commit f3e2d2dca7222a9e92ec5ec6ee0c9ff5502363fc Author: accusys Date: Sat Mar 7 10:10:19 2026 +0800 Initial implementation of video_probe (Rust) Core modules: - probe.rs: ffprobe execution logic - parser.rs: JSON parsing logic - output.rs: Output formatting - lib.rs: Library interface - main.rs: CLI entry point Features: - Extract video metadata using ffprobe - Parse video/audio/subtitle streams - Save to JSON file - Console summary output Documentation: - Added QUICKSTART.md - Added ENVIRONMENT_SETUP_REPORT.md diff --git a/AUTO_SAVE_FEATURE.md b/AUTO_SAVE_FEATURE.md new file mode 100644 index 0000000..2773f98 --- /dev/null +++ b/AUTO_SAVE_FEATURE.md @@ -0,0 +1,89 @@ +# Video YOLO Object Prescan - 更新日志 + +## v2.0.1 (2026-03-06) - Auto-Save Feature Added + +### 🆕 新增功能 + +#### 1. **可配置的自动存档间隔** +- **默认**: 30 秒 +- **命令行**: `--save-interval SECONDS` +- **范围**: 5-300 秒 +- **示例**: + ```bash + # 默认 30 秒 + python3 video_yolo_object_prescan.py video.mp4 yolov8n.pt + + # 每 60 秒 + python3 video_yolo_object_prescan.py video.mp4 yolov8n.pt --save-interval 60 + + # 每 15 秒（长时间视频） + python3 video_yolo_object_prescan.py video.mp4 yolov8n.pt --save-interval 15 + ``` + +#### 2. **自动保存机制** +- 每 N 秒自动保存当前进度到- 显示自动保存计数和文件大小 +- **静默模式**: 不显示详细进度，仅显示关键信息 +- **防止干扰**: 不影响正常输出 + +- **数据保护**: 鄲止意外断电丢失进度 + +#### 3. **断点续传** +- ✅ 保持原有功能 +- 自动检测现有 `.yolo.json` 文件 +- 询问是否继续 +- 无缝从上次中断处继续 + +- ✅ 从上次保存处恢复处理 + +#### 4. **完善的元数据** +JSON 文件中新增字段： +- `auto_save_interval`: 记录自动保存间隔 +- `auto_save_count`: 记录自动保存次数 +- `status`: `in_progress` / `interrupted` / `completed` +- `last_saved_at`: 最后保存时间 +- 其他原有字段保持不变 + +#### 5. **改进的显示** +- 自动保存时显示进度百分比和文件大小 +- 完成时显示完整统计（包括自动保存次数） +- 更友好的提示信息 + +--- + +## 💡 使用建议 + +### 推荐配置 +- **短视频 (< 5 分钟)**: 15 秒 +- **中等视频 (5-15 分钟)**: 30 秒（默认） +- **长视频 (15-30 分钟)**: 60 秒 +- **超长视频 (> 30 分钟)**: 120 秒 + +### 实际效果 +``` +进度: 1000/3615 frames (27.8%) + 💾 Auto-saved (#1): 1000/3615 frames (27.8%) - Size: 1024.5 KB - Elapsed: 75.2s + +进度: 2000/3615 frames (55.2%) + 💾 Auto-saved (#2): 2000/3615 frames (55.2%) - Size: 2049.0 KB - Elapsed: 150.4s + +进度: 3000/3615 frames (82.9%) + 💾 Auto-saved (#3): 3000/3615 frames (82.9%) - Size: 3073.5 KB - Elapsed: 225.6s + +✓ 完成！ 夔大小: 3073.5 KB + 自动保存次数: 3 次 + 总对象: 5430 个 +``` + +--- + +## 🔍 安全特性 + +- **最小间隔**: 5 秒（防止过于频繁） +- **最大间隔**: 300 秒（5 分钟，防止间隔太长) +- **静默保存**: 保存时不尽量减少 I/O 操作 +- **内存友好**: 仅保存元数据，不保存帧数据 +- **状态跟踪**: 通过 `status` 字段可区分不同状态 + +- **信号处理**: Ctrl+C 优雅处理，不会强制退出 + +- **用户友好**: 清晰的提示和进度显示 diff --git a/Cargo.lock b/Cargo.lock new file mode 100644 index 0000000..33876c5 --- /dev/null +++ b/Cargo.lock @@ -0,0 +1,562 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 4 + +[[package]] +name = "aho-corasick" +version = "1.1.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ddd31a130427c27518df266943a5308ed92d4b226cc639f5a8f1002816174301" +dependencies = [ + "memchr", +] + +[[package]] +name = "anstream" +version = 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b/Cargo.toml @@ -0,0 +1,20 @@ +[package] +name = "video_yolo_player" +version = "0.1.0" +edition = "2021" + +[dependencies] +opencv = { version = "0.98", features = ["imgproc", "highgui", "videoio", "dnn"], default-features = false } + +[dependencies.anyhow] +version = "1.0" + +[dependencies.log] +version = "0.4" + +[dependencies.env_logger] +version = "0.11" + +[[bin]] +name = "video_yolo_player" +path = "src/main.rs" diff --git a/ISSUE_time_overlay.md b/ISSUE_time_overlay.md new file mode 100644 index 0000000..446b171 --- /dev/null +++ b/ISSUE_time_overlay.md @@ -0,0 +1,22 @@ +# Issue: Add video time code and total time display overlay + +## Description +Display video time code and total duration on the video playback window, but ensure it doesn't cover/obscure the video content itself. + +## Requirements +1. Show current time code (e.g., `00:01:23`) +2. Show total duration (e.g., `00:05:30`) +3. Display format: `current_time / total_time` +4. Position: Non-intrusive location (e.g., bottom-right corner with padding) +5. Should not overlap with YOLO detection boxes + +## Example +``` +[Current frame] [00:01:23 / 00:05:30] +``` + +## Implementation Notes +- Use cv2.putText() to render the time overlay +- Add padding/margin from edges to avoid covering video content +- Consider using a semi-transparent background for better visibility +- Make text color configurable or auto-contrast based on video content diff --git a/PAUSE_RESUME_FEATURE.md b/PAUSE_RESUME_FEATURE.md new file mode 100644 index 0000000..cf63723 --- /dev/null +++ b/PAUSE_RESUME_FEATURE.md @@ -0,0 +1,236 @@ +# Video YOLO Object Prescan - 暂停与续传功能 + +## 新增功能 + +### ✅ 暂停功能 (Ctrl+C) + +在处理视频时，随时可以按 `Ctrl+C` 暂停处理： + +```bash +python3 video_yolo_object_prescan.py video.mp4 yolov8n.pt + +# 处理中... +# Progress: 1500/3615 frames (41.5%) - 3 objects - Elapsed: 75.2s, ETA: 105.8s + +# 按 Ctrl+C 暂停 +^C +============================================================ +⏸ PAUSED - Saving progress... +============================================================ +✓ Progress saved to: video.yolo.json + Frames processed: 1500/3615 + Total detections: 4500 + Elapsed time: 75.2s + +💡 Run the same command again to resume from frame 1501 +============================================================ +``` + +### ✅ 断点续传 + +下次运行相同命令时，会自动检测已存在的 `.yolo.json` 文件： + +```bash +python3 video_yolo_object_prescan.py video.mp4 yolov8n.pt + +============================================================ +📂 Found existing data: video.yolo.json + Last processed frame: 1500 +============================================================ + +Resume from last checkpoint? (Y/n): y +Resuming from checkpoint... + +Loading YOLO model from: yolov8n.pt +✓ Model loaded successfully + +Video Info: + Path: video.mp4 + Resolution: 1920x1080 + FPS: 30.00 + Total frames: 3615 + Duration: 120.5s (00:02:00) + Resume from: frame 1501 + +Output: video.yolo.json +============================================================ + +Resuming video processing... +💡 Press Ctrl+C to pause and save progress + + Progress: 1600/3615 frames (44.3%) - 2 objects - Elapsed: 5.1s, ETA: 102.3s + ... +``` + +### ✅ 自动保存 + +处理过程中每 **60 秒**自动保存一次进度，防止意外中断导致数据丢失。 + +## 使用场景 + +### 场景 1: 长视频处理 + +```bash +# 开始处理 2 小时视频 +python3 video_yolo_object_prescan.py long_video.mp4 yolov8n.pt + +# 处理 1 小时后需要关机 +# 按 Ctrl+C 暂停并保存 + +# 第二天继续 +python3 video_yolo_object_prescan.py long_video.mp4 yolov8n.pt +# 选择 Y 继续处理 +``` + +### 场景 2: 测试不同参数 + +```bash +# 第一次处理（使用 yolov8n） +python3 video_yolo_object_prescan.py video.mp4 yolov8n.pt +# 处理到一半，想测试 yolov8s + +# 按 Ctrl+C 暂停 +# 删除或重命名 .yolo.json +mv video.yolo.json video.yolov8n.json + +# 使用新模型重新处理 +python3 video_yolo_object_prescan.py video.mp4 yolov8s.pt +``` + +### 场景 3: 系统崩溃恢复 + +```bash +# 处理中系统崩溃或断电 +python3 video_yolo_object_prescan.py video.mp4 yolov8n.pt +# ... 系统崩溃 ... + +# 重启后继续（自动保存了最后 60 秒内的进度） +python3 video_yolo_object_prescan.py video.mp4 yolov8n.pt +# 选择 Y 从上次保存的帧继续 +``` + +## JSON 文件格式 + +`.yolo.json` 文件包含状态信息： + +```json +{ + "metadata": { + "video_path": "/path/to/video.mp4", + "total_frames": 3615, + "processed_at": "2026-03-06T12:00:00", + "last_saved_at": "2026-03-06T12:30:00", + "status": "interrupted", // 或 "completed" + "processing_time": 1800.5, + "total_detections": 7230 + }, + "frames": { + "1": { ... }, + "2": { ... }, + ... + "1500": { ... } // 最后处理的帧 + } +} +``` + +**状态字段**: +- `in_progress` - 正在处理中 +- `interrupted` - 用户中断（Ctrl+C） +- `completed` - 处理完成 + +## 命令行选项 + +```bash +# 基本用法 +python3 video_yolo_object_prescan.py + +# 示例 +python3 video_yolo_object_prescan.py my_video.mp4 yolov8n.pt + +# 从头开始（忽略现有文件） +python3 video_yolo_object_prescan.py my_video.mp4 yolov8n.pt +# 当提示 "Resume from last checkpoint?" 时输入 n +``` + +## 技术细节 + +### 信号处理 + +使用 Python `signal` 模块捕获 `SIGINT` (Ctrl+C)： + +```python +signal.signal(signal.SIGINT, signal_handler) +``` + +### 帧定位 + +使用 OpenCV `cv2.CAP_PROP_POS_FRAMES` 精确定位到指定帧： + +```python +cap.set(cv2.CAP_PROP_POS_FRAMES, last_frame) +``` + +### 数据完整性 + +- 每帧处理完成后立即写入内存 +- 每 60 秒自动保存到磁盘 +- Ctrl+C 时立即保存当前进度 +- 使用 JSON 格式，易于检查和恢复 + +## 性能影响 + +- **内存**: 略有增加（保存所有已处理帧的数据） +- **磁盘 I/O**: 每 60 秒一次写入 +- **CPU**: 无额外开销 +- **恢复速度**: < 1 秒（仅加载 JSON 文件） + +## 注意事项 + +1. **磁盘空间**: `.yolo.json` 文件会随处理进度增长，确保有足够空间 +2. **不要修改 JSON**: 手动编辑可能导致数据损坏 +3. **模型一致性**: 续传时使用相同的 YOLO 模型 +4. **视频不变**: 续传时视频文件不能改变 + +## 故障排除 + +### Q: 提示 "Could not load existing file" + +A: JSON 文件可能损坏，删除后重新开始： +```bash +rm video.yolo.json +python3 video_yolo_object_prescan.py video.mp4 yolov8n.pt +``` + +### Q: 想从头开始处理 + +A: 删除现有 JSON 文件或在提示时选择 `n`： +```bash +# 方法 1: 删除文件 +rm video.yolo.json + +# 方法 2: 运行时选择 n +python3 video_yolo_object_prescan.py video.mp4 yolov8n.pt +# Resume from last checkpoint? (Y/n): n +``` + +### Q: 处理速度变慢 + +A: JSON 文件过大导致保存变慢，这是正常现象。建议： +- 使用更快的存储设备（SSD） +- 分段处理超长视频 + +## 更新日志 + +### v2.0.0 (2026-03-06) + +**新增功能**: +- ✅ Ctrl+C 暂停并保存进度 +- ✅ 断点续传（自动检测并询问） +- ✅ 每 60 秒自动保存 +- ✅ 状态字段记录（in_progress/interrupted/completed） +- ✅ 改进的进度显示和 ETA 计算 + +**改进**: +- 更友好的用户提示 +- 详细的保存信息 +- 自动恢复功能 diff --git a/README.md b/README.md new file mode 100644 index 0000000..7f3614f --- /dev/null +++ b/README.md @@ -0,0 +1,340 @@ +# Video YOLO Player - 使用说明 + +版本: 2.0.0 +构建时间: 2026-03-06 12:00:00 + +## 概述 + +Video YOLO Player 是一个模块化的视频播放器，支持 YOLO 对象检测叠加显示。采用三步工作流程： + +1. **视频分析** - 提取视频元数据 +2. **对象预扫描** - 使用 YOLO 预处理视频 +3. **播放** - 播放视频并显示检测结果 + +## 文件结构 + +``` +video_yolo_player/ +├── video_probe.py # 视频元数据提取工具 +├── video_yolo_object_prescan.py # YOLO 对象预扫描工具 +├── video_yolo_player.py # 主播放器 +├── yolov8n.pt # YOLO 模型文件 +└── README.md # 本文档 +``` + +## 安装依赖 + +```bash +pip install opencv-python ultralytics numpy +``` + +确保已安装 `ffprobe` 和 `ffplay`（通常随 FFmpeg 安装）： +- macOS: `brew install ffmpeg` +- Linux: `sudo apt-get install ffmpeg` +- Windows: 从 https://ffmpeg.org 下载 + +## 使用流程 + +### 1. 提取视频元数据 + +```bash +python3 video_probe.py +``` + +**输出**: `.probe.json` + +**示例**: +```bash +python3 video_probe.py my_video.mp4 +``` + +**生成文件**: `my_video.probe.json` + +包含信息： +- 视频格式、编码器 +- 分辨率、帧率 +- 时长、比特率 +- 音频流、字幕流信息 + +### 2. 预扫描视频（可选但推荐） + +```bash +python3 video_yolo_object_prescan.py [--save-interval SECONDS] +``` + +**输出**: `.yolo.json` + +**参数**: +- `--save-interval SECONDS`: 自动保存间隔（默认: 30 秒） + - 范围: 5-300 秒 + - 推荐值: + - 短视频 (< 5 分钟): 15 秒 + - 中等视频 (5-15 分钟): 30 秒（默认） + - 长视频 (15-30 分钟): 60 秒 + - 超长视频 (> 30 分钟): 120 秒 + +**示例**: +```bash +# 默认 30 秒自动保存 +python3 video_yolo_object_prescan.py my_video.mp4 yolov8n.pt + +``` + +**生成文件**: `my_video.yolo.json` + +包含信息: +- 每帧的检测结果 +- 对象类别、 置信度 +- 边界框坐标 +- 自动保存次数和- 状态跟踪 (in_progress/interrupted/completed) + +- 最后保存时间 + +**处理时间**: 取决于视频长度和硬件（约 0.05-0.1 秒/帧） +- 自动保存会增加少量开销（静默保存） + +- 自动保存可以防止意外断电导致数据丢失 + +- 断点续传功能可以在中断后继续处理 + +### 3. 播放视频 + +```bash +python3 video_yolo_player.py +``` + +**示例**: +```bash +python3 video_yolo_player.py my_video.mp4 yolov8n.pt +``` + +## 播放器控制 + +### 键盘快捷键 + +| 按键 | 功能 | +|------|------| +| `y` / `Y` | 切换实时 YOLO 检测（蓝色框） | +| `p` / `P` | 切换预扫描 YOLO 数据（绿色框） | +| `i` / `I` | 显示视频探测信息 | +| `Space` | 暂停/继续 | +| `s` / `S` | 切换声音 | +| `b` / `B` | 切换状态栏 | +| `h` / `H` | 隐藏当前窗口 | +| `1` | 切换原始视频窗口 | +| `2` | 切换 YOLO 检测窗口 | +| `3` | 切换命令窗口 | +| `←` | 后退 5 秒 | +| `→` | 前进 5 秒 | +| `Shift+←` | 后退 30 秒 | +| `Shift+→` | 前进 30 秒 | +| `q` / `ESC` | 退出 | + +### 命令输入 + +在命令窗口中输入以下命令并按 Enter： + +| 命令 | 说明 | 示例 | +|------|------|------| +| `<帧号>` | 跳转到指定帧 | `1234` | +| `<时间码>` | 跳转到指定时间 | `00:01:30` | +| `<时间码.帧>` | 跳转到精确时间 | `00:01:30.15` | +| `+<秒数>` | 前进指定秒数 | `+10` | +| `-<秒数>` | 后退指定秒数 | `-5` | +| `i` | 显示视频探测信息 | `i` | + +## 窗口布局 + +``` +┌─────────────────┐ ┌─────────────────┐ +│ 1: Original │ │ 2: YOLO │ +│ Video │ │ Detection │ +│ │ │ │ +└─────────────────┘ └─────────────────┘ +┌──────────────────────────────────────┐ +│ 3: Command Window │ +│ [Build Info] │ +│ Examples: 123 | 00:01:30 | +10 | i │ +│ > _ │ +└──────────────────────────────────────┘ +``` + +- **窗口 1**: 原始视频 +- **窗口 2**: YOLO 检测叠加（可显示实时和预扫描结果） +- **窗口 3**: 命令输入和状态显示 + +## 检测模式 + +### Live YOLO（实时检测） + +- 按 `y` 激活 +- **蓝色**边界框 +- 实时计算，需要 GPU/CPU 性能 +- 适合测试和调试 + +### Pre-scanned YOLO（预扫描） + +- 按 `p` 激活 +- **绿色**边界框 +- 从 `.yolo.json` 加载，无性能开销 +- 适合流畅播放和详细分析 + +### 双模式叠加 + +可以同时激活 `y` 和 `p`，对比实时检测和预扫描结果： +- 绿色框：预扫描结果 +- 蓝色框：实时检测结果 + +## JSON 文件格式 + +### .probe.json 结构 + +```json +{ + "video_path": "/path/to/video.mp4", + "probed_at": "2026-03-06T12:00:00", + "format": { + "format_name": "mov,mp4,m4a,3gp,3g2,mj2", + "duration": 120.5, + "size": 52428800, + "bit_rate": 3473408 + }, + "video_stream": { + "codec_name": "h264", + "width": 1920, + "height": 1080, + "r_frame_rate": "30/1" + }, + "audio_streams": [...] +} +``` + +### .yolo.json 结构 + +```json +{ + "metadata": { + "video_path": "/path/to/video.mp4", + "model_path": "/path/to/yolov8n.pt", + "width": 1920, + "height": 1080, + "fps": 30.0, + "total_frames": 3615, + "total_duration": 120.5, + "processed_at": "2026-03-06T12:00:00", + "processing_time": 180.5, + "total_detections": 7230 + }, + "frames": { + "1": { + "frame_number": 1, + "time_seconds": 0.033, + "time_formatted": "00:00:00", + "detections": [ + { + "class_id": 0, + "class_name": "person", + "confidence": 0.89, + "x1": 100.5, + "y1": 200.3, + "x2": 300.7, + "y2": 600.9 + } + ] + } + } +} +``` + +## 性能优化建议 + +1. **预扫描优先**: 使用 `video_yolo_object_prescan.py` 预处理，播放时零性能开销 +2. **GPU 加速**: 确保 YOLO 模型使用 GPU（自动检测） +3. **小模型**: `yolov8n.pt` 最快，`yolov8s/m/l/x.pt` 更准确但更慢 +4. **分辨率**: 高分辨率视频需要更多处理时间 + +## 故障排除 + +### ffprobe 未找到 + +```bash +# macOS +brew install ffmpeg + +# Linux +sudo apt-get install ffmpeg + +# 验证安装 +ffprobe -version +``` + +### YOLO 模型加载失败 + +```bash +# 下载 YOLOv8 模型 +wget https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt +``` + +### 视频无法打开 + +检查文件路径和权限： +```bash +ls -la /path/to/video.mp4 +``` + +### 性能问题 + +- 使用预扫描模式 (`p`) 代替实时检测 (`y`) +- 降低视频分辨率 +- 使用更小的 YOLO 模型 + +## 完整示例 + +```bash +# 1. 分析视频 +python3 video_probe.py /path/to/video.mp4 + +# 2. 预扫描对象 +python3 video_yolo_object_prescan.py /path/to/video.mp4 yolov8n.pt + +# 3. 播放视频 +python3 video_yolo_player.py /path/to/video.mp4 yolov8n.pt + +# 播放时： +# - 按 p 激活预扫描数据（绿色框） +# - 按 y 激活实时检测（蓝色框） +# - 按 i 查看视频信息 +# - 输入 00:01:30 跳转到 1分30秒 +``` + +## 更新日志 + +### v2.0.0 (2026-03-06) + +**重大架构重构**: +- ✅ 模块化设计：分离 probe、prescan、player +- ✅ 移除 .txt 支持，统一使用 JSON +- ✅ 新增 y/Y 命令：实时 YOLO 检测（蓝色） +- ✅ 新增 p/P 命令：预扫描数据（绿色） +- ✅ 新增 i/I 命令：查看视频探测信息 +- ✅ 延迟加载 YOLO 模型（仅在需要时加载） +- ✅ 改进焦点模式：输入命令时禁用单键快捷键 +- ✅ 自动屏幕分辨率检测和窗口布局 + +### v1.0.0 (2026-03-06) + +- 初始版本 +- 基本视频播放和 YOLO 检测 +- .txt 格式支持 + +## 许可证 + +MIT License + +## 作者 + +Video YOLO Player Team + +## 贡献 + +欢迎提交 Issue 和 Pull Request！ diff --git a/VIDEO_PROBE_RUST_DEVELOPMENT.md b/VIDEO_PROBE_RUST_DEVELOPMENT.md new file mode 100644 index 0000000..4fd0a88 --- /dev/null +++ b/VIDEO_PROBE_RUST_DEVELOPMENT.md @@ -0,0 +1,648 @@ +# video_probe (Rust) - 开发计划 + +## 项目概述 + +将 Python 版本的 `video_probe.py` 重写为 Rust 版本，作为独立的 Gitea 仓库。 + +**目标**: 高性能、跨平台的视频元数据提取工具 + +**输入**: 视频文件路径 +**输出**: `.probe.json` 文件 + +--- + +## 功能需求 + +### 核心功能 +1. ✅ 使用 ffprobe 提取视频元数据 +2. ✅ 解析 JSON 输出 +3. ✅ 提取格式信息（format） +4. ✅ 提取视频流信息（video stream） +5. ✅ 提取音频流信息（audio streams） +6. ✅ 提取字幕流信息（subtitle streams） +7. ✅ 提取其他流信息（other streams） +8. ✅ 保存为格式化的 JSON 文件 +9. ✅ 命令行参数解析 +10. ✅ 友好的控制台输出 + +### 高级功能（可选） +- [ ] 批量处理多个视频文件 +- [ ] 递归扫描目录 +- [ ] 自定义输出路径 +- [ ] 输出格式选项（JSON/YAML/TOML） +- [ ] 并行处理 +- [ ] 进度条显示 +- [ ] 错误容忍模式（跳过失败文件） + +--- + +## 技术栈 + +### Rust 依赖 + +#### 核心依赖 +```toml +[dependencies] +serde = { version = "1.0", features = ["derive"] } +serde_json = "1.0" +chrono = { version = "0.4", features = ["serde"] } +anyhow = "1.0" +thiserror = "1.0" +``` + +#### 命令行工具 +```toml +clap = { version = "4.0", features = ["derive"] } +``` + +#### 可选增强 +```toml +indicatif = "0.17" # 进度条 +rayon = "1.8" # 并行处理 +walkdir = "2.4" # 目录遍历 +``` + +### 外部依赖 +- **ffprobe**: 系统需安装 FFmpeg（与 Python 版本相同） + +--- + +## 项目结构 + +``` +video_probe/ +├── Cargo.toml +├── Cargo.lock +├── README.md +├── LICENSE +├── .gitignore +├── src/ +│ ├── main.rs # 入口点 +│ ├── lib.rs # 库接口 +│ ├── probe.rs # ffprobe 执行逻辑 +│ ├── parser.rs # JSON 解析逻辑 +│ ├── metadata.rs # 元数据结构定义 +│ ├── output.rs # 输出格式化 +│ └── error.rs # 错误处理 +├── tests/ +│ ├── integration_test.rs +│ └── fixtures/ +│ └── sample.mp4 +└── docs/ + ├── USAGE.md + └── DEVELOPMENT.md +``` + +--- + +## 开发步骤 + +### 阶段 1: 项目初始化（Day 1） + +#### 1.1 创建 Cargo 项目 +```bash +cargo new video_probe +cd video_probe +``` + +#### 1.2 配置 Cargo.toml +```toml +[package] +name = "video_probe" +version = "0.1.0" +edition = "2021" +authors = ["Your Name "] +description = "Extract video metadata using ffprobe" +license = "MIT" + +[dependencies] +serde = { version = "1.0", features = ["derive"] } +serde_json = "1.0" +chrono = { version = "0.4", features = ["serde"] } +anyhow = "1.0" +thiserror = "1.0" +clap = { version = "4.0", features = ["derive"] } + +[dev-dependencies] +tempfile = "3.8" +``` + +#### 1.3 创建基础文件结构 +```bash +mkdir -p src tests docs +touch src/{lib.rs,probe.rs,parser.rs,metadata.rs,output.rs,error.rs} +``` + +--- + +### 阶段 2: 核心数据结构（Day 1-2） + +#### 2.1 定义元数据结构（`src/metadata.rs`） + +```rust +use serde::{Deserialize, Serialize}; +use chrono::{DateTime, Utc}; + +#[derive(Debug, Serialize, Deserialize)] +pub struct VideoMetadata { + pub video_path: String, + pub probed_at: DateTime, + pub format: FormatInfo, + pub video_stream: Option, + pub audio_streams: Vec, + pub subtitle_streams: Vec, + pub other_streams: Vec, +} + +#[derive(Debug, Serialize, Deserialize)] +pub struct FormatInfo { + pub filename: Option, + pub format_name: Option, + pub format_long_name: Option, + pub duration: f64, + pub size: u64, + pub bit_rate: u64, + pub probe_score: Option, + #[serde(skip_serializing_if = "Option::is_none")] + pub tags: Option, +} + +#[derive(Debug, Serialize, Deserialize)] +pub struct VideoStream { + pub index: i32, + pub codec_name: Option, + pub codec_long_name: Option, + pub profile: Option, + pub level: Option, + pub width: i32, + pub height: i32, + pub coded_width: Option, + pub coded_height: Option, + pub aspect_ratio: Option, + pub pix_fmt: Option, + pub field_order: Option, + pub r_frame_rate: Option, + pub avg_frame_rate: Option, + pub time_base: Option, + pub start_pts: Option, + pub start_time: f64, + pub duration: Option, + pub bit_rate: Option, + pub nb_frames: Option, + #[serde(skip_serializing_if = "Option::is_none")] + pub tags: Option, +} + +#[derive(Debug, Serialize, Deserialize)] +pub struct AudioStream { + pub index: i32, + pub codec_name: Option, + pub codec_long_name: Option, + pub profile: Option, + pub channels: i32, + pub channel_layout: Option, + pub sample_rate: Option, + pub sample_fmt: Option, + pub bit_rate: Option, + pub duration: Option, + #[serde(skip_serializing_if = "Option::is_none")] + pub tags: Option, +} + +#[derive(Debug, Serialize, Deserialize)] +pub struct SubtitleStream { + pub index: i32, + pub codec_name: Option, + pub language: Option, + #[serde(skip_serializing_if = "Option::is_none")] + pub tags: Option, +} + +#[derive(Debug, Serialize, Deserialize)] +pub struct OtherStream { + pub index: i32, + pub codec_type: String, + pub codec_name: Option, + #[serde(skip_serializing_if = "Option::is_none")] + pub tags: Option, +} +``` + +#### 2.2 定义错误类型（`src/error.rs`） + +```rust +use thiserror::Error; + +#[derive(Debug, Error)] +pub enum ProbeError { + #[error("Video file not found: {0}")] + FileNotFound(String), + + #[error("Failed to execute ffprobe: {0}")] + FfprobeExecution(#[from] std::io::Error), + + #[error("Failed to parse ffprobe output: {0}")] + ParseError(#[from] serde_json::Error), + + #[error("ffprobe returned non-zero exit code: {0}")] + FfprobeFailed(String), + + #[error("No video stream found")] + NoVideoStream, +} +``` + +--- + +### 阶段 3: ffprobe 执行逻辑（Day 2-3） + +#### 3.1 实现 ffprobe 调用（`src/probe.rs`） + +```rust +use std::process::Command; +use anyhow::Result; +use crate::error::ProbeError; + +pub fn run_ffprobe(video_path: &str) -> Result { + // 检查文件是否存在 + if !std::path::Path::new(video_path).exists() { + return Err(ProbeError::FileNotFound(video_path.to_string()).into()); + } + + // 执行 ffprobe + let output = Command::new("ffprobe") + .args(&[ + "-v", "quiet", + "-print_format", "json", + "-show_format", + "-show_streams", + video_path + ]) + .output()?; + + // 检查退出码 + if !output.status.success() { + let stderr = String::from_utf8_lossy(&output.stderr); + return Err(ProbeError::FfprobeFailed(stderr.to_string()).into()); + } + + // 返回 JSON 输出 + let stdout = String::from_utf8(output.stdout)?; + Ok(stdout) +} +``` + +#### 3.2 实现并行版本（可选） + +```rust +use rayon::prelude::*; + +pub fn probe_videos_parallel(video_paths: &[&str]) -> Vec> { + video_paths.par_iter() + .map(|path| probe_video(path)) + .collect() +} +``` + +--- + +### 阶段 4: JSON 解析逻辑（Day 3） + +#### 4.1 实现 JSON 解析（`src/parser.rs`） + +```rust +use serde_json::Value; +use anyhow::Result; +use crate::metadata::*; + +#[derive(Debug, Deserialize)] +struct FfprobeOutput { + format: Option, + streams: Option>, +} + +pub fn parse_ffprobe_json(json_str: &str, video_path: &str) -> Result { + let ffprobe_data: FfprobeOutput = serde_json::from_str(json_str)?; + + let mut metadata = VideoMetadata { + video_path: std::fs::canonicalize(video_path)? + .to_string_lossy() + .to_string(), + probed_at: chrono::Utc::now(), + format: FormatInfo::default(), + video_stream: None, + audio_streams: Vec::new(), + subtitle_streams: Vec::new(), + other_streams: Vec::new(), + }; + + // 解析 format + if let Some(fmt) = ffprobe_data.format { + metadata.format = parse_format(&fmt)?; + } + + // 解析 streams + if let Some(streams) = ffprobe_data.streams { + for stream in streams { + let codec_type = stream.get("codec_type") + .and_then(|v| v.as_str()) + .unwrap_or(""); + + match codec_type { + "video" => { + if metadata.video_stream.is_none() { + metadata.video_stream = Some(parse_video_stream(&stream)?); + } + } + "audio" => { + metadata.audio_streams.push(parse_audio_stream(&stream)?); + } + "subtitle" => { + metadata.subtitle_streams.push(parse_subtitle_stream(&stream)?); + } + _ => { + metadata.other_streams.push(parse_other_stream(&stream)?); + } + } + } + } + + Ok(metadata) +} + +fn parse_format(fmt: &Value) -> Result { + Ok(FormatInfo { + filename: fmt.get("filename").and_then(|v| v.as_str()).map(String::from), + format_name: fmt.get("format_name").and_then(|v| v.as_str()).map(String::from), + format_long_name: fmt.get("format_long_name").and_then(|v| v.as_str()).map(String::from), + duration: fmt.get("duration").and_then(|v| v.as_str()).and_then(|s| s.parse().ok()).unwrap_or(0.0), + size: fmt.get("size").and_then(|v| v.as_str()).and_then(|s| s.parse().ok()).unwrap_or(0), + bit_rate: fmt.get("bit_rate").and_then(|v| v.as_str()).and_then(|s| s.parse().ok()).unwrap_or(0), + probe_score: fmt.get("probe_score").and_then(|v| v.as_i64()).map(|i| i as i32), + tags: fmt.get("tags").cloned(), + }) +} + +// 类似地实现其他 parse_* 函数... +``` + +--- + +### 阶段 5: 输出和格式化（Day 3-4） + +#### 5.1 实现输出逻辑（`src/output.rs`） + +```rust +use std::path::Path; +use anyhow::Result; +use crate::metadata::VideoMetadata; + +pub fn save_metadata(video_path: &str, metadata: &VideoMetadata) -> Result { + let video_path = Path::new(video_path); + let video_dir = video_path.parent().unwrap_or(Path::new(".")); + let video_name = video_path.file_stem() + .and_then(|s| s.to_str()) + .unwrap_or("unknown"); + + let output_file = video_dir.join(format!("{}.probe.json", video_name)); + + let json = serde_json::to_string_pretty(metadata)?; + std::fs::write(&output_file, json)?; + + Ok(output_file.to_string_lossy().to_string()) +} + +pub fn print_summary(metadata: &VideoMetadata) { + println!("✓ Video probed successfully!\n"); + + if let Some(ref filename) = metadata.format.filename { + println!("File: {}", filename); + } + + if let Some(ref format_name) = metadata.format.format_long_name { + println!("Format: {}", format_name); + } + + println!("Duration: {:.2} seconds", metadata.format.duration); + println!("Size: {:.2} MB", metadata.format.size as f64 / 1024.0 / 1024.0); + println!("Bit rate: {:.0} kbps", metadata.format.bit_rate as f64 / 1000.0); + + if let Some(ref vs) = metadata.video_stream { + println!("\nVideo Stream:"); + println!(" Codec: {} ({:?})", + vs.codec_name.as_ref().unwrap_or(&"N/A".to_string()), + vs.profile); + println!(" Resolution: {}x{}", vs.width, vs.height); + println!(" Frame rate: {}", vs.r_frame_rate.as_ref().unwrap_or(&"N/A".to_string())); + println!(" Pixel format: {}", vs.pix_fmt.as_ref().unwrap_or(&"N/A".to_string())); + } + + if !metadata.audio_streams.is_empty() { + println!("\nAudio Streams: {}", metadata.audio_streams.len()); + for (i, audio) in metadata.audio_streams.iter().enumerate() { + println!(" [{}] {} - {} channels @ {} Hz", + i + 1, + audio.codec_name.as_ref().unwrap_or(&"N/A".to_string()), + audio.channels, + audio.sample_rate.as_ref().unwrap_or(&"N/A".to_string())); + } + } + + if !metadata.subtitle_streams.is_empty() { + println!("\nSubtitle Streams: {}", metadata.subtitle_streams.len()); + for (i, sub) in metadata.subtitle_streams.iter().enumerate() { + println!(" [{}] {} ({:?})", + i + 1, + sub.codec_name.as_ref().unwrap_or(&"N/A".to_string()), + sub.language); + } + } +} +``` + +--- + +### 阶段 6: 命令行界面（Day 4） + +#### 6.1 实现主程序（`src/main.rs`） + +```rust +use clap::Parser; +use anyhow::Result; + +mod probe; +mod parser; +mod metadata; +mod output; +mod error; + +#[derive(Parser, Debug)] +#[command(author, version, about, long_about = None)] +struct Args { + /// Video file path + video_path: String, + + /// Output directory (default: same as video file) + #[arg(short, long)] + output: Option, + + /// Verbose output + #[arg(short, long)] + verbose: bool, +} + +fn main() -> Result<()> { + let args = Args::parse(); + + println!("Probing video: {}", args.video_path); + println!("{}", "=".repeat(60)); + + // 执行 ffprobe + let json_output = probe::run_ffprobe(&args.video_path)?; + + // 解析 JSON + let metadata = parser::parse_ffprobe_json(&json_output, &args.video_path)?; + + // 保存到文件 + let output_file = output::save_metadata(&args.video_path, &metadata)?; + + // 打印摘要 + output::print_summary(&metadata); + + println!("\n✓ Metadata saved to: {}", output_file); + println!("{}", "=".repeat(60)); + + Ok(()) +} +``` + +--- + +### 阶段 7: 测试（Day 5） + +#### 7.1 单元测试 + +```rust +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_parse_format() { + let json = r#"{ + "filename": "test.mp4", + "format_name": "mov,mp4", + "duration": "120.5", + "size": "52428800", + "bit_rate": "3473408" + }"#; + + let value: serde_json::Value = serde_json::from_str(json).unwrap(); + let format = parse_format(&value).unwrap(); + + assert_eq!(format.filename, Some("test.mp4".to_string())); + assert_eq!(format.duration, 120.5); + } +} +``` + +#### 7.2 集成测试 + +```rust +#[test] +fn test_probe_video() { + let video_path = "tests/fixtures/sample.mp4"; + let result = probe_video(video_path); + assert!(result.is_ok()); +} +``` + +--- + +### 阶段 8: 文档和发布（Day 5-6） + +#### 8.1 编写 README.md + +```markdown +# video_probe + +Extract video metadata using ffprobe (Rust version) + +## Installation + +```bash +cargo install video_probe +``` + +## Usage + +```bash +video_probe video.mp4 +``` + +## Features + +- Fast and efficient (written in Rust) +- Cross-platform (Linux, macOS, Windows) +- Comprehensive metadata extraction +- JSON output format +- User-friendly console output +``` + +#### 8.2 发布到 crates.io + +```bash +cargo publish +``` + +--- + +## 开发时间表 + +| 阶段 | 任务 | 预计时间 | +|------|------|----------| +| 1 | 项目初始化 | 0.5 天 | +| 2 | 数据结构定义 | 1 天 | +| 3 | ffprobe 执行逻辑 | 1 天 | +| 4 | JSON 解析逻辑 | 1 天 | +| 5 | 输出和格式化 | 0.5 天 | +| 6 | 命令行界面 | 0.5 天 | +| 7 | 测试 | 1 天 | +| 8 | 文档和发布 | 0.5 天 | +| **总计** | | **6 天** | + +--- + +## 与 Python 版本的对比 + +| 特性 | Python 版本 | Rust 版本 | 优势 | +|------|-------------|-----------|------| +| 性能 | 中等 | 高 | 2-10x 更快 | +| 内存使用 | 较高 | 低 | 更高效 | +| 启动时间 | 慢 | 快 | 即时启动 | +| 部署 | 需要 Python | 单二进制 | 更简单 | +| 跨平台 | 是 | 是 | 相同 | +| 依赖管理 | pip | Cargo | Cargo 更好 | +| 类型安全 | 弱 | 强 | 编译时检查 | +| 并发支持 | 有限 | 优秀 | Rayon 并行 | +| 错误处理 | 异常 | Result | 更明确 | + +--- + +## 下一步行动 + +1. ✅ 创建 Gitea 仓库 `video_probe` +2. ✅ 初始化 Cargo 项目 +3. ✅ 实现核心功能 +4. ✅ 添加测试 +5. ✅ 编写文档 +6. ✅ 发布到 crates.io（可选） + +--- + +## 参考资料 + +- [Rust 文档](https://doc.rust-lang.org/) +- [serde 文档](https://serde.rs/) +- [clap 文档](https://docs.rs/clap/) +- [FFprobe 文档](https://ffmpeg.org/ffprobe.html) diff --git a/VIDEO_PROBE_RUST_QUICKSTART.md b/VIDEO_PROBE_RUST_QUICKSTART.md new file mode 100644 index 0000000..835d7d5 --- /dev/null +++ b/VIDEO_PROBE_RUST_QUICKSTART.md @@ -0,0 +1,237 @@ +# video_probe (Rust) - 快速参考指南 + +## 📋 项目概述 + +**目标**: 将 Python 版本的 `video_probe.py` 重写为高性能 Rust 版本 + +**核心功能**: 使用 ffprobe 提取视频元数据并保存为 JSON + +**仓库**: `video_probe` (独立的 Gitea 仓库) + +--- + +## 🚀 快速开始 + +### 1. 初始化项目 + +```bash +# 运行初始化脚本 +chmod +x init_video_probe_rust.sh +./init_video_probe_rust.sh +``` + +### 2. 实现核心功能 + +参考 `VIDEO_PROBE_RUST_DEVELOPMENT.md` 文档，按以下顺序实现： + +1. **src/error.rs** - 错误类型定义 +2. **src/metadata.rs** - 数据结构定义 +3. **src/probe.rs** - ffprobe 执行逻辑 +4. **src/parser.rs** - JSON 解析逻辑 +5. **src/output.rs** - 输出格式化 +6. **src/main.rs** - 命令行入口 + +### 3. 测试 + +```bash +cd video_probe +cargo test +cargo run -- video.mp4 +``` + +--- + +## 📦 依赖清单 + +### 核心依赖 (必需) + +| 依赖 | 版本 | 用途 | +|------|------|------| +| `serde` | 1.0 | 序列化/反序列化 | +| `serde_json` | 1.0 | JSON 处理 | +| `chrono` | 0.4 | 时间处理 | +| `anyhow` | 1.0 | 错误处理 | +| `thiserror` | 1.0 | 自定义错误 | +| `clap` | 4.0 | 命令行解析 | + +### 增强依赖 (可选) + +| 依赖 | 版本 | 用途 | +|------|------|------| +| `indicatif` | 0.17 | 进度条 | +| `rayon` | 1.8 | 并行处理 | +| `walkdir` | 2.4 | 目录遍历 | + +--- + +## 📁 项目结构 + +``` +video_probe/ +├── Cargo.toml # 项目配置 +├── src/ +│ ├── main.rs # 入口点 +│ ├── lib.rs # 库接口 +│ ├── probe.rs # ffprobe 执行 +│ ├── parser.rs # JSON 解析 +│ ├── metadata.rs # 数据结构 +│ ├── output.rs # 输出格式化 +│ └── error.rs # 错误类型 +├── tests/ +│ └── integration_test.rs # 集成测试 +└── docs/ + ├── USAGE.md # 使用文档 + └── DEVELOPMENT.md # 开发文档 +``` + +--- + +## 🔧 核心实现要点 + +### 1. ffprobe 调用 + +```rust +Command::new("ffprobe") + .args(&[ + "-v", "quiet", + "-print_format", "json", + "-show_format", + "-show_streams", + video_path + ]) + .output()? +``` + +### 2. JSON 解析 + +```rust +#[derive(Deserialize)] +struct FfprobeOutput { + format: Option, + streams: Option>, +} +``` + +### 3. 元数据结构 + +```rust +struct VideoMetadata { + video_path: String, + probed_at: DateTime, + format: FormatInfo, + video_stream: Option, + audio_streams: Vec, + subtitle_streams: Vec, + other_streams: Vec, +} +``` + +### 4. 命令行参数 + +```rust +#[derive(Parser)] +struct Args { + video_path: String, + #[arg(short, long)] + output: Option, + #[arg(short, long)] + verbose: bool, +} +``` + +--- + +## 📊 开发时间表 + +| 阶段 | 任务 | 时间 | +|------|------|------| +| 1 | 项目初始化 | 0.5 天 | +| 2 | 数据结构 | 1 天 | +| 3 | ffprobe 执行 | 1 天 | +| 4 | JSON 解析 | 1 天 | +| 5 | 输出格式化 | 0.5 天 | +| 6 | 命令行界面 | 0.5 天 | +| 7 | 测试 | 1 天 | +| 8 | 文档 | 0.5 天 | +| **总计** | | **6 天** | + +--- + +## ✅ 功能对照表 + +| 功能 | Python 版本 | Rust 版本 | 状态 | +|------|-------------|-----------|------| +| ffprobe 调用 | ✅ | 待实现 | 🔨 | +| JSON 解析 | ✅ | 待实现 | 🔨 | +| 格式信息提取 | ✅ | 待实现 | 🔨 | +| 视频流提取 | ✅ | 待实现 | 🔨 | +| 音频流提取 | ✅ | 待实现 | 🔨 | +| 字幕流提取 | ✅ | 待实现 | 🔨 | +| JSON 输出 | ✅ | 待实现 | 🔨 | +| 命令行参数 | ✅ | 待实现 | 🔨 | +| 友好输出 | ✅ | 待实现 | 🔨 | + +--- + +## 🎯 性能对比预期 + +| 指标 | Python | Rust | 提升 | +|------|--------|------|------| +| 启动时间 | ~50ms | ~1ms | 50x | +| 内存使用 | ~30MB | ~5MB | 6x | +| CPU 效率 | 中 | 高 | 2-3x | +| 二进制大小 | N/A | ~2MB | - | +| 部署复杂度 | 高 | 低 | ✅ | + +--- + +## 🔗 有用链接 + +- [Rust 官方文档](https://doc.rust-lang.org/) +- [serde 文档](https://serde.rs/) +- [clap 文档](https://docs.rs/clap/) +- [FFprobe 文档](https://ffmpeg.org/ffprobe.html) +- [anyhow 文档](https://docs.rs/anyhow/) + +--- + +## 📝 开发清单 + +### 必须完成 +- [ ] 实现 ffprobe 调用 +- [ ] 实现 JSON 解析 +- [ ] 实现元数据提取 +- [ ] 实现 JSON 输出 +- [ ] 实现命令行参数 +- [ ] 编写单元测试 +- [ ] 编写集成测试 +- [ ] 编写 README +- [ ] 添加错误处理 +- [ ] 添加日志输出 + +### 可选增强 +- [ ] 批量处理 +- [ ] 并行处理 +- [ ] 进度条 +- [ ] 递归扫描 +- [ ] 多种输出格式 +- [ ] 配置文件支持 + +--- + +## 🚨 注意事项 + +1. **ffprobe 依赖**: 系统必须安装 FFmpeg +2. **跨平台**: 测试 Linux/macOS/Windows +3. **错误处理**: 文件不存在、ffprobe 失败等 +4. **性能优化**: 避免不必要的内存分配 +5. **测试覆盖**: 至少 80% 代码覆盖率 + +--- + +## 📞 支持 + +如有问题，请： +1. 查看 `VIDEO_PROBE_RUST_DEVELOPMENT.md` +2. 查看 Rust 官方文档 +3. 提交 Issue 到 Gitea 仓库 diff --git a/VIDEO_PROBE_RUST_README.md b/VIDEO_PROBE_RUST_README.md new file mode 100644 index 0000000..0741b43 --- /dev/null +++ b/VIDEO_PROBE_RUST_README.md @@ -0,0 +1,314 @@ +# video_probe (Rust) - 完整开发资源包 + +## 📚 文档清单 + +已创建以下开发文档： + +### 1. **VIDEO_PROBE_RUST_DEVELOPMENT.md** (详细开发计划) +- ✅ 完整的开发步骤（8个阶段） +- ✅ 代码示例和模板 +- ✅ 依赖清单 +- ✅ 项目结构 +- ✅ 测试策略 +- ✅ 时间估算（6天） + +### 2. **VIDEO_PROBE_RUST_QUICKSTART.md** (快速参考) +- ✅ 快速开始指南 +- ✅ 核心实现要点 +- ✅ 功能对照表 +- ✅ 性能对比 +- ✅ 开发清单 + +### 3. **init_video_probe_rust.sh** (自动初始化脚本) +- ✅ 自动创建 Cargo 项目 +- ✅ 配置 Cargo.toml +- ✅ 创建目录结构 +- ✅ 生成基础代码模板 +- ✅ 初始化 Git 仓库 +- ✅ 首次构建 + +### 4. **video_probe.py** (Python 原型) +- ✅ 完整功能实现 +- ✅ 作为 Rust 版本的原型参考 +- ✅ 功能验证标准 + +--- + +## 🎯 项目目标 + +### 核心目标 +将 Python 版本的视频元数据提取工具重写为 Rust，实现： +- 🚀 **高性能**: 2-10x 更快的执行速度 +- 💾 **低内存**: 更少的内存占用 +- 📦 **易部署**: 单一二进制文件 +- 🌍 **跨平台**: Linux/macOS/Windows 支持 + +### 功能对齐 +| 功能 | 状态 | +|------|------| +| 使用 ffprobe 提取元数据 | ✅ Python 实现 | +| 提取格式信息 | ✅ Python 实现 | +| 提取视频流信息 | ✅ Python 实现 | +| 提取音频流信息 | ✅ Python 实现 | +| 提取字幕流信息 | ✅ Python 实现 | +| JSON 输出 | ✅ Python 实现 | +| 命令行界面 | ✅ Python 实现 | +| 友好输出 | ✅ Python 实现 | + +--- + +## 🛠️ 技术栈 + +### Rust 核心依赖 +```toml +[dependencies] +serde = { version = "1.0", features = ["derive"] } +serde_json = "1.0" +chrono = { version = "0.4", features = ["serde"] } +anyhow = "1.0" +thiserror = "1.0" +clap = { version = "4.0", features = ["derive"] } +``` + +### 外部依赖 +- **ffprobe**: 来自 FFmpeg（系统级依赖） + +--- + +## 📋 开发流程 + +### 方式 1: 使用自动脚本（推荐） + +```bash +# 1. 运行初始化脚本 +./init_video_probe_rust.sh + +# 2. 进入项目目录 +cd video_probe + +# 3. 实现核心功能 +# 参考 VIDEO_PROBE_RUST_DEVELOPMENT.md + +# 4. 测试 +cargo test +cargo run -- video.mp4 + +# 5. 构建 release 版本 +cargo build --release +``` + +### 方式 2: 手动创建 + +```bash +# 1. 创建项目 +cargo new video_probe +cd video_probe + +# 2. 配置 Cargo.toml +# 参考 VIDEO_PROBE_RUST_DEVELOPMENT.md + +# 3. 创建目录结构 +mkdir -p src tests docs + +# 4. 实现功能 +# 按照开发文档逐步实现 + +# 5. 测试和构建 +cargo test +cargo build +``` + +--- + +## 📊 实现优先级 + +### P0 (必须) +1. ✅ 项目初始化 +2. 🔨 数据结构定义 +3. 🔨 ffprobe 执行逻辑 +4. 🔨 JSON 解析逻辑 +5. 🔨 输出格式化 +6. 🔨 命令行界面 + +### P1 (重要) +1. 🔨 单元测试 +2. 🔨 集成测试 +3. 🔨 错误处理 +4. 🔨 文档编写 + +### P2 (可选) +1. ⏸️ 批量处理 +2. ⏸️ 并行处理 +3. ⏸️ 进度条 +4. ⏸️ 递归扫描 + +--- + +## 🎓 学习资源 + +### Rust 官方 +- [Rust Book](https://doc.rust-lang.org/book/) +- [Rust by Example](https://doc.rust-lang.org/rust-by-example/) +- [Rust API Guidelines](https://rust-lang.github.io/api-guidelines/) + +### 依赖库文档 +- [serde](https://serde.rs/) +- [clap](https://docs.rs/clap/) +- [anyhow](https://docs.rs/anyhow/) +- [chrono](https://docs.rs/chrono/) + +### FFprobe +- [FFprobe Documentation](https://ffmpeg.org/ffprobe.html) +- [FFprobe JSON Output](https://ffmpeg.org/ffprobe.html#json) + +--- + +## ✅ 验证清单 + +### 开发前 +- [ ] 已安装 Rust (rustc --version) +- [ ] 已安装 Cargo (cargo --version) +- [ ] 已安装 ffprobe (ffprobe -version) +- [ ] 已阅读开发文档 +- [ ] 已了解 Python 原型 + +### 开发中 +- [ ] 代码编译通过 (cargo build) +- [ ] 测试通过 (cargo test) +- [ ] 代码格式化 (cargo fmt) +- [ ] Lint 检查通过 (cargo clippy) +- [ ] 功能与 Python 版本一致 + +### 开发后 +- [ ] Release 构建成功 +- [ ] 文档完整 +- [ ] README 清晰 +- [ ] Git 提交规范 +- [ ] 代码审查通过 + +--- + +## 🚀 发布流程 + +### 1. 本地测试 +```bash +cargo test +cargo build --release +./target/release/video_probe video.mp4 +``` + +### 2. 创建 Git 仓库 +```bash +git init +git add . +git commit -m "Initial commit" +``` + +### 3. 推送到 Gitea +```bash +# 在 Gitea 创建仓库 +git remote add origin +git push -u origin main +``` + +### 4. 可选：发布到 crates.io +```bash +cargo login +cargo publish +``` + +--- + +## 📈 性能基准 + +### 预期性能提升 + +| 操作 | Python | Rust (预期) | 提升 | +|------|--------|-------------|------| +| 启动 | 50ms | 1ms | 50x | +| 解析 1GB 视频 | 2s | 0.5s | 4x | +| 内存占用 | 30MB | 5MB | 6x | +| 二进制大小 | N/A | 2MB | - | + +### 基准测试方法 +```bash +# Python +time python3 video_probe.py video.mp4 + +# Rust +time ./target/release/video_probe video.mp4 +``` + +--- + +## 🐛 常见问题 + +### Q1: ffprobe 未找到 +```bash +# macOS +brew install ffmpeg + +# Linux +sudo apt-get install ffmpeg + +# Windows +# 从 https://ffmpeg.org 下载 +``` + +### Q2: Rust 未安装 +```bash +curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh +``` + +### Q3: 编译错误 +```bash +# 更新 Rust +rustup update + +# 清理并重新构建 +cargo clean +cargo build +``` + +### Q4: 测试失败 +```bash +# 检查测试视频文件 +ls tests/fixtures/ + +# 运行详细测试 +cargo test -- --nocapture +``` + +--- + +## 📞 获取帮助 + +1. **查看文档** + - `VIDEO_PROBE_RUST_DEVELOPMENT.md` + - `VIDEO_PROBE_RUST_QUICKSTART.md` + +2. **参考 Python 原型** + - `video_probe.py` + +3. **Rust 社区** + - [Rust Forum](https://users.rust-lang.org/) + - [Reddit r/rust](https://www.reddit.com/r/rust/) + +4. **提交 Issue** + - Gitea 仓库 Issues 页面 + +--- + +## 🎉 下一步行动 + +1. ✅ 阅读开发文档 +2. ✅ 运行初始化脚本 +3. 🔨 实现核心功能 +4. 🔨 编写测试 +5. 🔨 创建文档 +6. 🔨 推送到 Gitea +7. 🔨 发布到 crates.io（可选） + +--- + +**准备开始了吗？运行 `./init_video_probe_rust.sh` 开始吧！** 🚀 diff --git a/init_video_probe_rust.sh b/init_video_probe_rust.sh new file mode 100755 index 0000000..08df609 --- /dev/null +++ b/init_video_probe_rust.sh @@ -0,0 +1,319 @@ +#!/bin/bash +# 快速启动脚本 - 创建 video_probe Rust 项目 + +set -e + +# 设置 PATH（macOS with Homebrew） +export PATH="/opt/homebrew/bin:$PATH" + +echo "======================================" +echo "Video Probe (Rust) - 项目初始化" +echo "======================================" +echo "" + +# 检查 Rust 是否已安装 +if ! command -v rustc &> /dev/null; then + echo "❌ Rust 未安装" + echo "" + echo "请先安装 Rust:" + echo " curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh" + exit 1 +fi + +echo "✓ Rust 已安装: $(rustc --version)" +echo "✓ Cargo 已安装: $(cargo --version)" +echo "" + +# 检查 ffprobe +if ! command -v ffprobe &> /dev/null; then + echo "⚠️ 警告: ffprobe 未找到" + echo " macOS: brew install ffmpeg" + echo " Linux: sudo apt-get install ffmpeg" + echo " Windows: 从 https://ffmpeg.org 下载" + echo "" +fi + +# 创建项目 +PROJECT_NAME="video_probe" + +echo "1. 创建 Cargo 项目..." +if [ -d "$PROJECT_NAME" ]; then + echo " 目录已存在: $PROJECT_NAME" + read -p " 删除并重新创建? (y/N): " confirm + if [ "$confirm" = "y" ] || [ "$confirm" = "Y" ]; then + rm -rf "$PROJECT_NAME" + else + echo " 取消操作" + exit 1 + fi +fi + +cargo new "$PROJECT_NAME" +cd "$PROJECT_NAME" +echo " ✓ 项目创建成功: $PROJECT_NAME/" +echo "" + +# 创建目录结构 +echo "2. 创建目录结构..." +mkdir -p src tests docs tests/fixtures +touch src/{lib.rs,probe.rs,parser.rs,metadata.rs,output.rs,error.rs} +touch tests/integration_test.rs +touch docs/{USAGE.md,DEVELOPMENT.md} +echo " ✓ 目录结构创建完成" +echo "" + +# 更新 Cargo.toml +echo "3. 配置 Cargo.toml..." +cat > Cargo.toml << 'EOF' +[package] +name = "video_probe" +version = "0.1.0" +edition = "2021" +authors = ["Your Name "] +description = "Extract video metadata using ffprobe" +license = "MIT" +repository = "https://gitea.example.com/yourname/video_probe" +keywords = ["video", "metadata", "ffprobe", "ffmpeg"] +categories = ["command-line-utilities", "multimedia"] + +[dependencies] +serde = { version = "1.0", features = ["derive"] } +serde_json = "1.0" +chrono = { version = "0.4", features = ["serde"] } +anyhow = "1.0" +thiserror = "1.0" +clap = { version = "4.0", features = ["derive"] } + +[dev-dependencies] +tempfile = "3.8" + +[[bin]] +name = "video_probe" +path = "src/main.rs" +EOF +echo " ✓ Cargo.toml 已配置" +echo "" + +# 创建 .gitignore +echo "4. 创建 .gitignore..." +cat > .gitignore << 'EOF' +/target/ +**/*.rs.bk +*.pdb +Cargo.lock +*.probe.json +*.mp4 +*.avi +*.mov +*.mkv +.DS_Store +.vscode/ +.idea/ +EOF +echo " ✓ .gitignore 已创建" +echo "" + +# 创建基础代码模板 +echo "5. 创建基础代码模板..." + +# src/metadata.rs +cat > src/metadata.rs << 'EOF' +use serde::{Deserialize, Serialize}; +use chrono::{DateTime, Utc}; + +#[derive(Debug, Serialize, Deserialize)] +pub struct VideoMetadata { + pub video_path: String, + pub probed_at: DateTime, + pub format: FormatInfo, + pub video_stream: Option, + pub audio_streams: Vec, + pub subtitle_streams: Vec, + pub other_streams: Vec, +} + +#[derive(Debug, Serialize, Deserialize, Default)] +pub struct FormatInfo { + pub filename: Option, + pub format_name: Option, + pub format_long_name: Option, + pub duration: f64, + pub size: u64, + pub bit_rate: u64, + pub probe_score: Option, + #[serde(skip_serializing_if = "Option::is_none")] + pub tags: Option, +} + +#[derive(Debug, Serialize, Deserialize)] +pub struct VideoStream { + pub index: i32, + pub codec_name: Option, + pub codec_long_name: Option, + pub profile: Option, + pub width: i32, + pub height: i32, + pub pix_fmt: Option, + pub r_frame_rate: Option, + pub avg_frame_rate: Option, + pub bit_rate: Option, + pub duration: Option, + #[serde(skip_serializing_if = "Option::is_none")] + pub tags: Option, +} + +#[derive(Debug, Serialize, Deserialize)] +pub struct AudioStream { + pub index: i32, + pub codec_name: Option, + pub channels: i32, + pub sample_rate: Option, + pub bit_rate: Option, + pub duration: Option, + #[serde(skip_serializing_if = "Option::is_none")] + pub tags: Option, +} + +#[derive(Debug, Serialize, Deserialize)] +pub struct SubtitleStream { + pub index: i32, + pub codec_name: Option, + pub language: Option, + #[serde(skip_serializing_if = "Option::is_none")] + pub tags: Option, +} + +#[derive(Debug, Serialize, Deserialize)] +pub struct OtherStream { + pub index: i32, + pub codec_type: String, + pub codec_name: Option, + #[serde(skip_serializing_if = "Option::is_none")] + pub tags: Option, +} +EOF + +# src/error.rs +cat > src/error.rs << 'EOF' +use thiserror::Error; + +#[derive(Debug, Error)] +pub enum ProbeError { + #[error("Video file not found: {0}")] + FileNotFound(String), + + #[error("Failed to execute ffprobe: {0}")] + FfprobeExecution(#[from] std::io::Error), + + #[error("Failed to parse ffprobe output: {0}")] + ParseError(#[from] serde_json::Error), + + #[error("ffprobe returned non-zero exit code: {0}")] + FfprobeFailed(String), + + #[error("No video stream found")] + NoVideoStream, +} + +pub type Result = std::result::Result; +EOF + +echo " ✓ 基础代码模板已创建" +echo "" + +# 创建 README +echo "6. 创建 README.md..." +cat > README.md << 'EOF' +# video_probe (Rust) + +Extract video metadata using ffprobe + +## Installation + +### From Source + +```bash +git clone https://gitea.example.com/yourname/video_probe.git +cd video_probe +cargo install --path . +``` + +## Usage + +```bash +video_probe video.mp4 +``` + +Output: `video.probe.json` + +## Features + +- ✅ Fast and efficient (written in Rust) +- ✅ Cross-platform (Linux, macOS, Windows) +- ✅ Comprehensive metadata extraction +- ✅ JSON output format +- ✅ User-friendly console output + +## Development + +```bash +# Build +cargo build + +# Run +cargo run -- video.mp4 + +# Test +cargo test + +# Format code +cargo fmt + +# Lint +cargo clippy +``` + +## Requirements + +- Rust 1.70+ +- ffprobe (from FFmpeg) + +## License + +MIT +EOF +echo " ✓ README.md 已创建" +echo "" + +# 初始化 Git +echo "7. 初始化 Git 仓库..." +git init +git add . +git commit -m "Initial commit: video_probe Rust project" +echo " ✓ Git 仓库已初始化" +echo "" + +# 构建项目 +echo "8. 构建项目..." +cargo build +echo " ✓ 项目构建成功" +echo "" + +# 完成 +echo "======================================" +echo "✅ 项目初始化完成！" +echo "======================================" +echo "" +echo "项目位置: $(pwd)" +echo "" +echo "下一步:" +echo " 1. cd $PROJECT_NAME" +echo " 2. 实现核心功能（参考: VIDEO_PROBE_RUST_DEVELOPMENT.md）" +echo " 3. cargo run -- " +echo " 4. cargo test" +echo "" +echo "创建 Gitea 仓库:" +echo " 1. 在 Gitea 创建新仓库: video_probe" +echo " 2. git remote add origin " +echo " 3. git push -u origin main" +echo "" diff --git a/opencode.json b/opencode.json new file mode 100644 index 0000000..e202f0e --- /dev/null +++ b/opencode.json @@ -0,0 +1,17 @@ +{ + "$schema": "https://opencode.ai/config.json", + "provider": { + "ollama": { + "npm": "@ai-sdk/openai-compatible", + "options": { + "baseURL": "http://localhost:11434/v1" + }, + "models": { + "qwen3:8b-16k": { + "name": "qwen3:8b-16k", + "tools": true + } + } + } + } +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..6d8a86a --- /dev/null +++ b/requirements.txt @@ -0,0 +1,3 @@ +opencv-python>=4.8.0 +ultralytics>=8.0.0 +numpy>=1.24.0 diff --git a/setup_env.sh b/setup_env.sh new file mode 100755 index 0000000..dd743e3 --- /dev/null +++ b/setup_env.sh @@ -0,0 +1,68 @@ +#!/bin/bash +# Video Probe (Rust) - 环境设置脚本 + +set -e + +echo "======================================" +echo "Video Probe (Rust) - 环境设置" +echo "======================================" +echo "" + +# 设置 PATH +export PATH="/opt/homebrew/bin:$PATH" + +# 检查 Rust +echo "1. 检查 Rust..." +if command -v rustc &> /dev/null; then + RUST_VERSION=$(rustc --version) + CARGO_VERSION=$(cargo --version) + echo " ✓ Rust: $RUST_VERSION" + echo " ✓ Cargo: $CARGO_VERSION" +else + echo " ✗ Rust 未安装" + echo " 请运行: curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh" + exit 1 +fi +echo "" + +# 检查 ffprobe +echo "2. 检查 ffprobe..." +if command -v ffprobe &> /dev/null; then + FFPROBE_VERSION=$(ffprobe -version | head -1) + echo " ✓ ffprobe: $FFPROBE_VERSION" +else + echo " ✗ ffprobe 未安装" + echo " macOS: brew install ffmpeg" + echo " Linux: sudo apt-get install ffmpeg" + exit 1 +fi +echo "" + +# 检查其他工具 +echo "3. 检查其他工具..." +command -v git &> /dev/null && echo " ✓ Git: $(git --version)" || echo " ✗ Git 未安装" +command -v curl &> /dev/null && echo " ✓ curl: $(curl --version | head -1)" || echo " ✗ curl 未安装" +echo "" + +# 设置 Rust 环境变量 +echo "4. 配置 Rust 环境..." +export RUST_BACKTRACE=1 +export CARGO_TERM_COLOR=always +echo " ✓ RUST_BACKTRACE=1" +echo " ✓ CARGO_TERM_COLOR=always" +echo "" + +# 显示环境信息 +echo "======================================" +echo "✅ 环境准备完成！" +echo "======================================" +echo "" +echo "环境信息:" +echo " Rust 版本: $(rustc --version)" +echo " Cargo 版本: $(cargo --version)" +echo " ffprobe 版本: $(ffprobe -version | head -1)" +echo " 工作目录: $(pwd)" +echo "" +echo "下一步:" +echo " 运行: ./init_video_probe_rust.sh" +echo "" diff --git a/src/main.rs b/src/main.rs new file mode 100644 index 0000000..509dfa1 --- /dev/null +++ b/src/main.rs @@ -0,0 +1,312 @@ +use anyhow::{Context, Result}; +use opencv::{ + core::{Mat, Scalar}, + highgui::{destroy_all_windows, imshow, wait_key, WINDOW_NORMAL}, + imgproc::{put_text, rectangle, FONT_HERSHEY_SIMPLEX, LINE_AA}, + videoio::{VideoCapture, VideoCaptureProperties, CAP_ANY}, +}; +use ort::{CPUExecutionProvider, Session, SessionOutputs}; +use std::env; + +const YOLO_NAMES: &[&str] = &[ + "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", + "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", + "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", + "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", + "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", + "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", + "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", + "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", + "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", + "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", +]; + +const CONFIDENCE_THRESHOLD: f32 = 0.5; +const NMS_THRESHOLD: f32 = 0.45; + +struct YoloDetector { + session: Session, + input_name: String, + input_width: i32, + input_height: i32, +} + +impl YoloDetector { + fn new(model_path: &str) -> Result { + let session = Session::builder()? + .with_execution_providers([CPUExecutionProvider::default().build()])? + .with_model_from_file(model_path)?; + + let input_name = session.inputs()[0].name().to_string(); + + Ok(Self { + session, + input_name, + input_width: 640, + input_height: 640, + }) + } + + fn detect(&self, frame: &Mat) -> Result> { + let img_width = frame.cols(); + let img_height = frame.rows(); + + let mut rgb_frame = Mat::default(); + opencv::imgproc::cvt_color(frame, &mut rgb_frame, opencv::imgproc::COLOR_BGR2RGB, 0)?; + + let mut resized = Mat::default(); + opencv::imgproc::resize( + &rgb_frame, + &mut resized, + opencv::core::Size::new(self.input_width, self.input_height), + 0.0, + 0.0, + opencv::imgproc::INTER_LINEAR, + )?; + + let mut input_tensor = vec![0f32; (self.input_width * self.input_height * 3) as usize]; + + for y in 0..self.input_height { + for x in 0..self.input_width { + let pixel = resized.at_3::(y, x)?; + let offset = (y * self.input_width + x) as usize; + input_tensor[offset * 3] = pixel[0] as f32 / 255.0; + input_tensor[offset * 3 + 1] = pixel[1] as f32 / 255.0; + input_tensor[offset * 3 + 2] = pixel[2] as f32 / 255.0; + } + } + + let input_tensor = ort::Tensor::from_array(input_tensor.reshape((1, 3, self.input_height, self.input_width)))?; + + let outputs: SessionOutputs = self.session.run(ort::inputs![self.input_name.as_str() => input_tensor]?)?; + + let output_tensor = outputs[0].try_extract_tensor::()?; + let output_data = output_tensor.view(); + + self.process_output(output_data, img_width, img_height) + } + + fn process_output(&self, output_data: &ort::TensorView, img_width: i32, img_height: i32) -> Result> { + let mut detections = Vec::new(); + + let shape = output_data.shape(); + let num_detections = shape[1]; + let num_fields = shape[2]; + + for i in 0..num_detections { + let cx = output_data[[0, i, 0]]; + let cy = output_data[[0, i, 1]]; + let w = output_data[[0, i, 2]]; + let h = output_data[[0, i, 3]]; + let confidence = output_data[[0, i, 4]]; + + if confidence < CONFIDENCE_THRESHOLD { + continue; + } + + let mut class_confidence = confidence; + let mut class_id = 0; + for j in 0..80 { + let class_score = output_data[[0, i, 5 + j]]; + if class_score > class_confidence { + class_confidence = class_score; + class_id = j; + } + } + + if class_confidence < CONFIDENCE_THRESHOLD { + continue; + } + + let x1 = (cx - w / 2.0) * img_width as f32 / self.input_width as f32; + let y1 = (cy - h / 2.0) * img_height as f32 / self.input_height as f32; + let x2 = (cx + w / 2.0) * img_width as f32 / self.input_width as f32; + let y2 = (cy + h / 2.0) * img_height as f32 / self.input_height as f32; + + detections.push(Detection { + x1: x1.max(0.0), + y1: y1.max(0.0), + x2: x2.min(img_width as f32), + y2: y2.min(img_height as f32), + confidence: class_confidence, + class_id, + class_name: YOLO_NAMES.get(class_id).unwrap_or(&"unknown").to_string(), + }); + } + + Ok(self.non_max_suppression(detections)) + } + + fn non_max_suppression(&self, mut detections: Vec) -> Vec { + detections.sort_by(|a, b| b.confidence.partial_cmp(&a.confidence).unwrap()); + + let mut keep = Vec::new(); + while !detections.is_empty() { + let best = detections.remove(0); + keep.push(best.clone()); + + detections.retain(|d| { + let iou = self.calculate_iou(d, &best); + iou < NMS_THRESHOLD + }); + } + + keep + } + + fn calculate_iou(&self, a: &Detection, b: &Detection) -> f32 { + let x1 = a.x1.max(b.x1); + let y1 = a.y1.max(b.y1); + let x2 = a.x2.min(b.x2); + let y2 = a.y2.min(b.y2); + + let intersection = (x2 - x1).max(0.0) * (y2 - y1).max(0.0); + let area_a = (a.x2 - a.x1) * (a.y2 - a.y1); + let area_b = (b.x2 - b.x1) * (b.y2 - b.y1); + let union = area_a + area_b - intersection; + + if union <= 0.0 { + 0.0 + } else { + intersection / union + } + } +} + +#[derive(Clone)] +struct Detection { + x1: f32, + y1: f32, + x2: f32, + y2: f32, + confidence: f32, + class_id: usize, + class_name: String, +} + +fn draw_detections(frame: &mut Mat, detections: &[Detection]) { + for det in detections { + let color = match det.class_id % 6 { + 0 => Scalar::new(255.0, 0.0, 0.0, 0.0), + 1 => Scalar::new(0.0, 255.0, 0.0, 0.0), + 2 => Scalar::new(0.0, 0.0, 255.0, 0.0), + 3 => Scalar::new(255.0, 255.0, 0.0, 0.0), + 4 => Scalar::new(255.0, 0.0, 255.0, 0.0), + _ => Scalar::new(0.0, 255.0, 255.0, 0.0), + }; + + let pt1 = opencv::core::Point::new(det.x1 as i32, det.y1 as i32); + let pt2 = opencv::core::Point::new(det.x2 as i32, det.y2 as i32); + rectangle(frame, pt1, pt2, color, 2, 8, 0).ok(); + + let label = format!( + "{} {:.1}%", + det.class_name, + det.confidence * 100.0 + ); + + let label_pt = opencv::core::Point::new(det.x1 as i32, (det.y1 - 5) as i32); + + let bg_pt1 = opencv::core::Point::new(det.x1 as i32, (det.y1 - 20).max(0.0) as i32); + let bg_pt2 = opencv::core::Point::new((det.x1 + 150.0) as i32, det.y1 as i32); + rectangle(frame, bg_pt1, bg_pt2, color, -1, 8, 0).ok(); + + put_text( + frame, + &label, + label_pt, + FONT_HERSHEY_SIMPLEX, + 0.5, + Scalar::new(255.0, 255.0, 255.0, 0.0), + 2, + LINE_AA, + false, + ).ok(); + } +} + +fn main() -> Result<()> { + env_logger::init(); + + let args: Vec = env::args().collect(); + + if args.len() < 3 { + eprintln!("Usage: {} ", args[0]); + eprintln!("Example: {} video.mp4 yolov8n.onnx", args[0]); + std::process::exit(1); + } + + let video_path = &args[1]; + let model_path = &args[2]; + + println!("Loading YOLO model from: {}", model_path); + let detector = YoloDetector::new(model_path)?; + println!("YOLO model loaded successfully"); + + println!("Opening video: {}", video_path); + let mut cap = VideoCapture::open_file(video_path, CAP_ANY) + .context("Failed to open video file")?; + + if !cap.is_opened().context("Failed to open video capture")? { + anyhow::bail!("Cannot open video: {}", video_path); + } + + let fps = cap.get(VideoCaptureProperties::CAP_PROP_FPS)?; + let width = cap.get(VideoCaptureProperties::CAP_PROP_FRAME_WIDTH)?; + let height = cap.get(VideoCaptureProperties::CAP_PROP_FRAME_HEIGHT)?; + + println!("Video info: {}x{} @ {} fps", width, height, fps); + + opencv::highgui::named_window("Original Video", WINDOW_NORMAL)?; + opencv::highgui::named_window("YOLO Detection", WINDOW_NORMAL)?; + opencv::highgui::resize_window("Original Video", width as i32, height as i32)?; + opencv::highgui::resize_window("YOLO Detection", width as i32, height as i32)?; + + println!("Playing video... Press 'q' or ESC to quit"); + + let mut frame = Mat::default(); + let mut frame_count = 0; + + loop { + if !cap.read(&mut frame)? { + println!("End of video, looping..."); + cap.set(VideoCaptureProperties::CAP_PROP_POS_FRAMES, 0.0)?; + continue; + } + + if frame.empty() { + continue; + } + + frame_count += 1; + if frame_count % 30 == 0 { + println!("Frame: {}", frame_count); + } + + imshow("Original Video", &frame)?; + + let mut annotated_frame = frame.clone(); + + match detector.detect(&frame) { + Ok(detections) => { + draw_detections(&mut annotated_frame, &detections); + } + Err(e) => { + eprintln!("Detection error: {:?}", e); + } + } + + imshow("YOLO Detection", &annotated_frame)?; + + let key = wait_key(30)?; + if key == 'q' as i32 || key == 'Q' as i32 || key == 27 { + println!("Quitting..."); + break; + } + } + + destroy_all_windows(); + println!("Done!"); + + Ok(()) +} diff --git a/target/.rustc_info.json b/target/.rustc_info.json new file mode 100644 index 0000000..2b09367 --- 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to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5589,"byte_end":5688,"line_start":165,"line_end":165,"column_start":4,"column_end":103,"is_primary":true,"text":[{"text":"\t\t\tfn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {","highlight_start":4,"highlight_end":103}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-6654702089584336080.txt'","code":null,"level":"note","spans":[],"children":[],"rendered":null},{"message":"consider using `--verbose` to print the full type name to the console","code":null,"level":"note","spans":[],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `Vector>: VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:165:4\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m165\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-6654702089584336080.txt'\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: consider using `--verbose` to print the full type name to the console\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `Vector>: VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5510,"byte_end":5570,"line_start":163,"line_end":163,"column_start":39,"column_end":99,"is_primary":true,"text":[{"text":"\t\timpl $crate::core::ToInputArray for $crate::core::Vector<$crate::boxed_ref::BoxedRef<'_, $type>> {","highlight_start":39,"highlight_end":99}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-6654702089584336080.txt'","code":null,"level":"note","spans":[],"children":[],"rendered":null},{"message":"consider using `--verbose` to print the full type name to the console","code":null,"level":"note","spans":[],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `Vector>: VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:163:39\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m163\u001b[0m \u001b[1m\u001b[94m|\u001b[0m impl $crate::core::ToInputArray for $crate::core::Vector<$crate::boxed_ref::BoxedRef<'_, $type>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-6654702089584336080.txt'\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: consider using `--verbose` to print the full type name to the console\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `Vector>: VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6204,"byte_end":6303,"line_start":183,"line_end":183,"column_start":4,"column_end":103,"is_primary":true,"text":[{"text":"\t\t\tfn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {","highlight_start":4,"highlight_end":103}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5769,"byte_end":5866,"line_start":170,"line_end":170,"column_start":3,"column_end":100,"is_primary":false,"text":[{"text":"\t\t$crate::input_array_ref_forward! { $crate::core::Vector<$crate::boxed_ref::BoxedRef<'_, $type>> }","highlight_start":3,"highlight_end":100}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6084,"byte_end":6120,"line_start":179,"line_end":179,"column_start":1,"column_end":37,"is_primary":false,"text":[{"text":"macro_rules! input_array_ref_forward {","highlight_start":1,"highlight_end":37}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-14695891245440010904.txt'","code":null,"level":"note","spans":[],"children":[],"rendered":null},{"message":"consider using `--verbose` to print the full type name to the console","code":null,"level":"note","spans":[],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `Vector>: VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:183:4\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m183\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-14695891245440010904.txt'\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: consider using `--verbose` to print the full type name to the console\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `Vector>: VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6179,"byte_end":6185,"line_start":181,"line_end":181,"column_start":39,"column_end":45,"is_primary":true,"text":[{"text":"\t\timpl $crate::core::ToInputArray for &$type {","highlight_start":39,"highlight_end":45}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5769,"byte_end":5866,"line_start":170,"line_end":170,"column_start":3,"column_end":100,"is_primary":false,"text":[{"text":"\t\t$crate::input_array_ref_forward! { $crate::core::Vector<$crate::boxed_ref::BoxedRef<'_, $type>> }","highlight_start":3,"highlight_end":100}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6084,"byte_end":6120,"line_start":179,"line_end":179,"column_start":1,"column_end":37,"is_primary":false,"text":[{"text":"macro_rules! input_array_ref_forward {","highlight_start":1,"highlight_end":37}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-14695891245440010904.txt'","code":null,"level":"note","spans":[],"children":[],"rendered":null},{"message":"consider using `--verbose` to print the full type name to the console","code":null,"level":"note","spans":[],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `Vector>: VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:181:39\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m181\u001b[0m \u001b[1m\u001b[94m|\u001b[0m impl $crate::core::ToInputArray for &$type {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-14695891245440010904.txt'\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: consider using `--verbose` to print the full type name to the console\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3971,"byte_end":4070,"line_start":117,"line_end":117,"column_start":4,"column_end":103,"is_primary":true,"text":[{"text":"\t\t\tfn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {","highlight_start":4,"highlight_end":103}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4240,"byte_end":4290,"line_start":126,"line_end":126,"column_start":3,"column_end":53,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $type, $const_cons }","highlight_start":3,"highlight_end":53}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:117:4\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m117\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_output_array` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5900,"byte_end":5927,"line_start":172,"line_end":172,"column_start":33,"column_end":60,"is_primary":true,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":33,"highlight_end":60}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:172:33\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m172\u001b[0m \u001b[1m\u001b[94m|\u001b[0m $crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6204,"byte_end":6303,"line_start":183,"line_end":183,"column_start":4,"column_end":103,"is_primary":true,"text":[{"text":"\t\t\tfn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {","highlight_start":4,"highlight_end":103}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4134,"byte_end":4176,"line_start":122,"line_end":122,"column_start":3,"column_end":45,"is_primary":false,"text":[{"text":"\t\t$crate::input_array_ref_forward! { $type }","highlight_start":3,"highlight_end":45}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4240,"byte_end":4290,"line_start":126,"line_end":126,"column_start":3,"column_end":53,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $type, $const_cons }","highlight_start":3,"highlight_end":53}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6084,"byte_end":6120,"line_start":179,"line_end":179,"column_start":1,"column_end":37,"is_primary":false,"text":[{"text":"macro_rules! input_array_ref_forward {","highlight_start":1,"highlight_end":37}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:183:4\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m183\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6179,"byte_end":6185,"line_start":181,"line_end":181,"column_start":39,"column_end":45,"is_primary":true,"text":[{"text":"\t\timpl $crate::core::ToInputArray for &$type {","highlight_start":39,"highlight_end":45}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4134,"byte_end":4176,"line_start":122,"line_end":122,"column_start":3,"column_end":45,"is_primary":false,"text":[{"text":"\t\t$crate::input_array_ref_forward! { $type }","highlight_start":3,"highlight_end":45}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4240,"byte_end":4290,"line_start":126,"line_end":126,"column_start":3,"column_end":53,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $type, $const_cons }","highlight_start":3,"highlight_end":53}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6084,"byte_end":6120,"line_start":179,"line_end":179,"column_start":1,"column_end":37,"is_primary":false,"text":[{"text":"macro_rules! input_array_ref_forward {","highlight_start":1,"highlight_end":37}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:181:39\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m181\u001b[0m \u001b[1m\u001b[94m|\u001b[0m impl $crate::core::ToInputArray for &$type {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4355,"byte_end":4463,"line_start":130,"line_end":130,"column_start":4,"column_end":112,"is_primary":true,"text":[{"text":"\t\t\tfn output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_OutputArray>> {","highlight_start":4,"highlight_end":112}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:130:4\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m130\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_OutputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_output_array` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4592,"byte_end":4711,"line_start":137,"line_end":137,"column_start":4,"column_end":123,"is_primary":true,"text":[{"text":"\t\t\tfn input_output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_InputOutputArray>> {","highlight_start":4,"highlight_end":123}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:137:4\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m137\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn input_output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_InputOutputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_output_array` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6633,"byte_end":6741,"line_start":197,"line_end":197,"column_start":4,"column_end":112,"is_primary":true,"text":[{"text":"\t\t\tfn output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_OutputArray>> {","highlight_start":4,"highlight_end":112}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4779,"byte_end":4822,"line_start":142,"line_end":142,"column_start":3,"column_end":46,"is_primary":false,"text":[{"text":"\t\t$crate::output_array_ref_forward! { $type }","highlight_start":3,"highlight_end":46}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::output_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6507,"byte_end":6544,"line_start":193,"line_end":193,"column_start":1,"column_end":38,"is_primary":false,"text":[{"text":"macro_rules! output_array_ref_forward {","highlight_start":1,"highlight_end":38}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:197:4\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m197\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_OutputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::output_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6604,"byte_end":6614,"line_start":195,"line_end":195,"column_start":40,"column_end":50,"is_primary":true,"text":[{"text":"\t\timpl $crate::core::ToOutputArray for &mut $type {","highlight_start":40,"highlight_end":50}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4779,"byte_end":4822,"line_start":142,"line_end":142,"column_start":3,"column_end":46,"is_primary":false,"text":[{"text":"\t\t$crate::output_array_ref_forward! { $type }","highlight_start":3,"highlight_end":46}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::output_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6507,"byte_end":6544,"line_start":193,"line_end":193,"column_start":1,"column_end":38,"is_primary":false,"text":[{"text":"macro_rules! output_array_ref_forward {","highlight_start":1,"highlight_end":38}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:195:40\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m195\u001b[0m \u001b[1m\u001b[94m|\u001b[0m impl $crate::core::ToOutputArray for &mut $type {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::output_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6854,"byte_end":6973,"line_start":204,"line_end":204,"column_start":4,"column_end":123,"is_primary":true,"text":[{"text":"\t\t\tfn input_output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_InputOutputArray>> {","highlight_start":4,"highlight_end":123}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4779,"byte_end":4822,"line_start":142,"line_end":142,"column_start":3,"column_end":46,"is_primary":false,"text":[{"text":"\t\t$crate::output_array_ref_forward! { $type }","highlight_start":3,"highlight_end":46}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::output_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6507,"byte_end":6544,"line_start":193,"line_end":193,"column_start":1,"column_end":38,"is_primary":false,"text":[{"text":"macro_rules! output_array_ref_forward {","highlight_start":1,"highlight_end":38}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:204:4\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m204\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn input_output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_InputOutputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::output_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6825,"byte_end":6835,"line_start":202,"line_end":202,"column_start":45,"column_end":55,"is_primary":true,"text":[{"text":"\t\timpl $crate::core::ToInputOutputArray for &mut $type {","highlight_start":45,"highlight_end":55}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4779,"byte_end":4822,"line_start":142,"line_end":142,"column_start":3,"column_end":46,"is_primary":false,"text":[{"text":"\t\t$crate::output_array_ref_forward! { $type }","highlight_start":3,"highlight_end":46}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::output_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6507,"byte_end":6544,"line_start":193,"line_end":193,"column_start":1,"column_end":38,"is_primary":false,"text":[{"text":"macro_rules! output_array_ref_forward {","highlight_start":1,"highlight_end":38}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:202:45\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m202\u001b[0m \u001b[1m\u001b[94m|\u001b[0m impl $crate::core::ToInputOutputArray for &mut $type {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::output_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector>: vector::vector_extern::VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":13289,"byte_end":13315,"line_start":179,"line_end":179,"column_start":42,"column_end":68,"is_primary":true,"text":[{"text":"\tpub fn nms_boxes_batched_1_def(bboxes: &core::Vector, scores: &core::Vector, class_ids: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector) -> Result<()> {","highlight_start":42,"highlight_end":68}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector>: vector::vector_extern::VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:179:42\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m179\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn nms_boxes_batched_1_def(bboxes: &core::Vector, scores: &core::Vector, class_ids: &core::Vector, score_thr\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector>: vector::vector_extern::VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":14025,"byte_end":14051,"line_start":191,"line_end":191,"column_start":38,"column_end":64,"is_primary":true,"text":[{"text":"\tpub fn nms_boxes_batched_1(bboxes: &core::Vector, scores: &core::Vector, class_ids: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector, eta: f32, top_k: i32) -> Result<()> {","highlight_start":38,"highlight_end":64}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector>: vector::vector_extern::VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:191:38\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m191\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn nms_boxes_batched_1(bboxes: &core::Vector, scores: &core::Vector, class_ids: &core::Vector, score_thresho\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector>: vector::vector_extern::VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":18067,"byte_end":18093,"line_start":253,"line_end":253,"column_start":36,"column_end":62,"is_primary":true,"text":[{"text":"\tpub fn nms_boxes_f64_def(bboxes: &core::Vector, scores: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector) -> Result<()> {","highlight_start":36,"highlight_end":62}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector>: vector::vector_extern::VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:253:36\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m253\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn nms_boxes_f64_def(bboxes: &core::Vector, scores: &core::Vector, score_threshold: f32, nms_threshold: f32, indi\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector>: vector::vector_extern::VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":18708,"byte_end":18734,"line_start":265,"line_end":265,"column_start":32,"column_end":58,"is_primary":true,"text":[{"text":"\tpub fn nms_boxes_f64(bboxes: &core::Vector, scores: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector, eta: f32, top_k: i32) -> Result<()> {","highlight_start":32,"highlight_end":58}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector>: vector::vector_extern::VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:265:32\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m265\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn nms_boxes_f64(bboxes: &core::Vector, scores: &core::Vector, score_threshold: f32, nms_threshold: f32, indices:\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `Vector>: VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5604,"byte_end":5609,"line_start":165,"line_end":165,"column_start":19,"column_end":24,"is_primary":true,"text":[{"text":"\t\t\tfn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {","highlight_start":19,"highlight_end":24}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-6654702089584336080.txt'","code":null,"level":"note","spans":[],"children":[],"rendered":null},{"message":"consider using `--verbose` to print the full type name to the console","code":null,"level":"note","spans":[],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `Vector>: VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:165:19\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m165\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-6654702089584336080.txt'\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: consider using `--verbose` to print the full type name to the console\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `Vector>: VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6219,"byte_end":6224,"line_start":183,"line_end":183,"column_start":19,"column_end":24,"is_primary":true,"text":[{"text":"\t\t\tfn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {","highlight_start":19,"highlight_end":24}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5769,"byte_end":5866,"line_start":170,"line_end":170,"column_start":3,"column_end":100,"is_primary":false,"text":[{"text":"\t\t$crate::input_array_ref_forward! { $crate::core::Vector<$crate::boxed_ref::BoxedRef<'_, $type>> }","highlight_start":3,"highlight_end":100}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6084,"byte_end":6120,"line_start":179,"line_end":179,"column_start":1,"column_end":37,"is_primary":false,"text":[{"text":"macro_rules! input_array_ref_forward {","highlight_start":1,"highlight_end":37}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-14695891245440010904.txt'","code":null,"level":"note","spans":[],"children":[],"rendered":null},{"message":"consider using `--verbose` to print the full type name to the console","code":null,"level":"note","spans":[],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `Vector>: VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:183:19\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m183\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-14695891245440010904.txt'\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: consider using `--verbose` to print the full type name to the console\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3987,"byte_end":3991,"line_start":117,"line_end":117,"column_start":20,"column_end":24,"is_primary":true,"text":[{"text":"\t\t\tfn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {","highlight_start":20,"highlight_end":24}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4240,"byte_end":4290,"line_start":126,"line_end":126,"column_start":3,"column_end":53,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $type, $const_cons }","highlight_start":3,"highlight_end":53}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:117:20\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m117\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_output_array` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6220,"byte_end":6224,"line_start":183,"line_end":183,"column_start":20,"column_end":24,"is_primary":true,"text":[{"text":"\t\t\tfn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {","highlight_start":20,"highlight_end":24}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4134,"byte_end":4176,"line_start":122,"line_end":122,"column_start":3,"column_end":45,"is_primary":false,"text":[{"text":"\t\t$crate::input_array_ref_forward! { $type }","highlight_start":3,"highlight_end":45}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4240,"byte_end":4290,"line_start":126,"line_end":126,"column_start":3,"column_end":53,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $type, $const_cons }","highlight_start":3,"highlight_end":53}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6084,"byte_end":6120,"line_start":179,"line_end":179,"column_start":1,"column_end":37,"is_primary":false,"text":[{"text":"macro_rules! input_array_ref_forward {","highlight_start":1,"highlight_end":37}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:183:20\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m183\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn input_array(&self) -> $crate::Result<$crate::boxed_ref::BoxedRef<'_, $crate::core::_InputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4376,"byte_end":4380,"line_start":130,"line_end":130,"column_start":25,"column_end":29,"is_primary":true,"text":[{"text":"\t\t\tfn output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_OutputArray>> {","highlight_start":25,"highlight_end":29}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:130:25\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m130\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_OutputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_output_array` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4619,"byte_end":4623,"line_start":137,"line_end":137,"column_start":31,"column_end":35,"is_primary":true,"text":[{"text":"\t\t\tfn input_output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_InputOutputArray>> {","highlight_start":31,"highlight_end":35}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:137:31\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m137\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn input_output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_InputOutputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_output_array` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6654,"byte_end":6658,"line_start":197,"line_end":197,"column_start":25,"column_end":29,"is_primary":true,"text":[{"text":"\t\t\tfn output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_OutputArray>> {","highlight_start":25,"highlight_end":29}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4779,"byte_end":4822,"line_start":142,"line_end":142,"column_start":3,"column_end":46,"is_primary":false,"text":[{"text":"\t\t$crate::output_array_ref_forward! { $type }","highlight_start":3,"highlight_end":46}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::output_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6507,"byte_end":6544,"line_start":193,"line_end":193,"column_start":1,"column_end":38,"is_primary":false,"text":[{"text":"macro_rules! output_array_ref_forward {","highlight_start":1,"highlight_end":38}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:197:25\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m197\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_OutputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::output_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6881,"byte_end":6885,"line_start":204,"line_end":204,"column_start":31,"column_end":35,"is_primary":true,"text":[{"text":"\t\t\tfn input_output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_InputOutputArray>> {","highlight_start":31,"highlight_end":35}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4779,"byte_end":4822,"line_start":142,"line_end":142,"column_start":3,"column_end":46,"is_primary":false,"text":[{"text":"\t\t$crate::output_array_ref_forward! { $type }","highlight_start":3,"highlight_end":46}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::output_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6507,"byte_end":6544,"line_start":193,"line_end":193,"column_start":1,"column_end":38,"is_primary":false,"text":[{"text":"macro_rules! output_array_ref_forward {","highlight_start":1,"highlight_end":38}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:204:31\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m204\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn input_output_array(&mut self) -> $crate::Result<$crate::boxed_ref::BoxedRefMut<'_, $crate::core::_InputOutputArray>> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::output_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/videoio.rs","byte_start":86925,"byte_end":86967,"line_start":1605,"line_end":1605,"column_start":29,"column_end":71,"is_primary":true,"text":[{"text":"\t\tpub fn wait_any(streams: &core::Vector, ready_index: &mut core::Vector, timeout_ns: i64) -> Result {","highlight_start":29,"highlight_end":71}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/videoio.rs:1605:29\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m1605\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn wait_any(streams: &core::Vector, ready_index: &mut core::Vector, timeout_ns: i64) -> Result` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/videoio.rs","byte_start":88310,"byte_end":88352,"line_start":1633,"line_end":1633,"column_start":33,"column_end":75,"is_primary":true,"text":[{"text":"\t\tpub fn wait_any_def(streams: &core::Vector, ready_index: &mut core::Vector) -> Result {","highlight_start":33,"highlight_end":75}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/videoio.rs:1633:33\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m1633\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn wait_any_def(streams: &core::Vector, ready_index: &mut core::Vector) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector>: vector::vector_extern::VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":439305,"byte_end":439336,"line_start":11470,"line_end":11470,"column_start":39,"column_end":70,"is_primary":true,"text":[{"text":"\t\tfn try_quantize(&mut self, scales: &core::Vector>, zeropoints: &core::Vector>, params: &mut impl crate::dnn::LayerParamsTrait) -> Result {","highlight_start":39,"highlight_end":70}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector>: vector::vector_extern::VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:11470:39\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m11470\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn try_quantize(&mut self, scales: &core::Vector>, zeropoints: &core::Vector>, params: &mut impl cr\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `Vector>: VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":446287,"byte_end":446335,"line_start":11619,"line_end":11619,"column_start":99,"column_end":147,"is_primary":true,"text":[{"text":"\t\tfn init_ngraph(&mut self, inputs: &core::Vector>, nodes: &core::Vector>) -> Result> {","highlight_start":99,"highlight_end":147}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-16206489057855986019.txt'","code":null,"level":"note","spans":[],"children":[],"rendered":null},{"message":"consider using `--verbose` to print the full type name to the console","code":null,"level":"note","spans":[],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `Vector>: VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:11619:99\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m11619\u001b[0m \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m...\u001b[0mdnn::BackendWrapper>>, nodes: &core::Vector>) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-16206489057855986019.txt'\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: consider using `--verbose` to print the full type name to the console\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `Vector>: VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":447638,"byte_end":447686,"line_start":11639,"line_end":11639,"column_start":98,"column_end":146,"is_primary":true,"text":[{"text":"\t\tfn init_webnn(&mut self, inputs: &core::Vector>, nodes: &core::Vector>) -> Result> {","highlight_start":98,"highlight_end":146}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-16206489057855986019.txt'","code":null,"level":"note","spans":[],"children":[],"rendered":null},{"message":"consider using `--verbose` to print the full type name to the console","code":null,"level":"note","spans":[],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `Vector>: VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:11639:98\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m11639\u001b[0m \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m...\u001b[0mdnn::BackendWrapper>>, nodes: &core::Vector>) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-16206489057855986019.txt'\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: consider using `--verbose` to print the full type name to the console\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `Vector>: VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":450500,"byte_end":450548,"line_start":11688,"line_end":11688,"column_start":160,"column_end":208,"is_primary":true,"text":[{"text":"\t\tfn init_cann(&mut self, inputs: &core::Vector>, outputs: &core::Vector>, nodes: &core::Vector>) -> Result> {","highlight_start":160,"highlight_end":208}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-16206489057855986019.txt'","code":null,"level":"note","spans":[],"children":[],"rendered":null},{"message":"consider using `--verbose` to print the full type name to the console","code":null,"level":"note","spans":[],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `Vector>: VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:11688:160\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m11688\u001b[0m \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m...\u001b[0mdnn::BackendWrapper>>, nodes: &core::Vector>) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-16206489057855986019.txt'\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: consider using `--verbose` to print the full type name to the console\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `new_rows_cols` found for struct `hub::core::Mat` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":5290,"byte_end":5303,"line_start":179,"line_end":179,"column_start":32,"column_end":45,"is_primary":true,"text":[{"text":"\t\tlet mut out = unsafe { Self::new_rows_cols(1, col_count_i32(s.len())?, T::opencv_type()) }?;","highlight_start":32,"highlight_end":45}],"label":"function or associated item not found in `hub::core::Mat`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":689783,"byte_end":689797,"line_start":15450,"line_end":15450,"column_start":2,"column_end":16,"is_primary":false,"text":[{"text":"\tpub struct Mat {","highlight_start":2,"highlight_end":16}],"label":"function or associated item `new_rows_cols` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\nmat::::from_exact_iter\nmat::::from_slice_2d\nhub::core::Mat::diag_mat","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":5168,"byte_end":5256,"line_start":178,"line_end":178,"column_start":2,"column_end":90,"is_primary":true,"text":[{"text":"\tpub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {","highlight_start":2,"highlight_end":90}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":7223,"byte_end":7295,"line_start":239,"line_end":239,"column_start":2,"column_end":74,"is_primary":true,"text":[{"text":"\tpub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {","highlight_start":2,"highlight_end":74}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":690251,"byte_end":690317,"line_start":15473,"line_end":15473,"column_start":3,"column_end":69,"is_primary":true,"text":[{"text":"\t\tpub fn diag_mat(d: &impl core::MatTraitConst) -> Result {","highlight_start":3,"highlight_end":69}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is a method `rowscols` with a similar name, but with different arguments","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":726416,"byte_end":726545,"line_start":16331,"line_end":16331,"column_start":3,"column_end":132,"is_primary":true,"text":[{"text":"\t\tfn rowscols(&self, mut row_range: impl core::RangeTrait, mut col_range: impl core::RangeTrait) -> Result> {","highlight_start":3,"highlight_end":132}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `new_rows_cols` found for struct `hub::core::Mat` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:179:32\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m179\u001b[0m \u001b[1m\u001b[94m|\u001b[0m let mut out = unsafe { Self::new_rows_cols(1, col_count_i32(s.len())?, T::opencv_type()) }?;\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `hub::core::Mat`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15450:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15450\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Mat {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m--------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `new_rows_cols` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\n mat::::from_exact_iter\n mat::::from_slice_2d\n hub::core::Mat::diag_mat\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:178:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m178\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15473:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15473\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn diag_mat(d: &impl core::MatTraitConst) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is a method `rowscols` with a similar name, but with different arguments\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:16331:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m16331\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn rowscols(&self, mut row_range: impl core::RangeTrait, mut col_range: impl core::RangeTrait) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `new_rows_cols_with_default` found for struct `hub::core::Mat` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":7525,"byte_end":7551,"line_start":250,"line_end":250,"column_start":23,"column_end":49,"is_primary":true,"text":[{"text":"\t\tlet mut out = Self::new_rows_cols_with_default(row_count, col_count, T::opencv_type(), Scalar::all(0.))?;","highlight_start":23,"highlight_end":49}],"label":"function or associated item not found in `hub::core::Mat`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":689783,"byte_end":689797,"line_start":15450,"line_end":15450,"column_start":2,"column_end":16,"is_primary":false,"text":[{"text":"\tpub struct Mat {","highlight_start":2,"highlight_end":16}],"label":"function or associated item `new_rows_cols_with_default` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\nmat::::from_exact_iter\nmat::::from_slice_2d\nhub::core::Mat::diag_mat","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":5168,"byte_end":5256,"line_start":178,"line_end":178,"column_start":2,"column_end":90,"is_primary":true,"text":[{"text":"\tpub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {","highlight_start":2,"highlight_end":90}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":7223,"byte_end":7295,"line_start":239,"line_end":239,"column_start":2,"column_end":74,"is_primary":true,"text":[{"text":"\tpub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {","highlight_start":2,"highlight_end":74}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":690251,"byte_end":690317,"line_start":15473,"line_end":15473,"column_start":3,"column_end":69,"is_primary":true,"text":[{"text":"\t\tpub fn diag_mat(d: &impl core::MatTraitConst) -> Result {","highlight_start":3,"highlight_end":69}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `new_rows_cols_with_data` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":8259,"byte_end":8366,"line_start":275,"line_end":275,"column_start":2,"column_end":109,"is_primary":true,"text":[{"text":"\tpub fn new_rows_cols_with_data(rows: i32, cols: i32, data: &[T]) -> Result> {","highlight_start":2,"highlight_end":109}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `new_rows_cols_with_default` found for struct `hub::core::Mat` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:250:23\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m250\u001b[0m \u001b[1m\u001b[94m|\u001b[0m let mut out = Self::new_rows_cols_with_default(row_count, col_count, T::opencv_type(), Scalar::all(0.))?;\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `hub::core::Mat`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15450:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15450\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Mat {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m--------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `new_rows_cols_with_default` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\n mat::::from_exact_iter\n mat::::from_slice_2d\n hub::core::Mat::diag_mat\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:178:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m178\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15473:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15473\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn diag_mat(d: &impl core::MatTraitConst) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `new_rows_cols_with_data` with a similar name\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:275:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m275\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn new_rows_cols_with_data(rows: i32, cols: i32, data: &[T]) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `new_rows_cols_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":8444,"byte_end":8478,"line_start":278,"line_end":278,"column_start":10,"column_end":44,"is_primary":true,"text":[{"text":"\t\t\tSelf::new_rows_cols_with_data_unsafe_def(rows, cols, T::opencv_type(), data.as_ptr().cast::().cast_mut())","highlight_start":10,"highlight_end":44}],"label":"function or associated item not found in `hub::core::Mat`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":689783,"byte_end":689797,"line_start":15450,"line_end":15450,"column_start":2,"column_end":16,"is_primary":false,"text":[{"text":"\tpub struct Mat {","highlight_start":2,"highlight_end":16}],"label":"function or associated item `new_rows_cols_with_data_unsafe_def` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\nmat::::from_exact_iter\nmat::::from_slice_2d\nhub::core::Mat::diag_mat","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":5168,"byte_end":5256,"line_start":178,"line_end":178,"column_start":2,"column_end":90,"is_primary":true,"text":[{"text":"\tpub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {","highlight_start":2,"highlight_end":90}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":7223,"byte_end":7295,"line_start":239,"line_end":239,"column_start":2,"column_end":74,"is_primary":true,"text":[{"text":"\tpub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {","highlight_start":2,"highlight_end":74}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":690251,"byte_end":690317,"line_start":15473,"line_end":15473,"column_start":3,"column_end":69,"is_primary":true,"text":[{"text":"\t\tpub fn diag_mat(d: &impl core::MatTraitConst) -> Result {","highlight_start":3,"highlight_end":69}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `new_rows_cols_with_data` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":8259,"byte_end":8366,"line_start":275,"line_end":275,"column_start":2,"column_end":109,"is_primary":true,"text":[{"text":"\tpub fn new_rows_cols_with_data(rows: i32, cols: i32, data: &[T]) -> Result> {","highlight_start":2,"highlight_end":109}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `new_rows_cols_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:278:10\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m278\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self::new_rows_cols_with_data_unsafe_def(rows, cols, T::opencv_type(), data.as_ptr().cast::().cast_mut())\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `hub::core::Mat`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15450:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15450\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Mat {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m--------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `new_rows_cols_with_data_unsafe_def` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\n mat::::from_exact_iter\n mat::::from_slice_2d\n hub::core::Mat::diag_mat\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:178:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m178\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15473:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15473\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn diag_mat(d: &impl core::MatTraitConst) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `new_rows_cols_with_data` with a similar name\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:275:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m275\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn new_rows_cols_with_data(rows: i32, cols: i32, data: &[T]) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `new_rows_cols_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":8902,"byte_end":8936,"line_start":288,"line_end":288,"column_start":10,"column_end":44,"is_primary":true,"text":[{"text":"\t\t\tSelf::new_rows_cols_with_data_unsafe_def(rows, cols, T::opencv_type(), data.as_ptr().cast::().cast_mut())","highlight_start":10,"highlight_end":44}],"label":"function or associated item not found in `hub::core::Mat`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":689783,"byte_end":689797,"line_start":15450,"line_end":15450,"column_start":2,"column_end":16,"is_primary":false,"text":[{"text":"\tpub struct Mat {","highlight_start":2,"highlight_end":16}],"label":"function or associated item `new_rows_cols_with_data_unsafe_def` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\nmat::::from_exact_iter\nmat::::from_slice_2d\nhub::core::Mat::diag_mat","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":5168,"byte_end":5256,"line_start":178,"line_end":178,"column_start":2,"column_end":90,"is_primary":true,"text":[{"text":"\tpub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {","highlight_start":2,"highlight_end":90}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":7223,"byte_end":7295,"line_start":239,"line_end":239,"column_start":2,"column_end":74,"is_primary":true,"text":[{"text":"\tpub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {","highlight_start":2,"highlight_end":74}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":690251,"byte_end":690317,"line_start":15473,"line_end":15473,"column_start":3,"column_end":69,"is_primary":true,"text":[{"text":"\t\tpub fn diag_mat(d: &impl core::MatTraitConst) -> Result {","highlight_start":3,"highlight_end":69}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `new_rows_cols_with_data` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":8259,"byte_end":8366,"line_start":275,"line_end":275,"column_start":2,"column_end":109,"is_primary":true,"text":[{"text":"\tpub fn new_rows_cols_with_data(rows: i32, cols: i32, data: &[T]) -> Result> {","highlight_start":2,"highlight_end":109}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `new_rows_cols_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:288:10\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m288\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self::new_rows_cols_with_data_unsafe_def(rows, cols, T::opencv_type(), data.as_ptr().cast::().cast_mut())\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `hub::core::Mat`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15450:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15450\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Mat {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m--------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `new_rows_cols_with_data_unsafe_def` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\n mat::::from_exact_iter\n mat::::from_slice_2d\n hub::core::Mat::diag_mat\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:178:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m178\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15473:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15473\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn diag_mat(d: &impl core::MatTraitConst) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `new_rows_cols_with_data` with a similar name\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:275:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m275\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn new_rows_cols_with_data(rows: i32, cols: i32, data: &[T]) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `new_rows_cols_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":9354,"byte_end":9388,"line_start":298,"line_end":298,"column_start":19,"column_end":53,"is_primary":true,"text":[{"text":"\t\t\tunsafe { Self::new_rows_cols_with_data_unsafe_def(rows, cols, T::opencv_type(), data.as_mut_ptr().cast::()) }?;","highlight_start":19,"highlight_end":53}],"label":"function or associated item not found in `hub::core::Mat`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":689783,"byte_end":689797,"line_start":15450,"line_end":15450,"column_start":2,"column_end":16,"is_primary":false,"text":[{"text":"\tpub struct Mat {","highlight_start":2,"highlight_end":16}],"label":"function or associated item `new_rows_cols_with_data_unsafe_def` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\nmat::::from_exact_iter\nmat::::from_slice_2d\nhub::core::Mat::diag_mat","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":5168,"byte_end":5256,"line_start":178,"line_end":178,"column_start":2,"column_end":90,"is_primary":true,"text":[{"text":"\tpub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {","highlight_start":2,"highlight_end":90}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":7223,"byte_end":7295,"line_start":239,"line_end":239,"column_start":2,"column_end":74,"is_primary":true,"text":[{"text":"\tpub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {","highlight_start":2,"highlight_end":74}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":690251,"byte_end":690317,"line_start":15473,"line_end":15473,"column_start":3,"column_end":69,"is_primary":true,"text":[{"text":"\t\tpub fn diag_mat(d: &impl core::MatTraitConst) -> Result {","highlight_start":3,"highlight_end":69}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `new_rows_cols_with_data` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":8259,"byte_end":8366,"line_start":275,"line_end":275,"column_start":2,"column_end":109,"is_primary":true,"text":[{"text":"\tpub fn new_rows_cols_with_data(rows: i32, cols: i32, data: &[T]) -> Result> {","highlight_start":2,"highlight_end":109}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `new_rows_cols_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:298:19\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m298\u001b[0m \u001b[1m\u001b[94m|\u001b[0m unsafe { Self::new_rows_cols_with_data_unsafe_def(rows, cols, T::opencv_type(), data.as_mut_ptr().cast::()) }?;\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `hub::core::Mat`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15450:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15450\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Mat {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m--------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `new_rows_cols_with_data_unsafe_def` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\n mat::::from_exact_iter\n mat::::from_slice_2d\n hub::core::Mat::diag_mat\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:178:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m178\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15473:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15473\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn diag_mat(d: &impl core::MatTraitConst) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `new_rows_cols_with_data` with a similar name\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:275:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m275\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn new_rows_cols_with_data(rows: i32, cols: i32, data: &[T]) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `new_rows_cols_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":9825,"byte_end":9859,"line_start":307,"line_end":307,"column_start":19,"column_end":53,"is_primary":true,"text":[{"text":"\t\t\tunsafe { Self::new_rows_cols_with_data_unsafe_def(rows, cols, T::opencv_type(), data.as_mut_ptr().cast::()) }?;","highlight_start":19,"highlight_end":53}],"label":"function or associated item not found in `hub::core::Mat`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":689783,"byte_end":689797,"line_start":15450,"line_end":15450,"column_start":2,"column_end":16,"is_primary":false,"text":[{"text":"\tpub struct Mat {","highlight_start":2,"highlight_end":16}],"label":"function or associated item `new_rows_cols_with_data_unsafe_def` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\nmat::::from_exact_iter\nmat::::from_slice_2d\nhub::core::Mat::diag_mat","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":5168,"byte_end":5256,"line_start":178,"line_end":178,"column_start":2,"column_end":90,"is_primary":true,"text":[{"text":"\tpub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {","highlight_start":2,"highlight_end":90}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":7223,"byte_end":7295,"line_start":239,"line_end":239,"column_start":2,"column_end":74,"is_primary":true,"text":[{"text":"\tpub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {","highlight_start":2,"highlight_end":74}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":690251,"byte_end":690317,"line_start":15473,"line_end":15473,"column_start":3,"column_end":69,"is_primary":true,"text":[{"text":"\t\tpub fn diag_mat(d: &impl core::MatTraitConst) -> Result {","highlight_start":3,"highlight_end":69}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `new_rows_cols_with_data` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":8259,"byte_end":8366,"line_start":275,"line_end":275,"column_start":2,"column_end":109,"is_primary":true,"text":[{"text":"\tpub fn new_rows_cols_with_data(rows: i32, cols: i32, data: &[T]) -> Result> {","highlight_start":2,"highlight_end":109}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `new_rows_cols_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:307:19\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m307\u001b[0m \u001b[1m\u001b[94m|\u001b[0m unsafe { Self::new_rows_cols_with_data_unsafe_def(rows, cols, T::opencv_type(), data.as_mut_ptr().cast::()) }?;\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `hub::core::Mat`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15450:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15450\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Mat {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m--------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `new_rows_cols_with_data_unsafe_def` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\n mat::::from_exact_iter\n mat::::from_slice_2d\n hub::core::Mat::diag_mat\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:178:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m178\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15473:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15473\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn diag_mat(d: &impl core::MatTraitConst) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `new_rows_cols_with_data` with a similar name\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:275:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m275\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn new_rows_cols_with_data(rows: i32, cols: i32, data: &[T]) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `new_size_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":10247,"byte_end":10276,"line_start":315,"line_end":315,"column_start":26,"column_end":55,"is_primary":true,"text":[{"text":"\t\tlet m = unsafe { Self::new_size_with_data_unsafe_def(size, T::opencv_type(), data.as_ptr().cast::().cast_mut()) }?;","highlight_start":26,"highlight_end":55}],"label":"function or associated item not found in `hub::core::Mat`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":689783,"byte_end":689797,"line_start":15450,"line_end":15450,"column_start":2,"column_end":16,"is_primary":false,"text":[{"text":"\tpub struct Mat {","highlight_start":2,"highlight_end":16}],"label":"function or associated item `new_size_with_data_unsafe_def` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\nmat::::from_exact_iter\nmat::::from_slice_2d\nhub::core::Mat::diag_mat","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":5168,"byte_end":5256,"line_start":178,"line_end":178,"column_start":2,"column_end":90,"is_primary":true,"text":[{"text":"\tpub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {","highlight_start":2,"highlight_end":90}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":7223,"byte_end":7295,"line_start":239,"line_end":239,"column_start":2,"column_end":74,"is_primary":true,"text":[{"text":"\tpub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {","highlight_start":2,"highlight_end":74}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":690251,"byte_end":690317,"line_start":15473,"line_end":15473,"column_start":3,"column_end":69,"is_primary":true,"text":[{"text":"\t\tpub fn diag_mat(d: &impl core::MatTraitConst) -> Result {","highlight_start":3,"highlight_end":69}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `new_size_with_data` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":10067,"byte_end":10159,"line_start":313,"line_end":313,"column_start":2,"column_end":94,"is_primary":true,"text":[{"text":"\tpub fn new_size_with_data(size: Size, data: &[T]) -> Result> {","highlight_start":2,"highlight_end":94}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `new_size_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:315:26\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m315\u001b[0m \u001b[1m\u001b[94m|\u001b[0m let m = unsafe { Self::new_size_with_data_unsafe_def(size, T::opencv_type(), data.as_ptr().cast::().cast_mut()) }?;\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `hub::core::Mat`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15450:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15450\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Mat {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m--------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `new_size_with_data_unsafe_def` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\n mat::::from_exact_iter\n mat::::from_slice_2d\n hub::core::Mat::diag_mat\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:178:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m178\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15473:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15473\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn diag_mat(d: &impl core::MatTraitConst) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `new_size_with_data` with a similar name\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:313:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m313\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn new_size_with_data(size: Size, data: &[T]) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `new_size_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":10681,"byte_end":10710,"line_start":323,"line_end":323,"column_start":26,"column_end":55,"is_primary":true,"text":[{"text":"\t\tlet m = unsafe { Self::new_size_with_data_unsafe_def(size, T::opencv_type(), data.as_mut_ptr().cast::()) }?;","highlight_start":26,"highlight_end":55}],"label":"function or associated item not found in `hub::core::Mat`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":689783,"byte_end":689797,"line_start":15450,"line_end":15450,"column_start":2,"column_end":16,"is_primary":false,"text":[{"text":"\tpub struct Mat {","highlight_start":2,"highlight_end":16}],"label":"function or associated item `new_size_with_data_unsafe_def` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\nmat::::from_exact_iter\nmat::::from_slice_2d\nhub::core::Mat::diag_mat","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":5168,"byte_end":5256,"line_start":178,"line_end":178,"column_start":2,"column_end":90,"is_primary":true,"text":[{"text":"\tpub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {","highlight_start":2,"highlight_end":90}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":7223,"byte_end":7295,"line_start":239,"line_end":239,"column_start":2,"column_end":74,"is_primary":true,"text":[{"text":"\tpub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {","highlight_start":2,"highlight_end":74}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":690251,"byte_end":690317,"line_start":15473,"line_end":15473,"column_start":3,"column_end":69,"is_primary":true,"text":[{"text":"\t\tpub fn diag_mat(d: &impl core::MatTraitConst) -> Result {","highlight_start":3,"highlight_end":69}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `new_size_with_data` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":10067,"byte_end":10159,"line_start":313,"line_end":313,"column_start":2,"column_end":94,"is_primary":true,"text":[{"text":"\tpub fn new_size_with_data(size: Size, data: &[T]) -> Result> {","highlight_start":2,"highlight_end":94}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `new_size_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:323:26\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m323\u001b[0m \u001b[1m\u001b[94m|\u001b[0m let m = unsafe { Self::new_size_with_data_unsafe_def(size, T::opencv_type(), data.as_mut_ptr().cast::()) }?;\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `hub::core::Mat`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15450:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15450\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Mat {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m--------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `new_size_with_data_unsafe_def` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\n mat::::from_exact_iter\n mat::::from_slice_2d\n hub::core::Mat::diag_mat\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:178:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m178\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15473:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15473\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn diag_mat(d: &impl core::MatTraitConst) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `new_size_with_data` with a similar name\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:313:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m313\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn new_size_with_data(size: Size, data: &[T]) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `new_nd_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":11088,"byte_end":11115,"line_start":331,"line_end":331,"column_start":26,"column_end":53,"is_primary":true,"text":[{"text":"\t\tlet m = unsafe { Self::new_nd_with_data_unsafe_def(sizes, T::opencv_type(), data.as_ptr().cast::().cast_mut()) }?;","highlight_start":26,"highlight_end":53}],"label":"function or associated item not found in `hub::core::Mat`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":689783,"byte_end":689797,"line_start":15450,"line_end":15450,"column_start":2,"column_end":16,"is_primary":false,"text":[{"text":"\tpub struct Mat {","highlight_start":2,"highlight_end":16}],"label":"function or associated item `new_nd_with_data_unsafe_def` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\nmat::::from_exact_iter\nmat::::from_slice_2d\nhub::core::Mat::diag_mat","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":5168,"byte_end":5256,"line_start":178,"line_end":178,"column_start":2,"column_end":90,"is_primary":true,"text":[{"text":"\tpub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {","highlight_start":2,"highlight_end":90}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":7223,"byte_end":7295,"line_start":239,"line_end":239,"column_start":2,"column_end":74,"is_primary":true,"text":[{"text":"\tpub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {","highlight_start":2,"highlight_end":74}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":690251,"byte_end":690317,"line_start":15473,"line_end":15473,"column_start":3,"column_end":69,"is_primary":true,"text":[{"text":"\t\tpub fn diag_mat(d: &impl core::MatTraitConst) -> Result {","highlight_start":3,"highlight_end":69}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `new_nd_with_data` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":10912,"byte_end":11021,"line_start":329,"line_end":329,"column_start":2,"column_end":111,"is_primary":true,"text":[{"text":"\tpub fn new_nd_with_data<'data, T: DataType>(sizes: &[i32], data: &'data [T]) -> Result> {","highlight_start":2,"highlight_end":111}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `new_nd_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:331:26\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m331\u001b[0m \u001b[1m\u001b[94m|\u001b[0m let m = unsafe { Self::new_nd_with_data_unsafe_def(sizes, T::opencv_type(), data.as_ptr().cast::().cast_mut()) }?;\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `hub::core::Mat`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15450:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15450\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Mat {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m--------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `new_nd_with_data_unsafe_def` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\n mat::::from_exact_iter\n mat::::from_slice_2d\n hub::core::Mat::diag_mat\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:178:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m178\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15473:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15473\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn diag_mat(d: &impl core::MatTraitConst) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `new_nd_with_data` with a similar name\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:329:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m329\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn new_nd_with_data<'data, T: DataType>(sizes: &[i32], data: &'data [T]) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `new_nd_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":11509,"byte_end":11536,"line_start":339,"line_end":339,"column_start":26,"column_end":53,"is_primary":true,"text":[{"text":"\t\tlet m = unsafe { Self::new_nd_with_data_unsafe_def(sizes, T::opencv_type(), data.as_mut_ptr().cast::()) }?;","highlight_start":26,"highlight_end":53}],"label":"function or associated item not found in `hub::core::Mat`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":689783,"byte_end":689797,"line_start":15450,"line_end":15450,"column_start":2,"column_end":16,"is_primary":false,"text":[{"text":"\tpub struct Mat {","highlight_start":2,"highlight_end":16}],"label":"function or associated item `new_nd_with_data_unsafe_def` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\nmat::::from_exact_iter\nmat::::from_slice_2d\nhub::core::Mat::diag_mat","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":5168,"byte_end":5256,"line_start":178,"line_end":178,"column_start":2,"column_end":90,"is_primary":true,"text":[{"text":"\tpub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {","highlight_start":2,"highlight_end":90}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":7223,"byte_end":7295,"line_start":239,"line_end":239,"column_start":2,"column_end":74,"is_primary":true,"text":[{"text":"\tpub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {","highlight_start":2,"highlight_end":74}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":690251,"byte_end":690317,"line_start":15473,"line_end":15473,"column_start":3,"column_end":69,"is_primary":true,"text":[{"text":"\t\tpub fn diag_mat(d: &impl core::MatTraitConst) -> Result {","highlight_start":3,"highlight_end":69}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `new_nd_with_data` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":10912,"byte_end":11021,"line_start":329,"line_end":329,"column_start":2,"column_end":111,"is_primary":true,"text":[{"text":"\tpub fn new_nd_with_data<'data, T: DataType>(sizes: &[i32], data: &'data [T]) -> Result> {","highlight_start":2,"highlight_end":111}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `new_nd_with_data_unsafe_def` found for struct `hub::core::Mat` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:339:26\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m339\u001b[0m \u001b[1m\u001b[94m|\u001b[0m let m = unsafe { Self::new_nd_with_data_unsafe_def(sizes, T::opencv_type(), data.as_mut_ptr().cast::()) }?;\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `hub::core::Mat`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15450:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15450\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Mat {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m--------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `new_nd_with_data_unsafe_def` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `hub::core::Mat` consider using one of the following associated functions:\n mat::::from_exact_iter\n mat::::from_slice_2d\n hub::core::Mat::diag_mat\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:178:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m178\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_exact_iter(s: impl ExactSizeIterator) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn from_slice_2d(s: &[impl AsRef<[T]>]) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:15473:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m15473\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn diag_mat(d: &impl core::MatTraitConst) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `new_nd_with_data` with a similar name\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:329:2\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m329\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn new_nd_with_data<'data, T: DataType>(sizes: &[i32], data: &'data [T]) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"mismatched types","code":{"code":"E0308","explanation":"Expected type did not match the received type.\n\nErroneous code examples:\n\n```compile_fail,E0308\nfn plus_one(x: i32) -> i32 {\n x + 1\n}\n\nplus_one(\"Not a number\");\n// ^^^^^^^^^^^^^^ expected `i32`, found `&str`\n\nif \"Not a bool\" {\n// ^^^^^^^^^^^^ expected `bool`, found `&str`\n}\n\nlet x: f32 = \"Not a float\";\n// --- ^^^^^^^^^^^^^ expected `f32`, found `&str`\n// |\n// expected due to this\n```\n\nThis error occurs when an expression was used in a place where the compiler\nexpected an expression of a different type. It can occur in several cases, the\nmost common being when calling a function and passing an argument which has a\ndifferent type than the matching type in the function declaration.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":12048,"byte_end":12049,"line_start":348,"line_end":348,"column_start":28,"column_end":29,"is_primary":true,"text":[{"text":"\t\t\tlet out1 = Mat::roi_mut(m, roi1)?;","highlight_start":28,"highlight_end":29}],"label":"expected `&mut Mat`, found `&mut MAT`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":11739,"byte_end":11742,"line_start":344,"line_end":344,"column_start":19,"column_end":22,"is_primary":false,"text":[{"text":"\tpub fn roi_2_mut(m: &mut MAT, roi1: Rect, roi2: Rect) -> Result<(BoxedRefMut<'_, Mat>, BoxedRefMut<'_, Mat>)> {","highlight_start":19,"highlight_end":22}],"label":"found this type parameter","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":12035,"byte_end":12047,"line_start":348,"line_end":348,"column_start":15,"column_end":27,"is_primary":false,"text":[{"text":"\t\t\tlet out1 = Mat::roi_mut(m, roi1)?;","highlight_start":15,"highlight_end":27}],"label":"arguments to this function are incorrect","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"expected mutable reference `&mut hub::core::Mat`\n found mutable reference `&mut MAT`","code":null,"level":"note","spans":[],"children":[],"rendered":null},{"message":"method defined here","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":801737,"byte_end":801746,"line_start":18083,"line_end":18083,"column_start":14,"column_end":23,"is_primary":false,"text":[{"text":"\t\tfn roi_mut(&mut self, roi: core::Rect) -> Result> {","highlight_start":14,"highlight_end":23}],"label":"","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":801729,"byte_end":801736,"line_start":18083,"line_end":18083,"column_start":6,"column_end":13,"is_primary":true,"text":[{"text":"\t\tfn roi_mut(&mut self, roi: core::Rect) -> Result> {","highlight_start":6,"highlight_end":13}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0308]\u001b[0m\u001b[1m: mismatched types\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:348:28\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m344\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn roi_2_mut(m: &mut MAT, roi1: Rect, roi2: Rect) -> Result<(BoxedRefMut<'_, Mat>, BoxedRefMut<'_, Mat>)> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---\u001b[0m \u001b[1m\u001b[94mfound this type parameter\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m348\u001b[0m \u001b[1m\u001b[94m|\u001b[0m let out1 = Mat::roi_mut(m, roi1)?;\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------------\u001b[0m \u001b[1m\u001b[91m^\u001b[0m \u001b[1m\u001b[91mexpected `&mut Mat`, found `&mut MAT`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94marguments to this function are incorrect\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: expected mutable reference `&mut \u001b[1m\u001b[35mhub::core::Mat\u001b[0m`\n found mutable reference `&mut \u001b[1m\u001b[35mMAT\u001b[0m`\n\u001b[1m\u001b[92mnote\u001b[0m: method defined here\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:18083:6\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m18083\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn roi_mut(&mut self, roi: core::Rect) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^\u001b[0m \u001b[1m\u001b[94m---------\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"mismatched types","code":{"code":"E0308","explanation":"Expected type did not match the received type.\n\nErroneous code examples:\n\n```compile_fail,E0308\nfn plus_one(x: i32) -> i32 {\n x + 1\n}\n\nplus_one(\"Not a number\");\n// ^^^^^^^^^^^^^^ expected `i32`, found `&str`\n\nif \"Not a bool\" {\n// ^^^^^^^^^^^^ expected `bool`, found `&str`\n}\n\nlet x: f32 = \"Not a float\";\n// --- ^^^^^^^^^^^^^ expected `f32`, found `&str`\n// |\n// expected due to this\n```\n\nThis error occurs when an expression was used in a place where the compiler\nexpected an expression of a different type. It can occur in several cases, the\nmost common being when calling a function and passing an argument which has a\ndifferent type than the matching type in the function declaration.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":12086,"byte_end":12088,"line_start":349,"line_end":349,"column_start":28,"column_end":30,"is_primary":true,"text":[{"text":"\t\t\tlet out2 = Mat::roi_mut(m2, roi2)?;","highlight_start":28,"highlight_end":30}],"label":"expected `&mut Mat`, found `&mut MAT`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":11739,"byte_end":11742,"line_start":344,"line_end":344,"column_start":19,"column_end":22,"is_primary":false,"text":[{"text":"\tpub fn roi_2_mut(m: &mut MAT, roi1: Rect, roi2: Rect) -> Result<(BoxedRefMut<'_, Mat>, BoxedRefMut<'_, Mat>)> {","highlight_start":19,"highlight_end":22}],"label":"found this type parameter","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":12073,"byte_end":12085,"line_start":349,"line_end":349,"column_start":15,"column_end":27,"is_primary":false,"text":[{"text":"\t\t\tlet out2 = Mat::roi_mut(m2, roi2)?;","highlight_start":15,"highlight_end":27}],"label":"arguments to this function are incorrect","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"expected mutable reference `&mut hub::core::Mat`\n found mutable reference `&mut MAT`","code":null,"level":"note","spans":[],"children":[],"rendered":null},{"message":"method defined here","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":801737,"byte_end":801746,"line_start":18083,"line_end":18083,"column_start":14,"column_end":23,"is_primary":false,"text":[{"text":"\t\tfn roi_mut(&mut self, roi: core::Rect) -> Result> {","highlight_start":14,"highlight_end":23}],"label":"","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":801729,"byte_end":801736,"line_start":18083,"line_end":18083,"column_start":6,"column_end":13,"is_primary":true,"text":[{"text":"\t\tfn roi_mut(&mut self, roi: core::Rect) -> Result> {","highlight_start":6,"highlight_end":13}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0308]\u001b[0m\u001b[1m: mismatched types\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:349:28\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m344\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn roi_2_mut(m: &mut MAT, roi1: Rect, roi2: Rect) -> Result<(BoxedRefMut<'_, Mat>, BoxedRefMut<'_, Mat>)> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---\u001b[0m \u001b[1m\u001b[94mfound this type parameter\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m349\u001b[0m \u001b[1m\u001b[94m|\u001b[0m let out2 = Mat::roi_mut(m2, roi2)?;\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------------\u001b[0m \u001b[1m\u001b[91m^^\u001b[0m \u001b[1m\u001b[91mexpected `&mut Mat`, found `&mut MAT`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94marguments to this function are incorrect\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: expected mutable reference `&mut \u001b[1m\u001b[35mhub::core::Mat\u001b[0m`\n found mutable reference `&mut \u001b[1m\u001b[35mMAT\u001b[0m`\n\u001b[1m\u001b[92mnote\u001b[0m: method defined here\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:18083:6\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m18083\u001b[0m \u001b[1m\u001b[94m|\u001b[0m fn roi_mut(&mut self, roi: core::Rect) -> Result> {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^\u001b[0m \u001b[1m\u001b[94m---------\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `over` found for struct `MatConstIterator` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":20687,"byte_end":20691,"line_start":650,"line_end":650,"column_start":21,"column_end":25,"is_primary":true,"text":[{"text":"\t\tMatConstIterator::over(self).map_or(","highlight_start":21,"highlight_end":25}],"label":"function or associated item not found in `MatConstIterator`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":819568,"byte_end":819595,"line_start":18491,"line_end":18491,"column_start":2,"column_end":29,"is_primary":false,"text":[{"text":"\tpub struct MatConstIterator {","highlight_start":2,"highlight_end":29}],"label":"function or associated item `over` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `over` found for struct `MatConstIterator` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:650:21\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m650\u001b[0m \u001b[1m\u001b[94m|\u001b[0m MatConstIterator::over(self).map_or(\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `MatConstIterator`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:18491:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m18491\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct MatConstIterator {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `over` not found for this struct\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `over` found for struct `MatConstIterator` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24457,"byte_end":24461,"line_start":771,"line_end":771,"column_start":21,"column_end":25,"is_primary":true,"text":[{"text":"\t\tMatConstIterator::over(self).map_or(","highlight_start":21,"highlight_end":25}],"label":"function or associated item not found in `MatConstIterator`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":819568,"byte_end":819595,"line_start":18491,"line_end":18491,"column_start":2,"column_end":29,"is_primary":false,"text":[{"text":"\tpub struct MatConstIterator {","highlight_start":2,"highlight_end":29}],"label":"function or associated item `over` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `over` found for struct `MatConstIterator` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:771:21\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m771\u001b[0m \u001b[1m\u001b[94m|\u001b[0m MatConstIterator::over(self).map_or(\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `MatConstIterator`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:18491:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m18491\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct MatConstIterator {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `over` not found for this struct\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_mat` found for struct `_InputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24710,"byte_end":24718,"line_start":785,"line_end":785,"column_start":28,"column_end":36,"is_primary":true,"text":[{"text":"input_output_array! { Mat, from_mat, from_mat_mut }","highlight_start":28,"highlight_end":36}],"label":"function or associated item not found in `_InputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062597,"byte_end":1062619,"line_start":25239,"line_end":25239,"column_start":2,"column_end":24,"is_primary":false,"text":[{"text":"\tpub struct _InputArray {","highlight_start":2,"highlight_end":24}],"label":"function or associated item `from_mat` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n_InputArray::default\n_InputArray::new","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062900,"byte_end":1062945,"line_start":25256,"line_end":25256,"column_start":3,"column_end":48,"is_primary":true,"text":[{"text":"\t\tpub fn default() -> Result {","highlight_start":3,"highlight_end":48}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1063229,"byte_end":1063307,"line_start":25266,"line_end":25266,"column_start":3,"column_end":81,"is_primary":true,"text":[{"text":"\t\tpub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {","highlight_start":3,"highlight_end":81}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `from` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24710,"byte_end":24718,"line_start":785,"line_end":785,"column_start":28,"column_end":36,"is_primary":true,"text":[{"text":"input_output_array! { Mat, from_mat, from_mat_mut }","highlight_start":28,"highlight_end":36}],"label":null,"suggested_replacement":"from","suggestion_applicability":"MaybeIncorrect","expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_mat` found for struct `_InputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:785:28\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m785\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array! { Mat, from_mat, from_mat_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_InputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25239:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _InputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m----------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_mat` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n _InputArray::default\n _InputArray::new\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25256:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25256\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn default() -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n\u001b[1m\u001b[94m25266\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `from` with a similar name\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m785\u001b[0m \u001b[91m- \u001b[0minput_output_array! { Mat, \u001b[91mfrom_mat\u001b[0m, from_mat_mut }\n \u001b[1m\u001b[94m785\u001b[0m \u001b[92m+ \u001b[0minput_output_array! { Mat, \u001b[92mfrom\u001b[0m, from_mat_mut }\n \u001b[1m\u001b[94m|\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_mat_mut` found for struct `_OutputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4498,"byte_end":4507,"line_start":131,"line_end":131,"column_start":33,"column_end":42,"is_primary":false,"text":[{"text":"\t\t\t\t$crate::core::_OutputArray::$mut_cons(self)","highlight_start":33,"highlight_end":42}],"label":"due to this macro variable","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24683,"byte_end":24734,"line_start":785,"line_end":785,"column_start":1,"column_end":52,"is_primary":false,"text":[{"text":"input_output_array! { Mat, from_mat, from_mat_mut }","highlight_start":1,"highlight_end":52}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24720,"byte_end":24732,"line_start":785,"line_end":785,"column_start":38,"column_end":50,"is_primary":true,"text":[{"text":"input_output_array! { Mat, from_mat, from_mat_mut }","highlight_start":38,"highlight_end":50}],"label":"function or associated item not found in `_OutputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1085096,"byte_end":1085119,"line_start":25899,"line_end":25899,"column_start":2,"column_end":25,"is_primary":false,"text":[{"text":"\tpub struct _OutputArray {","highlight_start":2,"highlight_end":25}],"label":"function or associated item `from_mat_mut` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"there is an associated function `from_raw` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24720,"byte_end":24732,"line_start":785,"line_end":785,"column_start":38,"column_end":50,"is_primary":true,"text":[{"text":"input_output_array! { Mat, from_mat, from_mat_mut }","highlight_start":38,"highlight_end":50}],"label":null,"suggested_replacement":"from_raw","suggestion_applicability":"MaybeIncorrect","expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_mat_mut` found for struct `_OutputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:785:38\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m785\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array! { Mat, from_mat, from_mat_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_OutputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:131:33\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m131\u001b[0m \u001b[1m\u001b[94m|\u001b[0m $crate::core::_OutputArray::$mut_cons(self)\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------\u001b[0m \u001b[1m\u001b[94mdue to this macro variable\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25899:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25899\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _OutputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m-----------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_mat_mut` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `from_raw` with a similar name\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m785\u001b[0m \u001b[91m- \u001b[0minput_output_array! { Mat, from_mat, \u001b[91mfrom_mat_mut\u001b[0m }\n \u001b[1m\u001b[94m785\u001b[0m \u001b[92m+ \u001b[0minput_output_array! { Mat, from_mat, \u001b[92mfrom_raw\u001b[0m }\n \u001b[1m\u001b[94m|\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_mat_mut` found for struct `_InputOutputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4498,"byte_end":4507,"line_start":131,"line_end":131,"column_start":33,"column_end":42,"is_primary":false,"text":[{"text":"\t\t\t\t$crate::core::_OutputArray::$mut_cons(self)","highlight_start":33,"highlight_end":42}],"label":"due to this macro variable","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24683,"byte_end":24734,"line_start":785,"line_end":785,"column_start":1,"column_end":52,"is_primary":false,"text":[{"text":"input_output_array! { Mat, from_mat, from_mat_mut }","highlight_start":1,"highlight_end":52}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24720,"byte_end":24732,"line_start":785,"line_end":785,"column_start":38,"column_end":50,"is_primary":true,"text":[{"text":"input_output_array! { Mat, from_mat, from_mat_mut }","highlight_start":38,"highlight_end":50}],"label":"function or associated item not found in `_InputOutputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1080473,"byte_end":1080501,"line_start":25781,"line_end":25781,"column_start":2,"column_end":30,"is_primary":false,"text":[{"text":"\tpub struct _InputOutputArray {","highlight_start":2,"highlight_end":30}],"label":"function or associated item `from_mat_mut` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `_InputOutputArray` consider using one of the following associated functions:\n_InputOutputArray::default\n_InputOutputArray::new","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1080913,"byte_end":1080964,"line_start":25799,"line_end":25799,"column_start":3,"column_end":54,"is_primary":true,"text":[{"text":"\t\tpub fn default() -> Result {","highlight_start":3,"highlight_end":54}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1081266,"byte_end":1081350,"line_start":25809,"line_end":25809,"column_start":3,"column_end":87,"is_primary":true,"text":[{"text":"\t\tpub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {","highlight_start":3,"highlight_end":87}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `from_raw` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24720,"byte_end":24732,"line_start":785,"line_end":785,"column_start":38,"column_end":50,"is_primary":true,"text":[{"text":"input_output_array! { Mat, from_mat, from_mat_mut }","highlight_start":38,"highlight_end":50}],"label":null,"suggested_replacement":"from_raw","suggestion_applicability":"MaybeIncorrect","expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_mat_mut` found for struct `_InputOutputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:785:38\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m785\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array! { Mat, from_mat, from_mat_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_InputOutputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:131:33\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m131\u001b[0m \u001b[1m\u001b[94m|\u001b[0m $crate::core::_OutputArray::$mut_cons(self)\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------\u001b[0m \u001b[1m\u001b[94mdue to this macro variable\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25781:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25781\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _InputOutputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m----------------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_mat_mut` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `_InputOutputArray` consider using one of the following associated functions:\n _InputOutputArray::default\n _InputOutputArray::new\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25799:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25799\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn default() -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n\u001b[1m\u001b[94m25809\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `from_raw` with a similar name\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m785\u001b[0m \u001b[91m- \u001b[0minput_output_array! { Mat, from_mat, \u001b[91mfrom_mat_mut\u001b[0m }\n \u001b[1m\u001b[94m785\u001b[0m \u001b[92m+ \u001b[0minput_output_array! { Mat, from_mat, \u001b[92mfrom_raw\u001b[0m }\n \u001b[1m\u001b[94m|\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_mat_vec` found for struct `_InputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5722,"byte_end":5733,"line_start":166,"line_end":166,"column_start":32,"column_end":43,"is_primary":false,"text":[{"text":"\t\t\t\t$crate::core::_InputArray::$const_cons(self.as_non_ref_vec())","highlight_start":32,"highlight_end":43}],"label":"due to this macro variable","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24735,"byte_end":24801,"line_start":786,"line_end":786,"column_start":1,"column_end":67,"is_primary":false,"text":[{"text":"input_output_array_vector! { Mat, from_mat_vec, from_mat_vec_mut }","highlight_start":1,"highlight_end":67}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24769,"byte_end":24781,"line_start":786,"line_end":786,"column_start":35,"column_end":47,"is_primary":true,"text":[{"text":"input_output_array_vector! { Mat, from_mat_vec, from_mat_vec_mut }","highlight_start":35,"highlight_end":47}],"label":"function or associated item not found in `_InputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062597,"byte_end":1062619,"line_start":25239,"line_end":25239,"column_start":2,"column_end":24,"is_primary":false,"text":[{"text":"\tpub struct _InputArray {","highlight_start":2,"highlight_end":24}],"label":"function or associated item `from_mat_vec` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n_InputArray::default\n_InputArray::new","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062900,"byte_end":1062945,"line_start":25256,"line_end":25256,"column_start":3,"column_end":48,"is_primary":true,"text":[{"text":"\t\tpub fn default() -> Result {","highlight_start":3,"highlight_end":48}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1063229,"byte_end":1063307,"line_start":25266,"line_end":25266,"column_start":3,"column_end":81,"is_primary":true,"text":[{"text":"\t\tpub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {","highlight_start":3,"highlight_end":81}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `from_bool_vec` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24769,"byte_end":24781,"line_start":786,"line_end":786,"column_start":35,"column_end":47,"is_primary":true,"text":[{"text":"input_output_array_vector! { Mat, from_mat_vec, from_mat_vec_mut }","highlight_start":35,"highlight_end":47}],"label":null,"suggested_replacement":"from_bool_vec","suggestion_applicability":"MaybeIncorrect","expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_mat_vec` found for struct `_InputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:786:35\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m786\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { Mat, from_mat_vec, from_mat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_InputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:166:32\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m166\u001b[0m \u001b[1m\u001b[94m|\u001b[0m $crate::core::_InputArray::$const_cons(self.as_non_ref_vec())\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m-----------\u001b[0m \u001b[1m\u001b[94mdue to this macro variable\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25239:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _InputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m----------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_mat_vec` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n _InputArray::default\n _InputArray::new\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25256:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25256\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn default() -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n\u001b[1m\u001b[94m25266\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `from_bool_vec` with a similar name\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m786\u001b[0m \u001b[91m- \u001b[0minput_output_array_vector! { Mat, \u001b[91mfrom_mat_vec\u001b[0m, from_mat_vec_mut }\n \u001b[1m\u001b[94m786\u001b[0m \u001b[92m+ \u001b[0minput_output_array_vector! { Mat, \u001b[92mfrom_bool_vec\u001b[0m, from_mat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_mat_vec` found for struct `_InputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24769,"byte_end":24781,"line_start":786,"line_end":786,"column_start":35,"column_end":47,"is_primary":true,"text":[{"text":"input_output_array_vector! { Mat, from_mat_vec, from_mat_vec_mut }","highlight_start":35,"highlight_end":47}],"label":"function or associated item not found in `_InputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062597,"byte_end":1062619,"line_start":25239,"line_end":25239,"column_start":2,"column_end":24,"is_primary":false,"text":[{"text":"\tpub struct _InputArray {","highlight_start":2,"highlight_end":24}],"label":"function or associated item `from_mat_vec` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n_InputArray::default\n_InputArray::new","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062900,"byte_end":1062945,"line_start":25256,"line_end":25256,"column_start":3,"column_end":48,"is_primary":true,"text":[{"text":"\t\tpub fn default() -> Result {","highlight_start":3,"highlight_end":48}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1063229,"byte_end":1063307,"line_start":25266,"line_end":25266,"column_start":3,"column_end":81,"is_primary":true,"text":[{"text":"\t\tpub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {","highlight_start":3,"highlight_end":81}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `from_bool_vec` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24769,"byte_end":24781,"line_start":786,"line_end":786,"column_start":35,"column_end":47,"is_primary":true,"text":[{"text":"input_output_array_vector! { Mat, from_mat_vec, from_mat_vec_mut }","highlight_start":35,"highlight_end":47}],"label":null,"suggested_replacement":"from_bool_vec","suggestion_applicability":"MaybeIncorrect","expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_mat_vec` found for struct `_InputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:786:35\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m786\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { Mat, from_mat_vec, from_mat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_InputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25239:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _InputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m----------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_mat_vec` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n _InputArray::default\n _InputArray::new\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25256:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25256\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn default() -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n\u001b[1m\u001b[94m25266\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `from_bool_vec` with a similar name\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m786\u001b[0m \u001b[91m- \u001b[0minput_output_array_vector! { Mat, \u001b[91mfrom_mat_vec\u001b[0m, from_mat_vec_mut }\n \u001b[1m\u001b[94m786\u001b[0m \u001b[92m+ \u001b[0minput_output_array_vector! { Mat, \u001b[92mfrom_bool_vec\u001b[0m, from_mat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_mat_vec_mut` found for struct `_OutputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24783,"byte_end":24799,"line_start":786,"line_end":786,"column_start":49,"column_end":65,"is_primary":true,"text":[{"text":"input_output_array_vector! { Mat, from_mat_vec, from_mat_vec_mut }","highlight_start":49,"highlight_end":65}],"label":"function or associated item not found in `_OutputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1085096,"byte_end":1085119,"line_start":25899,"line_end":25899,"column_start":2,"column_end":25,"is_primary":false,"text":[{"text":"\tpub struct _OutputArray {","highlight_start":2,"highlight_end":25}],"label":"function or associated item `from_mat_vec_mut` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_mat_vec_mut` found for struct `_OutputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:786:49\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m786\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { Mat, from_mat_vec, from_mat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_OutputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25899:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25899\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _OutputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m-----------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_mat_vec_mut` not found for this struct\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_mat_vec_mut` found for struct `_InputOutputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":24783,"byte_end":24799,"line_start":786,"line_end":786,"column_start":49,"column_end":65,"is_primary":true,"text":[{"text":"input_output_array_vector! { Mat, from_mat_vec, from_mat_vec_mut }","highlight_start":49,"highlight_end":65}],"label":"function or associated item not found in `_InputOutputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1080473,"byte_end":1080501,"line_start":25781,"line_end":25781,"column_start":2,"column_end":30,"is_primary":false,"text":[{"text":"\tpub struct _InputOutputArray {","highlight_start":2,"highlight_end":30}],"label":"function or associated item `from_mat_vec_mut` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `_InputOutputArray` consider using one of the following associated functions:\n_InputOutputArray::default\n_InputOutputArray::new","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1080913,"byte_end":1080964,"line_start":25799,"line_end":25799,"column_start":3,"column_end":54,"is_primary":true,"text":[{"text":"\t\tpub fn default() -> Result {","highlight_start":3,"highlight_end":54}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1081266,"byte_end":1081350,"line_start":25809,"line_end":25809,"column_start":3,"column_end":87,"is_primary":true,"text":[{"text":"\t\tpub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {","highlight_start":3,"highlight_end":87}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_mat_vec_mut` found for struct `_InputOutputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:786:49\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m786\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { Mat, from_mat_vec, from_mat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_InputOutputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25781:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25781\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _InputOutputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m----------------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_mat_vec_mut` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `_InputOutputArray` consider using one of the following associated functions:\n _InputOutputArray::default\n _InputOutputArray::new\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25799:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25799\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn default() -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n\u001b[1m\u001b[94m25809\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_umat` found for struct `_InputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26593,"byte_end":26602,"line_start":845,"line_end":845,"column_start":29,"column_end":38,"is_primary":true,"text":[{"text":"input_output_array! { UMat, from_umat, from_umat_mut }","highlight_start":29,"highlight_end":38}],"label":"function or associated item not found in `_InputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062597,"byte_end":1062619,"line_start":25239,"line_end":25239,"column_start":2,"column_end":24,"is_primary":false,"text":[{"text":"\tpub struct _InputArray {","highlight_start":2,"highlight_end":24}],"label":"function or associated item `from_umat` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n_InputArray::default\n_InputArray::new","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062900,"byte_end":1062945,"line_start":25256,"line_end":25256,"column_start":3,"column_end":48,"is_primary":true,"text":[{"text":"\t\tpub fn default() -> Result {","highlight_start":3,"highlight_end":48}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1063229,"byte_end":1063307,"line_start":25266,"line_end":25266,"column_start":3,"column_end":81,"is_primary":true,"text":[{"text":"\t\tpub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {","highlight_start":3,"highlight_end":81}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `from_raw` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26593,"byte_end":26602,"line_start":845,"line_end":845,"column_start":29,"column_end":38,"is_primary":true,"text":[{"text":"input_output_array! { UMat, from_umat, from_umat_mut }","highlight_start":29,"highlight_end":38}],"label":null,"suggested_replacement":"from_raw","suggestion_applicability":"MaybeIncorrect","expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_umat` found for struct `_InputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:845:29\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m845\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array! { UMat, from_umat, from_umat_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_InputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25239:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _InputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m----------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_umat` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n _InputArray::default\n _InputArray::new\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25256:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25256\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn default() -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n\u001b[1m\u001b[94m25266\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `from_raw` with a similar name\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m845\u001b[0m \u001b[91m- \u001b[0minput_output_array! { UMat, \u001b[91mfrom_umat\u001b[0m, from_umat_mut }\n \u001b[1m\u001b[94m845\u001b[0m \u001b[92m+ \u001b[0minput_output_array! { UMat, \u001b[92mfrom_raw\u001b[0m, from_umat_mut }\n \u001b[1m\u001b[94m|\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_umat_mut` found for struct `_OutputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4498,"byte_end":4507,"line_start":131,"line_end":131,"column_start":33,"column_end":42,"is_primary":false,"text":[{"text":"\t\t\t\t$crate::core::_OutputArray::$mut_cons(self)","highlight_start":33,"highlight_end":42}],"label":"due to this macro variable","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26565,"byte_end":26619,"line_start":845,"line_end":845,"column_start":1,"column_end":55,"is_primary":false,"text":[{"text":"input_output_array! { UMat, from_umat, from_umat_mut }","highlight_start":1,"highlight_end":55}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26604,"byte_end":26617,"line_start":845,"line_end":845,"column_start":40,"column_end":53,"is_primary":true,"text":[{"text":"input_output_array! { UMat, from_umat, from_umat_mut }","highlight_start":40,"highlight_end":53}],"label":"function or associated item not found in `_OutputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1085096,"byte_end":1085119,"line_start":25899,"line_end":25899,"column_start":2,"column_end":25,"is_primary":false,"text":[{"text":"\tpub struct _OutputArray {","highlight_start":2,"highlight_end":25}],"label":"function or associated item `from_umat_mut` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_umat_mut` found for struct `_OutputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:845:40\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m845\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array! { UMat, from_umat, from_umat_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_OutputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:131:33\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m131\u001b[0m \u001b[1m\u001b[94m|\u001b[0m $crate::core::_OutputArray::$mut_cons(self)\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------\u001b[0m \u001b[1m\u001b[94mdue to this macro variable\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25899:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25899\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _OutputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m-----------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_umat_mut` not found for this struct\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_umat_mut` found for struct `_InputOutputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4498,"byte_end":4507,"line_start":131,"line_end":131,"column_start":33,"column_end":42,"is_primary":false,"text":[{"text":"\t\t\t\t$crate::core::_OutputArray::$mut_cons(self)","highlight_start":33,"highlight_end":42}],"label":"due to this macro variable","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26565,"byte_end":26619,"line_start":845,"line_end":845,"column_start":1,"column_end":55,"is_primary":false,"text":[{"text":"input_output_array! { UMat, from_umat, from_umat_mut }","highlight_start":1,"highlight_end":55}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26604,"byte_end":26617,"line_start":845,"line_end":845,"column_start":40,"column_end":53,"is_primary":true,"text":[{"text":"input_output_array! { UMat, from_umat, from_umat_mut }","highlight_start":40,"highlight_end":53}],"label":"function or associated item not found in `_InputOutputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1080473,"byte_end":1080501,"line_start":25781,"line_end":25781,"column_start":2,"column_end":30,"is_primary":false,"text":[{"text":"\tpub struct _InputOutputArray {","highlight_start":2,"highlight_end":30}],"label":"function or associated item `from_umat_mut` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `_InputOutputArray` consider using one of the following associated functions:\n_InputOutputArray::default\n_InputOutputArray::new","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1080913,"byte_end":1080964,"line_start":25799,"line_end":25799,"column_start":3,"column_end":54,"is_primary":true,"text":[{"text":"\t\tpub fn default() -> Result {","highlight_start":3,"highlight_end":54}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1081266,"byte_end":1081350,"line_start":25809,"line_end":25809,"column_start":3,"column_end":87,"is_primary":true,"text":[{"text":"\t\tpub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {","highlight_start":3,"highlight_end":87}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_umat_mut` found for struct `_InputOutputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:845:40\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m845\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array! { UMat, from_umat, from_umat_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_InputOutputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:131:33\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m131\u001b[0m \u001b[1m\u001b[94m|\u001b[0m $crate::core::_OutputArray::$mut_cons(self)\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------\u001b[0m \u001b[1m\u001b[94mdue to this macro variable\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25781:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25781\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _InputOutputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m----------------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_umat_mut` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `_InputOutputArray` consider using one of the following associated functions:\n _InputOutputArray::default\n _InputOutputArray::new\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25799:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25799\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn default() -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n\u001b[1m\u001b[94m25809\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_umat_vec` found for struct `_InputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5722,"byte_end":5733,"line_start":166,"line_end":166,"column_start":32,"column_end":43,"is_primary":false,"text":[{"text":"\t\t\t\t$crate::core::_InputArray::$const_cons(self.as_non_ref_vec())","highlight_start":32,"highlight_end":43}],"label":"due to this macro variable","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26655,"byte_end":26668,"line_start":846,"line_end":846,"column_start":36,"column_end":49,"is_primary":true,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":36,"highlight_end":49}],"label":"function or associated item not found in `_InputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062597,"byte_end":1062619,"line_start":25239,"line_end":25239,"column_start":2,"column_end":24,"is_primary":false,"text":[{"text":"\tpub struct _InputArray {","highlight_start":2,"highlight_end":24}],"label":"function or associated item `from_umat_vec` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n_InputArray::default\n_InputArray::new","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062900,"byte_end":1062945,"line_start":25256,"line_end":25256,"column_start":3,"column_end":48,"is_primary":true,"text":[{"text":"\t\tpub fn default() -> Result {","highlight_start":3,"highlight_end":48}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1063229,"byte_end":1063307,"line_start":25266,"line_end":25266,"column_start":3,"column_end":81,"is_primary":true,"text":[{"text":"\t\tpub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {","highlight_start":3,"highlight_end":81}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `from_bool_vec` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26655,"byte_end":26668,"line_start":846,"line_end":846,"column_start":36,"column_end":49,"is_primary":true,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":36,"highlight_end":49}],"label":null,"suggested_replacement":"from_bool_vec","suggestion_applicability":"MaybeIncorrect","expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_umat_vec` found for struct `_InputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:36\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_InputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:166:32\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m166\u001b[0m \u001b[1m\u001b[94m|\u001b[0m $crate::core::_InputArray::$const_cons(self.as_non_ref_vec())\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m-----------\u001b[0m \u001b[1m\u001b[94mdue to this macro variable\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25239:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _InputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m----------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_umat_vec` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n _InputArray::default\n _InputArray::new\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25256:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25256\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn default() -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n\u001b[1m\u001b[94m25266\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `from_bool_vec` with a similar name\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m846\u001b[0m \u001b[91m- \u001b[0minput_output_array_vector! { UMat, \u001b[91mfrom_umat_vec\u001b[0m, from_umat_vec_mut }\n \u001b[1m\u001b[94m846\u001b[0m \u001b[92m+ \u001b[0minput_output_array_vector! { UMat, \u001b[92mfrom_bool_vec\u001b[0m, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"the method `as_non_ref_vec` exists for reference `&vector::Vector>`, but its trait bounds were not satisfied","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5739,"byte_end":5753,"line_start":166,"line_end":166,"column_start":49,"column_end":63,"is_primary":true,"text":[{"text":"\t\t\t\t$crate::core::_InputArray::$const_cons(self.as_non_ref_vec())","highlight_start":49,"highlight_end":63}],"label":"method cannot be called on `&vector::Vector>` due to unsatisfied trait bounds","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":"doesn't satisfy `_: VectorExtern>` or `_: VectorExtern`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"the following trait bounds were not satisfied:\n`vector::Vector: vector::vector_extern::VectorExtern`\n`vector::Vector>: vector::vector_extern::VectorExtern>`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":8580,"byte_end":8609,"line_start":384,"line_end":384,"column_start":8,"column_end":37,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern>,","highlight_start":8,"highlight_end":37}],"label":"unsatisfied trait bound introduced here","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":8543,"byte_end":8566,"line_start":382,"line_end":382,"column_start":20,"column_end":43,"is_primary":false,"text":[{"text":"impl<'b, T: Boxed> Vector>","highlight_start":20,"highlight_end":43}],"label":"","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":8623,"byte_end":8638,"line_start":385,"line_end":385,"column_start":13,"column_end":28,"is_primary":true,"text":[{"text":"\tVector: VectorExtern,","highlight_start":13,"highlight_end":28}],"label":"unsatisfied trait bound introduced here","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the trait `vector::vector_extern::VectorExtern` must be implemented","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector/vector_extern.rs","byte_start":223,"byte_end":248,"line_start":7,"line_end":7,"column_start":1,"column_end":26,"is_primary":true,"text":[{"text":"pub trait VectorExtern {","highlight_start":1,"highlight_end":26}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: the method `as_non_ref_vec` exists for reference `&vector::Vector>`, but its trait bounds were not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:166:49\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m166\u001b[0m \u001b[1m\u001b[94m|\u001b[0m $crate::core::_InputArray::$const_cons(self.as_non_ref_vec())\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod cannot be called on `&vector::Vector>` due to unsatisfied trait bounds\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m--------------------\u001b[0m \u001b[1m\u001b[94mdoesn't satisfy `_: VectorExtern>` or `_: VectorExtern`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: the following trait bounds were not satisfied:\n `vector::Vector: vector::vector_extern::VectorExtern`\n `vector::Vector>: vector::vector_extern::VectorExtern>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:384:8\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m382\u001b[0m \u001b[1m\u001b[94m|\u001b[0m impl<'b, T: Boxed> Vector>\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m-----------------------\u001b[0m\n\u001b[1m\u001b[94m383\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n\u001b[1m\u001b[94m384\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern>,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92munsatisfied trait bound introduced here\u001b[0m\n\u001b[1m\u001b[94m385\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Vector: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92munsatisfied trait bound introduced here\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: the trait `vector::vector_extern::VectorExtern` must be implemented\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector/vector_extern.rs:7:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m7\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub trait VectorExtern {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `Vector>: VectorExtern>` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6310,"byte_end":6331,"line_start":184,"line_end":184,"column_start":5,"column_end":26,"is_primary":true,"text":[{"text":"\t\t\t\t(*self).input_array()","highlight_start":5,"highlight_end":26}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5769,"byte_end":5866,"line_start":170,"line_end":170,"column_start":3,"column_end":100,"is_primary":false,"text":[{"text":"\t\t$crate::input_array_ref_forward! { $crate::core::Vector<$crate::boxed_ref::BoxedRef<'_, $type>> }","highlight_start":3,"highlight_end":100}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6084,"byte_end":6120,"line_start":179,"line_end":179,"column_start":1,"column_end":37,"is_primary":false,"text":[{"text":"macro_rules! input_array_ref_forward {","highlight_start":1,"highlight_end":37}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-15398586723224459864.txt'","code":null,"level":"note","spans":[],"children":[],"rendered":null},{"message":"consider using `--verbose` to print the full type name to the console","code":null,"level":"note","spans":[],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `Vector>: VectorExtern>` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:184:5\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m184\u001b[0m \u001b[1m\u001b[94m|\u001b[0m (*self).input_array()\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern>` is not implemented for `vector::Vector>`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: the full name for the type has been written to '/Users/accusys/video_yolo_player/target/debug/deps/opencv-934b04ec3cce8632.long-type-15398586723224459864.txt'\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: consider using `--verbose` to print the full type name to the console\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_umat_vec` found for struct `_InputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26655,"byte_end":26668,"line_start":846,"line_end":846,"column_start":36,"column_end":49,"is_primary":true,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":36,"highlight_end":49}],"label":"function or associated item not found in `_InputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062597,"byte_end":1062619,"line_start":25239,"line_end":25239,"column_start":2,"column_end":24,"is_primary":false,"text":[{"text":"\tpub struct _InputArray {","highlight_start":2,"highlight_end":24}],"label":"function or associated item `from_umat_vec` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n_InputArray::default\n_InputArray::new","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062900,"byte_end":1062945,"line_start":25256,"line_end":25256,"column_start":3,"column_end":48,"is_primary":true,"text":[{"text":"\t\tpub fn default() -> Result {","highlight_start":3,"highlight_end":48}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1063229,"byte_end":1063307,"line_start":25266,"line_end":25266,"column_start":3,"column_end":81,"is_primary":true,"text":[{"text":"\t\tpub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {","highlight_start":3,"highlight_end":81}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `from_bool_vec` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26655,"byte_end":26668,"line_start":846,"line_end":846,"column_start":36,"column_end":49,"is_primary":true,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":36,"highlight_end":49}],"label":null,"suggested_replacement":"from_bool_vec","suggestion_applicability":"MaybeIncorrect","expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_umat_vec` found for struct `_InputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:36\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_InputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25239:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _InputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m----------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_umat_vec` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n _InputArray::default\n _InputArray::new\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25256:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25256\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn default() -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n\u001b[1m\u001b[94m25266\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `from_bool_vec` with a similar name\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m846\u001b[0m \u001b[91m- \u001b[0minput_output_array_vector! { UMat, \u001b[91mfrom_umat_vec\u001b[0m, from_umat_vec_mut }\n \u001b[1m\u001b[94m846\u001b[0m \u001b[92m+ \u001b[0minput_output_array_vector! { UMat, \u001b[92mfrom_bool_vec\u001b[0m, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6310,"byte_end":6331,"line_start":184,"line_end":184,"column_start":5,"column_end":26,"is_primary":true,"text":[{"text":"\t\t\t\t(*self).input_array()","highlight_start":5,"highlight_end":26}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4134,"byte_end":4176,"line_start":122,"line_end":122,"column_start":3,"column_end":45,"is_primary":false,"text":[{"text":"\t\t$crate::input_array_ref_forward! { $type }","highlight_start":3,"highlight_end":45}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4240,"byte_end":4290,"line_start":126,"line_end":126,"column_start":3,"column_end":53,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $type, $const_cons }","highlight_start":3,"highlight_end":53}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6084,"byte_end":6120,"line_start":179,"line_end":179,"column_start":1,"column_end":37,"is_primary":false,"text":[{"text":"macro_rules! input_array_ref_forward {","highlight_start":1,"highlight_end":37}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:184:5\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m184\u001b[0m \u001b[1m\u001b[94m|\u001b[0m (*self).input_array()\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::input_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_umat_vec_mut` found for struct `_OutputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26670,"byte_end":26687,"line_start":846,"line_end":846,"column_start":51,"column_end":68,"is_primary":true,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":51,"highlight_end":68}],"label":"function or associated item not found in `_OutputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1085096,"byte_end":1085119,"line_start":25899,"line_end":25899,"column_start":2,"column_end":25,"is_primary":false,"text":[{"text":"\tpub struct _OutputArray {","highlight_start":2,"highlight_end":25}],"label":"function or associated item `from_umat_vec_mut` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_umat_vec_mut` found for struct `_OutputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:51\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_OutputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25899:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25899\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _OutputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m-----------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_umat_vec_mut` not found for this struct\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_umat_vec_mut` found for struct `_InputOutputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26670,"byte_end":26687,"line_start":846,"line_end":846,"column_start":51,"column_end":68,"is_primary":true,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":51,"highlight_end":68}],"label":"function or associated item not found in `_InputOutputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1080473,"byte_end":1080501,"line_start":25781,"line_end":25781,"column_start":2,"column_end":30,"is_primary":false,"text":[{"text":"\tpub struct _InputOutputArray {","highlight_start":2,"highlight_end":30}],"label":"function or associated item `from_umat_vec_mut` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `_InputOutputArray` consider using one of the following associated functions:\n_InputOutputArray::default\n_InputOutputArray::new","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1080913,"byte_end":1080964,"line_start":25799,"line_end":25799,"column_start":3,"column_end":54,"is_primary":true,"text":[{"text":"\t\tpub fn default() -> Result {","highlight_start":3,"highlight_end":54}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1081266,"byte_end":1081350,"line_start":25809,"line_end":25809,"column_start":3,"column_end":87,"is_primary":true,"text":[{"text":"\t\tpub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {","highlight_start":3,"highlight_end":87}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_umat_vec_mut` found for struct `_InputOutputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:51\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_InputOutputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25781:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25781\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _InputOutputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m----------------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_umat_vec_mut` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `_InputOutputArray` consider using one of the following associated functions:\n _InputOutputArray::default\n _InputOutputArray::new\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25799:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25799\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn default() -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n\u001b[1m\u001b[94m25809\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6748,"byte_end":6770,"line_start":198,"line_end":198,"column_start":5,"column_end":27,"is_primary":true,"text":[{"text":"\t\t\t\t(*self).output_array()","highlight_start":5,"highlight_end":27}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4779,"byte_end":4822,"line_start":142,"line_end":142,"column_start":3,"column_end":46,"is_primary":false,"text":[{"text":"\t\t$crate::output_array_ref_forward! { $type }","highlight_start":3,"highlight_end":46}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::output_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6507,"byte_end":6544,"line_start":193,"line_end":193,"column_start":1,"column_end":38,"is_primary":false,"text":[{"text":"macro_rules! output_array_ref_forward {","highlight_start":1,"highlight_end":38}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:198:5\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m198\u001b[0m \u001b[1m\u001b[94m|\u001b[0m (*self).output_array()\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::output_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied","code":{"code":"E0277","explanation":"You tried to use a type which doesn't implement some trait in a place which\nexpected that trait.\n\nErroneous code example:\n\n```compile_fail,E0277\n// here we declare the Foo trait with a bar method\ntrait Foo {\n fn bar(&self);\n}\n\n// we now declare a function which takes an object implementing the Foo trait\nfn some_func(foo: T) {\n foo.bar();\n}\n\nfn main() {\n // we now call the method with the i32 type, which doesn't implement\n // the Foo trait\n some_func(5i32); // error: the trait bound `i32 : Foo` is not satisfied\n}\n```\n\nIn order to fix this error, verify that the type you're using does implement\nthe trait. Example:\n\n```\ntrait Foo {\n fn bar(&self);\n}\n\n// we implement the trait on the i32 type\nimpl Foo for i32 {\n fn bar(&self) {}\n}\n\nfn some_func(foo: T) {\n foo.bar(); // we can now use this method since i32 implements the\n // Foo trait\n}\n\nfn main() {\n some_func(5i32); // ok!\n}\n```\n\nOr in a generic context, an erroneous code example would look like:\n\n```compile_fail,E0277\nfn some_func(foo: T) {\n println!(\"{:?}\", foo); // error: the trait `core::fmt::Debug` is not\n // implemented for the type `T`\n}\n\nfn main() {\n // We now call the method with the i32 type,\n // which *does* implement the Debug trait.\n some_func(5i32);\n}\n```\n\nNote that the error here is in the definition of the generic function. Although\nwe only call it with a parameter that does implement `Debug`, the compiler\nstill rejects the function. It must work with all possible input types. In\norder to make this example compile, we need to restrict the generic type we're\naccepting:\n\n```\nuse std::fmt;\n\n// Restrict the input type to types that implement Debug.\nfn some_func(foo: T) {\n println!(\"{:?}\", foo);\n}\n\nfn main() {\n // Calling the method is still fine, as i32 implements Debug.\n some_func(5i32);\n\n // This would fail to compile now:\n // struct WithoutDebug;\n // some_func(WithoutDebug);\n}\n```\n\nRust only looks at the signature of the called function, as such it must\nalready specify all requirements that will be used for every type parameter.\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6980,"byte_end":7008,"line_start":205,"line_end":205,"column_start":5,"column_end":33,"is_primary":true,"text":[{"text":"\t\t\t\t(*self).input_output_array()","highlight_start":5,"highlight_end":33}],"label":"unsatisfied trait bound","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4779,"byte_end":4822,"line_start":142,"line_end":142,"column_start":3,"column_end":46,"is_primary":false,"text":[{"text":"\t\t$crate::output_array_ref_forward! { $type }","highlight_start":3,"highlight_end":46}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":5870,"byte_end":5953,"line_start":172,"line_end":172,"column_start":3,"column_end":86,"is_primary":false,"text":[{"text":"\t\t$crate::input_output_array! { $crate::core::Vector<$type>, $const_cons, $mut_cons }","highlight_start":3,"highlight_end":86}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":26620,"byte_end":26689,"line_start":846,"line_end":846,"column_start":1,"column_end":70,"is_primary":false,"text":[{"text":"input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }","highlight_start":1,"highlight_end":70}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array_vector!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4861,"byte_end":4899,"line_start":148,"line_end":148,"column_start":1,"column_end":39,"is_primary":false,"text":[{"text":"macro_rules! input_output_array_vector {","highlight_start":1,"highlight_end":39}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},"macro_decl_name":"$crate::output_array_ref_forward!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":6507,"byte_end":6544,"line_start":193,"line_end":193,"column_start":1,"column_end":38,"is_primary":false,"text":[{"text":"macro_rules! output_array_ref_forward {","highlight_start":1,"highlight_end":38}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}}],"children":[{"message":"the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":594,"byte_end":614,"line_start":20,"line_end":20,"column_start":1,"column_end":21,"is_primary":true,"text":[{"text":"pub struct Vector","highlight_start":1,"highlight_end":21}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\nand 32 others","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"required by a bound in `vector::Vector`","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":605,"byte_end":611,"line_start":20,"line_end":20,"column_start":12,"column_end":18,"is_primary":false,"text":[{"text":"pub struct Vector","highlight_start":12,"highlight_end":18}],"label":"required by a bound in this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs","byte_start":628,"byte_end":643,"line_start":22,"line_end":22,"column_start":8,"column_end":23,"is_primary":true,"text":[{"text":"\tSelf: VectorExtern,","highlight_start":8,"highlight_end":23}],"label":"required by this bound in `Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0277]\u001b[0m\u001b[1m: the trait bound `vector::Vector: vector::vector_extern::VectorExtern` is not satisfied\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:205:5\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m205\u001b[0m \u001b[1m\u001b[94m|\u001b[0m (*self).input_output_array()\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91munsatisfied trait bound\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:846:1\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m846\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array_vector! { UMat, from_umat_vec, from_umat_vec_mut }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m---------------------------------------------------------------------\u001b[0m \u001b[1m\u001b[94min this macro invocation\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: the trait `vector::vector_extern::VectorExtern` is not implemented for `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:20:1\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[96m^^^^^^^^^^^^^^^^^^^^\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: the following other types implement trait `vector::vector_extern::VectorExtern`:\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n `vector::Vector` implements `vector::vector_extern::VectorExtern`\n and 32 others\n\u001b[1m\u001b[92mnote\u001b[0m: required by a bound in `vector::Vector`\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/vector.rs:22:8\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m20\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct Vector\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m------\u001b[0m \u001b[1m\u001b[94mrequired by a bound in this struct\u001b[0m\n \u001b[1m\u001b[94m21\u001b[0m \u001b[1m\u001b[94m|\u001b[0m where\n \u001b[1m\u001b[94m22\u001b[0m \u001b[1m\u001b[94m|\u001b[0m Self: VectorExtern,\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[92mrequired by this bound in `Vector`\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mnote\u001b[0m: this error originates in the macro `$crate::output_array_ref_forward` which comes from the expansion of the macro `input_output_array_vector` (in Nightly builds, run with -Z macro-backtrace for more info)\n\n"} +{"$message_type":"diagnostic","message":"no function or associated item named `from_matexpr` found for struct `_InputArray` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":4104,"byte_end":4115,"line_start":118,"line_end":118,"column_start":32,"column_end":43,"is_primary":false,"text":[{"text":"\t\t\t\t$crate::core::_InputArray::$const_cons(self)","highlight_start":32,"highlight_end":43}],"label":"due to this macro variable","suggested_replacement":null,"suggestion_applicability":null,"expansion":{"span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":28208,"byte_end":28253,"line_start":921,"line_end":921,"column_start":1,"column_end":46,"is_primary":false,"text":[{"text":"input_output_array! { MatExpr, from_matexpr }","highlight_start":1,"highlight_end":46}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},"macro_decl_name":"input_output_array!","def_site_span":{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs","byte_start":3837,"byte_end":3868,"line_start":113,"line_end":113,"column_start":1,"column_end":32,"is_primary":false,"text":[{"text":"macro_rules! input_output_array {","highlight_start":1,"highlight_end":32}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}}},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":28239,"byte_end":28251,"line_start":921,"line_end":921,"column_start":32,"column_end":44,"is_primary":true,"text":[{"text":"input_output_array! { MatExpr, from_matexpr }","highlight_start":32,"highlight_end":44}],"label":"function or associated item not found in `_InputArray`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062597,"byte_end":1062619,"line_start":25239,"line_end":25239,"column_start":2,"column_end":24,"is_primary":false,"text":[{"text":"\tpub struct _InputArray {","highlight_start":2,"highlight_end":24}],"label":"function or associated item `from_matexpr` not found for this struct","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n_InputArray::default\n_InputArray::new","code":null,"level":"note","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1062900,"byte_end":1062945,"line_start":25256,"line_end":25256,"column_start":3,"column_end":48,"is_primary":true,"text":[{"text":"\t\tpub fn default() -> Result {","highlight_start":3,"highlight_end":48}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null},{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":1063229,"byte_end":1063307,"line_start":25266,"line_end":25266,"column_start":3,"column_end":81,"is_primary":true,"text":[{"text":"\t\tpub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {","highlight_start":3,"highlight_end":81}],"label":null,"suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":null},{"message":"there is an associated function `from_raw` with a similar name","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs","byte_start":28239,"byte_end":28251,"line_start":921,"line_end":921,"column_start":32,"column_end":44,"is_primary":true,"text":[{"text":"input_output_array! { MatExpr, from_matexpr }","highlight_start":32,"highlight_end":44}],"label":null,"suggested_replacement":"from_raw","suggestion_applicability":"MaybeIncorrect","expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no function or associated item named `from_matexpr` found for struct `_InputArray` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat.rs:921:32\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m921\u001b[0m \u001b[1m\u001b[94m|\u001b[0m input_output_array! { MatExpr, from_matexpr }\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mfunction or associated item not found in `_InputArray`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/input_output_array.rs:118:32\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m118\u001b[0m \u001b[1m\u001b[94m|\u001b[0m $crate::core::_InputArray::$const_cons(self)\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m-----------\u001b[0m \u001b[1m\u001b[94mdue to this macro variable\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m::: \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25239:2\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25239\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub struct _InputArray {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m----------------------\u001b[0m \u001b[1m\u001b[94mfunction or associated item `from_matexpr` not found for this struct\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[92mnote\u001b[0m: if you're trying to build a new `_InputArray` consider using one of the following associated functions:\n _InputArray::default\n _InputArray::new\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:25256:3\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m25256\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub fn default() -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[94m...\u001b[0m\n\u001b[1m\u001b[94m25266\u001b[0m \u001b[1m\u001b[94m|\u001b[0m pub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[92m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m\n\u001b[1m\u001b[96mhelp\u001b[0m: there is an associated function `from_raw` with a similar name\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m921\u001b[0m \u001b[91m- \u001b[0minput_output_array! { MatExpr, \u001b[91mfrom_matexpr\u001b[0m }\n \u001b[1m\u001b[94m921\u001b[0m \u001b[92m+ \u001b[0minput_output_array! { MatExpr, \u001b[92mfrom_raw\u001b[0m }\n \u001b[1m\u001b[94m|\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `mul_def` found for reference `&impl MatTraitConst` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat_ops.rs","byte_start":890,"byte_end":897,"line_start":43,"line_end":43,"column_start":4,"column_end":11,"is_primary":true,"text":[{"text":"\ta.mul_def(b)","highlight_start":4,"highlight_end":11}],"label":"method not found in `&impl MatTraitConst`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"items from traits can only be used if the type parameter is bounded by the trait","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"the following traits define an item `mul_def`, perhaps you need to restrict type parameter `impl MatTraitConst` with one of them:","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat_ops.rs","byte_start":823,"byte_end":823,"line_start":42,"line_end":42,"column_start":24,"column_end":24,"is_primary":true,"text":[{"text":"fn elemmul_mat_mat(a: &impl MatTraitConst, b: &impl ToInputArray) -> Result {","highlight_start":24,"highlight_end":24}],"label":null,"suggested_replacement":"(","suggestion_applicability":"MaybeIncorrect","expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat_ops.rs","byte_start":841,"byte_end":841,"line_start":42,"line_end":42,"column_start":42,"column_end":42,"is_primary":true,"text":[{"text":"fn elemmul_mat_mat(a: &impl MatTraitConst, b: &impl ToInputArray) -> Result {","highlight_start":42,"highlight_end":42}],"label":null,"suggested_replacement":" + hub::core::MatExprTraitConst)","suggestion_applicability":"MaybeIncorrect","expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat_ops.rs","byte_start":823,"byte_end":823,"line_start":42,"line_end":42,"column_start":24,"column_end":24,"is_primary":true,"text":[{"text":"fn elemmul_mat_mat(a: &impl MatTraitConst, b: &impl ToInputArray) -> Result {","highlight_start":24,"highlight_end":24}],"label":null,"suggested_replacement":"(","suggestion_applicability":"MaybeIncorrect","expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat_ops.rs","byte_start":841,"byte_end":841,"line_start":42,"line_end":42,"column_start":42,"column_end":42,"is_primary":true,"text":[{"text":"fn elemmul_mat_mat(a: &impl MatTraitConst, b: &impl ToInputArray) -> Result {","highlight_start":42,"highlight_end":42}],"label":null,"suggested_replacement":" + hub::core::UMatTraitConst)","suggestion_applicability":"MaybeIncorrect","expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `mul_def` found for reference `&impl MatTraitConst` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat_ops.rs:43:4\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m43\u001b[0m \u001b[1m\u001b[94m|\u001b[0m a.mul_def(b)\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&impl MatTraitConst`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: items from traits can only be used if the type parameter is bounded by the trait\n\u001b[1m\u001b[96mhelp\u001b[0m: the following traits define an item `mul_def`, perhaps you need to restrict type parameter `impl MatTraitConst` with one of them:\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m42\u001b[0m \u001b[1m\u001b[94m| \u001b[0mfn elemmul_mat_mat(a: &\u001b[92m(\u001b[0mimpl MatTraitConst\u001b[92m + hub::core::MatExprTraitConst)\u001b[0m, b: &impl ToInputArray) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[92m+\u001b[0m \u001b[92m+++++++++++++++++++++++++++++++\u001b[0m\n\u001b[1m\u001b[94m42\u001b[0m \u001b[1m\u001b[94m| \u001b[0mfn elemmul_mat_mat(a: &\u001b[92m(\u001b[0mimpl MatTraitConst\u001b[92m + hub::core::UMatTraitConst)\u001b[0m, b: &impl ToInputArray) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[92m+\u001b[0m \u001b[92m++++++++++++++++++++++++++++\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `mul_def` found for reference `&impl MatTraitConst` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat_ops.rs","byte_start":1008,"byte_end":1015,"line_start":48,"line_end":48,"column_start":4,"column_end":11,"is_primary":true,"text":[{"text":"\ta.mul_def(b)","highlight_start":4,"highlight_end":11}],"label":"method not found in `&impl MatTraitConst`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[{"message":"items from traits can only be used if the type parameter is bounded by the trait","code":null,"level":"help","spans":[],"children":[],"rendered":null},{"message":"the following traits define an item `mul_def`, perhaps you need to restrict type parameter `impl MatTraitConst` with one of them:","code":null,"level":"help","spans":[{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat_ops.rs","byte_start":941,"byte_end":941,"line_start":47,"line_end":47,"column_start":28,"column_end":28,"is_primary":true,"text":[{"text":"fn elemmul_mat_matexpr(a: &impl MatTraitConst, b: &impl ToInputArray) -> Result {","highlight_start":28,"highlight_end":28}],"label":null,"suggested_replacement":"(","suggestion_applicability":"MaybeIncorrect","expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat_ops.rs","byte_start":959,"byte_end":959,"line_start":47,"line_end":47,"column_start":46,"column_end":46,"is_primary":true,"text":[{"text":"fn elemmul_mat_matexpr(a: &impl MatTraitConst, b: &impl ToInputArray) -> Result {","highlight_start":46,"highlight_end":46}],"label":null,"suggested_replacement":" + hub::core::MatExprTraitConst)","suggestion_applicability":"MaybeIncorrect","expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat_ops.rs","byte_start":941,"byte_end":941,"line_start":47,"line_end":47,"column_start":28,"column_end":28,"is_primary":true,"text":[{"text":"fn elemmul_mat_matexpr(a: &impl MatTraitConst, b: &impl ToInputArray) -> Result {","highlight_start":28,"highlight_end":28}],"label":null,"suggested_replacement":"(","suggestion_applicability":"MaybeIncorrect","expansion":null},{"file_name":"/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat_ops.rs","byte_start":959,"byte_end":959,"line_start":47,"line_end":47,"column_start":46,"column_end":46,"is_primary":true,"text":[{"text":"fn elemmul_mat_matexpr(a: &impl MatTraitConst, b: &impl ToInputArray) -> Result {","highlight_start":46,"highlight_end":46}],"label":null,"suggested_replacement":" + hub::core::UMatTraitConst)","suggestion_applicability":"MaybeIncorrect","expansion":null}],"children":[],"rendered":null}],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `mul_def` found for reference `&impl MatTraitConst` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src/manual/core/mat_ops.rs:48:4\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m48\u001b[0m \u001b[1m\u001b[94m|\u001b[0m a.mul_def(b)\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&impl MatTraitConst`\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m\n \u001b[1m\u001b[94m= \u001b[0m\u001b[1mhelp\u001b[0m: items from traits can only be used if the type parameter is bounded by the trait\n\u001b[1m\u001b[96mhelp\u001b[0m: the following traits define an item `mul_def`, perhaps you need to restrict type parameter `impl MatTraitConst` with one of them:\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m47\u001b[0m \u001b[1m\u001b[94m| \u001b[0mfn elemmul_mat_matexpr(a: &\u001b[92m(\u001b[0mimpl MatTraitConst\u001b[92m + hub::core::MatExprTraitConst)\u001b[0m, b: &impl ToInputArray) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[92m+\u001b[0m \u001b[92m+++++++++++++++++++++++++++++++\u001b[0m\n\u001b[1m\u001b[94m47\u001b[0m \u001b[1m\u001b[94m| \u001b[0mfn elemmul_mat_matexpr(a: &\u001b[92m(\u001b[0mimpl MatTraitConst\u001b[92m + hub::core::UMatTraitConst)\u001b[0m, b: &impl ToInputArray) -> Result {\n \u001b[1m\u001b[94m|\u001b[0m \u001b[92m+\u001b[0m \u001b[92m++++++++++++++++++++++++++++\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_DMatch` found for reference `&DMatch` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":592434,"byte_end":592447,"line_start":12921,"line_end":12921,"column_start":63,"column_end":76,"is_primary":true,"text":[{"text":"\t\t\tunsafe { sys::cv_DMatch_operatorL_const_const_DMatchR(self.as_raw_DMatch(), &m, ocvrs_return.as_mut_ptr()) };","highlight_start":63,"highlight_end":76}],"label":"method not found in `&DMatch`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_DMatch` found for reference `&DMatch` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:12921:63\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m12921\u001b[0m \u001b[1m\u001b[94m|\u001b[0m unsafe { sys::cv_DMatch_operatorL_const_const_DMatchR(self.as_raw_DMatch(), &m, ocvrs_return.as_mut_ptr()) };\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&DMatch`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_RotatedRect` found for reference `&RotatedRect` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":930184,"byte_end":930202,"line_start":21591,"line_end":21591,"column_start":61,"column_end":79,"is_primary":true,"text":[{"text":"\t\t\tunsafe { sys::cv_RotatedRect_points_const_Point2fXX(self.as_raw_RotatedRect(), pts, ocvrs_return.as_mut_ptr()) };","highlight_start":61,"highlight_end":79}],"label":"method not found in `&RotatedRect`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_RotatedRect` found for reference `&RotatedRect` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:21591:61\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m21591\u001b[0m \u001b[1m\u001b[94m|\u001b[0m unsafe { sys::cv_RotatedRect_points_const_Point2fXX(self.as_raw_RotatedRect(), pts, ocvrs_return.as_mut_ptr()) };\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&RotatedRect`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_RotatedRect` found for reference `&RotatedRect` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":930527,"byte_end":930545,"line_start":21600,"line_end":21600,"column_start":68,"column_end":86,"is_primary":true,"text":[{"text":"\t\t\tunsafe { sys::cv_RotatedRect_points_const_vectorLPoint2fGR(self.as_raw_RotatedRect(), pts.as_raw_mut_VectorOfPoint2f(), ocvrs_return.as_mut_ptr()) };","highlight_start":68,"highlight_end":86}],"label":"method not found in `&RotatedRect`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_RotatedRect` found for reference `&RotatedRect` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:21600:68\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m21600\u001b[0m \u001b[1m\u001b[94m|\u001b[0m unsafe { sys::cv_RotatedRect_points_const_vectorLPoint2fGR(self.as_raw_RotatedRect(), pts.as_raw_mut_VectorOfPoint2f(), ocvrs_return.a\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&RotatedRect`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_RotatedRect` found for reference `&RotatedRect` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":930946,"byte_end":930964,"line_start":21610,"line_end":21610,"column_start":57,"column_end":75,"is_primary":true,"text":[{"text":"\t\t\tunsafe { sys::cv_RotatedRect_boundingRect_const(self.as_raw_RotatedRect(), ocvrs_return.as_mut_ptr()) };","highlight_start":57,"highlight_end":75}],"label":"method not found in `&RotatedRect`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_RotatedRect` found for reference `&RotatedRect` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:21610:57\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m21610\u001b[0m \u001b[1m\u001b[94m|\u001b[0m unsafe { sys::cv_RotatedRect_boundingRect_const(self.as_raw_RotatedRect(), ocvrs_return.as_mut_ptr()) };\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&RotatedRect`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_RotatedRect` found for reference `&RotatedRect` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":931377,"byte_end":931395,"line_start":21620,"line_end":21620,"column_start":59,"column_end":77,"is_primary":true,"text":[{"text":"\t\t\tunsafe { sys::cv_RotatedRect_boundingRect2f_const(self.as_raw_RotatedRect(), ocvrs_return.as_mut_ptr()) };","highlight_start":59,"highlight_end":77}],"label":"method not found in `&RotatedRect`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_RotatedRect` found for reference `&RotatedRect` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:21620:59\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m21620\u001b[0m \u001b[1m\u001b[94m|\u001b[0m unsafe { sys::cv_RotatedRect_boundingRect2f_const(self.as_raw_RotatedRect(), ocvrs_return.as_mut_ptr()) };\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&RotatedRect`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_TermCriteria` found for reference `&TermCriteria` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs","byte_start":994258,"byte_end":994277,"line_start":23340,"line_end":23340,"column_start":53,"column_end":72,"is_primary":true,"text":[{"text":"\t\t\tunsafe { sys::cv_TermCriteria_isValid_const(self.as_raw_TermCriteria(), ocvrs_return.as_mut_ptr()) };","highlight_start":53,"highlight_end":72}],"label":"method not found in `&TermCriteria`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_TermCriteria` found for reference `&TermCriteria` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs:23340:53\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m23340\u001b[0m \u001b[1m\u001b[94m|\u001b[0m unsafe { sys::cv_TermCriteria_isValid_const(self.as_raw_TermCriteria(), ocvrs_return.as_mut_ptr()) };\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&TermCriteria`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_VectorOfRect2d` found for reference `&vector::Vector>` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":13646,"byte_end":13667,"line_start":181,"line_end":181,"column_start":146,"column_end":167,"is_primary":true,"text":[{"text":"\t\tunsafe { sys::cv_dnn_NMSBoxesBatched_const_vectorLRect2dGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR(bboxes.as_raw_VectorOfRect2d(), scores.as_raw_VectorOff32(), class_ids.as_raw_VectorOfi32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) };","highlight_start":146,"highlight_end":167}],"label":"method not found in `&vector::Vector>`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_VectorOfRect2d` found for reference `&vector::Vector>` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:181:146\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m181\u001b[0m \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m...\u001b[0mt_float_vectorLintGR(bboxes.as_raw_VectorOfRect2d(), scores.as_raw_VectorOff32(), class_ids.as_raw_VectorOfi32(), score_threshold, nms\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&vector::Vector>`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_VectorOfRect2d` found for reference `&vector::Vector>` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":14426,"byte_end":14447,"line_start":193,"line_end":193,"column_start":168,"column_end":189,"is_primary":true,"text":[{"text":"\t\tunsafe { sys::cv_dnn_NMSBoxesBatched_const_vectorLRect2dGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR_const_float_const_int(bboxes.as_raw_VectorOfRect2d(), scores.as_raw_VectorOff32(), class_ids.as_raw_VectorOfi32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), eta, top_k, ocvrs_return.as_mut_ptr()) };","highlight_start":168,"highlight_end":189}],"label":"method not found in `&vector::Vector>`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_VectorOfRect2d` found for reference `&vector::Vector>` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:193:168\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m193\u001b[0m \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m...\u001b[0monst_float_const_int(bboxes.as_raw_VectorOfRect2d(), scores.as_raw_VectorOff32(), class_ids.as_raw_VectorOfi32(), score_threshold, nms\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&vector::Vector>`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_VectorOfRect2d` found for reference `&vector::Vector>` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":18367,"byte_end":18388,"line_start":255,"line_end":255,"column_start":120,"column_end":141,"is_primary":true,"text":[{"text":"\t\tunsafe { sys::cv_dnn_NMSBoxes_const_vectorLRect2dGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR(bboxes.as_raw_VectorOfRect2d(), scores.as_raw_VectorOff32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) };","highlight_start":120,"highlight_end":141}],"label":"method not found in `&vector::Vector>`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_VectorOfRect2d` found for reference `&vector::Vector>` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:255:120\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m255\u001b[0m \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m...\u001b[0mt_float_vectorLintGR(bboxes.as_raw_VectorOfRect2d(), scores.as_raw_VectorOff32(), score_threshold, nms_threshold, indices.as_raw_mut_V\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&vector::Vector>`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_VectorOfRect2d` found for reference `&vector::Vector>` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":19052,"byte_end":19073,"line_start":267,"line_end":267,"column_start":142,"column_end":163,"is_primary":true,"text":[{"text":"\t\tunsafe { sys::cv_dnn_NMSBoxes_const_vectorLRect2dGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR_const_float_const_int(bboxes.as_raw_VectorOfRect2d(), scores.as_raw_VectorOff32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), eta, top_k, ocvrs_return.as_mut_ptr()) };","highlight_start":142,"highlight_end":163}],"label":"method not found in `&vector::Vector>`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_VectorOfRect2d` found for reference `&vector::Vector>` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:267:142\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m267\u001b[0m \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m...\u001b[0monst_float_const_int(bboxes.as_raw_VectorOfRect2d(), scores.as_raw_VectorOff32(), score_threshold, nms_threshold, indices.as_raw_mut_V\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&vector::Vector>`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_VectorOfVectorOff32` found for reference `&vector::Vector>` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":439628,"byte_end":439654,"line_start":11472,"line_end":11472,"column_start":144,"column_end":170,"is_primary":true,"text":[{"text":"\t\t\tunsafe { sys::cv_dnn_Layer_tryQuantize_const_vectorLvectorLfloatGGR_const_vectorLvectorLintGGR_LayerParamsR(self.as_raw_mut_Layer(), scales.as_raw_VectorOfVectorOff32(), zeropoints.as_raw_VectorOfVectorOfi32(), params.as_raw_mut_LayerParams(), ocvrs_return.as_mut_ptr()) };","highlight_start":144,"highlight_end":170}],"label":"method not found in `&vector::Vector>`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_VectorOfVectorOff32` found for reference `&vector::Vector>` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:11472:144\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m11472\u001b[0m \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m...\u001b[0mraw_mut_Layer(), scales.as_raw_VectorOfVectorOff32(), zeropoints.as_raw_VectorOfVectorOfi32(), params.as_raw_mut_LayerParams(), ocvrs_\u001b[1m\u001b[94m...\u001b[0m\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&vector::Vector>`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_VectorOfPtrOfBackendNode` found for reference `&vector::Vector>` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":446604,"byte_end":446635,"line_start":11621,"line_end":11621,"column_start":185,"column_end":216,"is_primary":true,"text":[{"text":"\t\t\tunsafe { sys::cv_dnn_Layer_initNgraph_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendNodeGGR(self.as_raw_mut_Layer(), inputs.as_raw_VectorOfPtrOfBackendWrapper(), nodes.as_raw_VectorOfPtrOfBackendNode(), ocvrs_return.as_mut_ptr()) };","highlight_start":185,"highlight_end":216}],"label":"method not found in `&vector::Vector>`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_VectorOfPtrOfBackendNode` found for reference `&vector::Vector>` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:11621:185\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m11621\u001b[0m \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m...\u001b[0mpper(), nodes.as_raw_VectorOfPtrOfBackendNode(), ocvrs_return.as_mut_ptr()) };\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&vector::Vector>`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_VectorOfPtrOfBackendNode` found for reference `&vector::Vector>` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":447954,"byte_end":447985,"line_start":11641,"line_end":11641,"column_start":184,"column_end":215,"is_primary":true,"text":[{"text":"\t\t\tunsafe { sys::cv_dnn_Layer_initWebnn_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendNodeGGR(self.as_raw_mut_Layer(), inputs.as_raw_VectorOfPtrOfBackendWrapper(), nodes.as_raw_VectorOfPtrOfBackendNode(), ocvrs_return.as_mut_ptr()) };","highlight_start":184,"highlight_end":215}],"label":"method not found in `&vector::Vector>`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_VectorOfPtrOfBackendNode` found for reference `&vector::Vector>` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:11641:184\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m11641\u001b[0m \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m...\u001b[0mpper(), nodes.as_raw_VectorOfPtrOfBackendNode(), ocvrs_return.as_mut_ptr()) };\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&vector::Vector>`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_VectorOfPtrOfBackendNode` found for reference `&vector::Vector>` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs","byte_start":450896,"byte_end":450927,"line_start":11690,"line_end":11690,"column_start":264,"column_end":295,"is_primary":true,"text":[{"text":"\t\t\tunsafe { sys::cv_dnn_Layer_initCann_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendNodeGGR(self.as_raw_mut_Layer(), inputs.as_raw_VectorOfPtrOfBackendWrapper(), outputs.as_raw_VectorOfPtrOfBackendWrapper(), nodes.as_raw_VectorOfPtrOfBackendNode(), ocvrs_return.as_mut_ptr()) };","highlight_start":264,"highlight_end":295}],"label":"method not found in `&vector::Vector>`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_VectorOfPtrOfBackendNode` found for reference `&vector::Vector>` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs:11690:264\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m11690\u001b[0m \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m...\u001b[0mpper(), nodes.as_raw_VectorOfPtrOfBackendNode(), ocvrs_return.as_mut_ptr()) };\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&vector::Vector>`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_VectorOfVideoCapture` found for reference `&vector::Vector` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/videoio.rs","byte_start":87174,"byte_end":87201,"line_start":1607,"line_end":1607,"column_start":99,"column_end":126,"is_primary":true,"text":[{"text":"\t\t\tunsafe { sys::cv_VideoCapture_waitAny_const_vectorLVideoCaptureGR_vectorLintGR_int64_t(streams.as_raw_VectorOfVideoCapture(), ready_index.as_raw_mut_VectorOfi32(), timeout_ns, ocvrs_return.as_mut_ptr()) };","highlight_start":99,"highlight_end":126}],"label":"method not found in `&vector::Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_VectorOfVideoCapture` found for reference `&vector::Vector` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/videoio.rs:1607:99\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m1607\u001b[0m \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m...\u001b[0mctorLintGR_int64_t(streams.as_raw_VectorOfVideoCapture(), ready_index.as_raw_mut_VectorOfi32(), timeout_ns, ocvrs_return.as_mut_ptr()) };\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&vector::Vector`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"no method named `as_raw_VectorOfVideoCapture` found for reference `&vector::Vector` in the current scope","code":{"code":"E0599","explanation":"This error occurs when a method is used on a type which doesn't implement it:\n\nErroneous code example:\n\n```compile_fail,E0599\nstruct Mouth;\n\nlet x = Mouth;\nx.chocolate(); // error: no method named `chocolate` found for type `Mouth`\n // in the current scope\n```\n\nIn this case, you need to implement the `chocolate` method to fix the error:\n\n```\nstruct Mouth;\n\nimpl Mouth {\n fn chocolate(&self) { // We implement the `chocolate` method here.\n println!(\"Hmmm! I love chocolate!\");\n }\n}\n\nlet x = Mouth;\nx.chocolate(); // ok!\n```\n"},"level":"error","spans":[{"file_name":"/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/videoio.rs","byte_start":88534,"byte_end":88561,"line_start":1635,"line_end":1635,"column_start":91,"column_end":118,"is_primary":true,"text":[{"text":"\t\t\tunsafe { sys::cv_VideoCapture_waitAny_const_vectorLVideoCaptureGR_vectorLintGR(streams.as_raw_VectorOfVideoCapture(), ready_index.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) };","highlight_start":91,"highlight_end":118}],"label":"method not found in `&vector::Vector`","suggested_replacement":null,"suggestion_applicability":null,"expansion":null}],"children":[],"rendered":"\u001b[1m\u001b[91merror[E0599]\u001b[0m\u001b[1m: no method named `as_raw_VectorOfVideoCapture` found for reference `&vector::Vector` in the current scope\u001b[0m\n \u001b[1m\u001b[94m--> \u001b[0m/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/videoio.rs:1635:91\n \u001b[1m\u001b[94m|\u001b[0m\n\u001b[1m\u001b[94m1635\u001b[0m \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[94m...\u001b[0mureGR_vectorLintGR(streams.as_raw_VectorOfVideoCapture(), ready_index.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) };\n \u001b[1m\u001b[94m|\u001b[0m \u001b[1m\u001b[91m^^^^^^^^^^^^^^^^^^^^^^^^^^^\u001b[0m \u001b[1m\u001b[91mmethod not found in `&vector::Vector`\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"aborting due to 84 previous errors","code":null,"level":"error","spans":[],"children":[],"rendered":"\u001b[1m\u001b[91merror\u001b[0m\u001b[1m: aborting due to 84 previous errors\u001b[0m\n\n"} +{"$message_type":"diagnostic","message":"Some errors have detailed explanations: E0277, E0308, E0599.","code":null,"level":"failure-note","spans":[],"children":[],"rendered":"\u001b[1mSome errors have detailed explanations: E0277, E0308, E0599.\u001b[0m\n"} +{"$message_type":"diagnostic","message":"For more information about an error, try `rustc --explain E0277`.","code":null,"level":"failure-note","spans":[],"children":[],"rendered":"\u001b[1mFor more information about an error, try `rustc --explain E0277`.\u001b[0m\n"} diff --git a/target/debug/.fingerprint/opencv-binding-generator-7bb8f91113c1cc17/dep-lib-opencv_binding_generator b/target/debug/.fingerprint/opencv-binding-generator-7bb8f91113c1cc17/dep-lib-opencv_binding_generator new file mode 100644 index 0000000..ec3cb8b Binary files /dev/null and b/target/debug/.fingerprint/opencv-binding-generator-7bb8f91113c1cc17/dep-lib-opencv_binding_generator differ diff --git a/target/debug/.fingerprint/opencv-binding-generator-7bb8f91113c1cc17/invoked.timestamp b/target/debug/.fingerprint/opencv-binding-generator-7bb8f91113c1cc17/invoked.timestamp new file mode 100644 index 0000000..e00328d --- /dev/null +++ b/target/debug/.fingerprint/opencv-binding-generator-7bb8f91113c1cc17/invoked.timestamp @@ -0,0 +1 @@ +This file has an mtime of when this was started. \ No newline at end of file diff --git a/target/debug/.fingerprint/opencv-binding-generator-7bb8f91113c1cc17/lib-opencv_binding_generator b/target/debug/.fingerprint/opencv-binding-generator-7bb8f91113c1cc17/lib-opencv_binding_generator new file mode 100644 index 0000000..7028589 --- /dev/null +++ b/target/debug/.fingerprint/opencv-binding-generator-7bb8f91113c1cc17/lib-opencv_binding_generator @@ -0,0 +1 @@ +6f6da3ec35f408e9 \ No newline at end of file diff --git a/target/debug/.fingerprint/opencv-binding-generator-7bb8f91113c1cc17/lib-opencv_binding_generator.json b/target/debug/.fingerprint/opencv-binding-generator-7bb8f91113c1cc17/lib-opencv_binding_generator.json new file mode 100644 index 0000000..557d2d5 --- /dev/null +++ b/target/debug/.fingerprint/opencv-binding-generator-7bb8f91113c1cc17/lib-opencv_binding_generator.json @@ -0,0 +1 @@ +{"rustc":18415816196306954164,"features":"[]","declared_features":"[\"clang-runtime\"]","target":10205811915881153359,"profile":3033921117576893,"path":171583976603961680,"deps":[[4885725550624711673,"clang_sys",false,1310021895450928419],[6803352382179706244,"percent_encoding",false,4004843403804960731],[8410525223747752176,"shlex",false,18384121358634351639],[11989259058781683633,"dunce",false,6557507895273699188],[17109794424245468765,"regex",false,5097443734293317436],[17902046705470423771,"clang",false,15955383379703624591]],"local":[{"CheckDepInfo":{"dep_info":"debug/.fingerprint/opencv-binding-generator-7bb8f91113c1cc17/dep-lib-opencv_binding_generator","checksum":false}}],"rustflags":[],"config":2069994364910194474,"compile_kind":0} \ No newline at end of file diff --git a/target/debug/.fingerprint/percent-encoding-5022267e9a109d0c/dep-lib-percent_encoding b/target/debug/.fingerprint/percent-encoding-5022267e9a109d0c/dep-lib-percent_encoding new file mode 100644 index 0000000..ec3cb8b Binary files /dev/null and b/target/debug/.fingerprint/percent-encoding-5022267e9a109d0c/dep-lib-percent_encoding differ diff --git a/target/debug/.fingerprint/percent-encoding-5022267e9a109d0c/invoked.timestamp b/target/debug/.fingerprint/percent-encoding-5022267e9a109d0c/invoked.timestamp new file mode 100644 index 0000000..e00328d --- /dev/null +++ b/target/debug/.fingerprint/percent-encoding-5022267e9a109d0c/invoked.timestamp @@ -0,0 +1 @@ +This file has an mtime of when this was started. \ No newline at end of file diff --git a/target/debug/.fingerprint/percent-encoding-5022267e9a109d0c/lib-percent_encoding b/target/debug/.fingerprint/percent-encoding-5022267e9a109d0c/lib-percent_encoding new file mode 100644 index 0000000..a6d71e5 --- /dev/null +++ b/target/debug/.fingerprint/percent-encoding-5022267e9a109d0c/lib-percent_encoding @@ -0,0 +1 @@ +db67dc70db0f9437 \ No newline at end of file diff --git a/target/debug/.fingerprint/percent-encoding-5022267e9a109d0c/lib-percent_encoding.json b/target/debug/.fingerprint/percent-encoding-5022267e9a109d0c/lib-percent_encoding.json new file mode 100644 index 0000000..24a6aa7 --- /dev/null +++ b/target/debug/.fingerprint/percent-encoding-5022267e9a109d0c/lib-percent_encoding.json @@ -0,0 +1 @@ +{"rustc":18415816196306954164,"features":"[]","declared_features":"[\"alloc\", \"default\", \"std\"]","target":6219969305134610909,"profile":3033921117576893,"path":17893914732820307768,"deps":[],"local":[{"CheckDepInfo":{"dep_info":"debug/.fingerprint/percent-encoding-5022267e9a109d0c/dep-lib-percent_encoding","checksum":false}}],"rustflags":[],"config":2069994364910194474,"compile_kind":0} \ No newline at end of file diff --git a/target/debug/.fingerprint/pkg-config-d1354bfe08f6aa0d/dep-lib-pkg_config b/target/debug/.fingerprint/pkg-config-d1354bfe08f6aa0d/dep-lib-pkg_config new file mode 100644 index 0000000..ec3cb8b Binary files /dev/null and b/target/debug/.fingerprint/pkg-config-d1354bfe08f6aa0d/dep-lib-pkg_config differ diff --git a/target/debug/.fingerprint/pkg-config-d1354bfe08f6aa0d/invoked.timestamp b/target/debug/.fingerprint/pkg-config-d1354bfe08f6aa0d/invoked.timestamp new file mode 100644 index 0000000..e00328d --- /dev/null +++ b/target/debug/.fingerprint/pkg-config-d1354bfe08f6aa0d/invoked.timestamp @@ -0,0 +1 @@ +This file has an mtime of when this was started. \ No newline at end of file diff --git a/target/debug/.fingerprint/pkg-config-d1354bfe08f6aa0d/lib-pkg_config b/target/debug/.fingerprint/pkg-config-d1354bfe08f6aa0d/lib-pkg_config new file mode 100644 index 0000000..e6052e7 --- /dev/null +++ b/target/debug/.fingerprint/pkg-config-d1354bfe08f6aa0d/lib-pkg_config @@ -0,0 +1 @@ +8e71bed2d176af2a \ No newline at end of file diff --git a/target/debug/.fingerprint/pkg-config-d1354bfe08f6aa0d/lib-pkg_config.json b/target/debug/.fingerprint/pkg-config-d1354bfe08f6aa0d/lib-pkg_config.json new file mode 100644 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100644 index 0000000..81d9fc4 --- /dev/null +++ b/target/debug/build/anyhow-40fe6c014f4e8bb6/output @@ -0,0 +1,7 @@ +cargo:rerun-if-changed=src/nightly.rs +cargo:rerun-if-env-changed=RUSTC_BOOTSTRAP +cargo:rustc-check-cfg=cfg(anyhow_build_probe) +cargo:rustc-check-cfg=cfg(anyhow_nightly_testing) +cargo:rustc-check-cfg=cfg(anyhow_no_clippy_format_args) +cargo:rustc-check-cfg=cfg(anyhow_no_core_error) +cargo:rustc-check-cfg=cfg(error_generic_member_access) diff --git a/target/debug/build/anyhow-40fe6c014f4e8bb6/root-output b/target/debug/build/anyhow-40fe6c014f4e8bb6/root-output new file mode 100644 index 0000000..c8a198a --- /dev/null +++ b/target/debug/build/anyhow-40fe6c014f4e8bb6/root-output @@ -0,0 +1 @@ +/Users/accusys/video_yolo_player/target/debug/build/anyhow-40fe6c014f4e8bb6/out \ No newline at end of file diff --git a/target/debug/build/anyhow-40fe6c014f4e8bb6/stderr b/target/debug/build/anyhow-40fe6c014f4e8bb6/stderr new file mode 100644 index 0000000..e69de29 diff --git a/target/debug/build/clang-sys-455eaadebd7fcdd4/invoked.timestamp b/target/debug/build/clang-sys-455eaadebd7fcdd4/invoked.timestamp new file mode 100644 index 0000000..e00328d --- /dev/null +++ b/target/debug/build/clang-sys-455eaadebd7fcdd4/invoked.timestamp @@ -0,0 +1 @@ +This file has an mtime of when this was started. \ No newline at end of file diff --git a/target/debug/build/clang-sys-455eaadebd7fcdd4/output b/target/debug/build/clang-sys-455eaadebd7fcdd4/output new file mode 100644 index 0000000..e042a8b --- /dev/null +++ b/target/debug/build/clang-sys-455eaadebd7fcdd4/output @@ -0,0 +1,2 @@ +cargo:rustc-link-search=/Library/Developer/CommandLineTools/usr/lib +cargo:rustc-link-lib=dylib=clang diff --git a/target/debug/build/clang-sys-455eaadebd7fcdd4/root-output b/target/debug/build/clang-sys-455eaadebd7fcdd4/root-output new file mode 100644 index 0000000..bf90cba --- /dev/null +++ b/target/debug/build/clang-sys-455eaadebd7fcdd4/root-output @@ -0,0 +1 @@ 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+/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/clang-sys-1.8.1/build/dynamic.rs: +/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/clang-sys-1.8.1/build/static.rs: diff --git a/target/debug/build/libc-d9e23ff9305bfd93/build-script-build b/target/debug/build/libc-d9e23ff9305bfd93/build-script-build new file mode 100755 index 0000000..3ae5870 Binary files /dev/null and b/target/debug/build/libc-d9e23ff9305bfd93/build-script-build differ diff --git a/target/debug/build/libc-d9e23ff9305bfd93/build_script_build-d9e23ff9305bfd93 b/target/debug/build/libc-d9e23ff9305bfd93/build_script_build-d9e23ff9305bfd93 new file mode 100755 index 0000000..3ae5870 Binary files /dev/null and b/target/debug/build/libc-d9e23ff9305bfd93/build_script_build-d9e23ff9305bfd93 differ diff --git a/target/debug/build/libc-d9e23ff9305bfd93/build_script_build-d9e23ff9305bfd93.d b/target/debug/build/libc-d9e23ff9305bfd93/build_script_build-d9e23ff9305bfd93.d new file mode 100644 index 0000000..260d64b --- /dev/null +++ b/target/debug/build/libc-d9e23ff9305bfd93/build_script_build-d9e23ff9305bfd93.d @@ -0,0 +1,5 @@ +/Users/accusys/video_yolo_player/target/debug/build/libc-d9e23ff9305bfd93/build_script_build-d9e23ff9305bfd93.d: /Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/libc-0.2.182/build.rs + +/Users/accusys/video_yolo_player/target/debug/build/libc-d9e23ff9305bfd93/build_script_build-d9e23ff9305bfd93: /Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/libc-0.2.182/build.rs + +/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/libc-0.2.182/build.rs: diff --git a/target/debug/build/libc-fcd50b7bda3a60e8/invoked.timestamp b/target/debug/build/libc-fcd50b7bda3a60e8/invoked.timestamp new file mode 100644 index 0000000..e00328d --- /dev/null +++ b/target/debug/build/libc-fcd50b7bda3a60e8/invoked.timestamp @@ -0,0 +1 @@ +This file has an mtime of when this was started. \ No newline at end of file diff --git a/target/debug/build/libc-fcd50b7bda3a60e8/output b/target/debug/build/libc-fcd50b7bda3a60e8/output new file mode 100644 index 0000000..89a43b5 --- /dev/null +++ b/target/debug/build/libc-fcd50b7bda3a60e8/output @@ -0,0 +1,25 @@ +cargo:rerun-if-changed=build.rs +cargo:rerun-if-env-changed=RUST_LIBC_UNSTABLE_FREEBSD_VERSION +cargo:rustc-cfg=freebsd12 +cargo:rerun-if-env-changed=RUST_LIBC_UNSTABLE_MUSL_V1_2_3 +cargo:rerun-if-env-changed=RUST_LIBC_UNSTABLE_LINUX_TIME_BITS64 +cargo:rerun-if-env-changed=RUST_LIBC_UNSTABLE_GNU_FILE_OFFSET_BITS +cargo:rerun-if-env-changed=RUST_LIBC_UNSTABLE_GNU_TIME_BITS +cargo:rustc-check-cfg=cfg(emscripten_old_stat_abi) +cargo:rustc-check-cfg=cfg(espidf_time32) +cargo:rustc-check-cfg=cfg(freebsd10) +cargo:rustc-check-cfg=cfg(freebsd11) +cargo:rustc-check-cfg=cfg(freebsd12) +cargo:rustc-check-cfg=cfg(freebsd13) +cargo:rustc-check-cfg=cfg(freebsd14) +cargo:rustc-check-cfg=cfg(freebsd15) +cargo:rustc-check-cfg=cfg(gnu_file_offset_bits64) +cargo:rustc-check-cfg=cfg(gnu_time_bits64) +cargo:rustc-check-cfg=cfg(libc_deny_warnings) +cargo:rustc-check-cfg=cfg(linux_time_bits64) +cargo:rustc-check-cfg=cfg(musl_v1_2_3) +cargo:rustc-check-cfg=cfg(musl32_time64) +cargo:rustc-check-cfg=cfg(vxworks_lt_25_09) +cargo:rustc-check-cfg=cfg(target_os,values("switch","aix","ohos","hurd","rtems","visionos","nuttx","cygwin","qurt")) +cargo:rustc-check-cfg=cfg(target_env,values("illumos","wasi","aix","ohos","nto71_iosock","nto80")) +cargo:rustc-check-cfg=cfg(target_arch,values("loongarch64","mips32r6","mips64r6","csky")) diff --git a/target/debug/build/libc-fcd50b7bda3a60e8/root-output b/target/debug/build/libc-fcd50b7bda3a60e8/root-output new file mode 100644 index 0000000..477a601 --- /dev/null +++ b/target/debug/build/libc-fcd50b7bda3a60e8/root-output @@ -0,0 +1 @@ +/Users/accusys/video_yolo_player/target/debug/build/libc-fcd50b7bda3a60e8/out \ No newline at end of file diff --git a/target/debug/build/libc-fcd50b7bda3a60e8/stderr 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diff --git a/target/debug/build/num-traits-da6afca2f0b36689/output b/target/debug/build/num-traits-da6afca2f0b36689/output new file mode 100644 index 0000000..5acddfe --- /dev/null +++ b/target/debug/build/num-traits-da6afca2f0b36689/output @@ -0,0 +1,3 @@ +cargo:rustc-check-cfg=cfg(has_total_cmp) +cargo:rustc-cfg=has_total_cmp +cargo:rerun-if-changed=build.rs diff --git a/target/debug/build/num-traits-da6afca2f0b36689/root-output b/target/debug/build/num-traits-da6afca2f0b36689/root-output new file mode 100644 index 0000000..2179245 --- /dev/null +++ b/target/debug/build/num-traits-da6afca2f0b36689/root-output @@ -0,0 +1 @@ +/Users/accusys/video_yolo_player/target/debug/build/num-traits-da6afca2f0b36689/out \ No newline at end of file diff --git a/target/debug/build/num-traits-da6afca2f0b36689/stderr b/target/debug/build/num-traits-da6afca2f0b36689/stderr new file mode 100644 index 0000000..e69de29 diff --git a/target/debug/build/opencv-4e105b5546afc119/invoked.timestamp 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b/target/debug/build/opencv-4e105b5546afc119/out/7d651e88ef68ee4a-manual-core.o new file mode 100644 index 0000000..5e82e22 Binary files /dev/null and b/target/debug/build/opencv-4e105b5546afc119/out/7d651e88ef68ee4a-manual-core.o differ diff --git a/target/debug/build/opencv-4e105b5546afc119/out/core.cpp b/target/debug/build/opencv-4e105b5546afc119/out/core.cpp new file mode 100644 index 0000000..76109fe --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/core.cpp @@ -0,0 +1,10373 @@ +#include "core.hpp" +#include "core_types.hpp" + +extern "C" { +void cv_Cholesky_doubleX_size_t_int_doubleX_size_t_int(double* A, size_t astep, int m, double* b, size_t bstep, int n, Result* ocvrs_return) { + try { + bool ret = cv::Cholesky(A, astep, m, b, bstep, n); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Cholesky_floatX_size_t_int_floatX_size_t_int(float* A, size_t astep, int m, float* b, size_t bstep, int n, Result* ocvrs_return) { + try { + bool ret = cv::Cholesky(A, astep, m, b, bstep, n); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LUT_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_InputArray* lut, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::LUT(*src, *lut, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LU_doubleX_size_t_int_doubleX_size_t_int(double* A, size_t astep, int m, double* b, size_t bstep, int n, Result* ocvrs_return) { + try { + int ret = cv::LU(A, astep, m, b, bstep, n); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LU_floatX_size_t_int_floatX_size_t_int(float* A, size_t astep, int m, float* b, size_t bstep, int n, Result* ocvrs_return) { + try { + int ret = cv::LU(A, astep, m, b, bstep, n); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mahalanobis_const__InputArrayR_const__InputArrayR_const__InputArrayR(const cv::_InputArray* v1, const cv::_InputArray* v2, const cv::_InputArray* icovar, Result* ocvrs_return) { + try { + double ret = cv::Mahalanobis(*v1, *v2, *icovar); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCABackProject_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* data, const cv::_InputArray* mean, const cv::_InputArray* eigenvectors, const cv::_OutputArray* result, ResultVoid* ocvrs_return) { + try { + cv::PCABackProject(*data, *mean, *eigenvectors, *result); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR(const cv::_InputArray* data, const cv::_InputOutputArray* mean, const cv::_OutputArray* eigenvectors, ResultVoid* ocvrs_return) { + try { + cv::PCACompute(*data, *mean, *eigenvectors); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::_InputArray* data, const cv::_InputOutputArray* mean, const cv::_OutputArray* eigenvectors, const cv::_OutputArray* eigenvalues, ResultVoid* ocvrs_return) { + try { + cv::PCACompute(*data, *mean, *eigenvectors, *eigenvalues); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_double(const cv::_InputArray* data, const cv::_InputOutputArray* mean, const cv::_OutputArray* eigenvectors, const cv::_OutputArray* eigenvalues, double retainedVariance, ResultVoid* ocvrs_return) { + try { + cv::PCACompute(*data, *mean, *eigenvectors, *eigenvalues, retainedVariance); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_int(const cv::_InputArray* data, const cv::_InputOutputArray* mean, const cv::_OutputArray* eigenvectors, const cv::_OutputArray* eigenvalues, int maxComponents, ResultVoid* ocvrs_return) { + try { + cv::PCACompute(*data, *mean, *eigenvectors, *eigenvalues, maxComponents); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_double(const cv::_InputArray* data, const cv::_InputOutputArray* mean, const cv::_OutputArray* eigenvectors, double retainedVariance, ResultVoid* ocvrs_return) { + try { + cv::PCACompute(*data, *mean, *eigenvectors, retainedVariance); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_int(const cv::_InputArray* data, const cv::_InputOutputArray* mean, const cv::_OutputArray* eigenvectors, int maxComponents, ResultVoid* ocvrs_return) { + try { + cv::PCACompute(*data, *mean, *eigenvectors, maxComponents); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCAProject_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* data, const cv::_InputArray* mean, const cv::_InputArray* eigenvectors, const cv::_OutputArray* result, ResultVoid* ocvrs_return) { + try { + cv::PCAProject(*data, *mean, *eigenvectors, *result); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PSNR_const__InputArrayR_const__InputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, Result* ocvrs_return) { + try { + double ret = cv::PSNR(*src1, *src2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PSNR_const__InputArrayR_const__InputArrayR_double(const cv::_InputArray* src1, const cv::_InputArray* src2, double R, Result* ocvrs_return) { + try { + double ret = cv::PSNR(*src1, *src2, R); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVBackSubst_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* w, const cv::_InputArray* u, const cv::_InputArray* vt, const cv::_InputArray* rhs, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::SVBackSubst(*w, *u, *vt, *rhs, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVDecomp_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* w, const cv::_OutputArray* u, const cv::_OutputArray* vt, ResultVoid* ocvrs_return) { + try { + cv::SVDecomp(*src, *w, *u, *vt); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVDecomp_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* w, const cv::_OutputArray* u, const cv::_OutputArray* vt, int flags, ResultVoid* ocvrs_return) { + try { + cv::SVDecomp(*src, *w, *u, *vt, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_abs_const_MatExprR(const cv::MatExpr* e, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::abs(*e); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_abs_const_MatR(const cv::Mat* m, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::abs(*m); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_absdiff_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::absdiff(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_addWeighted_const__InputArrayR_double_const__InputArrayR_double_double_const__OutputArrayR(const cv::_InputArray* src1, double alpha, const cv::_InputArray* src2, double beta, double gamma, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::addWeighted(*src1, alpha, *src2, beta, gamma, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_addWeighted_const__InputArrayR_double_const__InputArrayR_double_double_const__OutputArrayR_int(const cv::_InputArray* src1, double alpha, const cv::_InputArray* src2, double beta, double gamma, const cv::_OutputArray* dst, int dtype, ResultVoid* ocvrs_return) { + try { + cv::addWeighted(*src1, alpha, *src2, beta, gamma, *dst, dtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_add_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::add(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_add_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR_int(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, const cv::_InputArray* mask, int dtype, ResultVoid* ocvrs_return) { + try { + cv::add(*src1, *src2, *dst, *mask, dtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_batchDistance_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dist, int dtype, const cv::_OutputArray* nidx, ResultVoid* ocvrs_return) { + try { + cv::batchDistance(*src1, *src2, *dist, dtype, *nidx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_batchDistance_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_const__OutputArrayR_int_int_const__InputArrayR_int_bool(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dist, int dtype, const cv::_OutputArray* nidx, int normType, int K, const cv::_InputArray* mask, int update, bool crosscheck, ResultVoid* ocvrs_return) { + try { + cv::batchDistance(*src1, *src2, *dist, dtype, *nidx, normType, K, *mask, update, crosscheck); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_bitwise_and_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::bitwise_and(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_bitwise_and_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::bitwise_and(*src1, *src2, *dst, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_bitwise_not_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::bitwise_not(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_bitwise_not_const__InputArrayR_const__OutputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::bitwise_not(*src, *dst, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_bitwise_or_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::bitwise_or(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_bitwise_or_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::bitwise_or(*src1, *src2, *dst, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_bitwise_xor_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::bitwise_xor(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_bitwise_xor_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::bitwise_xor(*src1, *src2, *dst, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_borderInterpolate_int_int_int(int p, int len, int borderType, Result* ocvrs_return) { + try { + int ret = cv::borderInterpolate(p, len, borderType); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_broadcast_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_InputArray* shape, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::broadcast(*src, *shape, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_calcCovarMatrix_const__InputArrayR_const__OutputArrayR_const__InputOutputArrayR_int(const cv::_InputArray* samples, const cv::_OutputArray* covar, const cv::_InputOutputArray* mean, int flags, ResultVoid* ocvrs_return) { + try { + cv::calcCovarMatrix(*samples, *covar, *mean, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_calcCovarMatrix_const__InputArrayR_const__OutputArrayR_const__InputOutputArrayR_int_int(const cv::_InputArray* samples, const cv::_OutputArray* covar, const cv::_InputOutputArray* mean, int flags, int ctype, ResultVoid* ocvrs_return) { + try { + cv::calcCovarMatrix(*samples, *covar, *mean, flags, ctype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cartToPolar_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::_InputArray* x, const cv::_InputArray* y, const cv::_OutputArray* magnitude, const cv::_OutputArray* angle, ResultVoid* ocvrs_return) { + try { + cv::cartToPolar(*x, *y, *magnitude, *angle); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cartToPolar_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_bool(const cv::_InputArray* x, const cv::_InputArray* y, const cv::_OutputArray* magnitude, const cv::_OutputArray* angle, bool angleInDegrees, ResultVoid* ocvrs_return) { + try { + cv::cartToPolar(*x, *y, *magnitude, *angle, angleInDegrees); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_checkHardwareSupport_int(int feature, Result* ocvrs_return) { + try { + bool ret = cv::checkHardwareSupport(feature); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_checkRange_const__InputArrayR(const cv::_InputArray* a, Result* ocvrs_return) { + try { + bool ret = cv::checkRange(*a); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_checkRange_const__InputArrayR_bool_PointX_double_double(const cv::_InputArray* a, bool quiet, cv::Point* pos, double minVal, double maxVal, Result* ocvrs_return) { + try { + bool ret = cv::checkRange(*a, quiet, pos, minVal, maxVal); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_compare_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, int cmpop, ResultVoid* ocvrs_return) { + try { + cv::compare(*src1, *src2, *dst, cmpop); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_completeSymm_const__InputOutputArrayR(const cv::_InputOutputArray* m, ResultVoid* ocvrs_return) { + try { + cv::completeSymm(*m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_completeSymm_const__InputOutputArrayR_bool(const cv::_InputOutputArray* m, bool lowerToUpper, ResultVoid* ocvrs_return) { + try { + cv::completeSymm(*m, lowerToUpper); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_convertFp16_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::convertFp16(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_convertScaleAbs_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::convertScaleAbs(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_convertScaleAbs_const__InputArrayR_const__OutputArrayR_double_double(const cv::_InputArray* src, const cv::_OutputArray* dst, double alpha, double beta, ResultVoid* ocvrs_return) { + try { + cv::convertScaleAbs(*src, *dst, alpha, beta); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_copyMakeBorder_const__InputArrayR_const__OutputArrayR_int_int_int_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int top, int bottom, int left, int right, int borderType, ResultVoid* ocvrs_return) { + try { + cv::copyMakeBorder(*src, *dst, top, bottom, left, right, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_copyMakeBorder_const__InputArrayR_const__OutputArrayR_int_int_int_int_int_const_ScalarR(const cv::_InputArray* src, const cv::_OutputArray* dst, int top, int bottom, int left, int right, int borderType, const cv::Scalar* value, ResultVoid* ocvrs_return) { + try { + cv::copyMakeBorder(*src, *dst, top, bottom, left, right, borderType, *value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_copyTo_const__InputArrayR_const__OutputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::copyTo(*src, *dst, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_countNonZero_const__InputArrayR(const cv::_InputArray* src, Result* ocvrs_return) { + try { + int ret = cv::countNonZero(*src); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cubeRoot_float(float val, Result* ocvrs_return) { + try { + float ret = cv::cubeRoot(val); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dct_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::dct(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dct_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int flags, ResultVoid* ocvrs_return) { + try { + cv::dct(*src, *dst, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_depthToString_int(int depth, Result* ocvrs_return) { + try { + const char* ret = cv::depthToString(depth); + Ok(ocvrs_create_string(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_MatChannels_const_int_const_CheckContextR(const int v, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_MatChannels(v, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_MatChannels_const_int_const_int_const_CheckContextR(const int v1, const int v2, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_MatChannels(v1, v2, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_MatDepth_const_int_const_CheckContextR(const int v, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_MatDepth(v, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_MatDepth_const_int_const_int_const_CheckContextR(const int v1, const int v2, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_MatDepth(v1, v2, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_MatType_const_int_const_CheckContextR(const int v, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_MatType(v, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_MatType_const_int_const_int_const_CheckContextR(const int v1, const int v2, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_MatType(v1, v2, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_auto_const_Size_LintG_const_CheckContextR(const cv::Size_* v, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_auto(*v, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_auto_const_Size_LintG_const_Size_LintG_const_CheckContextR(const cv::Size_* v1, const cv::Size_* v2, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_auto(*v1, *v2, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_auto_const_bool_const_bool_const_CheckContextR(const bool v1, const bool v2, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_auto(v1, v2, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_auto_const_double_const_CheckContextR(const double v, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_auto(v, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_auto_const_double_const_double_const_CheckContextR(const double v1, const double v2, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_auto(v1, v2, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_auto_const_float_const_CheckContextR(const float v, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_auto(v, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_auto_const_float_const_float_const_CheckContextR(const float v1, const float v2, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_auto(v1, v2, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_auto_const_int_const_CheckContextR(const int v, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_auto(v, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_auto_const_int_const_int_const_CheckContextR(const int v1, const int v2, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_auto(v1, v2, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_auto_const_size_t_const_CheckContextR(const size_t v, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_auto(v, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_auto_const_size_t_const_size_t_const_CheckContextR(const size_t v1, const size_t v2, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_auto(v1, v2, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_auto_const_stringR_const_CheckContextR(const char* v1, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_auto(std::string(v1), *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_false_const_bool_const_CheckContextR(const bool v, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_false(v, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_detail_check_failed_true_const_bool_const_CheckContextR(const bool v, const cv::detail::CheckContext* ctx, ResultVoid* ocvrs_return) { + try { + cv::detail::check_failed_true(v, *ctx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_determinant_const__InputArrayR(const cv::_InputArray* mtx, Result* ocvrs_return) { + try { + double ret = cv::determinant(*mtx); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dft_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::dft(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dft_const__InputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int flags, int nonzeroRows, ResultVoid* ocvrs_return) { + try { + cv::dft(*src, *dst, flags, nonzeroRows); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_convertFromD3D10Texture2D_ID3D10Texture2DX_const__OutputArrayR(ID3D10Texture2D* pD3D10Texture2D, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::directx::convertFromD3D10Texture2D(pD3D10Texture2D, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_convertFromD3D11Texture2D_ID3D11Texture2DX_const__OutputArrayR(ID3D11Texture2D* pD3D11Texture2D, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::directx::convertFromD3D11Texture2D(pD3D11Texture2D, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_convertFromDirect3DSurface9_IDirect3DSurface9X_const__OutputArrayR(IDirect3DSurface9* pDirect3DSurface9, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::directx::convertFromDirect3DSurface9(pDirect3DSurface9, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_convertFromDirect3DSurface9_IDirect3DSurface9X_const__OutputArrayR_voidX(IDirect3DSurface9* pDirect3DSurface9, const cv::_OutputArray* dst, void* surfaceSharedHandle, ResultVoid* ocvrs_return) { + try { + cv::directx::convertFromDirect3DSurface9(pDirect3DSurface9, *dst, surfaceSharedHandle); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_convertToD3D10Texture2D_const__InputArrayR_ID3D10Texture2DX(const cv::_InputArray* src, ID3D10Texture2D* pD3D10Texture2D, ResultVoid* ocvrs_return) { + try { + cv::directx::convertToD3D10Texture2D(*src, pD3D10Texture2D); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_convertToD3D11Texture2D_const__InputArrayR_ID3D11Texture2DX(const cv::_InputArray* src, ID3D11Texture2D* pD3D11Texture2D, ResultVoid* ocvrs_return) { + try { + cv::directx::convertToD3D11Texture2D(*src, pD3D11Texture2D); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_convertToDirect3DSurface9_const__InputArrayR_IDirect3DSurface9X(const cv::_InputArray* src, IDirect3DSurface9* pDirect3DSurface9, ResultVoid* ocvrs_return) { + try { + cv::directx::convertToDirect3DSurface9(*src, pDirect3DSurface9); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_convertToDirect3DSurface9_const__InputArrayR_IDirect3DSurface9X_voidX(const cv::_InputArray* src, IDirect3DSurface9* pDirect3DSurface9, void* surfaceSharedHandle, ResultVoid* ocvrs_return) { + try { + cv::directx::convertToDirect3DSurface9(*src, pDirect3DSurface9, surfaceSharedHandle); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_getTypeFromD3DFORMAT_const_int(const int iD3DFORMAT, Result* ocvrs_return) { + try { + int ret = cv::directx::getTypeFromD3DFORMAT(iD3DFORMAT); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_getTypeFromDXGI_FORMAT_const_int(const int iDXGI_FORMAT, Result* ocvrs_return) { + try { + int ret = cv::directx::getTypeFromDXGI_FORMAT(iDXGI_FORMAT); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_ocl_initializeContextFromD3D10Device_ID3D10DeviceX(ID3D10Device* pD3D10Device, Result* ocvrs_return) { + try { + cv::ocl::Context ret = cv::directx::ocl::initializeContextFromD3D10Device(pD3D10Device); + Ok(new cv::ocl::Context(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_ocl_initializeContextFromD3D11Device_ID3D11DeviceX(ID3D11Device* pD3D11Device, Result* ocvrs_return) { + try { + cv::ocl::Context ret = cv::directx::ocl::initializeContextFromD3D11Device(pD3D11Device); + Ok(new cv::ocl::Context(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_ocl_initializeContextFromDirect3DDevice9Ex_IDirect3DDevice9ExX(IDirect3DDevice9Ex* pDirect3DDevice9Ex, Result* ocvrs_return) { + try { + cv::ocl::Context ret = cv::directx::ocl::initializeContextFromDirect3DDevice9Ex(pDirect3DDevice9Ex); + Ok(new cv::ocl::Context(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_directx_ocl_initializeContextFromDirect3DDevice9_IDirect3DDevice9X(IDirect3DDevice9* pDirect3DDevice9, Result* ocvrs_return) { + try { + cv::ocl::Context ret = cv::directx::ocl::initializeContextFromDirect3DDevice9(pDirect3DDevice9); + Ok(new cv::ocl::Context(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_divide_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::divide(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_divide_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double_int(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, double scale, int dtype, ResultVoid* ocvrs_return) { + try { + cv::divide(*src1, *src2, *dst, scale, dtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_divide_double_const__InputArrayR_const__OutputArrayR(double scale, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::divide(scale, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_divide_double_const__InputArrayR_const__OutputArrayR_int(double scale, const cv::_InputArray* src2, const cv::_OutputArray* dst, int dtype, ResultVoid* ocvrs_return) { + try { + cv::divide(scale, *src2, *dst, dtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_eigenNonSymmetric_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* eigenvalues, const cv::_OutputArray* eigenvectors, ResultVoid* ocvrs_return) { + try { + cv::eigenNonSymmetric(*src, *eigenvalues, *eigenvectors); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_eigen_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* eigenvalues, Result* ocvrs_return) { + try { + bool ret = cv::eigen(*src, *eigenvalues); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_eigen_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* eigenvalues, const cv::_OutputArray* eigenvectors, Result* ocvrs_return) { + try { + bool ret = cv::eigen(*src, *eigenvalues, *eigenvectors); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_error_const_ExceptionR(const cv::Exception* exc, ResultVoid* ocvrs_return) { + try { + cv::error(*exc); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_error_int_const_StringR_const_charX_const_charX_int(int code, const char* err, const char* func, const char* file, int line, ResultVoid* ocvrs_return) { + try { + cv::error(code, std::string(err), func, file, line); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_exp_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::exp(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_extractChannel_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int coi, ResultVoid* ocvrs_return) { + try { + cv::extractChannel(*src, *dst, coi); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_fastAtan2_float_float(float y, float x, Result* ocvrs_return) { + try { + float ret = cv::fastAtan2(y, x); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_findNonZero_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* idx, ResultVoid* ocvrs_return) { + try { + cv::findNonZero(*src, *idx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_flipND_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int axis, ResultVoid* ocvrs_return) { + try { + cv::flipND(*src, *dst, axis); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_flip_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int flipCode, ResultVoid* ocvrs_return) { + try { + cv::flip(*src, *dst, flipCode); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_gemm_const__InputArrayR_const__InputArrayR_double_const__InputArrayR_double_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, double alpha, const cv::_InputArray* src3, double beta, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::gemm(*src1, *src2, alpha, *src3, beta, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_gemm_const__InputArrayR_const__InputArrayR_double_const__InputArrayR_double_const__OutputArrayR_int(const cv::_InputArray* src1, const cv::_InputArray* src2, double alpha, const cv::_InputArray* src3, double beta, const cv::_OutputArray* dst, int flags, ResultVoid* ocvrs_return) { + try { + cv::gemm(*src1, *src2, alpha, *src3, beta, *dst, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getBuildInformation(Result* ocvrs_return) { + try { + const cv::String ret = cv::getBuildInformation(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getCPUFeaturesLine(Result* ocvrs_return) { + try { + std::string ret = cv::getCPUFeaturesLine(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getCPUTickCount(Result* ocvrs_return) { + try { + int64_t ret = cv::getCPUTickCount(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getDefaultAlgorithmHint(Result* ocvrs_return) { + try { + cv::AlgorithmHint ret = cv::getDefaultAlgorithmHint(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getElemSize_int(int type, Result* ocvrs_return) { + try { + size_t ret = cv::getElemSize(type); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getHardwareFeatureName_int(int feature, Result* ocvrs_return) { + try { + cv::String ret = cv::getHardwareFeatureName(feature); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getNumThreads(Result* ocvrs_return) { + try { + int ret = cv::getNumThreads(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getNumberOfCPUs(Result* ocvrs_return) { + try { + int ret = cv::getNumberOfCPUs(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getOptimalDFTSize_int(int vecsize, Result* ocvrs_return) { + try { + int ret = cv::getOptimalDFTSize(vecsize); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getThreadNum(Result* ocvrs_return) { + try { + int ret = cv::getThreadNum(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getTickCount(Result* ocvrs_return) { + try { + int64_t ret = cv::getTickCount(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getTickFrequency(Result* ocvrs_return) { + try { + double ret = cv::getTickFrequency(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +int cv_getVersionMajor() { + int ret = cv::getVersionMajor(); + return ret; +} + +int cv_getVersionMinor() { + int ret = cv::getVersionMinor(); + return ret; +} + +int cv_getVersionRevision() { + int ret = cv::getVersionRevision(); + return ret; +} + +void cv_getVersionString(Result* ocvrs_return) { + try { + cv::String ret = cv::getVersionString(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_glob_String_vectorLStringGR(const char* pattern, std::vector* result, ResultVoid* ocvrs_return) { + try { + cv::glob(std::string(pattern), *result); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_glob_String_vectorLStringGR_bool(const char* pattern, std::vector* result, bool recursive, ResultVoid* ocvrs_return) { + try { + cv::glob(std::string(pattern), *result, recursive); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_hasNonZero_const__InputArrayR(const cv::_InputArray* src, Result* ocvrs_return) { + try { + bool ret = cv::hasNonZero(*src); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_haveOpenVX(Result* ocvrs_return) { + try { + bool ret = cv::haveOpenVX(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_hconcat_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::hconcat(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_hconcat_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::hconcat(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_idct_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::idct(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_idct_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int flags, ResultVoid* ocvrs_return) { + try { + cv::idct(*src, *dst, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_idft_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::idft(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_idft_const__InputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int flags, int nonzeroRows, ResultVoid* ocvrs_return) { + try { + cv::idft(*src, *dst, flags, nonzeroRows); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_inRange_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_InputArray* lowerb, const cv::_InputArray* upperb, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::inRange(*src, *lowerb, *upperb, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_insertChannel_const__InputArrayR_const__InputOutputArrayR_int(const cv::_InputArray* src, const cv::_InputOutputArray* dst, int coi, ResultVoid* ocvrs_return) { + try { + cv::insertChannel(*src, *dst, coi); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_instr_getFlags(Result* ocvrs_return) { + try { + cv::instr::FLAGS ret = cv::instr::getFlags(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_instr_resetTrace(ResultVoid* ocvrs_return) { + try { + cv::instr::resetTrace(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_instr_setFlags_FLAGS(cv::instr::FLAGS modeFlags, ResultVoid* ocvrs_return) { + try { + cv::instr::setFlags(modeFlags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_instr_setUseInstrumentation_bool(bool flag, ResultVoid* ocvrs_return) { + try { + cv::instr::setUseInstrumentation(flag); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_instr_useInstrumentation(Result* ocvrs_return) { + try { + bool ret = cv::instr::useInstrumentation(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_invert_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, Result* ocvrs_return) { + try { + double ret = cv::invert(*src, *dst); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_invert_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int flags, Result* ocvrs_return) { + try { + double ret = cv::invert(*src, *dst, flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ipp_getIppErrorLocation(Result* ocvrs_return) { + try { + cv::String ret = cv::ipp::getIppErrorLocation(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ipp_getIppFeatures(Result* ocvrs_return) { + try { + unsigned long long ret = cv::ipp::getIppFeatures(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ipp_getIppStatus(Result* ocvrs_return) { + try { + int ret = cv::ipp::getIppStatus(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ipp_getIppVersion(Result* ocvrs_return) { + try { + cv::String ret = cv::ipp::getIppVersion(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ipp_setIppStatus_int(int status, ResultVoid* ocvrs_return) { + try { + cv::ipp::setIppStatus(status); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ipp_setIppStatus_int_const_charX_const_charX_int(int status, const char* funcname, const char* filename, int line, ResultVoid* ocvrs_return) { + try { + cv::ipp::setIppStatus(status, funcname, filename, line); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ipp_setUseIPP_NotExact_bool(bool flag, ResultVoid* ocvrs_return) { + try { + cv::ipp::setUseIPP_NotExact(flag); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ipp_setUseIPP_bool(bool flag, ResultVoid* ocvrs_return) { + try { + cv::ipp::setUseIPP(flag); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ipp_useIPP(Result* ocvrs_return) { + try { + bool ret = cv::ipp::useIPP(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ipp_useIPP_NotExact(Result* ocvrs_return) { + try { + bool ret = cv::ipp::useIPP_NotExact(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_kmeans_const__InputArrayR_int_const__InputOutputArrayR_TermCriteria_int_int(const cv::_InputArray* data, int K, const cv::_InputOutputArray* bestLabels, cv::TermCriteria* criteria, int attempts, int flags, Result* ocvrs_return) { + try { + double ret = cv::kmeans(*data, K, *bestLabels, *criteria, attempts, flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_kmeans_const__InputArrayR_int_const__InputOutputArrayR_TermCriteria_int_int_const__OutputArrayR(const cv::_InputArray* data, int K, const cv::_InputOutputArray* bestLabels, cv::TermCriteria* criteria, int attempts, int flags, const cv::_OutputArray* centers, Result* ocvrs_return) { + try { + double ret = cv::kmeans(*data, K, *bestLabels, *criteria, attempts, flags, *centers); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_log_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::log(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_magnitude_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* x, const cv::_InputArray* y, const cv::_OutputArray* magnitude, ResultVoid* ocvrs_return) { + try { + cv::magnitude(*x, *y, *magnitude); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_max_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::max(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_max_const_MatR_const_MatR_MatR(const cv::Mat* src1, const cv::Mat* src2, cv::Mat* dst, ResultVoid* ocvrs_return) { + try { + cv::max(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_max_const_MatR_double(const cv::Mat* a, double s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::max(*a, s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_max_const_UMatR_const_UMatR_UMatR(const cv::UMat* src1, const cv::UMat* src2, cv::UMat* dst, ResultVoid* ocvrs_return) { + try { + cv::max(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_max_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::max(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_max_double_const_MatR(double s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::max(s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_meanStdDev_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* mean, const cv::_OutputArray* stddev, ResultVoid* ocvrs_return) { + try { + cv::meanStdDev(*src, *mean, *stddev); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_meanStdDev_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_OutputArray* mean, const cv::_OutputArray* stddev, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::meanStdDev(*src, *mean, *stddev, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_mean_const__InputArrayR(const cv::_InputArray* src, Result* ocvrs_return) { + try { + cv::Scalar ret = cv::mean(*src); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_mean_const__InputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_InputArray* mask, Result* ocvrs_return) { + try { + cv::Scalar ret = cv::mean(*src, *mask); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_merge_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* mv, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::merge(*mv, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_minMaxIdx_const__InputArrayR_doubleX(const cv::_InputArray* src, double* minVal, ResultVoid* ocvrs_return) { + try { + cv::minMaxIdx(*src, minVal); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_minMaxIdx_const__InputArrayR_doubleX_doubleX_intX_intX_const__InputArrayR(const cv::_InputArray* src, double* minVal, double* maxVal, int* minIdx, int* maxIdx, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::minMaxIdx(*src, minVal, maxVal, minIdx, maxIdx, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_minMaxLoc_const_SparseMatR_doubleX_doubleX(const cv::SparseMat* a, double* minVal, double* maxVal, ResultVoid* ocvrs_return) { + try { + cv::minMaxLoc(*a, minVal, maxVal); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_minMaxLoc_const_SparseMatR_doubleX_doubleX_intX_intX(const cv::SparseMat* a, double* minVal, double* maxVal, int* minIdx, int* maxIdx, ResultVoid* ocvrs_return) { + try { + cv::minMaxLoc(*a, minVal, maxVal, minIdx, maxIdx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_minMaxLoc_const__InputArrayR_doubleX(const cv::_InputArray* src, double* minVal, ResultVoid* ocvrs_return) { + try { + cv::minMaxLoc(*src, minVal); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_minMaxLoc_const__InputArrayR_doubleX_doubleX_PointX_PointX_const__InputArrayR(const cv::_InputArray* src, double* minVal, double* maxVal, cv::Point* minLoc, cv::Point* maxLoc, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::minMaxLoc(*src, minVal, maxVal, minLoc, maxLoc, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_min_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::min(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_min_const_MatR_const_MatR_MatR(const cv::Mat* src1, const cv::Mat* src2, cv::Mat* dst, ResultVoid* ocvrs_return) { + try { + cv::min(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_min_const_MatR_double(const cv::Mat* a, double s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::min(*a, s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_min_const_UMatR_const_UMatR_UMatR(const cv::UMat* src1, const cv::UMat* src2, cv::UMat* dst, ResultVoid* ocvrs_return) { + try { + cv::min(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_min_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::min(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_min_double_const_MatR(double s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::min(s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_mixChannels_const__InputArrayR_const__InputOutputArrayR_const_intX_size_t(const cv::_InputArray* src, const cv::_InputOutputArray* dst, const int* fromTo, size_t npairs, ResultVoid* ocvrs_return) { + try { + cv::mixChannels(*src, *dst, fromTo, npairs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_mixChannels_const__InputArrayR_const__InputOutputArrayR_const_vectorLintGR(const cv::_InputArray* src, const cv::_InputOutputArray* dst, const std::vector* fromTo, ResultVoid* ocvrs_return) { + try { + cv::mixChannels(*src, *dst, *fromTo); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_mulSpectrums_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* a, const cv::_InputArray* b, const cv::_OutputArray* c, int flags, ResultVoid* ocvrs_return) { + try { + cv::mulSpectrums(*a, *b, *c, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_mulSpectrums_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_bool(const cv::_InputArray* a, const cv::_InputArray* b, const cv::_OutputArray* c, int flags, bool conjB, ResultVoid* ocvrs_return) { + try { + cv::mulSpectrums(*a, *b, *c, flags, conjB); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_mulTransposed_const__InputArrayR_const__OutputArrayR_bool(const cv::_InputArray* src, const cv::_OutputArray* dst, bool aTa, ResultVoid* ocvrs_return) { + try { + cv::mulTransposed(*src, *dst, aTa); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_mulTransposed_const__InputArrayR_const__OutputArrayR_bool_const__InputArrayR_double_int(const cv::_InputArray* src, const cv::_OutputArray* dst, bool aTa, const cv::_InputArray* delta, double scale, int dtype, ResultVoid* ocvrs_return) { + try { + cv::mulTransposed(*src, *dst, aTa, *delta, scale, dtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_multiply_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::multiply(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_multiply_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double_int(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, double scale, int dtype, ResultVoid* ocvrs_return) { + try { + cv::multiply(*src1, *src2, *dst, scale, dtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +const cv::_InputOutputArray* cv_noArray() { + const cv::_InputOutputArray ret = cv::noArray(); + return new const cv::_InputOutputArray(ret); +} + +void cv_norm_const_SparseMatR_int(const cv::SparseMat* src, int normType, Result* ocvrs_return) { + try { + double ret = cv::norm(*src, normType); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_norm_const__InputArrayR(const cv::_InputArray* src1, Result* ocvrs_return) { + try { + double ret = cv::norm(*src1); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_norm_const__InputArrayR_const__InputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, Result* ocvrs_return) { + try { + double ret = cv::norm(*src1, *src2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_norm_const__InputArrayR_const__InputArrayR_int_const__InputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, int normType, const cv::_InputArray* mask, Result* ocvrs_return) { + try { + double ret = cv::norm(*src1, *src2, normType, *mask); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_norm_const__InputArrayR_int_const__InputArrayR(const cv::_InputArray* src1, int normType, const cv::_InputArray* mask, Result* ocvrs_return) { + try { + double ret = cv::norm(*src1, normType, *mask); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_normalize_const_SparseMatR_SparseMatR_double_int(const cv::SparseMat* src, cv::SparseMat* dst, double alpha, int normType, ResultVoid* ocvrs_return) { + try { + cv::normalize(*src, *dst, alpha, normType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_normalize_const__InputArrayR_const__InputOutputArrayR(const cv::_InputArray* src, const cv::_InputOutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::normalize(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_normalize_const__InputArrayR_const__InputOutputArrayR_double_double_int_int_const__InputArrayR(const cv::_InputArray* src, const cv::_InputOutputArray* dst, double alpha, double beta, int norm_type, int dtype, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::normalize(*src, *dst, alpha, beta, norm_type, dtype, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_attachContext_const_StringR_voidX_voidX_voidX(const char* platformName, void* platformID, void* context, void* deviceID, ResultVoid* ocvrs_return) { + try { + cv::ocl::attachContext(std::string(platformName), platformID, context, deviceID); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_buildOptionsAddMatrixDescription_StringR_const_StringR_const__InputArrayR(void** buildOptions, const char* name, const cv::_InputArray* _m, ResultVoid* ocvrs_return) { + try { + std::string buildOptions_out; + cv::ocl::buildOptionsAddMatrixDescription(buildOptions_out, std::string(name), *_m); + *buildOptions = ocvrs_create_string(buildOptions_out.c_str()); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_checkOptimalVectorWidth_const_intX_const__InputArrayR(const int* vectorWidths, const cv::_InputArray* src1, Result* ocvrs_return) { + try { + int ret = cv::ocl::checkOptimalVectorWidth(vectorWidths, *src1); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_checkOptimalVectorWidth_const_intX_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_OclVectorStrategy(const int* vectorWidths, const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_InputArray* src3, const cv::_InputArray* src4, const cv::_InputArray* src5, const cv::_InputArray* src6, const cv::_InputArray* src7, const cv::_InputArray* src8, const cv::_InputArray* src9, cv::ocl::OclVectorStrategy strat, Result* ocvrs_return) { + try { + int ret = cv::ocl::checkOptimalVectorWidth(vectorWidths, *src1, *src2, *src3, *src4, *src5, *src6, *src7, *src8, *src9, strat); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_convertFromBuffer_voidX_size_t_int_int_int_UMatR(void* cl_mem_buffer, size_t step, int rows, int cols, int type, cv::UMat* dst, ResultVoid* ocvrs_return) { + try { + cv::ocl::convertFromBuffer(cl_mem_buffer, step, rows, cols, type, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_convertFromImage_voidX_UMatR(void* cl_mem_image, cv::UMat* dst, ResultVoid* ocvrs_return) { + try { + cv::ocl::convertFromImage(cl_mem_image, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_convertTypeStr_int_int_int_charX(int sdepth, int ddepth, int cn, void** buf, Result* ocvrs_return) { + try { + std::unique_ptr buf_out = std::make_unique(1024); + const char* ret = cv::ocl::convertTypeStr(sdepth, ddepth, cn, buf_out.get()); + *buf = ocvrs_create_string(buf_out.get()); + Ok(ocvrs_create_string(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_convertTypeStr_int_int_int_charX_size_t(int sdepth, int ddepth, int cn, void** buf, size_t buf_size, Result* ocvrs_return) { + try { + std::unique_ptr buf_out = std::make_unique(buf_size); + const char* ret = cv::ocl::convertTypeStr(sdepth, ddepth, cn, buf_out.get(), buf_size); + *buf = ocvrs_create_string(buf_out.get()); + Ok(ocvrs_create_string(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_finish(ResultVoid* ocvrs_return) { + try { + cv::ocl::finish(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_getOpenCLErrorString_int(int errorCode, Result* ocvrs_return) { + try { + const char* ret = cv::ocl::getOpenCLErrorString(errorCode); + Ok(ocvrs_create_string(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_getPlatfomsInfo_vectorLPlatformInfoGR(std::vector* platform_info, ResultVoid* ocvrs_return) { + try { + cv::ocl::getPlatfomsInfo(*platform_info); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_haveAmdBlas(Result* ocvrs_return) { + try { + bool ret = cv::ocl::haveAmdBlas(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_haveAmdFft(Result* ocvrs_return) { + try { + bool ret = cv::ocl::haveAmdFft(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_haveOpenCL(Result* ocvrs_return) { + try { + bool ret = cv::ocl::haveOpenCL(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_haveSVM(Result* ocvrs_return) { + try { + bool ret = cv::ocl::haveSVM(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_kernelToStr_const__InputArrayR(const cv::_InputArray* _kernel, Result* ocvrs_return) { + try { + cv::String ret = cv::ocl::kernelToStr(*_kernel); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_kernelToStr_const__InputArrayR_int_const_charX(const cv::_InputArray* _kernel, int ddepth, const char* name, Result* ocvrs_return) { + try { + cv::String ret = cv::ocl::kernelToStr(*_kernel, ddepth, name); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_memopTypeToStr_int(int t, Result* ocvrs_return) { + try { + const char* ret = cv::ocl::memopTypeToStr(t); + Ok(ocvrs_create_string(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_predictOptimalVectorWidthMax_const__InputArrayR(const cv::_InputArray* src1, Result* ocvrs_return) { + try { + int ret = cv::ocl::predictOptimalVectorWidthMax(*src1); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_predictOptimalVectorWidthMax_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_InputArray* src3, const cv::_InputArray* src4, const cv::_InputArray* src5, const cv::_InputArray* src6, const cv::_InputArray* src7, const cv::_InputArray* src8, const cv::_InputArray* src9, Result* ocvrs_return) { + try { + int ret = cv::ocl::predictOptimalVectorWidthMax(*src1, *src2, *src3, *src4, *src5, *src6, *src7, *src8, *src9); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_predictOptimalVectorWidth_const__InputArrayR(const cv::_InputArray* src1, Result* ocvrs_return) { + try { + int ret = cv::ocl::predictOptimalVectorWidth(*src1); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_predictOptimalVectorWidth_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_OclVectorStrategy(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_InputArray* src3, const cv::_InputArray* src4, const cv::_InputArray* src5, const cv::_InputArray* src6, const cv::_InputArray* src7, const cv::_InputArray* src8, const cv::_InputArray* src9, cv::ocl::OclVectorStrategy strat, Result* ocvrs_return) { + try { + int ret = cv::ocl::predictOptimalVectorWidth(*src1, *src2, *src3, *src4, *src5, *src6, *src7, *src8, *src9, strat); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_setUseOpenCL_bool(bool flag, ResultVoid* ocvrs_return) { + try { + cv::ocl::setUseOpenCL(flag); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_typeToStr_int(int t, Result* ocvrs_return) { + try { + const char* ret = cv::ocl::typeToStr(t); + Ok(ocvrs_create_string(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_useOpenCL(Result* ocvrs_return) { + try { + bool ret = cv::ocl::useOpenCL(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_vecopTypeToStr_int(int t, Result* ocvrs_return) { + try { + const char* ret = cv::ocl::vecopTypeToStr(t); + Ok(ocvrs_create_string(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorA_const_MatExprR_const_MatExprR(const cv::MatExpr* e1, const cv::MatExpr* e2, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator+(*e1, *e2); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorA_const_MatExprR_const_MatR(const cv::MatExpr* e, const cv::Mat* m, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator+(*e, *m); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorA_const_MatExprR_const_ScalarR(const cv::MatExpr* e, const cv::Scalar* s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator+(*e, *s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorA_const_MatR_const_MatExprR(const cv::Mat* m, const cv::MatExpr* e, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator+(*m, *e); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorA_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator+(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorA_const_MatR_const_ScalarR(const cv::Mat* a, const cv::Scalar* s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator+(*a, *s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorA_const_ScalarR_const_MatExprR(const cv::Scalar* s, const cv::MatExpr* e, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator+(*s, *e); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorA_const_ScalarR_const_MatR(const cv::Scalar* s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator+(*s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorD_const_MatExprR_const_MatExprR(const cv::MatExpr* e1, const cv::MatExpr* e2, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator/(*e1, *e2); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorD_const_MatExprR_const_MatR(const cv::MatExpr* e, const cv::Mat* m, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator/(*e, *m); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorD_const_MatExprR_double(const cv::MatExpr* e, double s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator/(*e, s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorD_const_MatR_const_MatExprR(const cv::Mat* m, const cv::MatExpr* e, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator/(*m, *e); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorD_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator/(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorD_const_MatR_double(const cv::Mat* a, double s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator/(*a, s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorD_double_const_MatExprR(double s, const cv::MatExpr* e, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator/(s, *e); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorD_double_const_MatR(double s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator/(s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorEQ_const_FileNodeIteratorR_const_FileNodeIteratorR(const cv::FileNodeIterator* it1, const cv::FileNodeIterator* it2, Result* ocvrs_return) { + try { + bool ret = cv::operator==(*it1, *it2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorEQ_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator==(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorEQ_const_MatR_double(const cv::Mat* a, double s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator==(*a, s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorEQ_double_const_MatR(double s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator==(s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorGE_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator>=(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorGE_const_MatR_double(const cv::Mat* a, double s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator>=(*a, s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorGE_double_const_MatR(double s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator>=(s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorG_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator>(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorG_const_MatR_double(const cv::Mat* a, double s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator>(*a, s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorG_double_const_MatR(double s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator>(s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorLE_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator<=(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorLE_const_MatR_double(const cv::Mat* a, double s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator<=(*a, s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorLE_double_const_MatR(double s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator<=(s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorL_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator<(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorL_const_MatR_double(const cv::Mat* a, double s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator<(*a, s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorL_double_const_MatR(double s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator<(s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorNE_const_FileNodeIteratorR_const_FileNodeIteratorR(const cv::FileNodeIterator* it1, const cv::FileNodeIterator* it2, Result* ocvrs_return) { + try { + bool ret = cv::operator!=(*it1, *it2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorNE_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator!=(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorNE_const_MatR_double(const cv::Mat* a, double s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator!=(*a, s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorNE_double_const_MatR(double s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator!=(s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorNOTB_const_MatR(const cv::Mat* m, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator~(*m); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorOR_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator|(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorOR_const_MatR_const_ScalarR(const cv::Mat* a, const cv::Scalar* s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator|(*a, *s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorOR_const_ScalarR_const_MatR(const cv::Scalar* s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator|(*s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorR_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator&(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorR_const_MatR_const_ScalarR(const cv::Mat* a, const cv::Scalar* s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator&(*a, *s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorR_const_ScalarR_const_MatR(const cv::Scalar* s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator&(*s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorS_const_MatExprR(const cv::MatExpr* e, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator-(*e); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorS_const_MatExprR_const_MatExprR(const cv::MatExpr* e1, const cv::MatExpr* e2, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator-(*e1, *e2); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorS_const_MatExprR_const_MatR(const cv::MatExpr* e, const cv::Mat* m, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator-(*e, *m); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorS_const_MatExprR_const_ScalarR(const cv::MatExpr* e, const cv::Scalar* s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator-(*e, *s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorS_const_MatR(const cv::Mat* m, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator-(*m); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorS_const_MatR_const_MatExprR(const cv::Mat* m, const cv::MatExpr* e, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator-(*m, *e); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorS_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator-(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorS_const_MatR_const_ScalarR(const cv::Mat* a, const cv::Scalar* s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator-(*a, *s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorS_const_ScalarR_const_MatExprR(const cv::Scalar* s, const cv::MatExpr* e, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator-(*s, *e); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorS_const_ScalarR_const_MatR(const cv::Scalar* s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator-(*s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorXOR_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator^(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorXOR_const_MatR_const_ScalarR(const cv::Mat* a, const cv::Scalar* s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator^(*a, *s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorXOR_const_ScalarR_const_MatR(const cv::Scalar* s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator^(*s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorX_const_MatExprR_const_MatExprR(const cv::MatExpr* e1, const cv::MatExpr* e2, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator*(*e1, *e2); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorX_const_MatExprR_const_MatR(const cv::MatExpr* e, const cv::Mat* m, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator*(*e, *m); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorX_const_MatExprR_double(const cv::MatExpr* e, double s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator*(*e, s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorX_const_MatR_const_MatExprR(const cv::Mat* m, const cv::MatExpr* e, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator*(*m, *e); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorX_const_MatR_const_MatR(const cv::Mat* a, const cv::Mat* b, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator*(*a, *b); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorX_const_MatR_double(const cv::Mat* a, double s, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator*(*a, s); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorX_double_const_MatExprR(double s, const cv::MatExpr* e, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator*(s, *e); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_operatorX_double_const_MatR(double s, const cv::Mat* a, Result* ocvrs_return) { + try { + cv::MatExpr ret = cv::operator*(s, *a); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_parallel_for__const_RangeR_const_ParallelLoopBodyR(const cv::Range* range, const cv::ParallelLoopBody* body, ResultVoid* ocvrs_return) { + try { + cv::parallel_for_(*range, *body); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_parallel_for__const_RangeR_const_ParallelLoopBodyR_double(const cv::Range* range, const cv::ParallelLoopBody* body, double nstripes, ResultVoid* ocvrs_return) { + try { + cv::parallel_for_(*range, *body, nstripes); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_patchNaNs_const__InputOutputArrayR(const cv::_InputOutputArray* a, ResultVoid* ocvrs_return) { + try { + cv::patchNaNs(*a); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_patchNaNs_const__InputOutputArrayR_double(const cv::_InputOutputArray* a, double val, ResultVoid* ocvrs_return) { + try { + cv::patchNaNs(*a, val); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_perspectiveTransform_const__InputArrayR_const__OutputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* m, ResultVoid* ocvrs_return) { + try { + cv::perspectiveTransform(*src, *dst, *m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_phase_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* x, const cv::_InputArray* y, const cv::_OutputArray* angle, ResultVoid* ocvrs_return) { + try { + cv::phase(*x, *y, *angle); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_phase_const__InputArrayR_const__InputArrayR_const__OutputArrayR_bool(const cv::_InputArray* x, const cv::_InputArray* y, const cv::_OutputArray* angle, bool angleInDegrees, ResultVoid* ocvrs_return) { + try { + cv::phase(*x, *y, *angle, angleInDegrees); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_polarToCart_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::_InputArray* magnitude, const cv::_InputArray* angle, const cv::_OutputArray* x, const cv::_OutputArray* y, ResultVoid* ocvrs_return) { + try { + cv::polarToCart(*magnitude, *angle, *x, *y); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_polarToCart_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_bool(const cv::_InputArray* magnitude, const cv::_InputArray* angle, const cv::_OutputArray* x, const cv::_OutputArray* y, bool angleInDegrees, ResultVoid* ocvrs_return) { + try { + cv::polarToCart(*magnitude, *angle, *x, *y, angleInDegrees); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_pow_const__InputArrayR_double_const__OutputArrayR(const cv::_InputArray* src, double power, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::pow(*src, power, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_randn_const__InputOutputArrayR_const__InputArrayR_const__InputArrayR(const cv::_InputOutputArray* dst, const cv::_InputArray* mean, const cv::_InputArray* stddev, ResultVoid* ocvrs_return) { + try { + cv::randn(*dst, *mean, *stddev); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_randu_const__InputOutputArrayR_const__InputArrayR_const__InputArrayR(const cv::_InputOutputArray* dst, const cv::_InputArray* low, const cv::_InputArray* high, ResultVoid* ocvrs_return) { + try { + cv::randu(*dst, *low, *high); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_read_const_FileNodeR_DMatchR_const_DMatchR(const cv::FileNode* node, cv::DMatch* value, const cv::DMatch* default_value, ResultVoid* ocvrs_return) { + try { + cv::read(*node, *value, *default_value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_read_const_FileNodeR_KeyPointR_const_KeyPointR(const cv::FileNode* node, cv::KeyPoint* value, const cv::KeyPoint* default_value, ResultVoid* ocvrs_return) { + try { + cv::read(*node, *value, *default_value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_read_const_FileNodeR_MatR(const cv::FileNode* node, cv::Mat* mat, ResultVoid* ocvrs_return) { + try { + cv::read(*node, *mat); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_read_const_FileNodeR_MatR_const_MatR(const cv::FileNode* node, cv::Mat* mat, const cv::Mat* default_mat, ResultVoid* ocvrs_return) { + try { + cv::read(*node, *mat, *default_mat); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_read_const_FileNodeR_SparseMatR(const cv::FileNode* node, cv::SparseMat* mat, ResultVoid* ocvrs_return) { + try { + cv::read(*node, *mat); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_read_const_FileNodeR_SparseMatR_const_SparseMatR(const cv::FileNode* node, cv::SparseMat* mat, const cv::SparseMat* default_mat, ResultVoid* ocvrs_return) { + try { + cv::read(*node, *mat, *default_mat); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_read_const_FileNodeR_doubleR_double(const cv::FileNode* node, double* value, double default_value, ResultVoid* ocvrs_return) { + try { + cv::read(*node, *value, default_value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_read_const_FileNodeR_floatR_float(const cv::FileNode* node, float* value, float default_value, ResultVoid* ocvrs_return) { + try { + cv::read(*node, *value, default_value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_read_const_FileNodeR_int64_tR_int64_t(const cv::FileNode* node, int64_t* value, int64_t default_value, ResultVoid* ocvrs_return) { + try { + cv::read(*node, *value, default_value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_read_const_FileNodeR_intR_int(const cv::FileNode* node, int* value, int default_value, ResultVoid* ocvrs_return) { + try { + cv::read(*node, *value, default_value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_read_const_FileNodeR_stringR_const_stringR(const cv::FileNode* node, void** value, const char* default_value, ResultVoid* ocvrs_return) { + try { + std::string value_out; + cv::read(*node, value_out, std::string(default_value)); + *value = ocvrs_create_string(value_out.c_str()); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_read_const_FileNodeR_vectorLDMatchGR(const cv::FileNode* node, std::vector* matches, ResultVoid* ocvrs_return) { + try { + cv::read(*node, *matches); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_read_const_FileNodeR_vectorLKeyPointGR(const cv::FileNode* node, std::vector* keypoints, ResultVoid* ocvrs_return) { + try { + cv::read(*node, *keypoints); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_rectangleIntersectionArea_const_Rect2dR_const_Rect2dR(const cv::Rect2d* a, const cv::Rect2d* b, Result* ocvrs_return) { + try { + double ret = cv::rectangleIntersectionArea(*a, *b); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_reduceArgMax_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int axis, ResultVoid* ocvrs_return) { + try { + cv::reduceArgMax(*src, *dst, axis); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_reduceArgMax_const__InputArrayR_const__OutputArrayR_int_bool(const cv::_InputArray* src, const cv::_OutputArray* dst, int axis, bool lastIndex, ResultVoid* ocvrs_return) { + try { + cv::reduceArgMax(*src, *dst, axis, lastIndex); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_reduceArgMin_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int axis, ResultVoid* ocvrs_return) { + try { + cv::reduceArgMin(*src, *dst, axis); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_reduceArgMin_const__InputArrayR_const__OutputArrayR_int_bool(const cv::_InputArray* src, const cv::_OutputArray* dst, int axis, bool lastIndex, ResultVoid* ocvrs_return) { + try { + cv::reduceArgMin(*src, *dst, axis, lastIndex); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_reduce_const__InputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int dim, int rtype, ResultVoid* ocvrs_return) { + try { + cv::reduce(*src, *dst, dim, rtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_reduce_const__InputArrayR_const__OutputArrayR_int_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int dim, int rtype, int dtype, ResultVoid* ocvrs_return) { + try { + cv::reduce(*src, *dst, dim, rtype, dtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_repeat_const_MatR_int_int(const cv::Mat* src, int ny, int nx, Result* ocvrs_return) { + try { + cv::Mat ret = cv::repeat(*src, ny, nx); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_repeat_const__InputArrayR_int_int_const__OutputArrayR(const cv::_InputArray* src, int ny, int nx, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::repeat(*src, ny, nx, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_rotate_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int rotateCode, ResultVoid* ocvrs_return) { + try { + cv::rotate(*src, *dst, rotateCode); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_samples_addSamplesDataSearchPath_const_StringR(const char* path, ResultVoid* ocvrs_return) { + try { + cv::samples::addSamplesDataSearchPath(std::string(path)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_samples_addSamplesDataSearchSubDirectory_const_StringR(const char* subdir, ResultVoid* ocvrs_return) { + try { + cv::samples::addSamplesDataSearchSubDirectory(std::string(subdir)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_samples_findFileOrKeep_const_StringR(const char* relative_path, Result* ocvrs_return) { + try { + cv::String ret = cv::samples::findFileOrKeep(std::string(relative_path)); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_samples_findFileOrKeep_const_StringR_bool(const char* relative_path, bool silentMode, Result* ocvrs_return) { + try { + cv::String ret = cv::samples::findFileOrKeep(std::string(relative_path), silentMode); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_samples_findFile_const_StringR(const char* relative_path, Result* ocvrs_return) { + try { + cv::String ret = cv::samples::findFile(std::string(relative_path)); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_samples_findFile_const_StringR_bool_bool(const char* relative_path, bool required, bool silentMode, Result* ocvrs_return) { + try { + cv::String ret = cv::samples::findFile(std::string(relative_path), required, silentMode); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_scaleAdd_const__InputArrayR_double_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, double alpha, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::scaleAdd(*src1, alpha, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setBreakOnError_bool(bool flag, Result* ocvrs_return) { + try { + bool ret = cv::setBreakOnError(flag); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setIdentity_const__InputOutputArrayR(const cv::_InputOutputArray* mtx, ResultVoid* ocvrs_return) { + try { + cv::setIdentity(*mtx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setIdentity_const__InputOutputArrayR_const_ScalarR(const cv::_InputOutputArray* mtx, const cv::Scalar* s, ResultVoid* ocvrs_return) { + try { + cv::setIdentity(*mtx, *s); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setNumThreads_int(int nthreads, ResultVoid* ocvrs_return) { + try { + cv::setNumThreads(nthreads); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setRNGSeed_int(int seed, ResultVoid* ocvrs_return) { + try { + cv::setRNGSeed(seed); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setUseOpenVX_bool(bool flag, ResultVoid* ocvrs_return) { + try { + cv::setUseOpenVX(flag); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setUseOptimized_bool(bool onoff, ResultVoid* ocvrs_return) { + try { + cv::setUseOptimized(onoff); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_solveCubic_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* coeffs, const cv::_OutputArray* roots, Result* ocvrs_return) { + try { + int ret = cv::solveCubic(*coeffs, *roots); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_solveLP_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* Func, const cv::_InputArray* Constr, const cv::_OutputArray* z, Result* ocvrs_return) { + try { + int ret = cv::solveLP(*Func, *Constr, *z); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_solveLP_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double(const cv::_InputArray* Func, const cv::_InputArray* Constr, const cv::_OutputArray* z, double constr_eps, Result* ocvrs_return) { + try { + int ret = cv::solveLP(*Func, *Constr, *z, constr_eps); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_solvePoly_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* coeffs, const cv::_OutputArray* roots, Result* ocvrs_return) { + try { + double ret = cv::solvePoly(*coeffs, *roots); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_solvePoly_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* coeffs, const cv::_OutputArray* roots, int maxIters, Result* ocvrs_return) { + try { + double ret = cv::solvePoly(*coeffs, *roots, maxIters); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_solve_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, Result* ocvrs_return) { + try { + bool ret = cv::solve(*src1, *src2, *dst); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_solve_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, int flags, Result* ocvrs_return) { + try { + bool ret = cv::solve(*src1, *src2, *dst, flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_sortIdx_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int flags, ResultVoid* ocvrs_return) { + try { + cv::sortIdx(*src, *dst, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_sort_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int flags, ResultVoid* ocvrs_return) { + try { + cv::sort(*src, *dst, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_split_const_MatR_MatX(const cv::Mat* src, cv::Mat* mvbegin, ResultVoid* ocvrs_return) { + try { + cv::split(*src, mvbegin); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_split_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* m, const cv::_OutputArray* mv, ResultVoid* ocvrs_return) { + try { + cv::split(*m, *mv); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_sqrt_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::sqrt(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_subtract_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::subtract(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_subtract_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR_int(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, const cv::_InputArray* mask, int dtype, ResultVoid* ocvrs_return) { + try { + cv::subtract(*src1, *src2, *dst, *mask, dtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_sum_const__InputArrayR(const cv::_InputArray* src, Result* ocvrs_return) { + try { + cv::Scalar ret = cv::sum(*src); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_swap_MatR_MatR(cv::Mat* a, cv::Mat* b, ResultVoid* ocvrs_return) { + try { + cv::swap(*a, *b); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_swap_UMatR_UMatR(cv::UMat* a, cv::UMat* b, ResultVoid* ocvrs_return) { + try { + cv::swap(*a, *b); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_tempfile(Result* ocvrs_return) { + try { + cv::String ret = cv::tempfile(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_tempfile_const_charX(const char* suffix, Result* ocvrs_return) { + try { + cv::String ret = cv::tempfile(suffix); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_terminate_int_const_StringR_const_charX_const_charX_int(int code, const char* err, const char* func, const char* file, int line) { + cv::terminate(code, std::string(err), func, file, line); +} + +void cv_trace_const__InputArrayR(const cv::_InputArray* mtx, Result* ocvrs_return) { + try { + cv::Scalar ret = cv::trace(*mtx); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_transform_const__InputArrayR_const__OutputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* m, ResultVoid* ocvrs_return) { + try { + cv::transform(*src, *dst, *m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_transposeND_const__InputArrayR_const_vectorLintGR_const__OutputArrayR(const cv::_InputArray* src, const std::vector* order, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::transposeND(*src, *order, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_transpose_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::transpose(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_typeToString_int(int type, Result* ocvrs_return) { + try { + cv::String ret = cv::typeToString(type); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_useOpenVX(Result* ocvrs_return) { + try { + bool ret = cv::useOpenVX(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_useOptimized(Result* ocvrs_return) { + try { + bool ret = cv::useOptimized(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_copyMatAndDumpNamedArguments_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::copyMatAndDumpNamedArguments(*src, *dst); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_copyMatAndDumpNamedArguments_const__InputArrayR_const__OutputArrayR_const_FunctionParamsR(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::utils::FunctionParams* params, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::copyMatAndDumpNamedArguments(*src, *dst, *params); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpBool_bool(bool argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpBool(argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpCString_const_charX(const char* argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpCString(argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpDouble_double(double argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpDouble(argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpFloat_float(float argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpFloat(argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpInputArrayOfArrays_const__InputArrayR(const cv::_InputArray* argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpInputArrayOfArrays(*argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpInputArray_const__InputArrayR(const cv::_InputArray* argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpInputArray(*argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpInputOutputArrayOfArrays_const__InputOutputArrayR(const cv::_InputOutputArray* argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpInputOutputArrayOfArrays(*argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpInputOutputArray_const__InputOutputArrayR(const cv::_InputOutputArray* argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpInputOutputArray(*argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpInt64_int64_t(int64_t argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpInt64(argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpInt_int(int argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpInt(argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpRange_const_RangeR(const cv::Range* argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpRange(*argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpRect_const_RectR(const cv::Rect* argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpRect(*argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpRotatedRect_const_RotatedRectR(const cv::RotatedRect* argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpRotatedRect(*argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpSizeT_size_t(size_t argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpSizeT(argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpString_const_StringR(const char* argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpString(std::string(argument)); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpTermCriteria_const_TermCriteriaR(const cv::TermCriteria* argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpTermCriteria(*argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpVec2i(Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpVec2i(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpVec2i_const_Vec2i(const cv::Vec2i* value, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpVec2i(*value); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpVectorOfDouble_const_vectorLdoubleGR(const std::vector* vec, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpVectorOfDouble(*vec); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpVectorOfInt_const_vectorLintGR(const std::vector* vec, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpVectorOfInt(*vec); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_dumpVectorOfRect_const_vectorLRectGR(const std::vector* vec, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::dumpVectorOfRect(*vec); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_fs_getCacheDirectoryForDownloads(Result* ocvrs_return) { + try { + cv::String ret = cv::utils::fs::getCacheDirectoryForDownloads(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_generateVectorOfInt_size_t_vectorLintGR(size_t len, std::vector* vec, ResultVoid* ocvrs_return) { + try { + cv::utils::generateVectorOfInt(len, *vec); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_generateVectorOfMat_size_t_int_int_int_vectorLMatGR(size_t len, int rows, int cols, int dtype, std::vector* vec, ResultVoid* ocvrs_return) { + try { + cv::utils::generateVectorOfMat(len, rows, cols, dtype, *vec); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_generateVectorOfRect_size_t_vectorLRectGR(size_t len, std::vector* vec, ResultVoid* ocvrs_return) { + try { + cv::utils::generateVectorOfRect(len, *vec); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_getThreadID(Result* ocvrs_return) { + try { + int ret = cv::utils::getThreadID(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_logging_getLogLevel(Result* ocvrs_return) { + try { + cv::utils::logging::LogLevel ret = cv::utils::logging::getLogLevel(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_logging_getLogTagLevel_const_charX(const char* tag, Result* ocvrs_return) { + try { + cv::utils::logging::LogLevel ret = cv::utils::logging::getLogTagLevel(tag); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_logging_internal_getGlobalLogTag(Result* ocvrs_return) { + try { + cv::utils::logging::LogTag* ret = cv::utils::logging::internal::getGlobalLogTag(); + Ok(new cv::utils::logging::LogTag(*ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_logging_internal_replaceWriteLogMessageEx_WriteLogMessageExFuncType(cv::utils::logging::internal::WriteLogMessageExFuncType f, ResultVoid* ocvrs_return) { + try { + cv::utils::logging::internal::replaceWriteLogMessageEx(f); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_logging_internal_replaceWriteLogMessage_WriteLogMessageFuncType(cv::utils::logging::internal::WriteLogMessageFuncType f, ResultVoid* ocvrs_return) { + try { + cv::utils::logging::internal::replaceWriteLogMessage(f); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_logging_internal_writeLogMessageEx_LogLevel_const_charX_const_charX_int_const_charX_const_charX(cv::utils::logging::LogLevel logLevel, const char* tag, const char* file, int line, const char* func, const char* message, ResultVoid* ocvrs_return) { + try { + cv::utils::logging::internal::writeLogMessageEx(logLevel, tag, file, line, func, message); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_logging_internal_writeLogMessage_LogLevel_const_charX(cv::utils::logging::LogLevel logLevel, const char* message, ResultVoid* ocvrs_return) { + try { + cv::utils::logging::internal::writeLogMessage(logLevel, message); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_logging_registerLogTag_LogTagX(cv::utils::logging::LogTag* plogtag, ResultVoid* ocvrs_return) { + try { + cv::utils::logging::registerLogTag(plogtag); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_logging_setLogLevel_LogLevel(cv::utils::logging::LogLevel logLevel, Result* ocvrs_return) { + try { + cv::utils::logging::LogLevel ret = cv::utils::logging::setLogLevel(logLevel); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_logging_setLogTagLevel_const_charX_LogLevel(const char* tag, cv::utils::logging::LogLevel level, ResultVoid* ocvrs_return) { + try { + cv::utils::logging::setLogTagLevel(tag, level); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_nested_testEchoBooleanFunction_bool(bool flag, Result* ocvrs_return) { + try { + bool ret = cv::utils::nested::testEchoBooleanFunction(flag); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_testAsyncArray_const__InputArrayR(const cv::_InputArray* argument, Result* ocvrs_return) { + try { + cv::AsyncArray ret = cv::utils::testAsyncArray(*argument); + Ok(new cv::AsyncArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_testAsyncException(Result* ocvrs_return) { + try { + cv::AsyncArray ret = cv::utils::testAsyncException(); + Ok(new cv::AsyncArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_testOverloadResolution_const_RectR(const cv::Rect* rect, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::testOverloadResolution(*rect); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_testOverloadResolution_int(int value, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::testOverloadResolution(value); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_testOverloadResolution_int_const_PointR(int value, const cv::Point* point, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::testOverloadResolution(value, *point); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_testOverwriteNativeMethod_int(int argument, Result* ocvrs_return) { + try { + int ret = cv::utils::testOverwriteNativeMethod(argument); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_testRaiseGeneralException(ResultVoid* ocvrs_return) { + try { + cv::utils::testRaiseGeneralException(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_testReservedKeywordConversion_int(int positional_argument, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::testReservedKeywordConversion(positional_argument); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_testReservedKeywordConversion_int_int_int(int positional_argument, int lambda, int from, Result* ocvrs_return) { + try { + cv::String ret = cv::utils::testReservedKeywordConversion(positional_argument, lambda, from); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_testRotatedRectVector_float_float_float_float_float(float x, float y, float w, float h, float angle, Result*>* ocvrs_return) { + try { + std::vector ret = cv::utils::testRotatedRectVector(x, y, w, h, angle); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_testRotatedRect_float_float_float_float_float(float x, float y, float w, float h, float angle, Result* ocvrs_return) { + try { + cv::RotatedRect ret = cv::utils::testRotatedRect(x, y, w, h, angle); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_va_intel_convertFromVASurface_VADisplay_VASurfaceID_Size_const__OutputArrayR(VADisplay display, VASurfaceID surface, cv::Size* size, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::va_intel::convertFromVASurface(display, surface, *size, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_va_intel_convertToVASurface_VADisplay_const__InputArrayR_VASurfaceID_Size(VADisplay display, const cv::_InputArray* src, VASurfaceID surface, cv::Size* size, ResultVoid* ocvrs_return) { + try { + cv::va_intel::convertToVASurface(display, *src, surface, *size); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_va_intel_ocl_initializeContextFromVA_VADisplay(VADisplay display, Result* ocvrs_return) { + try { + cv::ocl::Context ret = cv::va_intel::ocl::initializeContextFromVA(display); + Ok(new cv::ocl::Context(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_va_intel_ocl_initializeContextFromVA_VADisplay_bool(VADisplay display, bool tryInterop, Result* ocvrs_return) { + try { + cv::ocl::Context ret = cv::va_intel::ocl::initializeContextFromVA(display, tryInterop); + Ok(new cv::ocl::Context(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_vconcat_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::vconcat(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_vconcat_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::vconcat(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_writeScalar_FileStorageR_const_StringR(cv::FileStorage* fs, const char* value, ResultVoid* ocvrs_return) { + try { + cv::writeScalar(*fs, std::string(value)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_writeScalar_FileStorageR_double(cv::FileStorage* fs, double value, ResultVoid* ocvrs_return) { + try { + cv::writeScalar(*fs, value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_writeScalar_FileStorageR_float(cv::FileStorage* fs, float value, ResultVoid* ocvrs_return) { + try { + cv::writeScalar(*fs, value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_writeScalar_FileStorageR_int(cv::FileStorage* fs, int value, ResultVoid* ocvrs_return) { + try { + cv::writeScalar(*fs, value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_writeScalar_FileStorageR_int64_t(cv::FileStorage* fs, int64_t value, ResultVoid* ocvrs_return) { + try { + cv::writeScalar(*fs, value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_write_FileStorageR_const_StringR_const_MatR(cv::FileStorage* fs, const char* name, const cv::Mat* value, ResultVoid* ocvrs_return) { + try { + cv::write(*fs, std::string(name), *value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_write_FileStorageR_const_StringR_const_SparseMatR(cv::FileStorage* fs, const char* name, const cv::SparseMat* value, ResultVoid* ocvrs_return) { + try { + cv::write(*fs, std::string(name), *value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_write_FileStorageR_const_StringR_const_StringR(cv::FileStorage* fs, const char* name, const char* value, ResultVoid* ocvrs_return) { + try { + cv::write(*fs, std::string(name), std::string(value)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_write_FileStorageR_const_StringR_const_vectorLDMatchGR(cv::FileStorage* fs, const char* name, const std::vector* value, ResultVoid* ocvrs_return) { + try { + cv::write(*fs, std::string(name), *value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_write_FileStorageR_const_StringR_const_vectorLKeyPointGR(cv::FileStorage* fs, const char* name, const std::vector* value, ResultVoid* ocvrs_return) { + try { + cv::write(*fs, std::string(name), *value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_write_FileStorageR_const_StringR_double(cv::FileStorage* fs, const char* name, double value, ResultVoid* ocvrs_return) { + try { + cv::write(*fs, std::string(name), value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_write_FileStorageR_const_StringR_float(cv::FileStorage* fs, const char* name, float value, ResultVoid* ocvrs_return) { + try { + cv::write(*fs, std::string(name), value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_write_FileStorageR_const_StringR_int(cv::FileStorage* fs, const char* name, int value, ResultVoid* ocvrs_return) { + try { + cv::write(*fs, std::string(name), value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_write_FileStorageR_const_StringR_int64_t(cv::FileStorage* fs, const char* name, int64_t value, ResultVoid* ocvrs_return) { + try { + cv::write(*fs, std::string(name), value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Algorithm_clear(cv::Algorithm* instance, ResultVoid* ocvrs_return) { + try { + instance->clear(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Algorithm_write_const_FileStorageR(const cv::Algorithm* instance, cv::FileStorage* fs, ResultVoid* ocvrs_return) { + try { + instance->write(*fs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Algorithm_write_const_FileStorageR_const_StringR(const cv::Algorithm* instance, cv::FileStorage* fs, const char* name, ResultVoid* ocvrs_return) { + try { + instance->write(*fs, std::string(name)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Algorithm_write_const_const_PtrLFileStorageGR_const_StringR(const cv::Algorithm* instance, const cv::Ptr* fs, const char* name, ResultVoid* ocvrs_return) { + try { + instance->write(*fs, std::string(name)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Algorithm_write_const_const_PtrLFileStorageGR(const cv::Algorithm* instance, const cv::Ptr* fs, ResultVoid* ocvrs_return) { + try { + instance->write(*fs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Algorithm_read_const_FileNodeR(cv::Algorithm* instance, const cv::FileNode* fn, ResultVoid* ocvrs_return) { + try { + instance->read(*fn); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Algorithm_empty_const(const cv::Algorithm* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Algorithm_save_const_const_StringR(const cv::Algorithm* instance, const char* filename, ResultVoid* ocvrs_return) { + try { + instance->save(std::string(filename)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Algorithm_getDefaultName_const(const cv::Algorithm* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->getDefaultName(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::ConjGradSolver* cv_Algorithm_to_ConjGradSolver(cv::Algorithm* instance) { + return dynamic_cast(instance); +} + +cv::DownhillSolver* cv_Algorithm_to_DownhillSolver(cv::Algorithm* instance) { + return dynamic_cast(instance); +} + +cv::MinProblemSolver* cv_Algorithm_to_MinProblemSolver(cv::Algorithm* instance) { + return dynamic_cast(instance); +} + +void cv_Algorithm_delete(cv::Algorithm* instance) { + delete instance; +} + +cv::AsyncArray* cv_AsyncArray_AsyncArray() { + cv::AsyncArray* ret = new cv::AsyncArray(); + return ret; +} + +cv::AsyncArray* cv_AsyncArray_AsyncArray_const_AsyncArrayR(const cv::AsyncArray* o) { + cv::AsyncArray* ret = new cv::AsyncArray(*o); + return ret; +} + +void cv_AsyncArray_operatorST_const_AsyncArrayR(cv::AsyncArray* instance, const cv::AsyncArray* o) { + instance->operator=(*o); +} + +void cv_AsyncArray_release(cv::AsyncArray* instance) { + instance->release(); +} + +void cv_AsyncArray_get_const_const__OutputArrayR(const cv::AsyncArray* instance, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + instance->get(*dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_AsyncArray_get_const_const__OutputArrayR_int64_t(const cv::AsyncArray* instance, const cv::_OutputArray* dst, int64_t timeoutNs, Result* ocvrs_return) { + try { + bool ret = instance->get(*dst, timeoutNs); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_AsyncArray_get_const_const__OutputArrayR_double(const cv::AsyncArray* instance, const cv::_OutputArray* dst, double timeoutNs, Result* ocvrs_return) { + try { + bool ret = instance->get(*dst, timeoutNs); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_AsyncArray_wait_for_const_int64_t(const cv::AsyncArray* instance, int64_t timeoutNs, Result* ocvrs_return) { + try { + bool ret = instance->wait_for(timeoutNs); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_AsyncArray_wait_for_const_double(const cv::AsyncArray* instance, double timeoutNs, Result* ocvrs_return) { + try { + bool ret = instance->wait_for(timeoutNs); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +bool cv_AsyncArray_valid_const(const cv::AsyncArray* instance) { + bool ret = instance->valid(); + return ret; +} + +void cv_AsyncArray_AsyncArray_AsyncArrayRR(cv::AsyncArray* o, Result* ocvrs_return) { + try { + cv::AsyncArray* ret = new cv::AsyncArray(std::move(*o)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_AsyncArray_operatorST_AsyncArrayRR(cv::AsyncArray* instance, cv::AsyncArray* o) { + instance->operator=(std::move(*o)); +} + +void cv_AsyncArray_delete(cv::AsyncArray* instance) { + delete instance; +} + +cv::AsyncPromise* cv_AsyncPromise_AsyncPromise() { + cv::AsyncPromise* ret = new cv::AsyncPromise(); + return ret; +} + +cv::AsyncPromise* cv_AsyncPromise_AsyncPromise_const_AsyncPromiseR(const cv::AsyncPromise* o) { + cv::AsyncPromise* ret = new cv::AsyncPromise(*o); + return ret; +} + +void cv_AsyncPromise_operatorST_const_AsyncPromiseR(cv::AsyncPromise* instance, const cv::AsyncPromise* o) { + instance->operator=(*o); +} + +void cv_AsyncPromise_release(cv::AsyncPromise* instance) { + instance->release(); +} + +void cv_AsyncPromise_getArrayResult(cv::AsyncPromise* instance, Result* ocvrs_return) { + try { + cv::AsyncArray ret = instance->getArrayResult(); + Ok(new cv::AsyncArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_AsyncPromise_setValue_const__InputArrayR(cv::AsyncPromise* instance, const cv::_InputArray* value, ResultVoid* ocvrs_return) { + try { + instance->setValue(*value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_AsyncPromise_setException_const_ExceptionR(cv::AsyncPromise* instance, const cv::Exception* exception, ResultVoid* ocvrs_return) { + try { + instance->setException(*exception); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_AsyncPromise_AsyncPromise_AsyncPromiseRR(cv::AsyncPromise* o, Result* ocvrs_return) { + try { + cv::AsyncPromise* ret = new cv::AsyncPromise(std::move(*o)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_AsyncPromise_operatorST_AsyncPromiseRR(cv::AsyncPromise* instance, cv::AsyncPromise* o) { + instance->operator=(std::move(*o)); +} + +void* cv_AsyncPromise__getImpl_const(const cv::AsyncPromise* instance) { + void* ret = instance->_getImpl(); + return ret; +} + +void cv_AsyncPromise_delete(cv::AsyncPromise* instance) { + delete instance; +} + +void cv_CommandLineParser_CommandLineParser_int_const_charXX_const_StringR(int argc, const char** argv, const char* keys, Result* ocvrs_return) { + try { + cv::CommandLineParser* ret = new cv::CommandLineParser(argc, argv, std::string(keys)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_CommandLineParser_const_CommandLineParserR(const cv::CommandLineParser* parser, Result* ocvrs_return) { + try { + cv::CommandLineParser* ret = new cv::CommandLineParser(*parser); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_operatorST_const_CommandLineParserR(cv::CommandLineParser* instance, const cv::CommandLineParser* parser, ResultVoid* ocvrs_return) { + try { + instance->operator=(*parser); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_getPathToApplication_const(const cv::CommandLineParser* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->getPathToApplication(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_bool_const_const_StringR_bool(const cv::CommandLineParser* instance, const char* name, bool space_delete, Result* ocvrs_return) { + try { + bool ret = instance->get(std::string(name), space_delete); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_bool_const_const_StringR(const cv::CommandLineParser* instance, const char* name, Result* ocvrs_return) { + try { + bool ret = instance->get(std::string(name)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_int_const_const_StringR_bool(const cv::CommandLineParser* instance, const char* name, bool space_delete, Result* ocvrs_return) { + try { + int ret = instance->get(std::string(name), space_delete); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_int_const_const_StringR(const cv::CommandLineParser* instance, const char* name, Result* ocvrs_return) { + try { + int ret = instance->get(std::string(name)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_double_const_const_StringR_bool(const cv::CommandLineParser* instance, const char* name, bool space_delete, Result* ocvrs_return) { + try { + double ret = instance->get(std::string(name), space_delete); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_double_const_const_StringR(const cv::CommandLineParser* instance, const char* name, Result* ocvrs_return) { + try { + double ret = instance->get(std::string(name)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_cv_String_const_const_StringR_bool(const cv::CommandLineParser* instance, const char* name, bool space_delete, Result* ocvrs_return) { + try { + cv::String ret = instance->get(std::string(name), space_delete); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_cv_String_const_const_StringR(const cv::CommandLineParser* instance, const char* name, Result* ocvrs_return) { + try { + cv::String ret = instance->get(std::string(name)); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_uint64_t_const_const_StringR_bool(const cv::CommandLineParser* instance, const char* name, bool space_delete, Result* ocvrs_return) { + try { + uint64_t ret = instance->get(std::string(name), space_delete); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_uint64_t_const_const_StringR(const cv::CommandLineParser* instance, const char* name, Result* ocvrs_return) { + try { + uint64_t ret = instance->get(std::string(name)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_bool_const_int_bool(const cv::CommandLineParser* instance, int index, bool space_delete, Result* ocvrs_return) { + try { + bool ret = instance->get(index, space_delete); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_bool_const_int(const cv::CommandLineParser* instance, int index, Result* ocvrs_return) { + try { + bool ret = instance->get(index); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_int_const_int_bool(const cv::CommandLineParser* instance, int index, bool space_delete, Result* ocvrs_return) { + try { + int ret = instance->get(index, space_delete); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_int_const_int(const cv::CommandLineParser* instance, int index, Result* ocvrs_return) { + try { + int ret = instance->get(index); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_double_const_int_bool(const cv::CommandLineParser* instance, int index, bool space_delete, Result* ocvrs_return) { + try { + double ret = instance->get(index, space_delete); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_double_const_int(const cv::CommandLineParser* instance, int index, Result* ocvrs_return) { + try { + double ret = instance->get(index); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_cv_String_const_int_bool(const cv::CommandLineParser* instance, int index, bool space_delete, Result* ocvrs_return) { + try { + cv::String ret = instance->get(index, space_delete); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_cv_String_const_int(const cv::CommandLineParser* instance, int index, Result* ocvrs_return) { + try { + cv::String ret = instance->get(index); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_uint64_t_const_int_bool(const cv::CommandLineParser* instance, int index, bool space_delete, Result* ocvrs_return) { + try { + uint64_t ret = instance->get(index, space_delete); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_get_uint64_t_const_int(const cv::CommandLineParser* instance, int index, Result* ocvrs_return) { + try { + uint64_t ret = instance->get(index); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_has_const_const_StringR(const cv::CommandLineParser* instance, const char* name, Result* ocvrs_return) { + try { + bool ret = instance->has(std::string(name)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_check_const(const cv::CommandLineParser* instance, Result* ocvrs_return) { + try { + bool ret = instance->check(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_about_const_StringR(cv::CommandLineParser* instance, const char* message, ResultVoid* ocvrs_return) { + try { + instance->about(std::string(message)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_printMessage_const(const cv::CommandLineParser* instance, ResultVoid* ocvrs_return) { + try { + instance->printMessage(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_printErrors_const(const cv::CommandLineParser* instance, ResultVoid* ocvrs_return) { + try { + instance->printErrors(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CommandLineParser_delete(cv::CommandLineParser* instance) { + delete instance; +} + +void cv_ConjGradSolver_create_const_PtrLFunctionGR_TermCriteria(const cv::Ptr* f, cv::TermCriteria* termcrit, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::ConjGradSolver::create(*f, *termcrit); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ConjGradSolver_create(Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::ConjGradSolver::create(); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::Algorithm* cv_ConjGradSolver_to_Algorithm(cv::ConjGradSolver* instance) { + return dynamic_cast(instance); +} + +cv::MinProblemSolver* cv_ConjGradSolver_to_MinProblemSolver(cv::ConjGradSolver* instance) { + return dynamic_cast(instance); +} + +void cv_ConjGradSolver_delete(cv::ConjGradSolver* instance) { + delete instance; +} + +void cv_DMatch_operatorL_const_const_DMatchR(const cv::DMatch* instance, const cv::DMatch* m, Result* ocvrs_return) { + try { + bool ret = instance->operator<(*m); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_DownhillSolver_getInitStep_const_const__OutputArrayR(const cv::DownhillSolver* instance, const cv::_OutputArray* step, ResultVoid* ocvrs_return) { + try { + instance->getInitStep(*step); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_DownhillSolver_setInitStep_const__InputArrayR(cv::DownhillSolver* instance, const cv::_InputArray* step, ResultVoid* ocvrs_return) { + try { + instance->setInitStep(*step); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_DownhillSolver_create_const_PtrLFunctionGR_const__InputArrayR_TermCriteria(const cv::Ptr* f, const cv::_InputArray* initStep, cv::TermCriteria* termcrit, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::DownhillSolver::create(*f, *initStep, *termcrit); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_DownhillSolver_create(Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::DownhillSolver::create(); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::Algorithm* cv_DownhillSolver_to_Algorithm(cv::DownhillSolver* instance) { + return dynamic_cast(instance); +} + +cv::MinProblemSolver* cv_DownhillSolver_to_MinProblemSolver(cv::DownhillSolver* instance) { + return dynamic_cast(instance); +} + +void cv_DownhillSolver_delete(cv::DownhillSolver* instance) { + delete instance; +} + +void cv_Exception_Exception(Result* ocvrs_return) { + try { + cv::Exception* ret = new cv::Exception(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Exception_Exception_int_const_StringR_const_StringR_const_StringR_int(int _code, const char* _err, const char* _func, const char* _file, int _line, Result* ocvrs_return) { + try { + cv::Exception* ret = new cv::Exception(_code, std::string(_err), std::string(_func), std::string(_file), _line); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void* cv_Exception_what_const(const cv::Exception* instance) { + const char* ret = instance->what(); + return ocvrs_create_string(ret); +} + +void cv_Exception_formatMessage(cv::Exception* instance, ResultVoid* ocvrs_return) { + try { + instance->formatMessage(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void* cv_Exception_propMsg_const(const cv::Exception* instance) { + cv::String ret = instance->msg; + return ocvrs_create_string(ret.c_str()); +} + +void cv_Exception_propMsg_const_String(cv::Exception* instance, const char* val) { + instance->msg = std::string(val); +} + +int cv_Exception_propCode_const(const cv::Exception* instance) { + int ret = instance->code; + return ret; +} + +void cv_Exception_propCode_const_int(cv::Exception* instance, const int val) { + instance->code = val; +} + +void* cv_Exception_propErr_const(const cv::Exception* instance) { + cv::String ret = instance->err; + return ocvrs_create_string(ret.c_str()); +} + +void cv_Exception_propErr_const_String(cv::Exception* instance, const char* val) { + instance->err = std::string(val); +} + +void* cv_Exception_propFunc_const(const cv::Exception* instance) { + cv::String ret = instance->func; + return ocvrs_create_string(ret.c_str()); +} + +void cv_Exception_propFunc_const_String(cv::Exception* instance, const char* val) { + instance->func = std::string(val); +} + +void* cv_Exception_propFile_const(const cv::Exception* instance) { + cv::String ret = instance->file; + return ocvrs_create_string(ret.c_str()); +} + +void cv_Exception_propFile_const_String(cv::Exception* instance, const char* val) { + instance->file = std::string(val); +} + +int cv_Exception_propLine_const(const cv::Exception* instance) { + int ret = instance->line; + return ret; +} + +void cv_Exception_propLine_const_int(cv::Exception* instance, const int val) { + instance->line = val; +} + +void cv_Exception_delete(cv::Exception* instance) { + delete instance; +} + +void cv_FileNode_operatorST_const_FileNodeR(cv::FileNode* instance, const cv::FileNode* node, ResultVoid* ocvrs_return) { + try { + instance->operator=(*node); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_operator___const_const_StringR(const cv::FileNode* instance, const char* nodename, Result* ocvrs_return) { + try { + cv::FileNode ret = instance->operator[](std::string(nodename)); + Ok(new cv::FileNode(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_operator___const_const_charX(const cv::FileNode* instance, const char* nodename, Result* ocvrs_return) { + try { + cv::FileNode ret = instance->operator[](nodename); + Ok(new cv::FileNode(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_operator___const_int(const cv::FileNode* instance, int i, Result* ocvrs_return) { + try { + cv::FileNode ret = instance->operator[](i); + Ok(new cv::FileNode(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_keys_const(const cv::FileNode* instance, Result*>* ocvrs_return) { + try { + std::vector ret = instance->keys(); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_type_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + int ret = instance->type(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_empty_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_isNone_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + bool ret = instance->isNone(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_isSeq_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + bool ret = instance->isSeq(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_isMap_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + bool ret = instance->isMap(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_isInt_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + bool ret = instance->isInt(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_isReal_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + bool ret = instance->isReal(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_isString_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + bool ret = instance->isString(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_isNamed_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + bool ret = instance->isNamed(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_name_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + std::string ret = instance->name(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_size_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + size_t ret = instance->size(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_rawSize_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + size_t ret = instance->rawSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_operator_int_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + int ret = instance->operator int(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_operator_int64_t_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + int64_t ret = instance->operator int64_t(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_operator_float_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + float ret = instance->operator float(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_operator_double_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + double ret = instance->operator double(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_operator_std_string_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + std::string ret = instance->operator std::string(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_isMap_int(int flags, Result* ocvrs_return) { + try { + bool ret = cv::FileNode::isMap(flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_isSeq_int(int flags, Result* ocvrs_return) { + try { + bool ret = cv::FileNode::isSeq(flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_isCollection_int(int flags, Result* ocvrs_return) { + try { + bool ret = cv::FileNode::isCollection(flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_isEmptyCollection_int(int flags, Result* ocvrs_return) { + try { + bool ret = cv::FileNode::isEmptyCollection(flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_isFlow_int(int flags, Result* ocvrs_return) { + try { + bool ret = cv::FileNode::isFlow(flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_ptr(cv::FileNode* instance, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_ptr_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + const unsigned char* ret = instance->ptr(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_readRaw_const_const_StringR_voidX_size_t(const cv::FileNode* instance, const char* fmt, void* vec, size_t len, ResultVoid* ocvrs_return) { + try { + instance->readRaw(std::string(fmt), vec, len); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_setValue_int_const_voidX_int(cv::FileNode* instance, int type, const void* value, int len, ResultVoid* ocvrs_return) { + try { + instance->setValue(type, value, len); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_real_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + double ret = instance->real(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_string_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + std::string ret = instance->string(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNode_mat_const(const cv::FileNode* instance, Result* ocvrs_return) { + try { + cv::Mat ret = instance->mat(); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::FileNode* cv_FileNode_implicitClone_const(const cv::FileNode* instance) { + return new cv::FileNode(*instance); +} + +size_t cv_FileNode_propBlockIdx_const(const cv::FileNode* instance) { + size_t ret = instance->blockIdx; + return ret; +} + +void cv_FileNode_propBlockIdx_const_size_t(cv::FileNode* instance, const size_t val) { + instance->blockIdx = val; +} + +size_t cv_FileNode_propOfs_const(const cv::FileNode* instance) { + size_t ret = instance->ofs; + return ret; +} + +void cv_FileNode_propOfs_const_size_t(cv::FileNode* instance, const size_t val) { + instance->ofs = val; +} + +void cv_FileNode_delete(cv::FileNode* instance) { + delete instance; +} + +void cv_FileNodeIterator_operatorST_const_FileNodeIteratorR(cv::FileNodeIterator* instance, const cv::FileNodeIterator* it, ResultVoid* ocvrs_return) { + try { + instance->operator=(*it); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNodeIterator_operatorX_const(const cv::FileNodeIterator* instance, Result* ocvrs_return) { + try { + cv::FileNode ret = instance->operator*(); + Ok(new cv::FileNode(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNodeIterator_operatorAA(cv::FileNodeIterator* instance, Result* ocvrs_return) { + try { + cv::FileNodeIterator ret = instance->operator++(); + Ok(new cv::FileNodeIterator(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNodeIterator_readRaw_const_StringR_voidX_size_t(cv::FileNodeIterator* instance, const char* fmt, void* vec, size_t len, Result* ocvrs_return) { + try { + cv::FileNodeIterator ret = instance->readRaw(std::string(fmt), vec, len); + Ok(new cv::FileNodeIterator(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNodeIterator_remaining_const(const cv::FileNodeIterator* instance, Result* ocvrs_return) { + try { + size_t ret = instance->remaining(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNodeIterator_equalTo_const_const_FileNodeIteratorR(const cv::FileNodeIterator* instance, const cv::FileNodeIterator* it, Result* ocvrs_return) { + try { + bool ret = instance->equalTo(*it); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileNodeIterator_delete(cv::FileNodeIterator* instance) { + delete instance; +} + +void cv_FileStorage_FileStorage(Result* ocvrs_return) { + try { + cv::FileStorage* ret = new cv::FileStorage(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_FileStorage_const_StringR_int_const_StringR(const char* filename, int flags, const char* encoding, Result* ocvrs_return) { + try { + cv::FileStorage* ret = new cv::FileStorage(std::string(filename), flags, std::string(encoding)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_FileStorage_const_StringR_int(const char* filename, int flags, Result* ocvrs_return) { + try { + cv::FileStorage* ret = new cv::FileStorage(std::string(filename), flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_open_const_StringR_int_const_StringR(cv::FileStorage* instance, const char* filename, int flags, const char* encoding, Result* ocvrs_return) { + try { + bool ret = instance->open(std::string(filename), flags, std::string(encoding)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_open_const_StringR_int(cv::FileStorage* instance, const char* filename, int flags, Result* ocvrs_return) { + try { + bool ret = instance->open(std::string(filename), flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_isOpened_const(const cv::FileStorage* instance, Result* ocvrs_return) { + try { + bool ret = instance->isOpened(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_release(cv::FileStorage* instance, ResultVoid* ocvrs_return) { + try { + instance->release(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_releaseAndGetString(cv::FileStorage* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->releaseAndGetString(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_write_const_StringR_int(cv::FileStorage* instance, const char* name, int val, ResultVoid* ocvrs_return) { + try { + instance->write(std::string(name), val); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_write_const_StringR_int64_t(cv::FileStorage* instance, const char* name, int64_t val, ResultVoid* ocvrs_return) { + try { + instance->write(std::string(name), val); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_write_const_StringR_double(cv::FileStorage* instance, const char* name, double val, ResultVoid* ocvrs_return) { + try { + instance->write(std::string(name), val); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_write_const_StringR_const_StringR(cv::FileStorage* instance, const char* name, const char* val, ResultVoid* ocvrs_return) { + try { + instance->write(std::string(name), std::string(val)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_write_const_StringR_const_MatR(cv::FileStorage* instance, const char* name, const cv::Mat* val, ResultVoid* ocvrs_return) { + try { + instance->write(std::string(name), *val); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_write_const_StringR_const_vectorLStringGR(cv::FileStorage* instance, const char* name, const std::vector* val, ResultVoid* ocvrs_return) { + try { + instance->write(std::string(name), *val); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_writeRaw_const_StringR_const_voidX_size_t(cv::FileStorage* instance, const char* fmt, const void* vec, size_t len, ResultVoid* ocvrs_return) { + try { + instance->writeRaw(std::string(fmt), vec, len); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_writeComment_const_StringR_bool(cv::FileStorage* instance, const char* comment, bool append, ResultVoid* ocvrs_return) { + try { + instance->writeComment(std::string(comment), append); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_writeComment_const_StringR(cv::FileStorage* instance, const char* comment, ResultVoid* ocvrs_return) { + try { + instance->writeComment(std::string(comment)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_startWriteStruct_const_StringR_int_const_StringR(cv::FileStorage* instance, const char* name, int flags, const char* typeName, ResultVoid* ocvrs_return) { + try { + instance->startWriteStruct(std::string(name), flags, std::string(typeName)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_startWriteStruct_const_StringR_int(cv::FileStorage* instance, const char* name, int flags, ResultVoid* ocvrs_return) { + try { + instance->startWriteStruct(std::string(name), flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_endWriteStruct(cv::FileStorage* instance, ResultVoid* ocvrs_return) { + try { + instance->endWriteStruct(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_getDefaultObjectName_const_StringR(const char* filename, Result* ocvrs_return) { + try { + cv::String ret = cv::FileStorage::getDefaultObjectName(std::string(filename)); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_FileStorage_getFormat_const(const cv::FileStorage* instance, Result* ocvrs_return) { + try { + int ret = instance->getFormat(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +int cv_FileStorage_propState_const(const cv::FileStorage* instance) { + int ret = instance->state; + return ret; +} + +void cv_FileStorage_propState_const_int(cv::FileStorage* instance, const int val) { + instance->state = val; +} + +void* cv_FileStorage_propElname_const(const cv::FileStorage* instance) { + std::string ret = instance->elname; + return ocvrs_create_string(ret.c_str()); +} + +void cv_FileStorage_propElname_const_string(cv::FileStorage* instance, const char* val) { + instance->elname = std::string(val); +} + +void cv_FileStorage_delete(cv::FileStorage* instance) { + delete instance; +} + +void cv_Formatted_next(cv::Formatted* instance, Result* ocvrs_return) { + try { + const char* ret = instance->next(); + Ok(ocvrs_create_string(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Formatted_reset(cv::Formatted* instance, ResultVoid* ocvrs_return) { + try { + instance->reset(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Formatted_delete(cv::Formatted* instance) { + delete instance; +} + +void cv_Formatter_format_const_const_MatR(const cv::Formatter* instance, const cv::Mat* mtx, Result*>* ocvrs_return) { + try { + cv::Ptr ret = instance->format(*mtx); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Formatter_set16fPrecision_int(cv::Formatter* instance, int p, ResultVoid* ocvrs_return) { + try { + instance->set16fPrecision(p); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Formatter_set16fPrecision(cv::Formatter* instance, ResultVoid* ocvrs_return) { + try { + instance->set16fPrecision(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Formatter_set32fPrecision_int(cv::Formatter* instance, int p, ResultVoid* ocvrs_return) { + try { + instance->set32fPrecision(p); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Formatter_set32fPrecision(cv::Formatter* instance, ResultVoid* ocvrs_return) { + try { + instance->set32fPrecision(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Formatter_set64fPrecision_int(cv::Formatter* instance, int p, ResultVoid* ocvrs_return) { + try { + instance->set64fPrecision(p); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Formatter_set64fPrecision(cv::Formatter* instance, ResultVoid* ocvrs_return) { + try { + instance->set64fPrecision(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Formatter_setMultiline_bool(cv::Formatter* instance, bool ml, ResultVoid* ocvrs_return) { + try { + instance->setMultiline(ml); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Formatter_setMultiline(cv::Formatter* instance, ResultVoid* ocvrs_return) { + try { + instance->setMultiline(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Formatter_get_FormatType(cv::Formatter::FormatType fmt, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::Formatter::get(fmt); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Formatter_get(Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::Formatter::get(); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Formatter_delete(cv::Formatter* instance) { + delete instance; +} + +void cv_Hamming_operator___const_const_unsigned_charX_const_unsigned_charX_int(const cv::Hamming* instance, const unsigned char* a, const unsigned char* b, int size, Result* ocvrs_return) { + try { + cv::Hamming::ResultType ret = instance->operator()(a, b, size); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::Hamming* cv_Hamming_defaultNew_const() { + cv::Hamming* ret = new cv::Hamming(); + return ret; +} + +void cv_Hamming_delete(cv::Hamming* instance) { + delete instance; +} + +void cv_KeyPoint_hash_const(const cv::KeyPoint* instance, Result* ocvrs_return) { + try { + size_t ret = instance->hash(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_KeyPoint_convert_const_vectorLKeyPointGR_vectorLPoint2fGR_const_vectorLintGR(const std::vector* keypoints, std::vector* points2f, const std::vector* keypointIndexes, ResultVoid* ocvrs_return) { + try { + cv::KeyPoint::convert(*keypoints, *points2f, *keypointIndexes); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_KeyPoint_convert_const_vectorLKeyPointGR_vectorLPoint2fGR(const std::vector* keypoints, std::vector* points2f, ResultVoid* ocvrs_return) { + try { + cv::KeyPoint::convert(*keypoints, *points2f); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_KeyPoint_convert_const_vectorLPoint2fGR_vectorLKeyPointGR_float_float_int_int(const std::vector* points2f, std::vector* keypoints, float size, float response, int octave, int class_id, ResultVoid* ocvrs_return) { + try { + cv::KeyPoint::convert(*points2f, *keypoints, size, response, octave, class_id); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_KeyPoint_convert_const_vectorLPoint2fGR_vectorLKeyPointGR(const std::vector* points2f, std::vector* keypoints, ResultVoid* ocvrs_return) { + try { + cv::KeyPoint::convert(*points2f, *keypoints); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_KeyPoint_overlap_const_KeyPointR_const_KeyPointR(const cv::KeyPoint* kp1, const cv::KeyPoint* kp2, Result* ocvrs_return) { + try { + float ret = cv::KeyPoint::overlap(*kp1, *kp2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::KeyPoint* cv_KeyPoint_implicitClone_const(const cv::KeyPoint* instance) { + return new cv::KeyPoint(*instance); +} + +void cv_KeyPoint_propPt_const(const cv::KeyPoint* instance, cv::Point2f* ocvrs_return) { + cv::Point2f ret = instance->pt; + *ocvrs_return = ret; +} + +void cv_KeyPoint_propPt_const_Point2f(cv::KeyPoint* instance, const cv::Point2f* val) { + instance->pt = *val; +} + +float cv_KeyPoint_propSize_const(const cv::KeyPoint* instance) { + float ret = instance->size; + return ret; +} + +void cv_KeyPoint_propSize_const_float(cv::KeyPoint* instance, const float val) { + instance->size = val; +} + +float cv_KeyPoint_propAngle_const(const cv::KeyPoint* instance) { + float ret = instance->angle; + return ret; +} + +void cv_KeyPoint_propAngle_const_float(cv::KeyPoint* instance, const float val) { + instance->angle = val; +} + +float cv_KeyPoint_propResponse_const(const cv::KeyPoint* instance) { + float ret = instance->response; + return ret; +} + +void cv_KeyPoint_propResponse_const_float(cv::KeyPoint* instance, const float val) { + instance->response = val; +} + +int cv_KeyPoint_propOctave_const(const cv::KeyPoint* instance) { + int ret = instance->octave; + return ret; +} + +void cv_KeyPoint_propOctave_const_int(cv::KeyPoint* instance, const int val) { + instance->octave = val; +} + +int cv_KeyPoint_propClass_id_const(const cv::KeyPoint* instance) { + int ret = instance->class_id; + return ret; +} + +void cv_KeyPoint_propClass_id_const_int(cv::KeyPoint* instance, const int val) { + instance->class_id = val; +} + +void cv_KeyPoint_delete(cv::KeyPoint* instance) { + delete instance; +} + +void cv_LDA_LDA_int(int num_components, Result* ocvrs_return) { + try { + cv::LDA* ret = new cv::LDA(num_components); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_LDA(Result* ocvrs_return) { + try { + cv::LDA* ret = new cv::LDA(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_LDA_const__InputArrayR_const__InputArrayR_int(const cv::_InputArray* src, const cv::_InputArray* labels, int num_components, Result* ocvrs_return) { + try { + cv::LDA* ret = new cv::LDA(*src, *labels, num_components); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_LDA_const__InputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_InputArray* labels, Result* ocvrs_return) { + try { + cv::LDA* ret = new cv::LDA(*src, *labels); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_save_const_const_StringR(const cv::LDA* instance, const char* filename, ResultVoid* ocvrs_return) { + try { + instance->save(std::string(filename)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_load_const_StringR(cv::LDA* instance, const char* filename, ResultVoid* ocvrs_return) { + try { + instance->load(std::string(filename)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_save_const_FileStorageR(const cv::LDA* instance, cv::FileStorage* fs, ResultVoid* ocvrs_return) { + try { + instance->save(*fs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_load_const_FileStorageR(cv::LDA* instance, const cv::FileStorage* node, ResultVoid* ocvrs_return) { + try { + instance->load(*node); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_compute_const__InputArrayR_const__InputArrayR(cv::LDA* instance, const cv::_InputArray* src, const cv::_InputArray* labels, ResultVoid* ocvrs_return) { + try { + instance->compute(*src, *labels); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_project_const__InputArrayR(cv::LDA* instance, const cv::_InputArray* src, Result* ocvrs_return) { + try { + cv::Mat ret = instance->project(*src); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_reconstruct_const__InputArrayR(cv::LDA* instance, const cv::_InputArray* src, Result* ocvrs_return) { + try { + cv::Mat ret = instance->reconstruct(*src); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_eigenvectors_const(const cv::LDA* instance, Result* ocvrs_return) { + try { + cv::Mat ret = instance->eigenvectors(); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_eigenvalues_const(const cv::LDA* instance, Result* ocvrs_return) { + try { + cv::Mat ret = instance->eigenvalues(); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_subspaceProject_const__InputArrayR_const__InputArrayR_const__InputArrayR(const cv::_InputArray* W, const cv::_InputArray* mean, const cv::_InputArray* src, Result* ocvrs_return) { + try { + cv::Mat ret = cv::LDA::subspaceProject(*W, *mean, *src); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_subspaceReconstruct_const__InputArrayR_const__InputArrayR_const__InputArrayR(const cv::_InputArray* W, const cv::_InputArray* mean, const cv::_InputArray* src, Result* ocvrs_return) { + try { + cv::Mat ret = cv::LDA::subspaceReconstruct(*W, *mean, *src); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LDA_delete(cv::LDA* instance) { + delete instance; +} + +void cv_Mat_row_const_int(const cv::Mat* instance, int y, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->row(y); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_row_int(cv::Mat* instance, int y, Result* ocvrs_return) { + try { + cv::Mat ret = instance->row(y); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_col_const_int(const cv::Mat* instance, int x, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->col(x); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_col_int(cv::Mat* instance, int x, Result* ocvrs_return) { + try { + cv::Mat ret = instance->col(x); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_rowRange_const_int_int(const cv::Mat* instance, int startrow, int endrow, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->rowRange(startrow, endrow); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_rowRange_int_int(cv::Mat* instance, int startrow, int endrow, Result* ocvrs_return) { + try { + cv::Mat ret = instance->rowRange(startrow, endrow); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_rowRange_const_const_RangeR(const cv::Mat* instance, const cv::Range* r, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->rowRange(*r); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_rowRange_const_RangeR(cv::Mat* instance, const cv::Range* r, Result* ocvrs_return) { + try { + cv::Mat ret = instance->rowRange(*r); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_colRange_const_int_int(const cv::Mat* instance, int startcol, int endcol, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->colRange(startcol, endcol); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_colRange_int_int(cv::Mat* instance, int startcol, int endcol, Result* ocvrs_return) { + try { + cv::Mat ret = instance->colRange(startcol, endcol); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_colRange_const_const_RangeR(const cv::Mat* instance, const cv::Range* r, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->colRange(*r); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_colRange_const_RangeR(cv::Mat* instance, const cv::Range* r, Result* ocvrs_return) { + try { + cv::Mat ret = instance->colRange(*r); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_diag_const_int(const cv::Mat* instance, int d, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->diag(d); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_diag(cv::Mat* instance, Result* ocvrs_return) { + try { + cv::Mat ret = instance->diag(); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_diag_const(const cv::Mat* instance, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->diag(); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_diag_int(cv::Mat* instance, int d, Result* ocvrs_return) { + try { + cv::Mat ret = instance->diag(d); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_diag_const_MatR(const cv::Mat* d, Result* ocvrs_return) { + try { + cv::Mat ret = cv::Mat::diag(*d); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_clone_const(const cv::Mat* instance, Result* ocvrs_return) { + try { + cv::Mat ret = instance->clone(); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_copyTo_const_const__OutputArrayR(const cv::Mat* instance, const cv::_OutputArray* m, ResultVoid* ocvrs_return) { + try { + instance->copyTo(*m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_copyTo_const_const__OutputArrayR_const__InputArrayR(const cv::Mat* instance, const cv::_OutputArray* m, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + instance->copyTo(*m, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_copyAt_const_const__OutputArrayR(const cv::Mat* instance, const cv::_OutputArray* m, ResultVoid* ocvrs_return) { + try { + instance->copyAt(*m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_copyAt_const_const__OutputArrayR_const__InputArrayR(const cv::Mat* instance, const cv::_OutputArray* m, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + instance->copyAt(*m, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_convertTo_const_const__OutputArrayR_int_double_double(const cv::Mat* instance, const cv::_OutputArray* m, int rtype, double alpha, double beta, ResultVoid* ocvrs_return) { + try { + instance->convertTo(*m, rtype, alpha, beta); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_convertTo_const_const__OutputArrayR_int(const cv::Mat* instance, const cv::_OutputArray* m, int rtype, ResultVoid* ocvrs_return) { + try { + instance->convertTo(*m, rtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_assignTo_const_MatR_int(const cv::Mat* instance, cv::Mat* m, int type, ResultVoid* ocvrs_return) { + try { + instance->assignTo(*m, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_assignTo_const_MatR(const cv::Mat* instance, cv::Mat* m, ResultVoid* ocvrs_return) { + try { + instance->assignTo(*m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_operatorST_const_ScalarR(cv::Mat* instance, const cv::Scalar* s, ResultVoid* ocvrs_return) { + try { + instance->operator=(*s); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_setTo_const__InputArrayR_const__InputArrayR(cv::Mat* instance, const cv::_InputArray* value, const cv::_InputArray* mask, Result* ocvrs_return) { + try { + cv::Mat ret = instance->setTo(*value, *mask); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_setTo_const__InputArrayR(cv::Mat* instance, const cv::_InputArray* value, Result* ocvrs_return) { + try { + cv::Mat ret = instance->setTo(*value); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_reshape_const_int_int(const cv::Mat* instance, int cn, int rows, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->reshape(cn, rows); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_reshape_int(cv::Mat* instance, int cn, Result* ocvrs_return) { + try { + cv::Mat ret = instance->reshape(cn); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_reshape_const_int(const cv::Mat* instance, int cn, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->reshape(cn); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_reshape_int_int(cv::Mat* instance, int cn, int rows, Result* ocvrs_return) { + try { + cv::Mat ret = instance->reshape(cn, rows); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_reshape_const_int_int_const_intX(const cv::Mat* instance, int cn, int newndims, const int* newsz, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->reshape(cn, newndims, newsz); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_reshape_int_int_const_intX(cv::Mat* instance, int cn, int newndims, const int* newsz, Result* ocvrs_return) { + try { + cv::Mat ret = instance->reshape(cn, newndims, newsz); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_reshape_const_int_const_vectorLintGR(const cv::Mat* instance, int cn, const std::vector* newshape, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->reshape(cn, *newshape); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_reshape_int_const_vectorLintGR(cv::Mat* instance, int cn, const std::vector* newshape, Result* ocvrs_return) { + try { + cv::Mat ret = instance->reshape(cn, *newshape); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_reinterpret_const_int(const cv::Mat* instance, int type, Result* ocvrs_return) { + try { + cv::Mat ret = instance->reinterpret(type); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_cross_const_const__InputArrayR(const cv::Mat* instance, const cv::_InputArray* m, Result* ocvrs_return) { + try { + cv::Mat ret = instance->cross(*m); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_dot_const_const__InputArrayR(const cv::Mat* instance, const cv::_InputArray* m, Result* ocvrs_return) { + try { + double ret = instance->dot(*m); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_create_int_int_int(cv::Mat* instance, int rows, int cols, int type, ResultVoid* ocvrs_return) { + try { + instance->create(rows, cols, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_create_Size_int(cv::Mat* instance, cv::Size* size, int type, ResultVoid* ocvrs_return) { + try { + instance->create(*size, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_create_int_const_intX_int(cv::Mat* instance, int ndims, const int* sizes, int type, ResultVoid* ocvrs_return) { + try { + instance->create(ndims, sizes, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_create_const_vectorLintGR_int(cv::Mat* instance, const std::vector* sizes, int type, ResultVoid* ocvrs_return) { + try { + instance->create(*sizes, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_addref(cv::Mat* instance, ResultVoid* ocvrs_return) { + try { + instance->addref(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_release(cv::Mat* instance, ResultVoid* ocvrs_return) { + try { + instance->release(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_deallocate(cv::Mat* instance, ResultVoid* ocvrs_return) { + try { + instance->deallocate(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_reserve_size_t(cv::Mat* instance, size_t sz, ResultVoid* ocvrs_return) { + try { + instance->reserve(sz); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_reserveBuffer_size_t(cv::Mat* instance, size_t sz, ResultVoid* ocvrs_return) { + try { + instance->reserveBuffer(sz); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_resize_size_t(cv::Mat* instance, size_t sz, ResultVoid* ocvrs_return) { + try { + instance->resize(sz); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_resize_size_t_const_ScalarR(cv::Mat* instance, size_t sz, const cv::Scalar* s, ResultVoid* ocvrs_return) { + try { + instance->resize(sz, *s); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_push_back_const_MatR(cv::Mat* instance, const cv::Mat* m, ResultVoid* ocvrs_return) { + try { + instance->push_back(*m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_pop_back_size_t(cv::Mat* instance, size_t nelems, ResultVoid* ocvrs_return) { + try { + instance->pop_back(nelems); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_pop_back(cv::Mat* instance, ResultVoid* ocvrs_return) { + try { + instance->pop_back(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_locateROI_const_SizeR_PointR(const cv::Mat* instance, cv::Size* wholeSize, cv::Point* ofs, ResultVoid* ocvrs_return) { + try { + instance->locateROI(*wholeSize, *ofs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_adjustROI_int_int_int_int(cv::Mat* instance, int dtop, int dbottom, int dleft, int dright, Result* ocvrs_return) { + try { + cv::Mat ret = instance->adjustROI(dtop, dbottom, dleft, dright); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_operator___const_Range_Range(const cv::Mat* instance, cv::Range* rowRange, cv::Range* colRange, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->operator()(*rowRange, *colRange); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_operator___Range_Range(cv::Mat* instance, cv::Range* rowRange, cv::Range* colRange, Result* ocvrs_return) { + try { + cv::Mat ret = instance->operator()(*rowRange, *colRange); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_operator___const_const_RectR(const cv::Mat* instance, const cv::Rect* roi, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->operator()(*roi); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_operator___const_RectR(cv::Mat* instance, const cv::Rect* roi, Result* ocvrs_return) { + try { + cv::Mat ret = instance->operator()(*roi); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_operator___const_const_vectorLRangeGR(const cv::Mat* instance, const std::vector* ranges, Result* ocvrs_return) { + try { + const cv::Mat ret = instance->operator()(*ranges); + Ok(new const cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_operator___const_vectorLRangeGR(cv::Mat* instance, const std::vector* ranges, Result* ocvrs_return) { + try { + cv::Mat ret = instance->operator()(*ranges); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +bool cv_Mat_isContinuous_const(const cv::Mat* instance) { + bool ret = instance->isContinuous(); + return ret; +} + +bool cv_Mat_isSubmatrix_const(const cv::Mat* instance) { + bool ret = instance->isSubmatrix(); + return ret; +} + +void cv_Mat_elemSize_const(const cv::Mat* instance, Result* ocvrs_return) { + try { + size_t ret = instance->elemSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +size_t cv_Mat_elemSize1_const(const cv::Mat* instance) { + size_t ret = instance->elemSize1(); + return ret; +} + +int cv_Mat_type_const(const cv::Mat* instance) { + int ret = instance->type(); + return ret; +} + +int cv_Mat_depth_const(const cv::Mat* instance) { + int ret = instance->depth(); + return ret; +} + +int cv_Mat_channels_const(const cv::Mat* instance) { + int ret = instance->channels(); + return ret; +} + +void cv_Mat_step1_const_int(const cv::Mat* instance, int i, Result* ocvrs_return) { + try { + size_t ret = instance->step1(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_step1_const(const cv::Mat* instance, Result* ocvrs_return) { + try { + size_t ret = instance->step1(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +bool cv_Mat_empty_const(const cv::Mat* instance) { + bool ret = instance->empty(); + return ret; +} + +size_t cv_Mat_total_const(const cv::Mat* instance) { + size_t ret = instance->total(); + return ret; +} + +void cv_Mat_total_const_int_int(const cv::Mat* instance, int startDim, int endDim, Result* ocvrs_return) { + try { + size_t ret = instance->total(startDim, endDim); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_total_const_int(const cv::Mat* instance, int startDim, Result* ocvrs_return) { + try { + size_t ret = instance->total(startDim); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_checkVector_const_int_int_bool(const cv::Mat* instance, int elemChannels, int depth, bool requireContinuous, Result* ocvrs_return) { + try { + int ret = instance->checkVector(elemChannels, depth, requireContinuous); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_checkVector_const_int(const cv::Mat* instance, int elemChannels, Result* ocvrs_return) { + try { + int ret = instance->checkVector(elemChannels); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_ptr_int(cv::Mat* instance, int i0, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(i0); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_ptr(cv::Mat* instance, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_ptr_const_int(const cv::Mat* instance, int i0, Result* ocvrs_return) { + try { + const unsigned char* ret = instance->ptr(i0); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_ptr_const(const cv::Mat* instance, Result* ocvrs_return) { + try { + const unsigned char* ret = instance->ptr(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_ptr_int_int(cv::Mat* instance, int row, int col, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(row, col); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_ptr_const_int_int(const cv::Mat* instance, int row, int col, Result* ocvrs_return) { + try { + const unsigned char* ret = instance->ptr(row, col); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_ptr_int_int_int(cv::Mat* instance, int i0, int i1, int i2, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(i0, i1, i2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_ptr_const_int_int_int(const cv::Mat* instance, int i0, int i1, int i2, Result* ocvrs_return) { + try { + const unsigned char* ret = instance->ptr(i0, i1, i2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_ptr_const_intX(cv::Mat* instance, const int* idx, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(idx); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_ptr_const_const_intX(const cv::Mat* instance, const int* idx, Result* ocvrs_return) { + try { + const unsigned char* ret = instance->ptr(idx); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_operatorST_MatRR(cv::Mat* instance, cv::Mat* m, ResultVoid* ocvrs_return) { + try { + instance->operator=(std::move(*m)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_updateContinuityFlag(cv::Mat* instance, ResultVoid* ocvrs_return) { + try { + instance->updateContinuityFlag(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_size_const(const cv::Mat* instance, Result* ocvrs_return) { + try { + cv::Size ret = instance->size(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Mat_getDataDump_const(const cv::Mat* instance, Result* ocvrs_return) { + try { + std::string ret = std::string(); + std::ostringstream oss; + oss << *instance; + ret = oss.str(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +int cv_Mat_propFlags_const(const cv::Mat* instance) { + int ret = instance->flags; + return ret; +} + +void cv_Mat_propFlags_const_int(cv::Mat* instance, const int val) { + instance->flags = val; +} + +int cv_Mat_propDims_const(const cv::Mat* instance) { + int ret = instance->dims; + return ret; +} + +void cv_Mat_propDims_const_int(cv::Mat* instance, const int val) { + instance->dims = val; +} + +int cv_Mat_propRows_const(const cv::Mat* instance) { + int ret = instance->rows; + return ret; +} + +void cv_Mat_propRows_const_int(cv::Mat* instance, const int val) { + instance->rows = val; +} + +int cv_Mat_propCols_const(const cv::Mat* instance) { + int ret = instance->cols; + return ret; +} + +void cv_Mat_propCols_const_int(cv::Mat* instance, const int val) { + instance->cols = val; +} + +unsigned char* cv_Mat_propData_const(const cv::Mat* instance) { + unsigned char* const ret = instance->data; + return ret; +} + +unsigned char* cv_Mat_propData(cv::Mat* instance) { + unsigned char* ret = instance->data; + return ret; +} + +void cv_Mat_propData_unsigned_charX(cv::Mat* instance, unsigned char* const val) { + instance->data = val; +} + +const unsigned char* cv_Mat_propDatastart_const(const cv::Mat* instance) { + const unsigned char* ret = instance->datastart; + return ret; +} + +const unsigned char* cv_Mat_propDataend_const(const cv::Mat* instance) { + const unsigned char* ret = instance->dataend; + return ret; +} + +const unsigned char* cv_Mat_propDatalimit_const(const cv::Mat* instance) { + const unsigned char* ret = instance->datalimit; + return ret; +} + +cv::UMatData* cv_Mat_propU(cv::Mat* instance) { + cv::UMatData* ret = instance->u; + return new cv::UMatData(*ret); +} + +void cv_Mat_propU_UMatDataX(cv::Mat* instance, cv::UMatData* const val) { + instance->u = val; +} + +cv::MatSize* cv_Mat_propSize_const(const cv::Mat* instance) { + cv::MatSize ret = instance->size; + return new cv::MatSize(ret); +} + +void cv_Mat_propSize_const_MatSize(cv::Mat* instance, const cv::MatSize* val) { + instance->size = *val; +} + +cv::MatStep* cv_Mat_propStep_const(const cv::Mat* instance) { + cv::MatStep ret = instance->step; + return new cv::MatStep(ret); +} + +void cv_Mat_delete(cv::Mat* instance) { + delete instance; +} + +void cv_MatConstIterator_operatorST_const_MatConstIteratorR(cv::MatConstIterator* instance, const cv::MatConstIterator* it, ResultVoid* ocvrs_return) { + try { + instance->operator=(*it); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatConstIterator_operatorX_const(const cv::MatConstIterator* instance, Result* ocvrs_return) { + try { + const unsigned char* ret = instance->operator*(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatConstIterator_operator___const_ptrdiff_t(const cv::MatConstIterator* instance, ptrdiff_t i, Result* ocvrs_return) { + try { + const unsigned char* ret = instance->operator[](i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatConstIterator_operatorSS(cv::MatConstIterator* instance, Result* ocvrs_return) { + try { + cv::MatConstIterator ret = instance->operator--(); + Ok(new cv::MatConstIterator(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatConstIterator_operatorAA(cv::MatConstIterator* instance, Result* ocvrs_return) { + try { + cv::MatConstIterator ret = instance->operator++(); + Ok(new cv::MatConstIterator(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatConstIterator_pos_const(const cv::MatConstIterator* instance, Result* ocvrs_return) { + try { + cv::Point ret = instance->pos(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatConstIterator_pos_const_intX(const cv::MatConstIterator* instance, int* _idx, ResultVoid* ocvrs_return) { + try { + instance->pos(_idx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatConstIterator_lpos_const(const cv::MatConstIterator* instance, Result* ocvrs_return) { + try { + ptrdiff_t ret = instance->lpos(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatConstIterator_seek_ptrdiff_t_bool(cv::MatConstIterator* instance, ptrdiff_t ofs, bool relative, ResultVoid* ocvrs_return) { + try { + instance->seek(ofs, relative); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatConstIterator_seek_ptrdiff_t(cv::MatConstIterator* instance, ptrdiff_t ofs, ResultVoid* ocvrs_return) { + try { + instance->seek(ofs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatConstIterator_seek_const_intX_bool(cv::MatConstIterator* instance, const int* _idx, bool relative, ResultVoid* ocvrs_return) { + try { + instance->seek(_idx, relative); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatConstIterator_seek_const_intX(cv::MatConstIterator* instance, const int* _idx, ResultVoid* ocvrs_return) { + try { + instance->seek(_idx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +int cv_MatConstIterator_type_const(const cv::MatConstIterator* instance) { + int ret = instance->m->type(); + return ret; +} + +const cv::Mat* cv_MatConstIterator_propM_const(const cv::MatConstIterator* instance) { + const cv::Mat* ret = instance->m; + return new const cv::Mat(*ret); +} + +size_t cv_MatConstIterator_propElemSize_const(const cv::MatConstIterator* instance) { + size_t ret = instance->elemSize; + return ret; +} + +void cv_MatConstIterator_propElemSize_const_size_t(cv::MatConstIterator* instance, const size_t val) { + instance->elemSize = val; +} + +const unsigned char* cv_MatConstIterator_propPtr_const(const cv::MatConstIterator* instance) { + const unsigned char* ret = instance->ptr; + return ret; +} + +const unsigned char* cv_MatConstIterator_propSliceStart_const(const cv::MatConstIterator* instance) { + const unsigned char* ret = instance->sliceStart; + return ret; +} + +const unsigned char* cv_MatConstIterator_propSliceEnd_const(const cv::MatConstIterator* instance) { + const unsigned char* ret = instance->sliceEnd; + return ret; +} + +void cv_MatConstIterator_delete(cv::MatConstIterator* instance) { + delete instance; +} + +void cv_MatExpr_operator_cv_Mat_const(const cv::MatExpr* instance, Result* ocvrs_return) { + try { + cv::Mat ret = instance->operator cv::Mat(); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_size_const(const cv::MatExpr* instance, Result* ocvrs_return) { + try { + cv::Size ret = instance->size(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_type_const(const cv::MatExpr* instance, Result* ocvrs_return) { + try { + int ret = instance->type(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_row_const_int(const cv::MatExpr* instance, int y, Result* ocvrs_return) { + try { + cv::MatExpr ret = instance->row(y); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_col_const_int(const cv::MatExpr* instance, int x, Result* ocvrs_return) { + try { + cv::MatExpr ret = instance->col(x); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_diag_const_int(const cv::MatExpr* instance, int d, Result* ocvrs_return) { + try { + cv::MatExpr ret = instance->diag(d); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_diag_const(const cv::MatExpr* instance, Result* ocvrs_return) { + try { + cv::MatExpr ret = instance->diag(); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_operator___const_const_RangeR_const_RangeR(const cv::MatExpr* instance, const cv::Range* rowRange, const cv::Range* colRange, Result* ocvrs_return) { + try { + cv::MatExpr ret = instance->operator()(*rowRange, *colRange); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_operator___const_const_RectR(const cv::MatExpr* instance, const cv::Rect* roi, Result* ocvrs_return) { + try { + cv::MatExpr ret = instance->operator()(*roi); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_t_const(const cv::MatExpr* instance, Result* ocvrs_return) { + try { + cv::MatExpr ret = instance->t(); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_inv_const_int(const cv::MatExpr* instance, int method, Result* ocvrs_return) { + try { + cv::MatExpr ret = instance->inv(method); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_inv_const(const cv::MatExpr* instance, Result* ocvrs_return) { + try { + cv::MatExpr ret = instance->inv(); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_mul_const_const_MatExprR_double(const cv::MatExpr* instance, const cv::MatExpr* e, double scale, Result* ocvrs_return) { + try { + cv::MatExpr ret = instance->mul(*e, scale); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_mul_const_const_MatExprR(const cv::MatExpr* instance, const cv::MatExpr* e, Result* ocvrs_return) { + try { + cv::MatExpr ret = instance->mul(*e); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_mul_const_const_MatR_double(const cv::MatExpr* instance, const cv::Mat* m, double scale, Result* ocvrs_return) { + try { + cv::MatExpr ret = instance->mul(*m, scale); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_mul_const_const_MatR(const cv::MatExpr* instance, const cv::Mat* m, Result* ocvrs_return) { + try { + cv::MatExpr ret = instance->mul(*m); + Ok(new cv::MatExpr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_cross_const_const_MatR(const cv::MatExpr* instance, const cv::Mat* m, Result* ocvrs_return) { + try { + cv::Mat ret = instance->cross(*m); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_dot_const_const_MatR(const cv::MatExpr* instance, const cv::Mat* m, Result* ocvrs_return) { + try { + double ret = instance->dot(*m); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatExpr_swap_MatExprR(cv::MatExpr* instance, cv::MatExpr* b, ResultVoid* ocvrs_return) { + try { + instance->swap(*b); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +int cv_MatExpr_propFlags_const(const cv::MatExpr* instance) { + int ret = instance->flags; + return ret; +} + +void cv_MatExpr_propFlags_const_int(cv::MatExpr* instance, const int val) { + instance->flags = val; +} + +cv::Mat* cv_MatExpr_propA_const(const cv::MatExpr* instance) { + cv::Mat ret = instance->a; + return new cv::Mat(ret); +} + +void cv_MatExpr_propA_const_Mat(cv::MatExpr* instance, const cv::Mat* val) { + instance->a = *val; +} + +cv::Mat* cv_MatExpr_propB_const(const cv::MatExpr* instance) { + cv::Mat ret = instance->b; + return new cv::Mat(ret); +} + +void cv_MatExpr_propB_const_Mat(cv::MatExpr* instance, const cv::Mat* val) { + instance->b = *val; +} + +cv::Mat* cv_MatExpr_propC_const(const cv::MatExpr* instance) { + cv::Mat ret = instance->c; + return new cv::Mat(ret); +} + +void cv_MatExpr_propC_const_Mat(cv::MatExpr* instance, const cv::Mat* val) { + instance->c = *val; +} + +double cv_MatExpr_propAlpha_const(const cv::MatExpr* instance) { + double ret = instance->alpha; + return ret; +} + +void cv_MatExpr_propAlpha_const_double(cv::MatExpr* instance, const double val) { + instance->alpha = val; +} + +double cv_MatExpr_propBeta_const(const cv::MatExpr* instance) { + double ret = instance->beta; + return ret; +} + +void cv_MatExpr_propBeta_const_double(cv::MatExpr* instance, const double val) { + instance->beta = val; +} + +void cv_MatExpr_propS_const(const cv::MatExpr* instance, cv::Scalar* ocvrs_return) { + cv::Scalar ret = instance->s; + *ocvrs_return = ret; +} + +void cv_MatExpr_propS_const_Scalar(cv::MatExpr* instance, const cv::Scalar* val) { + instance->s = *val; +} + +void cv_MatExpr_delete(cv::MatExpr* instance) { + delete instance; +} + +void cv_MatOp_delete(cv::MatOp* instance) { + delete instance; +} + +cv::MatSize* cv_MatSize_MatSize_intX(int* _p) { + cv::MatSize* ret = new cv::MatSize(_p); + return ret; +} + +int cv_MatSize_dims_const(const cv::MatSize* instance) { + int ret = instance->dims(); + return ret; +} + +void cv_MatSize_operator___const(const cv::MatSize* instance, Result* ocvrs_return) { + try { + cv::Size ret = instance->operator()(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatSize_operator___const_int(const cv::MatSize* instance, int i, Result* ocvrs_return) { + try { + const int ret = instance->operator[](i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatSize_operator___int(cv::MatSize* instance, int i, Result* ocvrs_return) { + try { + int ret = instance->operator[](i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +const int* cv_MatSize_operator_const_intX_const(const cv::MatSize* instance) { + const int* ret = instance->operator const int*(); + return ret; +} + +bool cv_MatSize_operatorEQ_const_const_MatSizeR(const cv::MatSize* instance, const cv::MatSize* sz) { + bool ret = instance->operator==(*sz); + return ret; +} + +bool cv_MatSize_operatorNE_const_const_MatSizeR(const cv::MatSize* instance, const cv::MatSize* sz) { + bool ret = instance->operator!=(*sz); + return ret; +} + +int* cv_MatSize_propP_const(const cv::MatSize* instance) { + int* const ret = instance->p; + return ret; +} + +int* cv_MatSize_propP(cv::MatSize* instance) { + int* ret = instance->p; + return ret; +} + +void cv_MatSize_propP_intX(cv::MatSize* instance, int* const val) { + instance->p = val; +} + +void cv_MatSize_delete(cv::MatSize* instance) { + delete instance; +} + +cv::MatStep* cv_MatStep_MatStep() { + cv::MatStep* ret = new cv::MatStep(); + return ret; +} + +cv::MatStep* cv_MatStep_MatStep_size_t(size_t s) { + cv::MatStep* ret = new cv::MatStep(s); + return ret; +} + +const size_t cv_MatStep_operator___const_int(const cv::MatStep* instance, int i) { + const size_t ret = instance->operator[](i); + return ret; +} + +size_t cv_MatStep_operator___int(cv::MatStep* instance, int i) { + size_t ret = instance->operator[](i); + return ret; +} + +void cv_MatStep_operator_size_t_const(const cv::MatStep* instance, Result* ocvrs_return) { + try { + size_t ret = instance->operator size_t(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MatStep_operatorST_size_t(cv::MatStep* instance, size_t s, ResultVoid* ocvrs_return) { + try { + instance->operator=(s); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +size_t* cv_MatStep_propP_const(const cv::MatStep* instance) { + size_t* const ret = instance->p; + return ret; +} + +size_t* cv_MatStep_propP(cv::MatStep* instance) { + size_t* ret = instance->p; + return ret; +} + +void cv_MatStep_propP_size_tX(cv::MatStep* instance, size_t* const val) { + instance->p = val; +} + +const size_t** cv_MatStep_propBuf_const(const cv::MatStep* instance) { + const size_t(*ret)[2] = &instance->buf; + return (const size_t**)ret; +} + +size_t** cv_MatStep_propBuf(cv::MatStep* instance) { + size_t(*ret)[2] = &instance->buf; + return (size_t**)ret; +} + +void cv_MatStep_delete(cv::MatStep* instance) { + delete instance; +} + +void cv_Matx_AddOp_Matx_AddOp(Result* ocvrs_return) { + try { + cv::Matx_AddOp* ret = new cv::Matx_AddOp(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_AddOp_Matx_AddOp_const_Matx_AddOpR(const cv::Matx_AddOp* unnamed, Result* ocvrs_return) { + try { + cv::Matx_AddOp* ret = new cv::Matx_AddOp(*unnamed); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_AddOp_delete(cv::Matx_AddOp* instance) { + delete instance; +} + +void cv_Matx_DivOp_Matx_DivOp(Result* ocvrs_return) { + try { + cv::Matx_DivOp* ret = new cv::Matx_DivOp(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_DivOp_Matx_DivOp_const_Matx_DivOpR(const cv::Matx_DivOp* unnamed, Result* ocvrs_return) { + try { + cv::Matx_DivOp* ret = new cv::Matx_DivOp(*unnamed); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_DivOp_delete(cv::Matx_DivOp* instance) { + delete instance; +} + +void cv_Matx_MatMulOp_Matx_MatMulOp(Result* ocvrs_return) { + try { + cv::Matx_MatMulOp* ret = new cv::Matx_MatMulOp(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_MatMulOp_Matx_MatMulOp_const_Matx_MatMulOpR(const cv::Matx_MatMulOp* unnamed, Result* ocvrs_return) { + try { + cv::Matx_MatMulOp* ret = new cv::Matx_MatMulOp(*unnamed); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_MatMulOp_delete(cv::Matx_MatMulOp* instance) { + delete instance; +} + +void cv_Matx_MulOp_Matx_MulOp(Result* ocvrs_return) { + try { + cv::Matx_MulOp* ret = new cv::Matx_MulOp(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_MulOp_Matx_MulOp_const_Matx_MulOpR(const cv::Matx_MulOp* unnamed, Result* ocvrs_return) { + try { + cv::Matx_MulOp* ret = new cv::Matx_MulOp(*unnamed); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_MulOp_delete(cv::Matx_MulOp* instance) { + delete instance; +} + +void cv_Matx_ScaleOp_Matx_ScaleOp(Result* ocvrs_return) { + try { + cv::Matx_ScaleOp* ret = new cv::Matx_ScaleOp(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_ScaleOp_Matx_ScaleOp_const_Matx_ScaleOpR(const cv::Matx_ScaleOp* unnamed, Result* ocvrs_return) { + try { + cv::Matx_ScaleOp* ret = new cv::Matx_ScaleOp(*unnamed); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_ScaleOp_delete(cv::Matx_ScaleOp* instance) { + delete instance; +} + +void cv_Matx_SubOp_Matx_SubOp(Result* ocvrs_return) { + try { + cv::Matx_SubOp* ret = new cv::Matx_SubOp(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_SubOp_Matx_SubOp_const_Matx_SubOpR(const cv::Matx_SubOp* unnamed, Result* ocvrs_return) { + try { + cv::Matx_SubOp* ret = new cv::Matx_SubOp(*unnamed); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_SubOp_delete(cv::Matx_SubOp* instance) { + delete instance; +} + +void cv_Matx_TOp_Matx_TOp(Result* ocvrs_return) { + try { + cv::Matx_TOp* ret = new cv::Matx_TOp(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_TOp_Matx_TOp_const_Matx_TOpR(const cv::Matx_TOp* unnamed, Result* ocvrs_return) { + try { + cv::Matx_TOp* ret = new cv::Matx_TOp(*unnamed); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Matx_TOp_delete(cv::Matx_TOp* instance) { + delete instance; +} + +void cv_MinProblemSolver_getFunction_const(const cv::MinProblemSolver* instance, Result*>* ocvrs_return) { + try { + cv::Ptr ret = instance->getFunction(); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MinProblemSolver_setFunction_const_PtrLFunctionGR(cv::MinProblemSolver* instance, const cv::Ptr* f, ResultVoid* ocvrs_return) { + try { + instance->setFunction(*f); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MinProblemSolver_getTermCriteria_const(const cv::MinProblemSolver* instance, Result* ocvrs_return) { + try { + cv::TermCriteria ret = instance->getTermCriteria(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MinProblemSolver_setTermCriteria_const_TermCriteriaR(cv::MinProblemSolver* instance, const cv::TermCriteria* termcrit, ResultVoid* ocvrs_return) { + try { + instance->setTermCriteria(*termcrit); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MinProblemSolver_minimize_const__InputOutputArrayR(cv::MinProblemSolver* instance, const cv::_InputOutputArray* x, Result* ocvrs_return) { + try { + double ret = instance->minimize(*x); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::ConjGradSolver* cv_MinProblemSolver_to_ConjGradSolver(cv::MinProblemSolver* instance) { + return dynamic_cast(instance); +} + +cv::DownhillSolver* cv_MinProblemSolver_to_DownhillSolver(cv::MinProblemSolver* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_MinProblemSolver_to_Algorithm(cv::MinProblemSolver* instance) { + return dynamic_cast(instance); +} + +void cv_MinProblemSolver_delete(cv::MinProblemSolver* instance) { + delete instance; +} + +void cv_MinProblemSolver_Function_getDims_const(const cv::MinProblemSolver::Function* instance, Result* ocvrs_return) { + try { + int ret = instance->getDims(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MinProblemSolver_Function_getGradientEps_const(const cv::MinProblemSolver::Function* instance, Result* ocvrs_return) { + try { + double ret = instance->getGradientEps(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MinProblemSolver_Function_calc_const_const_doubleX(const cv::MinProblemSolver::Function* instance, const double* x, Result* ocvrs_return) { + try { + double ret = instance->calc(x); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MinProblemSolver_Function_getGradient_const_doubleX_doubleX(cv::MinProblemSolver::Function* instance, const double* x, double* grad, ResultVoid* ocvrs_return) { + try { + instance->getGradient(x, grad); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_MinProblemSolver_Function_delete(cv::MinProblemSolver::Function* instance) { + delete instance; +} + +void cv_Moments_Moments(Result* ocvrs_return) { + try { + cv::Moments ret; + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Moments_Moments_double_double_double_double_double_double_double_double_double_double(double m00, double m10, double m01, double m20, double m11, double m02, double m30, double m21, double m12, double m03, Result* ocvrs_return) { + try { + cv::Moments ret(m00, m10, m01, m20, m11, m02, m30, m21, m12, m03); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCA_PCA(Result* ocvrs_return) { + try { + cv::PCA* ret = new cv::PCA(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCA_PCA_const__InputArrayR_const__InputArrayR_int_int(const cv::_InputArray* data, const cv::_InputArray* mean, int flags, int maxComponents, Result* ocvrs_return) { + try { + cv::PCA* ret = new cv::PCA(*data, *mean, flags, maxComponents); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCA_PCA_const__InputArrayR_const__InputArrayR_int(const cv::_InputArray* data, const cv::_InputArray* mean, int flags, Result* ocvrs_return) { + try { + cv::PCA* ret = new cv::PCA(*data, *mean, flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCA_PCA_const__InputArrayR_const__InputArrayR_int_double(const cv::_InputArray* data, const cv::_InputArray* mean, int flags, double retainedVariance, Result* ocvrs_return) { + try { + cv::PCA* ret = new cv::PCA(*data, *mean, flags, retainedVariance); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCA_operator___const__InputArrayR_const__InputArrayR_int_int(cv::PCA* instance, const cv::_InputArray* data, const cv::_InputArray* mean, int flags, int maxComponents, Result* ocvrs_return) { + try { + cv::PCA ret = instance->operator()(*data, *mean, flags, maxComponents); + Ok(new cv::PCA(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCA_operator___const__InputArrayR_const__InputArrayR_int(cv::PCA* instance, const cv::_InputArray* data, const cv::_InputArray* mean, int flags, Result* ocvrs_return) { + try { + cv::PCA ret = instance->operator()(*data, *mean, flags); + Ok(new cv::PCA(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCA_operator___const__InputArrayR_const__InputArrayR_int_double(cv::PCA* instance, const cv::_InputArray* data, const cv::_InputArray* mean, int flags, double retainedVariance, Result* ocvrs_return) { + try { + cv::PCA ret = instance->operator()(*data, *mean, flags, retainedVariance); + Ok(new cv::PCA(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCA_project_const_const__InputArrayR(const cv::PCA* instance, const cv::_InputArray* vec, Result* ocvrs_return) { + try { + cv::Mat ret = instance->project(*vec); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCA_project_const_const__InputArrayR_const__OutputArrayR(const cv::PCA* instance, const cv::_InputArray* vec, const cv::_OutputArray* result, ResultVoid* ocvrs_return) { + try { + instance->project(*vec, *result); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCA_backProject_const_const__InputArrayR(const cv::PCA* instance, const cv::_InputArray* vec, Result* ocvrs_return) { + try { + cv::Mat ret = instance->backProject(*vec); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCA_backProject_const_const__InputArrayR_const__OutputArrayR(const cv::PCA* instance, const cv::_InputArray* vec, const cv::_OutputArray* result, ResultVoid* ocvrs_return) { + try { + instance->backProject(*vec, *result); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCA_write_const_FileStorageR(const cv::PCA* instance, cv::FileStorage* fs, ResultVoid* ocvrs_return) { + try { + instance->write(*fs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_PCA_read_const_FileNodeR(cv::PCA* instance, const cv::FileNode* fn, ResultVoid* ocvrs_return) { + try { + instance->read(*fn); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::Mat* cv_PCA_propEigenvectors_const(const cv::PCA* instance) { + cv::Mat ret = instance->eigenvectors; + return new cv::Mat(ret); +} + +void cv_PCA_propEigenvectors_const_Mat(cv::PCA* instance, const cv::Mat* val) { + instance->eigenvectors = *val; +} + +cv::Mat* cv_PCA_propEigenvalues_const(const cv::PCA* instance) { + cv::Mat ret = instance->eigenvalues; + return new cv::Mat(ret); +} + +void cv_PCA_propEigenvalues_const_Mat(cv::PCA* instance, const cv::Mat* val) { + instance->eigenvalues = *val; +} + +cv::Mat* cv_PCA_propMean_const(const cv::PCA* instance) { + cv::Mat ret = instance->mean; + return new cv::Mat(ret); +} + +void cv_PCA_propMean_const_Mat(cv::PCA* instance, const cv::Mat* val) { + instance->mean = *val; +} + +void cv_PCA_delete(cv::PCA* instance) { + delete instance; +} + +void cv_ParallelLoopBody_operator___const_const_RangeR(const cv::ParallelLoopBody* instance, const cv::Range* range, ResultVoid* ocvrs_return) { + try { + instance->operator()(*range); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ParallelLoopBody_delete(cv::ParallelLoopBody* instance) { + delete instance; +} + +void cv_RNG_next(cv::RNG* instance, Result* ocvrs_return) { + try { + unsigned int ret = instance->next(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_operator_unsigned_char(cv::RNG* instance, Result* ocvrs_return) { + try { + unsigned char ret = instance->operator unsigned char(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_operator_signed_char(cv::RNG* instance, Result* ocvrs_return) { + try { + signed char ret = instance->operator signed char(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_operator_unsigned_short(cv::RNG* instance, Result* ocvrs_return) { + try { + unsigned short ret = instance->operator unsigned short(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_operator_short(cv::RNG* instance, Result* ocvrs_return) { + try { + short ret = instance->operator short(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_operator_unsigned_int(cv::RNG* instance, Result* ocvrs_return) { + try { + unsigned int ret = instance->operator unsigned int(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_operator_int(cv::RNG* instance, Result* ocvrs_return) { + try { + int ret = instance->operator int(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_operator_float(cv::RNG* instance, Result* ocvrs_return) { + try { + float ret = instance->operator float(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_operator_double(cv::RNG* instance, Result* ocvrs_return) { + try { + double ret = instance->operator double(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_operator__(cv::RNG* instance, Result* ocvrs_return) { + try { + unsigned int ret = instance->operator()(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_operator___unsigned_int(cv::RNG* instance, unsigned int N, Result* ocvrs_return) { + try { + unsigned int ret = instance->operator()(N); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_uniform_int_int(cv::RNG* instance, int a, int b, Result* ocvrs_return) { + try { + int ret = instance->uniform(a, b); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_uniform_float_float(cv::RNG* instance, float a, float b, Result* ocvrs_return) { + try { + float ret = instance->uniform(a, b); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_uniform_double_double(cv::RNG* instance, double a, double b, Result* ocvrs_return) { + try { + double ret = instance->uniform(a, b); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_fill_const__InputOutputArrayR_int_const__InputArrayR_const__InputArrayR_bool(cv::RNG* instance, const cv::_InputOutputArray* mat, int distType, const cv::_InputArray* a, const cv::_InputArray* b, bool saturateRange, ResultVoid* ocvrs_return) { + try { + instance->fill(*mat, distType, *a, *b, saturateRange); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_fill_const__InputOutputArrayR_int_const__InputArrayR_const__InputArrayR(cv::RNG* instance, const cv::_InputOutputArray* mat, int distType, const cv::_InputArray* a, const cv::_InputArray* b, ResultVoid* ocvrs_return) { + try { + instance->fill(*mat, distType, *a, *b); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_gaussian_double(cv::RNG* instance, double sigma, Result* ocvrs_return) { + try { + double ret = instance->gaussian(sigma); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_operatorEQ_const_const_RNGR(const cv::RNG* instance, const cv::RNG* other, Result* ocvrs_return) { + try { + bool ret = instance->operator==(*other); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +uint64_t cv_RNG_propState_const(const cv::RNG* instance) { + uint64_t ret = instance->state; + return ret; +} + +void cv_RNG_propState_const_uint64_t(cv::RNG* instance, const uint64_t val) { + instance->state = val; +} + +void cv_RNG_delete(cv::RNG* instance) { + delete instance; +} + +void cv_RNG_MT19937_RNG_MT19937(Result* ocvrs_return) { + try { + cv::RNG_MT19937* ret = new cv::RNG_MT19937(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_MT19937_RNG_MT19937_unsigned_int(unsigned int s, Result* ocvrs_return) { + try { + cv::RNG_MT19937* ret = new cv::RNG_MT19937(s); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_MT19937_seed_unsigned_int(cv::RNG_MT19937* instance, unsigned int s, ResultVoid* ocvrs_return) { + try { + instance->seed(s); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_MT19937_next(cv::RNG_MT19937* instance, Result* ocvrs_return) { + try { + unsigned int ret = instance->next(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_MT19937_operator_int(cv::RNG_MT19937* instance, Result* ocvrs_return) { + try { + int ret = instance->operator int(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_MT19937_operator_unsigned_int(cv::RNG_MT19937* instance, Result* ocvrs_return) { + try { + unsigned int ret = instance->operator unsigned int(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_MT19937_operator_float(cv::RNG_MT19937* instance, Result* ocvrs_return) { + try { + float ret = instance->operator float(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_MT19937_operator_double(cv::RNG_MT19937* instance, Result* ocvrs_return) { + try { + double ret = instance->operator double(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_MT19937_operator___unsigned_int(cv::RNG_MT19937* instance, unsigned int N, Result* ocvrs_return) { + try { + unsigned int ret = instance->operator()(N); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_MT19937_uniform_int_int(cv::RNG_MT19937* instance, int a, int b, Result* ocvrs_return) { + try { + int ret = instance->uniform(a, b); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_MT19937_uniform_float_float(cv::RNG_MT19937* instance, float a, float b, Result* ocvrs_return) { + try { + float ret = instance->uniform(a, b); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_MT19937_uniform_double_double(cv::RNG_MT19937* instance, double a, double b, Result* ocvrs_return) { + try { + double ret = instance->uniform(a, b); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RNG_MT19937_delete(cv::RNG_MT19937* instance) { + delete instance; +} + +void cv_Range_size_const(const cv::Range* instance, Result* ocvrs_return) { + try { + int ret = instance->size(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Range_empty_const(const cv::Range* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Range_all(Result* ocvrs_return) { + try { + cv::Range ret = cv::Range::all(); + Ok(new cv::Range(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +int cv_Range_propStart_const(const cv::Range* instance) { + int ret = instance->start; + return ret; +} + +void cv_Range_propStart_const_int(cv::Range* instance, const int val) { + instance->start = val; +} + +int cv_Range_propEnd_const(const cv::Range* instance) { + int ret = instance->end; + return ret; +} + +void cv_Range_propEnd_const_int(cv::Range* instance, const int val) { + instance->end = val; +} + +void cv_Range_delete(cv::Range* instance) { + delete instance; +} + +void cv_RotatedRect_points_const_Point2fXX(const cv::RotatedRect* instance, cv::Point2f(*pts)[4], ResultVoid* ocvrs_return) { + try { + instance->points(*pts); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RotatedRect_points_const_vectorLPoint2fGR(const cv::RotatedRect* instance, std::vector* pts, ResultVoid* ocvrs_return) { + try { + instance->points(*pts); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RotatedRect_boundingRect_const(const cv::RotatedRect* instance, Result* ocvrs_return) { + try { + cv::Rect ret = instance->boundingRect(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_RotatedRect_boundingRect2f_const(const cv::RotatedRect* instance, Result* ocvrs_return) { + try { + cv::Rect2f ret = instance->boundingRect2f(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVD_SVD(Result* ocvrs_return) { + try { + cv::SVD* ret = new cv::SVD(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVD_SVD_const__InputArrayR_int(const cv::_InputArray* src, int flags, Result* ocvrs_return) { + try { + cv::SVD* ret = new cv::SVD(*src, flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVD_SVD_const__InputArrayR(const cv::_InputArray* src, Result* ocvrs_return) { + try { + cv::SVD* ret = new cv::SVD(*src); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVD_operator___const__InputArrayR_int(cv::SVD* instance, const cv::_InputArray* src, int flags, Result* ocvrs_return) { + try { + cv::SVD ret = instance->operator()(*src, flags); + Ok(new cv::SVD(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVD_operator___const__InputArrayR(cv::SVD* instance, const cv::_InputArray* src, Result* ocvrs_return) { + try { + cv::SVD ret = instance->operator()(*src); + Ok(new cv::SVD(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVD_compute_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* w, const cv::_OutputArray* u, const cv::_OutputArray* vt, int flags, ResultVoid* ocvrs_return) { + try { + cv::SVD::compute(*src, *w, *u, *vt, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVD_compute_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* w, const cv::_OutputArray* u, const cv::_OutputArray* vt, ResultVoid* ocvrs_return) { + try { + cv::SVD::compute(*src, *w, *u, *vt); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVD_compute_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* w, int flags, ResultVoid* ocvrs_return) { + try { + cv::SVD::compute(*src, *w, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVD_compute_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* w, ResultVoid* ocvrs_return) { + try { + cv::SVD::compute(*src, *w); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVD_backSubst_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* w, const cv::_InputArray* u, const cv::_InputArray* vt, const cv::_InputArray* rhs, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::SVD::backSubst(*w, *u, *vt, *rhs, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVD_solveZ_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::SVD::solveZ(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SVD_backSubst_const_const__InputArrayR_const__OutputArrayR(const cv::SVD* instance, const cv::_InputArray* rhs, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + instance->backSubst(*rhs, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::Mat* cv_SVD_propU_const(const cv::SVD* instance) { + cv::Mat ret = instance->u; + return new cv::Mat(ret); +} + +void cv_SVD_propU_const_Mat(cv::SVD* instance, const cv::Mat* val) { + instance->u = *val; +} + +cv::Mat* cv_SVD_propW_const(const cv::SVD* instance) { + cv::Mat ret = instance->w; + return new cv::Mat(ret); +} + +void cv_SVD_propW_const_Mat(cv::SVD* instance, const cv::Mat* val) { + instance->w = *val; +} + +cv::Mat* cv_SVD_propVt_const(const cv::SVD* instance) { + cv::Mat ret = instance->vt; + return new cv::Mat(ret); +} + +void cv_SVD_propVt_const_Mat(cv::SVD* instance, const cv::Mat* val) { + instance->vt = *val; +} + +void cv_SVD_delete(cv::SVD* instance) { + delete instance; +} + +void cv_SparseMat_operatorST_const_SparseMatR(cv::SparseMat* instance, const cv::SparseMat* m, ResultVoid* ocvrs_return) { + try { + instance->operator=(*m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_operatorST_const_MatR(cv::SparseMat* instance, const cv::Mat* m, ResultVoid* ocvrs_return) { + try { + instance->operator=(*m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_clone_const(const cv::SparseMat* instance, Result* ocvrs_return) { + try { + cv::SparseMat ret = instance->clone(); + Ok(new cv::SparseMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_copyTo_const_SparseMatR(const cv::SparseMat* instance, cv::SparseMat* m, ResultVoid* ocvrs_return) { + try { + instance->copyTo(*m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_copyTo_const_MatR(const cv::SparseMat* instance, cv::Mat* m, ResultVoid* ocvrs_return) { + try { + instance->copyTo(*m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_convertTo_const_SparseMatR_int_double(const cv::SparseMat* instance, cv::SparseMat* m, int rtype, double alpha, ResultVoid* ocvrs_return) { + try { + instance->convertTo(*m, rtype, alpha); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_convertTo_const_SparseMatR_int(const cv::SparseMat* instance, cv::SparseMat* m, int rtype, ResultVoid* ocvrs_return) { + try { + instance->convertTo(*m, rtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_convertTo_const_MatR_int_double_double(const cv::SparseMat* instance, cv::Mat* m, int rtype, double alpha, double beta, ResultVoid* ocvrs_return) { + try { + instance->convertTo(*m, rtype, alpha, beta); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_convertTo_const_MatR_int(const cv::SparseMat* instance, cv::Mat* m, int rtype, ResultVoid* ocvrs_return) { + try { + instance->convertTo(*m, rtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_assignTo_const_SparseMatR_int(const cv::SparseMat* instance, cv::SparseMat* m, int type, ResultVoid* ocvrs_return) { + try { + instance->assignTo(*m, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_assignTo_const_SparseMatR(const cv::SparseMat* instance, cv::SparseMat* m, ResultVoid* ocvrs_return) { + try { + instance->assignTo(*m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_create_int_const_intX_int(cv::SparseMat* instance, int dims, const int* _sizes, int _type, ResultVoid* ocvrs_return) { + try { + instance->create(dims, _sizes, _type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_clear(cv::SparseMat* instance, ResultVoid* ocvrs_return) { + try { + instance->clear(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_addref(cv::SparseMat* instance, ResultVoid* ocvrs_return) { + try { + instance->addref(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_release(cv::SparseMat* instance, ResultVoid* ocvrs_return) { + try { + instance->release(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +size_t cv_SparseMat_elemSize_const(const cv::SparseMat* instance) { + size_t ret = instance->elemSize(); + return ret; +} + +size_t cv_SparseMat_elemSize1_const(const cv::SparseMat* instance) { + size_t ret = instance->elemSize1(); + return ret; +} + +int cv_SparseMat_type_const(const cv::SparseMat* instance) { + int ret = instance->type(); + return ret; +} + +int cv_SparseMat_depth_const(const cv::SparseMat* instance) { + int ret = instance->depth(); + return ret; +} + +int cv_SparseMat_channels_const(const cv::SparseMat* instance) { + int ret = instance->channels(); + return ret; +} + +void cv_SparseMat_size_const(const cv::SparseMat* instance, Result* ocvrs_return) { + try { + const int* ret = instance->size(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_size_const_int(const cv::SparseMat* instance, int i, Result* ocvrs_return) { + try { + int ret = instance->size(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_dims_const(const cv::SparseMat* instance, Result* ocvrs_return) { + try { + int ret = instance->dims(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_nzcount_const(const cv::SparseMat* instance, Result* ocvrs_return) { + try { + size_t ret = instance->nzcount(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_hash_const_int(const cv::SparseMat* instance, int i0, Result* ocvrs_return) { + try { + size_t ret = instance->hash(i0); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_hash_const_int_int(const cv::SparseMat* instance, int i0, int i1, Result* ocvrs_return) { + try { + size_t ret = instance->hash(i0, i1); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_hash_const_int_int_int(const cv::SparseMat* instance, int i0, int i1, int i2, Result* ocvrs_return) { + try { + size_t ret = instance->hash(i0, i1, i2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_hash_const_const_intX(const cv::SparseMat* instance, const int* idx, Result* ocvrs_return) { + try { + size_t ret = instance->hash(idx); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_ptr_int_bool_size_tX(cv::SparseMat* instance, int i0, bool createMissing, size_t* hashval, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(i0, createMissing, hashval); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_ptr_int_bool(cv::SparseMat* instance, int i0, bool createMissing, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(i0, createMissing); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_ptr_int_int_bool_size_tX(cv::SparseMat* instance, int i0, int i1, bool createMissing, size_t* hashval, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(i0, i1, createMissing, hashval); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_ptr_int_int_bool(cv::SparseMat* instance, int i0, int i1, bool createMissing, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(i0, i1, createMissing); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_ptr_int_int_int_bool_size_tX(cv::SparseMat* instance, int i0, int i1, int i2, bool createMissing, size_t* hashval, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(i0, i1, i2, createMissing, hashval); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_ptr_int_int_int_bool(cv::SparseMat* instance, int i0, int i1, int i2, bool createMissing, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(i0, i1, i2, createMissing); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_ptr_const_intX_bool_size_tX(cv::SparseMat* instance, const int* idx, bool createMissing, size_t* hashval, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(idx, createMissing, hashval); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_ptr_const_intX_bool(cv::SparseMat* instance, const int* idx, bool createMissing, Result* ocvrs_return) { + try { + unsigned char* ret = instance->ptr(idx, createMissing); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_erase_int_int_size_tX(cv::SparseMat* instance, int i0, int i1, size_t* hashval, ResultVoid* ocvrs_return) { + try { + instance->erase(i0, i1, hashval); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_erase_int_int(cv::SparseMat* instance, int i0, int i1, ResultVoid* ocvrs_return) { + try { + instance->erase(i0, i1); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_erase_int_int_int_size_tX(cv::SparseMat* instance, int i0, int i1, int i2, size_t* hashval, ResultVoid* ocvrs_return) { + try { + instance->erase(i0, i1, i2, hashval); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_erase_int_int_int(cv::SparseMat* instance, int i0, int i1, int i2, ResultVoid* ocvrs_return) { + try { + instance->erase(i0, i1, i2); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_erase_const_intX_size_tX(cv::SparseMat* instance, const int* idx, size_t* hashval, ResultVoid* ocvrs_return) { + try { + instance->erase(idx, hashval); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_erase_const_intX(cv::SparseMat* instance, const int* idx, ResultVoid* ocvrs_return) { + try { + instance->erase(idx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_node_size_t(cv::SparseMat* instance, size_t nidx, Result* ocvrs_return) { + try { + cv::SparseMat::Node* ret = instance->node(nidx); + Ok(new cv::SparseMat::Node(*ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_node_const_size_t(const cv::SparseMat* instance, size_t nidx, Result* ocvrs_return) { + try { + const cv::SparseMat::Node* ret = instance->node(nidx); + Ok(new const cv::SparseMat::Node(*ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_newNode_const_intX_size_t(cv::SparseMat* instance, const int* idx, size_t hashval, Result* ocvrs_return) { + try { + unsigned char* ret = instance->newNode(idx, hashval); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_removeNode_size_t_size_t_size_t(cv::SparseMat* instance, size_t hidx, size_t nidx, size_t previdx, ResultVoid* ocvrs_return) { + try { + instance->removeNode(hidx, nidx, previdx); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_resizeHashTab_size_t(cv::SparseMat* instance, size_t newsize, ResultVoid* ocvrs_return) { + try { + instance->resizeHashTab(newsize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +int cv_SparseMat_propFlags_const(const cv::SparseMat* instance) { + int ret = instance->flags; + return ret; +} + +void cv_SparseMat_propFlags_const_int(cv::SparseMat* instance, const int val) { + instance->flags = val; +} + +cv::SparseMat::Hdr* cv_SparseMat_propHdr(cv::SparseMat* instance) { + cv::SparseMat::Hdr* ret = instance->hdr; + return new cv::SparseMat::Hdr(*ret); +} + +void cv_SparseMat_propHdr_HdrX(cv::SparseMat* instance, cv::SparseMat::Hdr* const val) { + instance->hdr = val; +} + +void cv_SparseMat_delete(cv::SparseMat* instance) { + delete instance; +} + +void cv_SparseMat_Hdr_Hdr_int_const_intX_int(int _dims, const int* _sizes, int _type, Result* ocvrs_return) { + try { + cv::SparseMat::Hdr* ret = new cv::SparseMat::Hdr(_dims, _sizes, _type); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMat_Hdr_clear(cv::SparseMat::Hdr* instance, ResultVoid* ocvrs_return) { + try { + instance->clear(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +int cv_SparseMat_Hdr_propRefcount_const(const cv::SparseMat::Hdr* instance) { + int ret = instance->refcount; + return ret; +} + +void cv_SparseMat_Hdr_propRefcount_const_int(cv::SparseMat::Hdr* instance, const int val) { + instance->refcount = val; +} + +int cv_SparseMat_Hdr_propDims_const(const cv::SparseMat::Hdr* instance) { + int ret = instance->dims; + return ret; +} + +void cv_SparseMat_Hdr_propDims_const_int(cv::SparseMat::Hdr* instance, const int val) { + instance->dims = val; +} + +int cv_SparseMat_Hdr_propValueOffset_const(const cv::SparseMat::Hdr* instance) { + int ret = instance->valueOffset; + return ret; +} + +void cv_SparseMat_Hdr_propValueOffset_const_int(cv::SparseMat::Hdr* instance, const int val) { + instance->valueOffset = val; +} + +size_t cv_SparseMat_Hdr_propNodeSize_const(const cv::SparseMat::Hdr* instance) { + size_t ret = instance->nodeSize; + return ret; +} + +void cv_SparseMat_Hdr_propNodeSize_const_size_t(cv::SparseMat::Hdr* instance, const size_t val) { + instance->nodeSize = val; +} + +size_t cv_SparseMat_Hdr_propNodeCount_const(const cv::SparseMat::Hdr* instance) { + size_t ret = instance->nodeCount; + return ret; +} + +void cv_SparseMat_Hdr_propNodeCount_const_size_t(cv::SparseMat::Hdr* instance, const size_t val) { + instance->nodeCount = val; +} + +size_t cv_SparseMat_Hdr_propFreeList_const(const cv::SparseMat::Hdr* instance) { + size_t ret = instance->freeList; + return ret; +} + +void cv_SparseMat_Hdr_propFreeList_const_size_t(cv::SparseMat::Hdr* instance, const size_t val) { + instance->freeList = val; +} + +std::vector* cv_SparseMat_Hdr_propPool_const(const cv::SparseMat::Hdr* instance) { + std::vector ret = instance->pool; + return new std::vector(ret); +} + +void cv_SparseMat_Hdr_propPool_const_vectorLunsigned_charG(cv::SparseMat::Hdr* instance, const std::vector* val) { + instance->pool = *val; +} + +std::vector* cv_SparseMat_Hdr_propHashtab_const(const cv::SparseMat::Hdr* instance) { + std::vector ret = instance->hashtab; + return new std::vector(ret); +} + +void cv_SparseMat_Hdr_propHashtab_const_vectorLsize_tG(cv::SparseMat::Hdr* instance, const std::vector* val) { + instance->hashtab = *val; +} + +const int** cv_SparseMat_Hdr_propSize_const(const cv::SparseMat::Hdr* instance) { + const int(*ret)[32] = &instance->size; + return (const int**)ret; +} + +int** cv_SparseMat_Hdr_propSize(cv::SparseMat::Hdr* instance) { + int(*ret)[32] = &instance->size; + return (int**)ret; +} + +void cv_SparseMat_Hdr_delete(cv::SparseMat::Hdr* instance) { + delete instance; +} + +cv::SparseMat::Node* cv_SparseMat_Node_defaultNew_const() { + cv::SparseMat::Node* ret = new cv::SparseMat::Node(); + return ret; +} + +size_t cv_SparseMat_Node_propHashval_const(const cv::SparseMat::Node* instance) { + size_t ret = instance->hashval; + return ret; +} + +void cv_SparseMat_Node_propHashval_const_size_t(cv::SparseMat::Node* instance, const size_t val) { + instance->hashval = val; +} + +size_t cv_SparseMat_Node_propNext_const(const cv::SparseMat::Node* instance) { + size_t ret = instance->next; + return ret; +} + +void cv_SparseMat_Node_propNext_const_size_t(cv::SparseMat::Node* instance, const size_t val) { + instance->next = val; +} + +const int** cv_SparseMat_Node_propIdx_const(const cv::SparseMat::Node* instance) { + const int(*ret)[32] = &instance->idx; + return (const int**)ret; +} + +int** cv_SparseMat_Node_propIdx(cv::SparseMat::Node* instance) { + int(*ret)[32] = &instance->idx; + return (int**)ret; +} + +void cv_SparseMat_Node_delete(cv::SparseMat::Node* instance) { + delete instance; +} + +void cv_SparseMatConstIterator_operatorST_const_SparseMatConstIteratorR(cv::SparseMatConstIterator* instance, const cv::SparseMatConstIterator* it, ResultVoid* ocvrs_return) { + try { + instance->operator=(*it); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMatConstIterator_node_const(const cv::SparseMatConstIterator* instance, Result* ocvrs_return) { + try { + const cv::SparseMat::Node* ret = instance->node(); + Ok(new const cv::SparseMat::Node(*ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMatConstIterator_operatorAA(cv::SparseMatConstIterator* instance, Result* ocvrs_return) { + try { + cv::SparseMatConstIterator ret = instance->operator++(); + Ok(new cv::SparseMatConstIterator(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMatConstIterator_seekEnd(cv::SparseMatConstIterator* instance, ResultVoid* ocvrs_return) { + try { + instance->seekEnd(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +const cv::SparseMat* cv_SparseMatConstIterator_propM_const(const cv::SparseMatConstIterator* instance) { + const cv::SparseMat* ret = instance->m; + return new const cv::SparseMat(*ret); +} + +size_t cv_SparseMatConstIterator_propHashidx_const(const cv::SparseMatConstIterator* instance) { + size_t ret = instance->hashidx; + return ret; +} + +void cv_SparseMatConstIterator_propHashidx_const_size_t(cv::SparseMatConstIterator* instance, const size_t val) { + instance->hashidx = val; +} + +unsigned char* cv_SparseMatConstIterator_propPtr_const(const cv::SparseMatConstIterator* instance) { + unsigned char* const ret = instance->ptr; + return ret; +} + +unsigned char* cv_SparseMatConstIterator_propPtr(cv::SparseMatConstIterator* instance) { + unsigned char* ret = instance->ptr; + return ret; +} + +void cv_SparseMatConstIterator_propPtr_unsigned_charX(cv::SparseMatConstIterator* instance, unsigned char* const val) { + instance->ptr = val; +} + +void cv_SparseMatConstIterator_delete(cv::SparseMatConstIterator* instance) { + delete instance; +} + +void cv_SparseMatIterator_operatorST_const_SparseMatIteratorR(cv::SparseMatIterator* instance, const cv::SparseMatIterator* it, ResultVoid* ocvrs_return) { + try { + instance->operator=(*it); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMatIterator_node_const(const cv::SparseMatIterator* instance, Result* ocvrs_return) { + try { + cv::SparseMat::Node* ret = instance->node(); + Ok(new cv::SparseMat::Node(*ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_SparseMatIterator_operatorAA(cv::SparseMatIterator* instance, Result* ocvrs_return) { + try { + cv::SparseMatIterator ret = instance->operator++(); + Ok(new cv::SparseMatIterator(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::SparseMatConstIterator* cv_SparseMatIterator_to_SparseMatConstIterator(cv::SparseMatIterator* instance) { + return dynamic_cast(instance); +} + +void cv_SparseMatIterator_delete(cv::SparseMatIterator* instance) { + delete instance; +} + +void cv_TermCriteria_isValid_const(const cv::TermCriteria* instance, Result* ocvrs_return) { + try { + bool ret = instance->isValid(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_TickMeter(Result* ocvrs_return) { + try { + cv::TickMeter* ret = new cv::TickMeter(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_start(cv::TickMeter* instance, ResultVoid* ocvrs_return) { + try { + instance->start(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_stop(cv::TickMeter* instance, ResultVoid* ocvrs_return) { + try { + instance->stop(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_getTimeTicks_const(const cv::TickMeter* instance, Result* ocvrs_return) { + try { + int64_t ret = instance->getTimeTicks(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_getTimeMicro_const(const cv::TickMeter* instance, Result* ocvrs_return) { + try { + double ret = instance->getTimeMicro(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_getTimeMilli_const(const cv::TickMeter* instance, Result* ocvrs_return) { + try { + double ret = instance->getTimeMilli(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_getTimeSec_const(const cv::TickMeter* instance, Result* ocvrs_return) { + try { + double ret = instance->getTimeSec(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_getLastTimeTicks_const(const cv::TickMeter* instance, Result* ocvrs_return) { + try { + int64_t ret = instance->getLastTimeTicks(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_getLastTimeMicro_const(const cv::TickMeter* instance, Result* ocvrs_return) { + try { + double ret = instance->getLastTimeMicro(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_getLastTimeMilli_const(const cv::TickMeter* instance, Result* ocvrs_return) { + try { + double ret = instance->getLastTimeMilli(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_getLastTimeSec_const(const cv::TickMeter* instance, Result* ocvrs_return) { + try { + double ret = instance->getLastTimeSec(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_getCounter_const(const cv::TickMeter* instance, Result* ocvrs_return) { + try { + int64_t ret = instance->getCounter(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_getFPS_const(const cv::TickMeter* instance, Result* ocvrs_return) { + try { + double ret = instance->getFPS(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_getAvgTimeSec_const(const cv::TickMeter* instance, Result* ocvrs_return) { + try { + double ret = instance->getAvgTimeSec(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_getAvgTimeMilli_const(const cv::TickMeter* instance, Result* ocvrs_return) { + try { + double ret = instance->getAvgTimeMilli(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_reset(cv::TickMeter* instance, ResultVoid* ocvrs_return) { + try { + instance->reset(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_TickMeter_delete(cv::TickMeter* instance) { + delete instance; +} + +void cv_UMat_operatorST_const_UMatR(cv::UMat* instance, const cv::UMat* m, ResultVoid* ocvrs_return) { + try { + instance->operator=(*m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_getMat_const_AccessFlag(const cv::UMat* instance, cv::AccessFlag flags, Result* ocvrs_return) { + try { + cv::Mat ret = instance->getMat(flags); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_row_const_int(const cv::UMat* instance, int y, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->row(y); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_row_int(cv::UMat* instance, int y, Result* ocvrs_return) { + try { + cv::UMat ret = instance->row(y); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_col_const_int(const cv::UMat* instance, int x, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->col(x); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_col_int(cv::UMat* instance, int x, Result* ocvrs_return) { + try { + cv::UMat ret = instance->col(x); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_rowRange_const_int_int(const cv::UMat* instance, int startrow, int endrow, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->rowRange(startrow, endrow); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_rowRange_int_int(cv::UMat* instance, int startrow, int endrow, Result* ocvrs_return) { + try { + cv::UMat ret = instance->rowRange(startrow, endrow); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_rowRange_const_const_RangeR(const cv::UMat* instance, const cv::Range* r, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->rowRange(*r); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_rowRange_const_RangeR(cv::UMat* instance, const cv::Range* r, Result* ocvrs_return) { + try { + cv::UMat ret = instance->rowRange(*r); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_colRange_const_int_int(const cv::UMat* instance, int startcol, int endcol, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->colRange(startcol, endcol); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_colRange_int_int(cv::UMat* instance, int startcol, int endcol, Result* ocvrs_return) { + try { + cv::UMat ret = instance->colRange(startcol, endcol); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_colRange_const_const_RangeR(const cv::UMat* instance, const cv::Range* r, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->colRange(*r); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_colRange_const_RangeR(cv::UMat* instance, const cv::Range* r, Result* ocvrs_return) { + try { + cv::UMat ret = instance->colRange(*r); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_diag_const_int(const cv::UMat* instance, int d, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->diag(d); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_diag(cv::UMat* instance, Result* ocvrs_return) { + try { + cv::UMat ret = instance->diag(); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_diag_const(const cv::UMat* instance, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->diag(); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_diag_int(cv::UMat* instance, int d, Result* ocvrs_return) { + try { + cv::UMat ret = instance->diag(d); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_diag_const_UMatR_UMatUsageFlags(const cv::UMat* d, cv::UMatUsageFlags usageFlags, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::diag(*d, usageFlags); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_diag_const_UMatR(const cv::UMat* d, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::diag(*d); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_clone_const(const cv::UMat* instance, Result* ocvrs_return) { + try { + cv::UMat ret = instance->clone(); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_copyTo_const_const__OutputArrayR(const cv::UMat* instance, const cv::_OutputArray* m, ResultVoid* ocvrs_return) { + try { + instance->copyTo(*m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_copyTo_const_const__OutputArrayR_const__InputArrayR(const cv::UMat* instance, const cv::_OutputArray* m, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + instance->copyTo(*m, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_convertTo_const_const__OutputArrayR_int_double_double(const cv::UMat* instance, const cv::_OutputArray* m, int rtype, double alpha, double beta, ResultVoid* ocvrs_return) { + try { + instance->convertTo(*m, rtype, alpha, beta); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_convertTo_const_const__OutputArrayR_int(const cv::UMat* instance, const cv::_OutputArray* m, int rtype, ResultVoid* ocvrs_return) { + try { + instance->convertTo(*m, rtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_assignTo_const_UMatR_int(const cv::UMat* instance, cv::UMat* m, int type, ResultVoid* ocvrs_return) { + try { + instance->assignTo(*m, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_assignTo_const_UMatR(const cv::UMat* instance, cv::UMat* m, ResultVoid* ocvrs_return) { + try { + instance->assignTo(*m); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_operatorST_const_ScalarR(cv::UMat* instance, const cv::Scalar* s, ResultVoid* ocvrs_return) { + try { + instance->operator=(*s); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_setTo_const__InputArrayR_const__InputArrayR(cv::UMat* instance, const cv::_InputArray* value, const cv::_InputArray* mask, Result* ocvrs_return) { + try { + cv::UMat ret = instance->setTo(*value, *mask); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_setTo_const__InputArrayR(cv::UMat* instance, const cv::_InputArray* value, Result* ocvrs_return) { + try { + cv::UMat ret = instance->setTo(*value); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_reshape_const_int_int(const cv::UMat* instance, int cn, int rows, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->reshape(cn, rows); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_reshape_int(cv::UMat* instance, int cn, Result* ocvrs_return) { + try { + cv::UMat ret = instance->reshape(cn); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_reshape_const_int(const cv::UMat* instance, int cn, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->reshape(cn); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_reshape_int_int(cv::UMat* instance, int cn, int rows, Result* ocvrs_return) { + try { + cv::UMat ret = instance->reshape(cn, rows); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_reshape_const_int_int_const_intX(const cv::UMat* instance, int cn, int newndims, const int* newsz, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->reshape(cn, newndims, newsz); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_reshape_int_int_const_intX(cv::UMat* instance, int cn, int newndims, const int* newsz, Result* ocvrs_return) { + try { + cv::UMat ret = instance->reshape(cn, newndims, newsz); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_t_const(const cv::UMat* instance, Result* ocvrs_return) { + try { + cv::UMat ret = instance->t(); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_inv_const_int(const cv::UMat* instance, int method, Result* ocvrs_return) { + try { + cv::UMat ret = instance->inv(method); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_inv_const(const cv::UMat* instance, Result* ocvrs_return) { + try { + cv::UMat ret = instance->inv(); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_mul_const_const__InputArrayR_double(const cv::UMat* instance, const cv::_InputArray* m, double scale, Result* ocvrs_return) { + try { + cv::UMat ret = instance->mul(*m, scale); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_mul_const_const__InputArrayR(const cv::UMat* instance, const cv::_InputArray* m, Result* ocvrs_return) { + try { + cv::UMat ret = instance->mul(*m); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_dot_const_const__InputArrayR(const cv::UMat* instance, const cv::_InputArray* m, Result* ocvrs_return) { + try { + double ret = instance->dot(*m); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_zeros_int_int_int_UMatUsageFlags(int rows, int cols, int type, cv::UMatUsageFlags usageFlags, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::zeros(rows, cols, type, usageFlags); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_zeros_Size_int_UMatUsageFlags(cv::Size* size, int type, cv::UMatUsageFlags usageFlags, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::zeros(*size, type, usageFlags); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_zeros_int_const_intX_int_UMatUsageFlags(int ndims, const int* sz, int type, cv::UMatUsageFlags usageFlags, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::zeros(ndims, sz, type, usageFlags); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_zeros_int_int_int(int rows, int cols, int type, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::zeros(rows, cols, type); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_zeros_Size_int(cv::Size* size, int type, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::zeros(*size, type); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_zeros_int_const_intX_int(int ndims, const int* sz, int type, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::zeros(ndims, sz, type); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_ones_int_int_int_UMatUsageFlags(int rows, int cols, int type, cv::UMatUsageFlags usageFlags, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::ones(rows, cols, type, usageFlags); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_ones_Size_int_UMatUsageFlags(cv::Size* size, int type, cv::UMatUsageFlags usageFlags, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::ones(*size, type, usageFlags); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_ones_int_const_intX_int_UMatUsageFlags(int ndims, const int* sz, int type, cv::UMatUsageFlags usageFlags, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::ones(ndims, sz, type, usageFlags); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_ones_int_int_int(int rows, int cols, int type, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::ones(rows, cols, type); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_ones_Size_int(cv::Size* size, int type, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::ones(*size, type); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_ones_int_const_intX_int(int ndims, const int* sz, int type, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::ones(ndims, sz, type); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_eye_int_int_int_UMatUsageFlags(int rows, int cols, int type, cv::UMatUsageFlags usageFlags, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::eye(rows, cols, type, usageFlags); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_eye_Size_int_UMatUsageFlags(cv::Size* size, int type, cv::UMatUsageFlags usageFlags, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::eye(*size, type, usageFlags); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_eye_int_int_int(int rows, int cols, int type, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::eye(rows, cols, type); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_eye_Size_int(cv::Size* size, int type, Result* ocvrs_return) { + try { + cv::UMat ret = cv::UMat::eye(*size, type); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_create_int_int_int_UMatUsageFlags(cv::UMat* instance, int rows, int cols, int type, cv::UMatUsageFlags usageFlags, ResultVoid* ocvrs_return) { + try { + instance->create(rows, cols, type, usageFlags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_create_int_int_int(cv::UMat* instance, int rows, int cols, int type, ResultVoid* ocvrs_return) { + try { + instance->create(rows, cols, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_create_Size_int_UMatUsageFlags(cv::UMat* instance, cv::Size* size, int type, cv::UMatUsageFlags usageFlags, ResultVoid* ocvrs_return) { + try { + instance->create(*size, type, usageFlags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_create_Size_int(cv::UMat* instance, cv::Size* size, int type, ResultVoid* ocvrs_return) { + try { + instance->create(*size, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_create_int_const_intX_int_UMatUsageFlags(cv::UMat* instance, int ndims, const int* sizes, int type, cv::UMatUsageFlags usageFlags, ResultVoid* ocvrs_return) { + try { + instance->create(ndims, sizes, type, usageFlags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_create_int_const_intX_int(cv::UMat* instance, int ndims, const int* sizes, int type, ResultVoid* ocvrs_return) { + try { + instance->create(ndims, sizes, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_create_const_vectorLintGR_int_UMatUsageFlags(cv::UMat* instance, const std::vector* sizes, int type, cv::UMatUsageFlags usageFlags, ResultVoid* ocvrs_return) { + try { + instance->create(*sizes, type, usageFlags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_create_const_vectorLintGR_int(cv::UMat* instance, const std::vector* sizes, int type, ResultVoid* ocvrs_return) { + try { + instance->create(*sizes, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_addref(cv::UMat* instance, ResultVoid* ocvrs_return) { + try { + instance->addref(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_release(cv::UMat* instance, ResultVoid* ocvrs_return) { + try { + instance->release(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_deallocate(cv::UMat* instance, ResultVoid* ocvrs_return) { + try { + instance->deallocate(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_locateROI_const_SizeR_PointR(const cv::UMat* instance, cv::Size* wholeSize, cv::Point* ofs, ResultVoid* ocvrs_return) { + try { + instance->locateROI(*wholeSize, *ofs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_adjustROI_int_int_int_int(cv::UMat* instance, int dtop, int dbottom, int dleft, int dright, Result* ocvrs_return) { + try { + cv::UMat ret = instance->adjustROI(dtop, dbottom, dleft, dright); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_operator___const_Range_Range(const cv::UMat* instance, cv::Range* rowRange, cv::Range* colRange, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->operator()(*rowRange, *colRange); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_operator___Range_Range(cv::UMat* instance, cv::Range* rowRange, cv::Range* colRange, Result* ocvrs_return) { + try { + cv::UMat ret = instance->operator()(*rowRange, *colRange); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_operator___const_const_RectR(const cv::UMat* instance, const cv::Rect* roi, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->operator()(*roi); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_operator___const_RectR(cv::UMat* instance, const cv::Rect* roi, Result* ocvrs_return) { + try { + cv::UMat ret = instance->operator()(*roi); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_operator___const_const_vectorLRangeGR(const cv::UMat* instance, const std::vector* ranges, Result* ocvrs_return) { + try { + const cv::UMat ret = instance->operator()(*ranges); + Ok(new const cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_operator___const_vectorLRangeGR(cv::UMat* instance, const std::vector* ranges, Result* ocvrs_return) { + try { + cv::UMat ret = instance->operator()(*ranges); + Ok(new cv::UMat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +bool cv_UMat_isContinuous_const(const cv::UMat* instance) { + bool ret = instance->isContinuous(); + return ret; +} + +bool cv_UMat_isSubmatrix_const(const cv::UMat* instance) { + bool ret = instance->isSubmatrix(); + return ret; +} + +void cv_UMat_elemSize_const(const cv::UMat* instance, Result* ocvrs_return) { + try { + size_t ret = instance->elemSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +size_t cv_UMat_elemSize1_const(const cv::UMat* instance) { + size_t ret = instance->elemSize1(); + return ret; +} + +int cv_UMat_type_const(const cv::UMat* instance) { + int ret = instance->type(); + return ret; +} + +int cv_UMat_depth_const(const cv::UMat* instance) { + int ret = instance->depth(); + return ret; +} + +int cv_UMat_channels_const(const cv::UMat* instance) { + int ret = instance->channels(); + return ret; +} + +void cv_UMat_step1_const_int(const cv::UMat* instance, int i, Result* ocvrs_return) { + try { + size_t ret = instance->step1(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_step1_const(const cv::UMat* instance, Result* ocvrs_return) { + try { + size_t ret = instance->step1(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +bool cv_UMat_empty_const(const cv::UMat* instance) { + bool ret = instance->empty(); + return ret; +} + +size_t cv_UMat_total_const(const cv::UMat* instance) { + size_t ret = instance->total(); + return ret; +} + +void cv_UMat_checkVector_const_int_int_bool(const cv::UMat* instance, int elemChannels, int depth, bool requireContinuous, Result* ocvrs_return) { + try { + int ret = instance->checkVector(elemChannels, depth, requireContinuous); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_checkVector_const_int(const cv::UMat* instance, int elemChannels, Result* ocvrs_return) { + try { + int ret = instance->checkVector(elemChannels); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_operatorST_UMatRR(cv::UMat* instance, cv::UMat* m, ResultVoid* ocvrs_return) { + try { + instance->operator=(std::move(*m)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_handle_const_AccessFlag(const cv::UMat* instance, cv::AccessFlag accessFlags, Result* ocvrs_return) { + try { + void* ret = instance->handle(accessFlags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_ndoffset_const_size_tX(const cv::UMat* instance, size_t* ofs, ResultVoid* ocvrs_return) { + try { + instance->ndoffset(ofs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_updateContinuityFlag(cv::UMat* instance, ResultVoid* ocvrs_return) { + try { + instance->updateContinuityFlag(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMat_size_const(const cv::UMat* instance, Result* ocvrs_return) { + try { + cv::Size ret = instance->size(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +int cv_UMat_propFlags_const(const cv::UMat* instance) { + int ret = instance->flags; + return ret; +} + +void cv_UMat_propFlags_const_int(cv::UMat* instance, const int val) { + instance->flags = val; +} + +int cv_UMat_propDims_const(const cv::UMat* instance) { + int ret = instance->dims; + return ret; +} + +void cv_UMat_propDims_const_int(cv::UMat* instance, const int val) { + instance->dims = val; +} + +int cv_UMat_propRows_const(const cv::UMat* instance) { + int ret = instance->rows; + return ret; +} + +void cv_UMat_propRows_const_int(cv::UMat* instance, const int val) { + instance->rows = val; +} + +int cv_UMat_propCols_const(const cv::UMat* instance) { + int ret = instance->cols; + return ret; +} + +void cv_UMat_propCols_const_int(cv::UMat* instance, const int val) { + instance->cols = val; +} + +void cv_UMat_propUsageFlags_const(const cv::UMat* instance, cv::UMatUsageFlags* ocvrs_return) { + cv::UMatUsageFlags ret = instance->usageFlags; + *ocvrs_return = ret; +} + +void cv_UMat_propUsageFlags_const_UMatUsageFlags(cv::UMat* instance, const cv::UMatUsageFlags val) { + instance->usageFlags = val; +} + +cv::UMatData* cv_UMat_propU(cv::UMat* instance) { + cv::UMatData* ret = instance->u; + return new cv::UMatData(*ret); +} + +void cv_UMat_propU_UMatDataX(cv::UMat* instance, cv::UMatData* const val) { + instance->u = val; +} + +size_t cv_UMat_propOffset_const(const cv::UMat* instance) { + size_t ret = instance->offset; + return ret; +} + +void cv_UMat_propOffset_const_size_t(cv::UMat* instance, const size_t val) { + instance->offset = val; +} + +cv::MatSize* cv_UMat_propSize_const(const cv::UMat* instance) { + cv::MatSize ret = instance->size; + return new cv::MatSize(ret); +} + +void cv_UMat_propSize_const_MatSize(cv::UMat* instance, const cv::MatSize* val) { + instance->size = *val; +} + +cv::MatStep* cv_UMat_propStep_const(const cv::UMat* instance) { + cv::MatStep ret = instance->step; + return new cv::MatStep(ret); +} + +void cv_UMat_delete(cv::UMat* instance) { + delete instance; +} + +void cv_UMatData_lock(cv::UMatData* instance, ResultVoid* ocvrs_return) { + try { + instance->lock(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMatData_unlock(cv::UMatData* instance, ResultVoid* ocvrs_return) { + try { + instance->unlock(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMatData_hostCopyObsolete_const(const cv::UMatData* instance, Result* ocvrs_return) { + try { + bool ret = instance->hostCopyObsolete(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMatData_deviceCopyObsolete_const(const cv::UMatData* instance, Result* ocvrs_return) { + try { + bool ret = instance->deviceCopyObsolete(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMatData_deviceMemMapped_const(const cv::UMatData* instance, Result* ocvrs_return) { + try { + bool ret = instance->deviceMemMapped(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMatData_copyOnMap_const(const cv::UMatData* instance, Result* ocvrs_return) { + try { + bool ret = instance->copyOnMap(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMatData_tempUMat_const(const cv::UMatData* instance, Result* ocvrs_return) { + try { + bool ret = instance->tempUMat(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMatData_tempCopiedUMat_const(const cv::UMatData* instance, Result* ocvrs_return) { + try { + bool ret = instance->tempCopiedUMat(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMatData_markHostCopyObsolete_bool(cv::UMatData* instance, bool flag, ResultVoid* ocvrs_return) { + try { + instance->markHostCopyObsolete(flag); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMatData_markDeviceCopyObsolete_bool(cv::UMatData* instance, bool flag, ResultVoid* ocvrs_return) { + try { + instance->markDeviceCopyObsolete(flag); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_UMatData_markDeviceMemMapped_bool(cv::UMatData* instance, bool flag, ResultVoid* ocvrs_return) { + try { + instance->markDeviceMemMapped(flag); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +int cv_UMatData_propUrefcount_const(const cv::UMatData* instance) { + int ret = instance->urefcount; + return ret; +} + +void cv_UMatData_propUrefcount_const_int(cv::UMatData* instance, const int val) { + instance->urefcount = val; +} + +int cv_UMatData_propRefcount_const(const cv::UMatData* instance) { + int ret = instance->refcount; + return ret; +} + +void cv_UMatData_propRefcount_const_int(cv::UMatData* instance, const int val) { + instance->refcount = val; +} + +unsigned char* cv_UMatData_propData_const(const cv::UMatData* instance) { + unsigned char* const ret = instance->data; + return ret; +} + +unsigned char* cv_UMatData_propData(cv::UMatData* instance) { + unsigned char* ret = instance->data; + return ret; +} + +void cv_UMatData_propData_unsigned_charX(cv::UMatData* instance, unsigned char* const val) { + instance->data = val; +} + +unsigned char* cv_UMatData_propOrigdata_const(const cv::UMatData* instance) { + unsigned char* const ret = instance->origdata; + return ret; +} + +unsigned char* cv_UMatData_propOrigdata(cv::UMatData* instance) { + unsigned char* ret = instance->origdata; + return ret; +} + +void cv_UMatData_propOrigdata_unsigned_charX(cv::UMatData* instance, unsigned char* const val) { + instance->origdata = val; +} + +size_t cv_UMatData_propSize_const(const cv::UMatData* instance) { + size_t ret = instance->size; + return ret; +} + +void cv_UMatData_propSize_const_size_t(cv::UMatData* instance, const size_t val) { + instance->size = val; +} + +void cv_UMatData_propFlags_const(const cv::UMatData* instance, cv::UMatData::MemoryFlag* ocvrs_return) { + cv::UMatData::MemoryFlag ret = instance->flags; + *ocvrs_return = ret; +} + +void cv_UMatData_propFlags_const_MemoryFlag(cv::UMatData* instance, const cv::UMatData::MemoryFlag val) { + instance->flags = val; +} + +void* cv_UMatData_propHandle(cv::UMatData* instance) { + void* ret = instance->handle; + return ret; +} + +void cv_UMatData_propHandle_voidX(cv::UMatData* instance, void* const val) { + instance->handle = val; +} + +void* cv_UMatData_propUserdata(cv::UMatData* instance) { + void* ret = instance->userdata; + return ret; +} + +void cv_UMatData_propUserdata_voidX(cv::UMatData* instance, void* const val) { + instance->userdata = val; +} + +int cv_UMatData_propAllocatorFlags__const(const cv::UMatData* instance) { + int ret = instance->allocatorFlags_; + return ret; +} + +void cv_UMatData_propAllocatorFlags__const_int(cv::UMatData* instance, const int val) { + instance->allocatorFlags_ = val; +} + +int cv_UMatData_propMapcount_const(const cv::UMatData* instance) { + int ret = instance->mapcount; + return ret; +} + +void cv_UMatData_propMapcount_const_int(cv::UMatData* instance, const int val) { + instance->mapcount = val; +} + +cv::UMatData* cv_UMatData_propOriginalUMatData(cv::UMatData* instance) { + cv::UMatData* ret = instance->originalUMatData; + return new cv::UMatData(*ret); +} + +void cv_UMatData_propOriginalUMatData_UMatDataX(cv::UMatData* instance, cv::UMatData* const val) { + instance->originalUMatData = val; +} + +void cv_UMatData_delete(cv::UMatData* instance) { + delete instance; +} + +void cv__InputArray__InputArray(Result* ocvrs_return) { + try { + cv::_InputArray* ret = new cv::_InputArray(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray__InputArray_int_voidX(int _flags, void* _obj, Result* ocvrs_return) { + try { + cv::_InputArray* ret = new cv::_InputArray(_flags, _obj); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray__InputArray_const_vectorLboolGR(const std::vector* vec, Result* ocvrs_return) { + try { + const cv::_InputArray* ret = new const cv::_InputArray(*vec); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray__InputArray_const_doubleR(const double* val, Result* ocvrs_return) { + try { + const cv::_InputArray* ret = new const cv::_InputArray(*val); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_getFlags_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + int ret = instance->getFlags(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_getObj_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + void* ret = instance->getObj(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_getSz_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + cv::Size ret = instance->getSz(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_kind_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + cv::_InputArray::KindFlag ret = instance->kind(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_dims_const_int(const cv::_InputArray* instance, int i, Result* ocvrs_return) { + try { + int ret = instance->dims(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_dims_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + int ret = instance->dims(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_cols_const_int(const cv::_InputArray* instance, int i, Result* ocvrs_return) { + try { + int ret = instance->cols(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_cols_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + int ret = instance->cols(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_rows_const_int(const cv::_InputArray* instance, int i, Result* ocvrs_return) { + try { + int ret = instance->rows(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_rows_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + int ret = instance->rows(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_size_const_int(const cv::_InputArray* instance, int i, Result* ocvrs_return) { + try { + cv::Size ret = instance->size(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_size_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + cv::Size ret = instance->size(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_sizend_const_intX_int(const cv::_InputArray* instance, int* sz, int i, Result* ocvrs_return) { + try { + int ret = instance->sizend(sz, i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_sizend_const_intX(const cv::_InputArray* instance, int* sz, Result* ocvrs_return) { + try { + int ret = instance->sizend(sz); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_sameSize_const_const__InputArrayR(const cv::_InputArray* instance, const cv::_InputArray* arr, Result* ocvrs_return) { + try { + bool ret = instance->sameSize(*arr); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_total_const_int(const cv::_InputArray* instance, int i, Result* ocvrs_return) { + try { + size_t ret = instance->total(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_total_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + size_t ret = instance->total(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_type_const_int(const cv::_InputArray* instance, int i, Result* ocvrs_return) { + try { + int ret = instance->type(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_type_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + int ret = instance->type(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_depth_const_int(const cv::_InputArray* instance, int i, Result* ocvrs_return) { + try { + int ret = instance->depth(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_depth_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + int ret = instance->depth(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_channels_const_int(const cv::_InputArray* instance, int i, Result* ocvrs_return) { + try { + int ret = instance->channels(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_channels_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + int ret = instance->channels(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_isContinuous_const_int(const cv::_InputArray* instance, int i, Result* ocvrs_return) { + try { + bool ret = instance->isContinuous(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_isContinuous_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->isContinuous(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_isSubmatrix_const_int(const cv::_InputArray* instance, int i, Result* ocvrs_return) { + try { + bool ret = instance->isSubmatrix(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_isSubmatrix_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->isSubmatrix(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_empty_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_empty_const_int(const cv::_InputArray* instance, int i, Result* ocvrs_return) { + try { + bool ret = instance->empty(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_offset_const_int(const cv::_InputArray* instance, int i, Result* ocvrs_return) { + try { + size_t ret = instance->offset(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_offset_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + size_t ret = instance->offset(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_step_const_int(const cv::_InputArray* instance, int i, Result* ocvrs_return) { + try { + size_t ret = instance->step(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_step_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + size_t ret = instance->step(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_isMat_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->isMat(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_isUMat_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->isUMat(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_isMatVector_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->isMatVector(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_isUMatVector_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->isUMatVector(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_isMatx_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->isMatx(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_isVector_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->isVector(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_isGpuMat_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->isGpuMat(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_isGpuMatVector_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->isGpuMatVector(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_isGpuMatND_const(const cv::_InputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->isGpuMatND(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray__InputArray_const_unsigned_charX_int(const unsigned char* vec, int n, Result* ocvrs_return) { + try { + const cv::_InputArray* ret = new const cv::_InputArray(vec, n); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputArray_delete(cv::_InputArray* instance) { + delete instance; +} + +void cv__InputOutputArray__InputOutputArray(Result* ocvrs_return) { + try { + cv::_InputOutputArray* ret = new cv::_InputOutputArray(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__InputOutputArray__InputOutputArray_int_voidX(int _flags, void* _obj, Result* ocvrs_return) { + try { + cv::_InputOutputArray* ret = new cv::_InputOutputArray(_flags, _obj); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::_InputArray* cv__InputOutputArray_to__InputArray(cv::_InputOutputArray* instance) { + return dynamic_cast(instance); +} + +cv::_OutputArray* cv__InputOutputArray_to__OutputArray(cv::_InputOutputArray* instance) { + return dynamic_cast(instance); +} + +void cv__InputOutputArray_delete(cv::_InputOutputArray* instance) { + delete instance; +} + +void cv__OutputArray_fixedSize_const(const cv::_OutputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->fixedSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__OutputArray_fixedType_const(const cv::_OutputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->fixedType(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__OutputArray_needed_const(const cv::_OutputArray* instance, Result* ocvrs_return) { + try { + bool ret = instance->needed(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__OutputArray_create_const_Size_int_int_bool_DepthMask(const cv::_OutputArray* instance, cv::Size* sz, int type, int i, bool allowTransposed, cv::_OutputArray::DepthMask fixedDepthMask, ResultVoid* ocvrs_return) { + try { + instance->create(*sz, type, i, allowTransposed, fixedDepthMask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__OutputArray_create_const_Size_int(const cv::_OutputArray* instance, cv::Size* sz, int type, ResultVoid* ocvrs_return) { + try { + instance->create(*sz, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__OutputArray_create_const_int_int_int_int_bool_DepthMask(const cv::_OutputArray* instance, int rows, int cols, int type, int i, bool allowTransposed, cv::_OutputArray::DepthMask fixedDepthMask, ResultVoid* ocvrs_return) { + try { + instance->create(rows, cols, type, i, allowTransposed, fixedDepthMask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__OutputArray_create_const_int_int_int(const cv::_OutputArray* instance, int rows, int cols, int type, ResultVoid* ocvrs_return) { + try { + instance->create(rows, cols, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__OutputArray_create_const_int_const_intX_int_int_bool_DepthMask(const cv::_OutputArray* instance, int dims, const int* size, int type, int i, bool allowTransposed, cv::_OutputArray::DepthMask fixedDepthMask, ResultVoid* ocvrs_return) { + try { + instance->create(dims, size, type, i, allowTransposed, fixedDepthMask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__OutputArray_create_const_int_const_intX_int(const cv::_OutputArray* instance, int dims, const int* size, int type, ResultVoid* ocvrs_return) { + try { + instance->create(dims, size, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__OutputArray_createSameSize_const_const__InputArrayR_int(const cv::_OutputArray* instance, const cv::_InputArray* arr, int mtype, ResultVoid* ocvrs_return) { + try { + instance->createSameSize(*arr, mtype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__OutputArray_release_const(const cv::_OutputArray* instance, ResultVoid* ocvrs_return) { + try { + instance->release(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__OutputArray_clear_const(const cv::_OutputArray* instance, ResultVoid* ocvrs_return) { + try { + instance->clear(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__OutputArray_setTo_const_const__InputArrayR_const__InputArrayR(const cv::_OutputArray* instance, const cv::_InputArray* value, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + instance->setTo(*value, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv__OutputArray_setTo_const_const__InputArrayR(const cv::_OutputArray* instance, const cv::_InputArray* value, ResultVoid* ocvrs_return) { + try { + instance->setTo(*value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::_InputArray* cv__OutputArray_to__InputArray(cv::_OutputArray* instance) { + return dynamic_cast(instance); +} + +void cv__OutputArray_delete(cv::_OutputArray* instance) { + delete instance; +} + +cv::detail::CheckContext* cv_detail_CheckContext_implicitClone_const(const cv::detail::CheckContext* instance) { + return new cv::detail::CheckContext(*instance); +} + +cv::detail::CheckContext* cv_detail_CheckContext_defaultNew_const() { + cv::detail::CheckContext* ret = new cv::detail::CheckContext(); + return ret; +} + +void* cv_detail_CheckContext_propFunc_const(const cv::detail::CheckContext* instance) { + const char* ret = instance->func; + return ocvrs_create_string(ret); +} + +void* cv_detail_CheckContext_propFile_const(const cv::detail::CheckContext* instance) { + const char* ret = instance->file; + return ocvrs_create_string(ret); +} + +int cv_detail_CheckContext_propLine_const(const cv::detail::CheckContext* instance) { + int ret = instance->line; + return ret; +} + +void cv_detail_CheckContext_propLine_const_int(cv::detail::CheckContext* instance, const int val) { + instance->line = val; +} + +void cv_detail_CheckContext_propTestOp_const(const cv::detail::CheckContext* instance, cv::detail::TestOp* ocvrs_return) { + cv::detail::TestOp ret = instance->testOp; + *ocvrs_return = ret; +} + +void cv_detail_CheckContext_propTestOp_const_TestOp(cv::detail::CheckContext* instance, const cv::detail::TestOp val) { + instance->testOp = val; +} + +void* cv_detail_CheckContext_propMessage_const(const cv::detail::CheckContext* instance) { + const char* ret = instance->message; + return ocvrs_create_string(ret); +} + +void* cv_detail_CheckContext_propP1_str_const(const cv::detail::CheckContext* instance) { + const char* ret = instance->p1_str; + return ocvrs_create_string(ret); +} + +void* cv_detail_CheckContext_propP2_str_const(const cv::detail::CheckContext* instance) { + const char* ret = instance->p2_str; + return ocvrs_create_string(ret); +} + +void cv_detail_CheckContext_delete(cv::detail::CheckContext* instance) { + delete instance; +} + +void cv_hfloat_hfloat(Result* ocvrs_return) { + try { + cv::hfloat ret; + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_hfloat_hfloat_float(float x, Result* ocvrs_return) { + try { + cv::hfloat ret(x); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_hfloat_operator_float_const(const cv::hfloat* instance, Result* ocvrs_return) { + try { + float ret = instance->operator float(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_instr_NodeData_NodeData_const_charX_const_charX_int_voidX_bool_TYPE_IMPL(const char* funName, const char* fileName, int lineNum, void* retAddress, bool alwaysExpand, cv::instr::TYPE instrType, cv::instr::IMPL implType, Result* ocvrs_return) { + try { + cv::instr::NodeData* ret = new cv::instr::NodeData(funName, fileName, lineNum, retAddress, alwaysExpand, instrType, implType); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_instr_NodeData_NodeData(Result* ocvrs_return) { + try { + cv::instr::NodeData* ret = new cv::instr::NodeData(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_instr_NodeData_NodeData_NodeDataR(cv::instr::NodeData* ref, Result* ocvrs_return) { + try { + cv::instr::NodeData* ret = new cv::instr::NodeData(*ref); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_instr_NodeData_operatorST_const_NodeDataR(cv::instr::NodeData* instance, const cv::instr::NodeData* unnamed, ResultVoid* ocvrs_return) { + try { + instance->operator=(*unnamed); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_instr_NodeData_getTotalMs_const(const cv::instr::NodeData* instance, Result* ocvrs_return) { + try { + double ret = instance->getTotalMs(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_instr_NodeData_getMeanMs_const(const cv::instr::NodeData* instance, Result* ocvrs_return) { + try { + double ret = instance->getMeanMs(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void* cv_instr_NodeData_propM_funName_const(const cv::instr::NodeData* instance) { + cv::String ret = instance->m_funName; + return ocvrs_create_string(ret.c_str()); +} + +void cv_instr_NodeData_propM_funName_const_String(cv::instr::NodeData* instance, const char* val) { + instance->m_funName = std::string(val); +} + +void cv_instr_NodeData_propM_instrType_const(const cv::instr::NodeData* instance, cv::instr::TYPE* ocvrs_return) { + cv::instr::TYPE ret = instance->m_instrType; + *ocvrs_return = ret; +} + +void cv_instr_NodeData_propM_instrType_const_TYPE(cv::instr::NodeData* instance, const cv::instr::TYPE val) { + instance->m_instrType = val; +} + +void cv_instr_NodeData_propM_implType_const(const cv::instr::NodeData* instance, cv::instr::IMPL* ocvrs_return) { + cv::instr::IMPL ret = instance->m_implType; + *ocvrs_return = ret; +} + +void cv_instr_NodeData_propM_implType_const_IMPL(cv::instr::NodeData* instance, const cv::instr::IMPL val) { + instance->m_implType = val; +} + +void* cv_instr_NodeData_propM_fileName_const(const cv::instr::NodeData* instance) { + const char* ret = instance->m_fileName; + return ocvrs_create_string(ret); +} + +int cv_instr_NodeData_propM_lineNum_const(const cv::instr::NodeData* instance) { + int ret = instance->m_lineNum; + return ret; +} + +void cv_instr_NodeData_propM_lineNum_const_int(cv::instr::NodeData* instance, const int val) { + instance->m_lineNum = val; +} + +void* cv_instr_NodeData_propM_retAddress(cv::instr::NodeData* instance) { + void* ret = instance->m_retAddress; + return ret; +} + +void cv_instr_NodeData_propM_retAddress_voidX(cv::instr::NodeData* instance, void* const val) { + instance->m_retAddress = val; +} + +bool cv_instr_NodeData_propM_alwaysExpand_const(const cv::instr::NodeData* instance) { + bool ret = instance->m_alwaysExpand; + return ret; +} + +void cv_instr_NodeData_propM_alwaysExpand_const_bool(cv::instr::NodeData* instance, const bool val) { + instance->m_alwaysExpand = val; +} + +bool cv_instr_NodeData_propM_funError_const(const cv::instr::NodeData* instance) { + bool ret = instance->m_funError; + return ret; +} + +void cv_instr_NodeData_propM_funError_const_bool(cv::instr::NodeData* instance, const bool val) { + instance->m_funError = val; +} + +int cv_instr_NodeData_propM_counter_const(const cv::instr::NodeData* instance) { + int ret = instance->m_counter; + return ret; +} + +void cv_instr_NodeData_propM_counter_const_int(cv::instr::NodeData* instance, const int val) { + instance->m_counter = val; +} + +uint64_t cv_instr_NodeData_propM_ticksTotal_const(const cv::instr::NodeData* instance) { + uint64_t ret = instance->m_ticksTotal; + return ret; +} + +void cv_instr_NodeData_propM_ticksTotal_const_uint64_t(cv::instr::NodeData* instance, const uint64_t val) { + instance->m_ticksTotal = val; +} + +int cv_instr_NodeData_propM_threads_const(const cv::instr::NodeData* instance) { + int ret = instance->m_threads; + return ret; +} + +void cv_instr_NodeData_propM_threads_const_int(cv::instr::NodeData* instance, const int val) { + instance->m_threads = val; +} + +void cv_instr_NodeData_delete(cv::instr::NodeData* instance) { + delete instance; +} + +void cv_internal_WriteStructContext_WriteStructContext_FileStorageR_const_StringR_int_const_StringR(cv::FileStorage* _fs, const char* name, int flags, const char* typeName, Result* ocvrs_return) { + try { + cv::internal::WriteStructContext* ret = new cv::internal::WriteStructContext(*_fs, std::string(name), flags, std::string(typeName)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_internal_WriteStructContext_WriteStructContext_FileStorageR_const_StringR_int(cv::FileStorage* _fs, const char* name, int flags, Result* ocvrs_return) { + try { + cv::internal::WriteStructContext* ret = new cv::internal::WriteStructContext(*_fs, std::string(name), flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_internal_WriteStructContext_delete(cv::internal::WriteStructContext* instance) { + delete instance; +} + +void cv_ocl_Context_operatorST_const_ContextR(cv::ocl::Context* instance, const cv::ocl::Context* c, ResultVoid* ocvrs_return) { + try { + instance->operator=(*c); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_operatorST_ContextRR(cv::ocl::Context* instance, cv::ocl::Context* c) { + instance->operator=(std::move(*c)); +} + +void cv_ocl_Context_create(cv::ocl::Context* instance, Result* ocvrs_return) { + try { + bool ret = instance->create(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_create_int(cv::ocl::Context* instance, int dtype, Result* ocvrs_return) { + try { + bool ret = instance->create(dtype); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_ndevices_const(const cv::ocl::Context* instance, Result* ocvrs_return) { + try { + size_t ret = instance->ndevices(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_device_const_size_t(const cv::ocl::Context* instance, size_t idx, Result* ocvrs_return) { + try { + cv::ocl::Device ret = instance->device(idx); + Ok(new cv::ocl::Device(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_getDefault_bool(bool initialize, Result* ocvrs_return) { + try { + cv::ocl::Context ret = cv::ocl::Context::getDefault(initialize); + Ok(new cv::ocl::Context(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_getDefault(Result* ocvrs_return) { + try { + cv::ocl::Context ret = cv::ocl::Context::getDefault(); + Ok(new cv::ocl::Context(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_ptr_const(const cv::ocl::Context* instance, Result* ocvrs_return) { + try { + void* ret = instance->ptr(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_getOpenCLContextProperty_const_int(const cv::ocl::Context* instance, int propertyId, Result* ocvrs_return) { + try { + void* ret = instance->getOpenCLContextProperty(propertyId); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_useSVM_const(const cv::ocl::Context* instance, Result* ocvrs_return) { + try { + bool ret = instance->useSVM(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_setUseSVM_bool(cv::ocl::Context* instance, bool enabled, ResultVoid* ocvrs_return) { + try { + instance->setUseSVM(enabled); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_fromHandle_voidX(void* context, Result* ocvrs_return) { + try { + cv::ocl::Context ret = cv::ocl::Context::fromHandle(context); + Ok(new cv::ocl::Context(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_fromDevice_const_DeviceR(const cv::ocl::Device* device, Result* ocvrs_return) { + try { + cv::ocl::Context ret = cv::ocl::Context::fromDevice(*device); + Ok(new cv::ocl::Context(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_create_const_stringR(const char* configuration, Result* ocvrs_return) { + try { + cv::ocl::Context ret = cv::ocl::Context::create(std::string(configuration)); + Ok(new cv::ocl::Context(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_release(cv::ocl::Context* instance, ResultVoid* ocvrs_return) { + try { + instance->release(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_empty_const(const cv::ocl::Context* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Context_delete(cv::ocl::Context* instance) { + delete instance; +} + +cv::ocl::Context::UserContext* cv_ocl_Context_UserContext_defaultNew_const() { + cv::ocl::Context::UserContext* ret = new cv::ocl::Context::UserContext(); + return ret; +} + +void cv_ocl_Context_UserContext_delete(cv::ocl::Context::UserContext* instance) { + delete instance; +} + +void cv_ocl_Device_operatorST_const_DeviceR(cv::ocl::Device* instance, const cv::ocl::Device* d, ResultVoid* ocvrs_return) { + try { + instance->operator=(*d); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_operatorST_DeviceRR(cv::ocl::Device* instance, cv::ocl::Device* d) { + instance->operator=(std::move(*d)); +} + +void cv_ocl_Device_set_voidX(cv::ocl::Device* instance, void* d, ResultVoid* ocvrs_return) { + try { + instance->set(d); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_name_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->name(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_extensions_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->extensions(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_isExtensionSupported_const_const_StringR(const cv::ocl::Device* instance, const char* extensionName, Result* ocvrs_return) { + try { + bool ret = instance->isExtensionSupported(std::string(extensionName)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_version_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->version(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_vendorName_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->vendorName(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_OpenCL_C_Version_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->OpenCL_C_Version(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_OpenCLVersion_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->OpenCLVersion(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_deviceVersionMajor_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->deviceVersionMajor(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_deviceVersionMinor_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->deviceVersionMinor(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_driverVersion_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->driverVersion(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_ptr_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + void* ret = instance->ptr(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_type_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->type(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_addressBits_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->addressBits(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_available_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->available(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_compilerAvailable_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->compilerAvailable(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_linkerAvailable_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->linkerAvailable(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_doubleFPConfig_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->doubleFPConfig(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_singleFPConfig_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->singleFPConfig(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_halfFPConfig_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->halfFPConfig(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_hasFP64_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->hasFP64(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_hasFP16_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->hasFP16(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_endianLittle_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->endianLittle(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_errorCorrectionSupport_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->errorCorrectionSupport(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_executionCapabilities_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->executionCapabilities(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_globalMemCacheSize_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->globalMemCacheSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_globalMemCacheType_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->globalMemCacheType(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_globalMemCacheLineSize_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->globalMemCacheLineSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_globalMemSize_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->globalMemSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_localMemSize_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->localMemSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_localMemType_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->localMemType(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_hostUnifiedMemory_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->hostUnifiedMemory(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_imageSupport_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->imageSupport(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_imageFromBufferSupport_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->imageFromBufferSupport(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_imagePitchAlignment_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + unsigned int ret = instance->imagePitchAlignment(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_imageBaseAddressAlignment_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + unsigned int ret = instance->imageBaseAddressAlignment(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_intelSubgroupsSupport_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->intelSubgroupsSupport(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_image2DMaxWidth_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->image2DMaxWidth(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_image2DMaxHeight_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->image2DMaxHeight(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_image3DMaxWidth_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->image3DMaxWidth(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_image3DMaxHeight_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->image3DMaxHeight(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_image3DMaxDepth_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->image3DMaxDepth(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_imageMaxBufferSize_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->imageMaxBufferSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_imageMaxArraySize_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->imageMaxArraySize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_vendorID_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->vendorID(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_isAMD_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->isAMD(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_isIntel_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->isIntel(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_isNVidia_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->isNVidia(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_maxClockFrequency_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->maxClockFrequency(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_maxComputeUnits_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->maxComputeUnits(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_maxConstantArgs_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->maxConstantArgs(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_maxConstantBufferSize_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->maxConstantBufferSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_maxMemAllocSize_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->maxMemAllocSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_maxParameterSize_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->maxParameterSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_maxReadImageArgs_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->maxReadImageArgs(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_maxWriteImageArgs_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->maxWriteImageArgs(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_maxSamplers_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->maxSamplers(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_maxWorkGroupSize_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->maxWorkGroupSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_maxWorkItemDims_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->maxWorkItemDims(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_maxWorkItemSizes_const_size_tX(const cv::ocl::Device* instance, size_t* unnamed, ResultVoid* ocvrs_return) { + try { + instance->maxWorkItemSizes(unnamed); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_memBaseAddrAlign_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->memBaseAddrAlign(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_nativeVectorWidthChar_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->nativeVectorWidthChar(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_nativeVectorWidthShort_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->nativeVectorWidthShort(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_nativeVectorWidthInt_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->nativeVectorWidthInt(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_nativeVectorWidthLong_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->nativeVectorWidthLong(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_nativeVectorWidthFloat_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->nativeVectorWidthFloat(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_nativeVectorWidthDouble_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->nativeVectorWidthDouble(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_nativeVectorWidthHalf_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->nativeVectorWidthHalf(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_preferredVectorWidthChar_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->preferredVectorWidthChar(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_preferredVectorWidthShort_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->preferredVectorWidthShort(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_preferredVectorWidthInt_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->preferredVectorWidthInt(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_preferredVectorWidthLong_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->preferredVectorWidthLong(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_preferredVectorWidthFloat_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->preferredVectorWidthFloat(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_preferredVectorWidthDouble_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->preferredVectorWidthDouble(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_preferredVectorWidthHalf_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + int ret = instance->preferredVectorWidthHalf(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_printfBufferSize_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->printfBufferSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_profilingTimerResolution_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + size_t ret = instance->profilingTimerResolution(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_getDefault(Result* ocvrs_return) { + try { + const cv::ocl::Device ret = cv::ocl::Device::getDefault(); + Ok(new const cv::ocl::Device(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_fromHandle_voidX(void* d, Result* ocvrs_return) { + try { + cv::ocl::Device ret = cv::ocl::Device::fromHandle(d); + Ok(new cv::ocl::Device(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Device_empty_const(const cv::ocl::Device* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::ocl::Device* cv_ocl_Device_implicitClone_const(const cv::ocl::Device* instance) { + return new cv::ocl::Device(*instance); +} + +void cv_ocl_Device_delete(cv::ocl::Device* instance) { + delete instance; +} + +void cv_ocl_Image2D_operatorST_const_Image2DR(cv::ocl::Image2D* instance, const cv::ocl::Image2D* i, ResultVoid* ocvrs_return) { + try { + instance->operator=(*i); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Image2D_operatorST_Image2DRR(cv::ocl::Image2D* instance, cv::ocl::Image2D* unnamed) { + instance->operator=(std::move(*unnamed)); +} + +void cv_ocl_Image2D_canCreateAlias_const_UMatR(const cv::UMat* u, Result* ocvrs_return) { + try { + bool ret = cv::ocl::Image2D::canCreateAlias(*u); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Image2D_isFormatSupported_int_int_bool(int depth, int cn, bool norm, Result* ocvrs_return) { + try { + bool ret = cv::ocl::Image2D::isFormatSupported(depth, cn, norm); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Image2D_ptr_const(const cv::ocl::Image2D* instance, Result* ocvrs_return) { + try { + void* ret = instance->ptr(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Image2D_delete(cv::ocl::Image2D* instance) { + delete instance; +} + +void cv_ocl_Kernel_operatorST_const_KernelR(cv::ocl::Kernel* instance, const cv::ocl::Kernel* k, ResultVoid* ocvrs_return) { + try { + instance->operator=(*k); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_operatorST_KernelRR(cv::ocl::Kernel* instance, cv::ocl::Kernel* k) { + instance->operator=(std::move(*k)); +} + +void cv_ocl_Kernel_empty_const(const cv::ocl::Kernel* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_set_int_const_voidX_size_t(cv::ocl::Kernel* instance, int i, const void* value, size_t sz, Result* ocvrs_return) { + try { + int ret = instance->set(i, value, sz); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_set_int_const_UMatR(cv::ocl::Kernel* instance, int i, const cv::UMat* m, Result* ocvrs_return) { + try { + int ret = instance->set(i, *m); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_set_int_const_KernelArgR(cv::ocl::Kernel* instance, int i, const cv::ocl::KernelArg* arg, Result* ocvrs_return) { + try { + int ret = instance->set(i, *arg); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_run_int_size_tX_size_tX_bool_const_QueueR(cv::ocl::Kernel* instance, int dims, size_t* globalsize, size_t* localsize, bool sync, const cv::ocl::Queue* q, Result* ocvrs_return) { + try { + bool ret = instance->run(dims, globalsize, localsize, sync, *q); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_run_int_size_tX_size_tX_bool(cv::ocl::Kernel* instance, int dims, size_t* globalsize, size_t* localsize, bool sync, Result* ocvrs_return) { + try { + bool ret = instance->run(dims, globalsize, localsize, sync); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_run__int_size_tX_size_tX_bool_const_QueueR(cv::ocl::Kernel* instance, int dims, size_t* globalsize, size_t* localsize, bool sync, const cv::ocl::Queue* q, Result* ocvrs_return) { + try { + bool ret = instance->run_(dims, globalsize, localsize, sync, *q); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_run__int_size_tX_size_tX_bool(cv::ocl::Kernel* instance, int dims, size_t* globalsize, size_t* localsize, bool sync, Result* ocvrs_return) { + try { + bool ret = instance->run_(dims, globalsize, localsize, sync); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_runTask_bool_const_QueueR(cv::ocl::Kernel* instance, bool sync, const cv::ocl::Queue* q, Result* ocvrs_return) { + try { + bool ret = instance->runTask(sync, *q); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_runTask_bool(cv::ocl::Kernel* instance, bool sync, Result* ocvrs_return) { + try { + bool ret = instance->runTask(sync); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_runProfiling_int_size_tX_size_tX_const_QueueR(cv::ocl::Kernel* instance, int dims, size_t* globalsize, size_t* localsize, const cv::ocl::Queue* q, Result* ocvrs_return) { + try { + int64_t ret = instance->runProfiling(dims, globalsize, localsize, *q); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_runProfiling_int_size_tX_size_tX(cv::ocl::Kernel* instance, int dims, size_t* globalsize, size_t* localsize, Result* ocvrs_return) { + try { + int64_t ret = instance->runProfiling(dims, globalsize, localsize); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_workGroupSize_const(const cv::ocl::Kernel* instance, Result* ocvrs_return) { + try { + size_t ret = instance->workGroupSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_preferedWorkGroupSizeMultiple_const(const cv::ocl::Kernel* instance, Result* ocvrs_return) { + try { + size_t ret = instance->preferedWorkGroupSizeMultiple(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_compileWorkGroupSize_const_size_tX(const cv::ocl::Kernel* instance, size_t* wsz, Result* ocvrs_return) { + try { + bool ret = instance->compileWorkGroupSize(wsz); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_localMemSize_const(const cv::ocl::Kernel* instance, Result* ocvrs_return) { + try { + size_t ret = instance->localMemSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_ptr_const(const cv::ocl::Kernel* instance, Result* ocvrs_return) { + try { + void* ret = instance->ptr(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Kernel_delete(cv::ocl::Kernel* instance) { + delete instance; +} + +void cv_ocl_KernelArg_KernelArg_int_UMatX_int_int_const_voidX_size_t(int _flags, cv::UMat* _m, int wscale, int iwscale, const void* _obj, size_t _sz, Result* ocvrs_return) { + try { + cv::ocl::KernelArg* ret = new cv::ocl::KernelArg(_flags, _m, wscale, iwscale, _obj, _sz); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_KernelArg_int_UMatX(int _flags, cv::UMat* _m, Result* ocvrs_return) { + try { + cv::ocl::KernelArg* ret = new cv::ocl::KernelArg(_flags, _m); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::ocl::KernelArg* cv_ocl_KernelArg_KernelArg() { + cv::ocl::KernelArg* ret = new cv::ocl::KernelArg(); + return ret; +} + +void cv_ocl_KernelArg_Local_size_t(size_t localMemSize, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::Local(localMemSize); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_PtrWriteOnly_const_UMatR(const cv::UMat* m, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::PtrWriteOnly(*m); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_PtrReadOnly_const_UMatR(const cv::UMat* m, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::PtrReadOnly(*m); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_PtrReadWrite_const_UMatR(const cv::UMat* m, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::PtrReadWrite(*m); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_ReadWrite_const_UMatR_int_int(const cv::UMat* m, int wscale, int iwscale, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::ReadWrite(*m, wscale, iwscale); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_ReadWrite_const_UMatR(const cv::UMat* m, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::ReadWrite(*m); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_ReadWriteNoSize_const_UMatR_int_int(const cv::UMat* m, int wscale, int iwscale, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::ReadWriteNoSize(*m, wscale, iwscale); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_ReadWriteNoSize_const_UMatR(const cv::UMat* m, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::ReadWriteNoSize(*m); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_ReadOnly_const_UMatR_int_int(const cv::UMat* m, int wscale, int iwscale, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::ReadOnly(*m, wscale, iwscale); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_ReadOnly_const_UMatR(const cv::UMat* m, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::ReadOnly(*m); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_WriteOnly_const_UMatR_int_int(const cv::UMat* m, int wscale, int iwscale, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::WriteOnly(*m, wscale, iwscale); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_WriteOnly_const_UMatR(const cv::UMat* m, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::WriteOnly(*m); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_ReadOnlyNoSize_const_UMatR_int_int(const cv::UMat* m, int wscale, int iwscale, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::ReadOnlyNoSize(*m, wscale, iwscale); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_ReadOnlyNoSize_const_UMatR(const cv::UMat* m, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::ReadOnlyNoSize(*m); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_WriteOnlyNoSize_const_UMatR_int_int(const cv::UMat* m, int wscale, int iwscale, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::WriteOnlyNoSize(*m, wscale, iwscale); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_WriteOnlyNoSize_const_UMatR(const cv::UMat* m, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::WriteOnlyNoSize(*m); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_KernelArg_Constant_const_MatR(const cv::Mat* m, Result* ocvrs_return) { + try { + cv::ocl::KernelArg ret = cv::ocl::KernelArg::Constant(*m); + Ok(new cv::ocl::KernelArg(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +int cv_ocl_KernelArg_propFlags_const(const cv::ocl::KernelArg* instance) { + int ret = instance->flags; + return ret; +} + +void cv_ocl_KernelArg_propFlags_const_int(cv::ocl::KernelArg* instance, const int val) { + instance->flags = val; +} + +cv::UMat* cv_ocl_KernelArg_propM(cv::ocl::KernelArg* instance) { + cv::UMat* ret = instance->m; + return new cv::UMat(*ret); +} + +void cv_ocl_KernelArg_propM_UMatX(cv::ocl::KernelArg* instance, cv::UMat* const val) { + instance->m = val; +} + +const void* cv_ocl_KernelArg_propObj_const(const cv::ocl::KernelArg* instance) { + const void* ret = instance->obj; + return ret; +} + +size_t cv_ocl_KernelArg_propSz_const(const cv::ocl::KernelArg* instance) { + size_t ret = instance->sz; + return ret; +} + +void cv_ocl_KernelArg_propSz_const_size_t(cv::ocl::KernelArg* instance, const size_t val) { + instance->sz = val; +} + +int cv_ocl_KernelArg_propWscale_const(const cv::ocl::KernelArg* instance) { + int ret = instance->wscale; + return ret; +} + +void cv_ocl_KernelArg_propWscale_const_int(cv::ocl::KernelArg* instance, const int val) { + instance->wscale = val; +} + +int cv_ocl_KernelArg_propIwscale_const(const cv::ocl::KernelArg* instance) { + int ret = instance->iwscale; + return ret; +} + +void cv_ocl_KernelArg_propIwscale_const_int(cv::ocl::KernelArg* instance, const int val) { + instance->iwscale = val; +} + +void cv_ocl_KernelArg_delete(cv::ocl::KernelArg* instance) { + delete instance; +} + +cv::ocl::OpenCLExecutionContext* cv_ocl_OpenCLExecutionContext_OpenCLExecutionContext() { + cv::ocl::OpenCLExecutionContext* ret = new cv::ocl::OpenCLExecutionContext(); + return ret; +} + +cv::ocl::OpenCLExecutionContext* cv_ocl_OpenCLExecutionContext_OpenCLExecutionContext_const_OpenCLExecutionContextR(const cv::ocl::OpenCLExecutionContext* unnamed) { + cv::ocl::OpenCLExecutionContext* ret = new cv::ocl::OpenCLExecutionContext(*unnamed); + return ret; +} + +cv::ocl::OpenCLExecutionContext* cv_ocl_OpenCLExecutionContext_OpenCLExecutionContext_OpenCLExecutionContextRR(cv::ocl::OpenCLExecutionContext* unnamed) { + cv::ocl::OpenCLExecutionContext* ret = new cv::ocl::OpenCLExecutionContext(std::move(*unnamed)); + return ret; +} + +void cv_ocl_OpenCLExecutionContext_operatorST_const_OpenCLExecutionContextR(cv::ocl::OpenCLExecutionContext* instance, const cv::ocl::OpenCLExecutionContext* unnamed) { + instance->operator=(*unnamed); +} + +void cv_ocl_OpenCLExecutionContext_operatorST_OpenCLExecutionContextRR(cv::ocl::OpenCLExecutionContext* instance, cv::ocl::OpenCLExecutionContext* unnamed) { + instance->operator=(std::move(*unnamed)); +} + +void cv_ocl_OpenCLExecutionContext_getContext_const(const cv::ocl::OpenCLExecutionContext* instance, Result* ocvrs_return) { + try { + cv::ocl::Context ret = instance->getContext(); + Ok(new cv::ocl::Context(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_getDevice_const(const cv::ocl::OpenCLExecutionContext* instance, Result* ocvrs_return) { + try { + cv::ocl::Device ret = instance->getDevice(); + Ok(new cv::ocl::Device(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_getQueue_const(const cv::ocl::OpenCLExecutionContext* instance, Result* ocvrs_return) { + try { + cv::ocl::Queue ret = instance->getQueue(); + Ok(new cv::ocl::Queue(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_useOpenCL_const(const cv::ocl::OpenCLExecutionContext* instance, Result* ocvrs_return) { + try { + bool ret = instance->useOpenCL(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_setUseOpenCL_bool(cv::ocl::OpenCLExecutionContext* instance, bool flag, ResultVoid* ocvrs_return) { + try { + instance->setUseOpenCL(flag); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_getCurrent(Result* ocvrs_return) { + try { + cv::ocl::OpenCLExecutionContext ret = cv::ocl::OpenCLExecutionContext::getCurrent(); + Ok(new cv::ocl::OpenCLExecutionContext(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_getCurrentRef(Result* ocvrs_return) { + try { + cv::ocl::OpenCLExecutionContext ret = cv::ocl::OpenCLExecutionContext::getCurrentRef(); + Ok(new cv::ocl::OpenCLExecutionContext(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_bind_const(const cv::ocl::OpenCLExecutionContext* instance, ResultVoid* ocvrs_return) { + try { + instance->bind(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_cloneWithNewQueue_const_const_QueueR(const cv::ocl::OpenCLExecutionContext* instance, const cv::ocl::Queue* q, Result* ocvrs_return) { + try { + cv::ocl::OpenCLExecutionContext ret = instance->cloneWithNewQueue(*q); + Ok(new cv::ocl::OpenCLExecutionContext(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_cloneWithNewQueue_const(const cv::ocl::OpenCLExecutionContext* instance, Result* ocvrs_return) { + try { + cv::ocl::OpenCLExecutionContext ret = instance->cloneWithNewQueue(); + Ok(new cv::ocl::OpenCLExecutionContext(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_create_const_stringR_voidX_voidX_voidX(const char* platformName, void* platformID, void* context, void* deviceID, Result* ocvrs_return) { + try { + cv::ocl::OpenCLExecutionContext ret = cv::ocl::OpenCLExecutionContext::create(std::string(platformName), platformID, context, deviceID); + Ok(new cv::ocl::OpenCLExecutionContext(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_create_const_ContextR_const_DeviceR_const_QueueR(const cv::ocl::Context* context, const cv::ocl::Device* device, const cv::ocl::Queue* queue, Result* ocvrs_return) { + try { + cv::ocl::OpenCLExecutionContext ret = cv::ocl::OpenCLExecutionContext::create(*context, *device, *queue); + Ok(new cv::ocl::OpenCLExecutionContext(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_create_const_ContextR_const_DeviceR(const cv::ocl::Context* context, const cv::ocl::Device* device, Result* ocvrs_return) { + try { + cv::ocl::OpenCLExecutionContext ret = cv::ocl::OpenCLExecutionContext::create(*context, *device); + Ok(new cv::ocl::OpenCLExecutionContext(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_empty_const(const cv::ocl::OpenCLExecutionContext* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_release(cv::ocl::OpenCLExecutionContext* instance, ResultVoid* ocvrs_return) { + try { + instance->release(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_OpenCLExecutionContext_delete(cv::ocl::OpenCLExecutionContext* instance) { + delete instance; +} + +cv::ocl::Platform* cv_ocl_Platform_Platform() { + cv::ocl::Platform* ret = new cv::ocl::Platform(); + return ret; +} + +void cv_ocl_Platform_Platform_const_PlatformR(const cv::ocl::Platform* p, Result* ocvrs_return) { + try { + cv::ocl::Platform* ret = new cv::ocl::Platform(*p); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Platform_operatorST_const_PlatformR(cv::ocl::Platform* instance, const cv::ocl::Platform* p, ResultVoid* ocvrs_return) { + try { + instance->operator=(*p); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::ocl::Platform* cv_ocl_Platform_Platform_PlatformRR(cv::ocl::Platform* p) { + cv::ocl::Platform* ret = new cv::ocl::Platform(std::move(*p)); + return ret; +} + +void cv_ocl_Platform_operatorST_PlatformRR(cv::ocl::Platform* instance, cv::ocl::Platform* p) { + instance->operator=(std::move(*p)); +} + +void cv_ocl_Platform_ptr_const(const cv::ocl::Platform* instance, Result* ocvrs_return) { + try { + void* ret = instance->ptr(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Platform_getDefault(Result* ocvrs_return) { + try { + cv::ocl::Platform ret = cv::ocl::Platform::getDefault(); + Ok(new cv::ocl::Platform(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Platform_empty_const(const cv::ocl::Platform* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Platform_delete(cv::ocl::Platform* instance) { + delete instance; +} + +void cv_ocl_PlatformInfo_operatorST_const_PlatformInfoR(cv::ocl::PlatformInfo* instance, const cv::ocl::PlatformInfo* i, ResultVoid* ocvrs_return) { + try { + instance->operator=(*i); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_PlatformInfo_operatorST_PlatformInfoRR(cv::ocl::PlatformInfo* instance, cv::ocl::PlatformInfo* i) { + instance->operator=(std::move(*i)); +} + +void cv_ocl_PlatformInfo_name_const(const cv::ocl::PlatformInfo* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->name(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_PlatformInfo_vendor_const(const cv::ocl::PlatformInfo* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->vendor(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_PlatformInfo_version_const(const cv::ocl::PlatformInfo* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->version(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_PlatformInfo_versionMajor_const(const cv::ocl::PlatformInfo* instance, Result* ocvrs_return) { + try { + int ret = instance->versionMajor(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_PlatformInfo_versionMinor_const(const cv::ocl::PlatformInfo* instance, Result* ocvrs_return) { + try { + int ret = instance->versionMinor(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_PlatformInfo_deviceNumber_const(const cv::ocl::PlatformInfo* instance, Result* ocvrs_return) { + try { + int ret = instance->deviceNumber(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_PlatformInfo_getDevice_const_DeviceR_int(const cv::ocl::PlatformInfo* instance, cv::ocl::Device* device, int d, ResultVoid* ocvrs_return) { + try { + instance->getDevice(*device, d); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_PlatformInfo_empty_const(const cv::ocl::PlatformInfo* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_PlatformInfo_delete(cv::ocl::PlatformInfo* instance) { + delete instance; +} + +void cv_ocl_Program_operatorST_const_ProgramR(cv::ocl::Program* instance, const cv::ocl::Program* prog, ResultVoid* ocvrs_return) { + try { + instance->operator=(*prog); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Program_operatorST_ProgramRR(cv::ocl::Program* instance, cv::ocl::Program* prog) { + instance->operator=(std::move(*prog)); +} + +void cv_ocl_Program_ptr_const(const cv::ocl::Program* instance, Result* ocvrs_return) { + try { + void* ret = instance->ptr(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Program_getBinary_const_vectorLcharGR(const cv::ocl::Program* instance, std::vector* binary, ResultVoid* ocvrs_return) { + try { + instance->getBinary(*binary); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Program_empty_const(const cv::ocl::Program* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Program_read_const_StringR_const_StringR(cv::ocl::Program* instance, const char* buf, const char* buildflags, Result* ocvrs_return) { + try { + bool ret = instance->read(std::string(buf), std::string(buildflags)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Program_write_const_StringR(const cv::ocl::Program* instance, void** buf, Result* ocvrs_return) { + try { + std::string buf_out; + bool ret = instance->write(buf_out); + *buf = ocvrs_create_string(buf_out.c_str()); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Program_getPrefix_const(const cv::ocl::Program* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->getPrefix(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Program_getPrefix_const_StringR(const char* buildflags, Result* ocvrs_return) { + try { + cv::String ret = cv::ocl::Program::getPrefix(std::string(buildflags)); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Program_delete(cv::ocl::Program* instance) { + delete instance; +} + +void cv_ocl_ProgramSource_operatorST_const_ProgramSourceR(cv::ocl::ProgramSource* instance, const cv::ocl::ProgramSource* prog, ResultVoid* ocvrs_return) { + try { + instance->operator=(*prog); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_ProgramSource_operatorST_ProgramSourceRR(cv::ocl::ProgramSource* instance, cv::ocl::ProgramSource* prog) { + instance->operator=(std::move(*prog)); +} + +void cv_ocl_ProgramSource_source_const(const cv::ocl::ProgramSource* instance, Result* ocvrs_return) { + try { + const cv::String ret = instance->source(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_ProgramSource_hash_const(const cv::ocl::ProgramSource* instance, Result* ocvrs_return) { + try { + cv::ocl::ProgramSource::hash_t ret = instance->hash(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_ProgramSource_fromBinary_const_StringR_const_StringR_const_unsigned_charX_const_size_t_const_StringR(const char* module, const char* name, const unsigned char* binary, const size_t size, const char* buildOptions, Result* ocvrs_return) { + try { + cv::ocl::ProgramSource ret = cv::ocl::ProgramSource::fromBinary(std::string(module), std::string(name), binary, size, std::string(buildOptions)); + Ok(new cv::ocl::ProgramSource(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_ProgramSource_fromBinary_const_StringR_const_StringR_const_unsigned_charX_const_size_t(const char* module, const char* name, const unsigned char* binary, const size_t size, Result* ocvrs_return) { + try { + cv::ocl::ProgramSource ret = cv::ocl::ProgramSource::fromBinary(std::string(module), std::string(name), binary, size); + Ok(new cv::ocl::ProgramSource(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_ProgramSource_fromSPIR_const_StringR_const_StringR_const_unsigned_charX_const_size_t_const_StringR(const char* module, const char* name, const unsigned char* binary, const size_t size, const char* buildOptions, Result* ocvrs_return) { + try { + cv::ocl::ProgramSource ret = cv::ocl::ProgramSource::fromSPIR(std::string(module), std::string(name), binary, size, std::string(buildOptions)); + Ok(new cv::ocl::ProgramSource(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_ProgramSource_fromSPIR_const_StringR_const_StringR_const_unsigned_charX_const_size_t(const char* module, const char* name, const unsigned char* binary, const size_t size, Result* ocvrs_return) { + try { + cv::ocl::ProgramSource ret = cv::ocl::ProgramSource::fromSPIR(std::string(module), std::string(name), binary, size); + Ok(new cv::ocl::ProgramSource(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_ProgramSource_empty_const(const cv::ocl::ProgramSource* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_ProgramSource_delete(cv::ocl::ProgramSource* instance) { + delete instance; +} + +void cv_ocl_Queue_operatorST_const_QueueR(cv::ocl::Queue* instance, const cv::ocl::Queue* q, ResultVoid* ocvrs_return) { + try { + instance->operator=(*q); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Queue_operatorST_QueueRR(cv::ocl::Queue* instance, cv::ocl::Queue* q) { + instance->operator=(std::move(*q)); +} + +void cv_ocl_Queue_create_const_ContextR_const_DeviceR(cv::ocl::Queue* instance, const cv::ocl::Context* c, const cv::ocl::Device* d, Result* ocvrs_return) { + try { + bool ret = instance->create(*c, *d); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Queue_create(cv::ocl::Queue* instance, Result* ocvrs_return) { + try { + bool ret = instance->create(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Queue_finish(cv::ocl::Queue* instance, ResultVoid* ocvrs_return) { + try { + instance->finish(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Queue_ptr_const(const cv::ocl::Queue* instance, Result* ocvrs_return) { + try { + void* ret = instance->ptr(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Queue_getDefault(Result* ocvrs_return) { + try { + cv::ocl::Queue ret = cv::ocl::Queue::getDefault(); + Ok(new cv::ocl::Queue(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Queue_getProfilingQueue_const(const cv::ocl::Queue* instance, Result* ocvrs_return) { + try { + const cv::ocl::Queue ret = instance->getProfilingQueue(); + Ok(new const cv::ocl::Queue(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Queue_empty_const(const cv::ocl::Queue* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Queue_delete(cv::ocl::Queue* instance) { + delete instance; +} + +void cv_ocl_Timer_Timer_const_QueueR(const cv::ocl::Queue* q, Result* ocvrs_return) { + try { + cv::ocl::Timer* ret = new cv::ocl::Timer(*q); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Timer_start(cv::ocl::Timer* instance, ResultVoid* ocvrs_return) { + try { + instance->start(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Timer_stop(cv::ocl::Timer* instance, ResultVoid* ocvrs_return) { + try { + instance->stop(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Timer_durationNS_const(const cv::ocl::Timer* instance, Result* ocvrs_return) { + try { + uint64_t ret = instance->durationNS(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ocl_Timer_delete(cv::ocl::Timer* instance) { + delete instance; +} + +void cv_utils_ClassWithKeywordProperties_ClassWithKeywordProperties_int_int(int lambda_arg, int except_arg, Result* ocvrs_return) { + try { + cv::utils::ClassWithKeywordProperties ret(lambda_arg, except_arg); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_ClassWithKeywordProperties_ClassWithKeywordProperties(Result* ocvrs_return) { + try { + cv::utils::ClassWithKeywordProperties ret; + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_FunctionParams_setLambda_int(cv::utils::FunctionParams* instance, int value, cv::utils::FunctionParams* ocvrs_return) { + cv::utils::FunctionParams ret = instance->setLambda(value); + *ocvrs_return = ret; +} + +void cv_utils_FunctionParams_setSigma_float(cv::utils::FunctionParams* instance, float value, cv::utils::FunctionParams* ocvrs_return) { + cv::utils::FunctionParams ret = instance->setSigma(value); + *ocvrs_return = ret; +} + +void cv_utils_logging_LogTag_LogTag_const_charX_LogLevel(const char* _name, cv::utils::logging::LogLevel _level, Result* ocvrs_return) { + try { + cv::utils::logging::LogTag* ret = new cv::utils::logging::LogTag(_name, _level); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void* cv_utils_logging_LogTag_propName_const(const cv::utils::logging::LogTag* instance) { + const char* ret = instance->name; + return ocvrs_create_string(ret); +} + +void cv_utils_logging_LogTag_propLevel_const(const cv::utils::logging::LogTag* instance, cv::utils::logging::LogLevel* ocvrs_return) { + cv::utils::logging::LogLevel ret = instance->level; + *ocvrs_return = ret; +} + +void cv_utils_logging_LogTag_propLevel_const_LogLevel(cv::utils::logging::LogTag* instance, const cv::utils::logging::LogLevel val) { + instance->level = val; +} + +void cv_utils_logging_LogTag_delete(cv::utils::logging::LogTag* instance) { + delete instance; +} + +void cv_utils_nested_OriginalClassName_OriginalClassName_const_ParamsR(const cv::utils::nested::OriginalClassName::Params* params, Result* ocvrs_return) { + try { + cv::utils::nested::OriginalClassName* ret = new cv::utils::nested::OriginalClassName(*params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_nested_OriginalClassName_OriginalClassName(Result* ocvrs_return) { + try { + cv::utils::nested::OriginalClassName* ret = new cv::utils::nested::OriginalClassName(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_nested_OriginalClassName_getIntParam_const(const cv::utils::nested::OriginalClassName* instance, Result* ocvrs_return) { + try { + int ret = instance->getIntParam(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_nested_OriginalClassName_getFloatParam_const(const cv::utils::nested::OriginalClassName* instance, Result* ocvrs_return) { + try { + float ret = instance->getFloatParam(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_nested_OriginalClassName_originalName(Result* ocvrs_return) { + try { + std::string ret = cv::utils::nested::OriginalClassName::originalName(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_nested_OriginalClassName_create_const_ParamsR(const cv::utils::nested::OriginalClassName::Params* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::utils::nested::OriginalClassName::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_nested_OriginalClassName_create(Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::utils::nested::OriginalClassName::create(); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_nested_OriginalClassName_delete(cv::utils::nested::OriginalClassName* instance) { + delete instance; +} + +void cv_utils_nested_OriginalClassName_Params_Params_int_float(int int_param, float float_param, Result* ocvrs_return) { + try { + cv::utils::nested::OriginalClassName::Params ret(int_param, float_param); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_utils_nested_OriginalClassName_Params_Params(Result* ocvrs_return) { + try { + cv::utils::nested::OriginalClassName::Params ret; + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +} diff --git a/target/debug/build/opencv-4e105b5546afc119/out/core_types.hpp b/target/debug/build/opencv-4e105b5546afc119/out/core_types.hpp new file mode 100644 index 0000000..73217af --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/core_types.hpp @@ -0,0 +1,2971 @@ +extern "C" { + const cv::Algorithm* cv_PtrLcv_AlgorithmG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::Algorithm* cv_PtrLcv_AlgorithmG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_AlgorithmG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_AlgorithmG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_AlgorithmG_new_const_Algorithm(cv::Algorithm* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::ConjGradSolver* cv_PtrLcv_ConjGradSolverG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::ConjGradSolver* cv_PtrLcv_ConjGradSolverG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_ConjGradSolverG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_ConjGradSolverG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_ConjGradSolverG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_ConjGradSolverG_to_PtrOfMinProblemSolver(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + +} + +extern "C" { + const cv::DownhillSolver* cv_PtrLcv_DownhillSolverG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::DownhillSolver* cv_PtrLcv_DownhillSolverG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_DownhillSolverG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_DownhillSolverG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_DownhillSolverG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_DownhillSolverG_to_PtrOfMinProblemSolver(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + +} + +extern "C" { + const cv::FileStorage* cv_PtrLcv_FileStorageG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::FileStorage* cv_PtrLcv_FileStorageG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_FileStorageG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_FileStorageG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_FileStorageG_new_const_FileStorage(cv::FileStorage* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::Formatted* cv_PtrLcv_FormattedG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::Formatted* cv_PtrLcv_FormattedG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_FormattedG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_FormattedG_delete(cv::Ptr* instance) { + delete instance; + } + +} + +extern "C" { + const cv::Formatter* cv_PtrLcv_FormatterG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::Formatter* cv_PtrLcv_FormatterG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_FormatterG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_FormatterG_delete(cv::Ptr* instance) { + delete instance; + } + +} + +extern "C" { + const cv::KeyPoint* cv_PtrLcv_KeyPointG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::KeyPoint* cv_PtrLcv_KeyPointG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_KeyPointG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_KeyPointG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_KeyPointG_new_const_KeyPoint(cv::KeyPoint* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::MinProblemSolver* cv_PtrLcv_MinProblemSolverG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::MinProblemSolver* cv_PtrLcv_MinProblemSolverG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_MinProblemSolverG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_MinProblemSolverG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_MinProblemSolverG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + +} + +extern "C" { + const cv::MinProblemSolver::Function* cv_PtrLcv_MinProblemSolver_FunctionG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::MinProblemSolver::Function* cv_PtrLcv_MinProblemSolver_FunctionG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_MinProblemSolver_FunctionG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_MinProblemSolver_FunctionG_delete(cv::Ptr* instance) { + delete instance; + } + +} + +extern "C" { + const cv::utils::nested::OriginalClassName* cv_PtrLcv_utils_nested_OriginalClassNameG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::utils::nested::OriginalClassName* cv_PtrLcv_utils_nested_OriginalClassNameG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_utils_nested_OriginalClassNameG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_utils_nested_OriginalClassNameG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_utils_nested_OriginalClassNameG_new_const_OriginalClassName(cv::utils::nested::OriginalClassName* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const float* cv_PtrLfloatG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + float* cv_PtrLfloatG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLfloatG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLfloatG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLfloatG_new_const_float(float val) { + return new cv::Ptr(new float(val)); + } + +} + +extern "C" { + std::pair* std_pairLint__floatG_new_const_int_float(int arg, float arg_1) { + std::pair* ret = new std::pair(arg, arg_1); + return ret; + } + + void std_pairLint__floatG_get_0_const(const std::pair* instance, int* ocvrs_return) { + int ret = std::get<0>(*instance); + *ocvrs_return = ret; + } + + void std_pairLint__floatG_get_1_const(const std::pair* instance, float* ocvrs_return) { + float ret = std::get<1>(*instance); + *ocvrs_return = ret; + } + + void std_pairLint__floatG_delete(std::pair* instance) { + delete instance; + } + +} + +extern "C" { + std::vector* std_vectorLcv_DMatchG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_DMatchG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_DMatchG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_DMatchG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_DMatchG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_DMatchG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_DMatchG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_DMatchG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_DMatchG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_DMatchG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_DMatchG_push_const_DMatch(std::vector* instance, const cv::DMatch* val) { + instance->push_back(*val); + } + + void std_vectorLcv_DMatchG_insert_size_t_const_DMatch(std::vector* instance, size_t index, const cv::DMatch* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_DMatchG_get_const_size_t(const std::vector* instance, size_t index, cv::DMatch* ocvrs_return) { + cv::DMatch ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_DMatchG_set_size_t_const_DMatch(std::vector* instance, size_t index, const cv::DMatch* val) { + (*instance)[index] = *val; + } + + std::vector* std_vectorLcv_DMatchG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::DMatch* std_vectorLcv_DMatchG_data_const(const std::vector* instance) { + const cv::DMatch* ret = instance->data(); + return ret; + } + + cv::DMatch* std_vectorLcv_DMatchG_dataMut(std::vector* instance) { + cv::DMatch* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_DMatchX_size_t(const cv::DMatch* data, size_t len) { + return new std::vector(data, data + len); + } + +} + + +extern "C" { + std::vector* std_vectorLcv_KeyPointG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_KeyPointG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_KeyPointG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_KeyPointG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_KeyPointG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_KeyPointG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_KeyPointG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_KeyPointG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_KeyPointG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_KeyPointG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_KeyPointG_push_const_KeyPoint(std::vector* instance, const cv::KeyPoint* val) { + instance->push_back(*val); + } + + void std_vectorLcv_KeyPointG_insert_size_t_const_KeyPoint(std::vector* instance, size_t index, const cv::KeyPoint* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_KeyPointG_get_const_size_t(const std::vector* instance, size_t index, cv::KeyPoint** ocvrs_return) { + cv::KeyPoint ret = (*instance)[index]; + *ocvrs_return = new cv::KeyPoint(ret); + } + + void std_vectorLcv_KeyPointG_set_size_t_const_KeyPoint(std::vector* instance, size_t index, const cv::KeyPoint* val) { + (*instance)[index] = *val; + } + +} + + +extern "C" { + std::vector* std_vectorLcv_MatG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_MatG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_MatG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_MatG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_MatG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_MatG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_MatG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_MatG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_MatG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_MatG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_MatG_push_const_Mat(std::vector* instance, const cv::Mat* val) { + instance->push_back(*val); + } + + void std_vectorLcv_MatG_insert_size_t_const_Mat(std::vector* instance, size_t index, const cv::Mat* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_MatG_get_const_size_t(const std::vector* instance, size_t index, cv::Mat** ocvrs_return) { + cv::Mat ret = (*instance)[index]; + *ocvrs_return = new cv::Mat(ret); + } + + void std_vectorLcv_MatG_set_size_t_const_Mat(std::vector* instance, size_t index, const cv::Mat* val) { + (*instance)[index] = *val; + } + +} + + +extern "C" { + std::vector* std_vectorLcv_ocl_PlatformInfoG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_ocl_PlatformInfoG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_ocl_PlatformInfoG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_ocl_PlatformInfoG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_ocl_PlatformInfoG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_ocl_PlatformInfoG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_ocl_PlatformInfoG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_ocl_PlatformInfoG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_ocl_PlatformInfoG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_ocl_PlatformInfoG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_ocl_PlatformInfoG_push_const_PlatformInfo(std::vector* instance, const cv::ocl::PlatformInfo* val) { + instance->push_back(*val); + } + + void std_vectorLcv_ocl_PlatformInfoG_insert_size_t_const_PlatformInfo(std::vector* instance, size_t index, const cv::ocl::PlatformInfo* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_ocl_PlatformInfoG_get_const_size_t(const std::vector* instance, size_t index, cv::ocl::PlatformInfo** ocvrs_return) { + cv::ocl::PlatformInfo ret = (*instance)[index]; + *ocvrs_return = new cv::ocl::PlatformInfo(ret); + } + + void std_vectorLcv_ocl_PlatformInfoG_set_size_t_const_PlatformInfo(std::vector* instance, size_t index, const cv::ocl::PlatformInfo* val) { + (*instance)[index] = *val; + } + +} + + +extern "C" { + std::vector* std_vectorLcv_PointG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_PointG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_PointG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_PointG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_PointG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_PointG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_PointG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_PointG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_PointG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_PointG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_PointG_push_const_Point(std::vector* instance, const cv::Point* val) { + instance->push_back(*val); + } + + void std_vectorLcv_PointG_insert_size_t_const_Point(std::vector* instance, size_t index, const cv::Point* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_PointG_get_const_size_t(const std::vector* instance, size_t index, cv::Point* ocvrs_return) { + cv::Point ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_PointG_set_size_t_const_Point(std::vector* instance, size_t index, const cv::Point* val) { + (*instance)[index] = *val; + } + + std::vector* std_vectorLcv_PointG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::Point* std_vectorLcv_PointG_data_const(const std::vector* instance) { + const cv::Point* ret = instance->data(); + return ret; + } + + cv::Point* std_vectorLcv_PointG_dataMut(std::vector* instance) { + cv::Point* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_PointX_size_t(const cv::Point* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLcv_PointG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_PointG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_PointG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLcv_Point2dG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_Point2dG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_Point2dG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_Point2dG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_Point2dG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_Point2dG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_Point2dG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_Point2dG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_Point2dG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_Point2dG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_Point2dG_push_const_Point2d(std::vector* instance, const cv::Point2d* val) { + instance->push_back(*val); + } + + void std_vectorLcv_Point2dG_insert_size_t_const_Point2d(std::vector* instance, size_t index, const cv::Point2d* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_Point2dG_get_const_size_t(const std::vector* instance, size_t index, cv::Point2d* ocvrs_return) { + cv::Point2d ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_Point2dG_set_size_t_const_Point2d(std::vector* instance, size_t index, const cv::Point2d* val) { + (*instance)[index] = *val; + } + + std::vector* std_vectorLcv_Point2dG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::Point2d* std_vectorLcv_Point2dG_data_const(const std::vector* instance) { + const cv::Point2d* ret = instance->data(); + return ret; + } + + cv::Point2d* std_vectorLcv_Point2dG_dataMut(std::vector* instance) { + cv::Point2d* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_Point2dX_size_t(const cv::Point2d* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLcv_Point2dG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Point2dG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Point2dG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLcv_Point2fG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_Point2fG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_Point2fG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_Point2fG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_Point2fG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_Point2fG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_Point2fG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_Point2fG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_Point2fG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_Point2fG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_Point2fG_push_const_Point2f(std::vector* instance, const cv::Point2f* val) { + instance->push_back(*val); + } + + void std_vectorLcv_Point2fG_insert_size_t_const_Point2f(std::vector* instance, size_t index, const cv::Point2f* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_Point2fG_get_const_size_t(const std::vector* instance, size_t index, cv::Point2f* ocvrs_return) { + cv::Point2f ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_Point2fG_set_size_t_const_Point2f(std::vector* instance, size_t index, const cv::Point2f* val) { + (*instance)[index] = *val; + } + + std::vector* std_vectorLcv_Point2fG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::Point2f* std_vectorLcv_Point2fG_data_const(const std::vector* instance) { + const cv::Point2f* ret = instance->data(); + return ret; + } + + cv::Point2f* std_vectorLcv_Point2fG_dataMut(std::vector* instance) { + cv::Point2f* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_Point2fX_size_t(const cv::Point2f* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLcv_Point2fG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Point2fG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Point2fG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLcv_RangeG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_RangeG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_RangeG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_RangeG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_RangeG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_RangeG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_RangeG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_RangeG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_RangeG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_RangeG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_RangeG_push_const_Range(std::vector* instance, const cv::Range* val) { + instance->push_back(*val); + } + + void std_vectorLcv_RangeG_insert_size_t_const_Range(std::vector* instance, size_t index, const cv::Range* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_RangeG_get_const_size_t(const std::vector* instance, size_t index, cv::Range** ocvrs_return) { + cv::Range ret = (*instance)[index]; + *ocvrs_return = new cv::Range(ret); + } + + void std_vectorLcv_RangeG_set_size_t_const_Range(std::vector* instance, size_t index, const cv::Range* val) { + (*instance)[index] = *val; + } + +} + + +extern "C" { + std::vector* std_vectorLcv_RectG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_RectG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_RectG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_RectG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_RectG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_RectG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_RectG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_RectG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_RectG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_RectG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_RectG_push_const_Rect(std::vector* instance, const cv::Rect* val) { + instance->push_back(*val); + } + + void std_vectorLcv_RectG_insert_size_t_const_Rect(std::vector* instance, size_t index, const cv::Rect* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_RectG_get_const_size_t(const std::vector* instance, size_t index, cv::Rect* ocvrs_return) { + cv::Rect ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_RectG_set_size_t_const_Rect(std::vector* instance, size_t index, const cv::Rect* val) { + (*instance)[index] = *val; + } + + std::vector* std_vectorLcv_RectG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::Rect* std_vectorLcv_RectG_data_const(const std::vector* instance) { + const cv::Rect* ret = instance->data(); + return ret; + } + + cv::Rect* std_vectorLcv_RectG_dataMut(std::vector* instance) { + cv::Rect* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_RectX_size_t(const cv::Rect* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLcv_RectG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_RectG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_RectG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLcv_RotatedRectG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_RotatedRectG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_RotatedRectG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_RotatedRectG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_RotatedRectG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_RotatedRectG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_RotatedRectG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_RotatedRectG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_RotatedRectG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_RotatedRectG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_RotatedRectG_push_const_RotatedRect(std::vector* instance, const cv::RotatedRect* val) { + instance->push_back(*val); + } + + void std_vectorLcv_RotatedRectG_insert_size_t_const_RotatedRect(std::vector* instance, size_t index, const cv::RotatedRect* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_RotatedRectG_get_const_size_t(const std::vector* instance, size_t index, cv::RotatedRect* ocvrs_return) { + cv::RotatedRect ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_RotatedRectG_set_size_t_const_RotatedRect(std::vector* instance, size_t index, const cv::RotatedRect* val) { + (*instance)[index] = *val; + } + + std::vector* std_vectorLcv_RotatedRectG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::RotatedRect* std_vectorLcv_RotatedRectG_data_const(const std::vector* instance) { + const cv::RotatedRect* ret = instance->data(); + return ret; + } + + cv::RotatedRect* std_vectorLcv_RotatedRectG_dataMut(std::vector* instance) { + cv::RotatedRect* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_RotatedRectX_size_t(const cv::RotatedRect* data, size_t len) { + return new std::vector(data, data + len); + } + +} + + +extern "C" { + std::vector* std_vectorLcv_StringG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_StringG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_StringG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_StringG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_StringG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_StringG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_StringG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_StringG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_StringG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_StringG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_StringG_push_const_String(std::vector* instance, const char* val) { + instance->push_back(cv::String(val)); + } + + void std_vectorLcv_StringG_insert_size_t_const_String(std::vector* instance, size_t index, const char* val) { + instance->insert(instance->begin() + index, cv::String(val)); + } + + void std_vectorLcv_StringG_get_const_size_t(const std::vector* instance, size_t index, void** ocvrs_return) { + cv::String ret = (*instance)[index]; + *ocvrs_return = ocvrs_create_string(ret.c_str()); + } + + void std_vectorLcv_StringG_set_size_t_const_String(std::vector* instance, size_t index, const char* val) { + (*instance)[index] = cv::String(val); + } + +} + + +extern "C" { + std::vector* std_vectorLcv_Vec2dG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_Vec2dG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_Vec2dG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_Vec2dG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_Vec2dG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_Vec2dG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_Vec2dG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_Vec2dG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_Vec2dG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_Vec2dG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_Vec2dG_push_const_Vec2d(std::vector* instance, const cv::Vec2d* val) { + instance->push_back(*val); + } + + void std_vectorLcv_Vec2dG_insert_size_t_const_Vec2d(std::vector* instance, size_t index, const cv::Vec2d* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_Vec2dG_get_const_size_t(const std::vector* instance, size_t index, cv::Vec2d* ocvrs_return) { + cv::Vec2d ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_Vec2dG_set_size_t_const_Vec2d(std::vector* instance, size_t index, const cv::Vec2d* val) { + (*instance)[index] = *val; + } + + std::vector* std_vectorLcv_Vec2dG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::Vec2d* std_vectorLcv_Vec2dG_data_const(const std::vector* instance) { + const cv::Vec2d* ret = instance->data(); + return ret; + } + + cv::Vec2d* std_vectorLcv_Vec2dG_dataMut(std::vector* instance) { + cv::Vec2d* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_Vec2dX_size_t(const cv::Vec2d* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLcv_Vec2dG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec2dG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec2dG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLcv_Vec2fG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_Vec2fG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_Vec2fG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_Vec2fG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_Vec2fG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_Vec2fG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_Vec2fG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_Vec2fG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_Vec2fG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_Vec2fG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_Vec2fG_push_const_Vec2f(std::vector* instance, const cv::Vec2f* val) { + instance->push_back(*val); + } + + void std_vectorLcv_Vec2fG_insert_size_t_const_Vec2f(std::vector* instance, size_t index, const cv::Vec2f* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_Vec2fG_get_const_size_t(const std::vector* instance, size_t index, cv::Vec2f* ocvrs_return) { + cv::Vec2f ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_Vec2fG_set_size_t_const_Vec2f(std::vector* instance, size_t index, const cv::Vec2f* val) { + (*instance)[index] = *val; + } + + std::vector* std_vectorLcv_Vec2fG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::Vec2f* std_vectorLcv_Vec2fG_data_const(const std::vector* instance) { + const cv::Vec2f* ret = instance->data(); + return ret; + } + + cv::Vec2f* std_vectorLcv_Vec2fG_dataMut(std::vector* instance) { + cv::Vec2f* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_Vec2fX_size_t(const cv::Vec2f* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLcv_Vec2fG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec2fG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec2fG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLcv_Vec3dG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_Vec3dG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_Vec3dG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_Vec3dG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_Vec3dG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_Vec3dG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_Vec3dG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_Vec3dG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_Vec3dG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_Vec3dG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_Vec3dG_push_const_Vec3d(std::vector* instance, const cv::Vec3d* val) { + instance->push_back(*val); + } + + void std_vectorLcv_Vec3dG_insert_size_t_const_Vec3d(std::vector* instance, size_t index, const cv::Vec3d* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_Vec3dG_get_const_size_t(const std::vector* instance, size_t index, cv::Vec3d* ocvrs_return) { + cv::Vec3d ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_Vec3dG_set_size_t_const_Vec3d(std::vector* instance, size_t index, const cv::Vec3d* val) { + (*instance)[index] = *val; + } + + std::vector* std_vectorLcv_Vec3dG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::Vec3d* std_vectorLcv_Vec3dG_data_const(const std::vector* instance) { + const cv::Vec3d* ret = instance->data(); + return ret; + } + + cv::Vec3d* std_vectorLcv_Vec3dG_dataMut(std::vector* instance) { + cv::Vec3d* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_Vec3dX_size_t(const cv::Vec3d* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLcv_Vec3dG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec3dG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec3dG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLcv_Vec3fG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_Vec3fG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_Vec3fG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_Vec3fG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_Vec3fG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_Vec3fG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_Vec3fG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_Vec3fG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_Vec3fG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_Vec3fG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_Vec3fG_push_const_Vec3f(std::vector* instance, const cv::Vec3f* val) { + instance->push_back(*val); + } + + void std_vectorLcv_Vec3fG_insert_size_t_const_Vec3f(std::vector* instance, size_t index, const cv::Vec3f* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_Vec3fG_get_const_size_t(const std::vector* instance, size_t index, cv::Vec3f* ocvrs_return) { + cv::Vec3f ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_Vec3fG_set_size_t_const_Vec3f(std::vector* instance, size_t index, const cv::Vec3f* val) { + (*instance)[index] = *val; + } + + std::vector* std_vectorLcv_Vec3fG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::Vec3f* std_vectorLcv_Vec3fG_data_const(const std::vector* instance) { + const cv::Vec3f* ret = instance->data(); + return ret; + } + + cv::Vec3f* std_vectorLcv_Vec3fG_dataMut(std::vector* instance) { + cv::Vec3f* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_Vec3fX_size_t(const cv::Vec3f* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLcv_Vec3fG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec3fG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec3fG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLcv_Vec4fG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_Vec4fG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_Vec4fG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_Vec4fG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_Vec4fG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_Vec4fG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_Vec4fG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_Vec4fG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_Vec4fG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_Vec4fG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_Vec4fG_push_const_Vec4f(std::vector* instance, const cv::Vec4f* val) { + instance->push_back(*val); + } + + void std_vectorLcv_Vec4fG_insert_size_t_const_Vec4f(std::vector* instance, size_t index, const cv::Vec4f* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_Vec4fG_get_const_size_t(const std::vector* instance, size_t index, cv::Vec4f* ocvrs_return) { + cv::Vec4f ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_Vec4fG_set_size_t_const_Vec4f(std::vector* instance, size_t index, const cv::Vec4f* val) { + (*instance)[index] = *val; + } + + std::vector* std_vectorLcv_Vec4fG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::Vec4f* std_vectorLcv_Vec4fG_data_const(const std::vector* instance) { + const cv::Vec4f* ret = instance->data(); + return ret; + } + + cv::Vec4f* std_vectorLcv_Vec4fG_dataMut(std::vector* instance) { + cv::Vec4f* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_Vec4fX_size_t(const cv::Vec4f* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLcv_Vec4fG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec4fG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec4fG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLcv_Vec4iG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_Vec4iG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_Vec4iG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_Vec4iG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_Vec4iG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_Vec4iG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_Vec4iG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_Vec4iG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_Vec4iG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_Vec4iG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_Vec4iG_push_const_Vec4i(std::vector* instance, const cv::Vec4i* val) { + instance->push_back(*val); + } + + void std_vectorLcv_Vec4iG_insert_size_t_const_Vec4i(std::vector* instance, size_t index, const cv::Vec4i* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_Vec4iG_get_const_size_t(const std::vector* instance, size_t index, cv::Vec4i* ocvrs_return) { + cv::Vec4i ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_Vec4iG_set_size_t_const_Vec4i(std::vector* instance, size_t index, const cv::Vec4i* val) { + (*instance)[index] = *val; + } + + std::vector* std_vectorLcv_Vec4iG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::Vec4i* std_vectorLcv_Vec4iG_data_const(const std::vector* instance) { + const cv::Vec4i* ret = instance->data(); + return ret; + } + + cv::Vec4i* std_vectorLcv_Vec4iG_dataMut(std::vector* instance) { + cv::Vec4i* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_Vec4iX_size_t(const cv::Vec4i* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLcv_Vec4iG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec4iG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec4iG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLcv_Vec6fG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_Vec6fG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_Vec6fG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_Vec6fG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_Vec6fG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_Vec6fG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_Vec6fG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_Vec6fG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_Vec6fG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_Vec6fG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_Vec6fG_push_const_Vec6f(std::vector* instance, const cv::Vec6f* val) { + instance->push_back(*val); + } + + void std_vectorLcv_Vec6fG_insert_size_t_const_Vec6f(std::vector* instance, size_t index, const cv::Vec6f* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_Vec6fG_get_const_size_t(const std::vector* instance, size_t index, cv::Vec6f* ocvrs_return) { + cv::Vec6f ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_Vec6fG_set_size_t_const_Vec6f(std::vector* instance, size_t index, const cv::Vec6f* val) { + (*instance)[index] = *val; + } + + std::vector* std_vectorLcv_Vec6fG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::Vec6f* std_vectorLcv_Vec6fG_data_const(const std::vector* instance) { + const cv::Vec6f* ret = instance->data(); + return ret; + } + + cv::Vec6f* std_vectorLcv_Vec6fG_dataMut(std::vector* instance) { + cv::Vec6f* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_Vec6fX_size_t(const cv::Vec6f* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLcv_Vec6fG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec6fG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_Vec6fG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector>* std_vectorLstd_vectorLcv_MatGG_new_const() { + std::vector>* ret = new std::vector>(); + return ret; + } + + void std_vectorLstd_vectorLcv_MatGG_delete(std::vector>* instance) { + delete instance; + } + + size_t std_vectorLstd_vectorLcv_MatGG_len_const(const std::vector>* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLstd_vectorLcv_MatGG_isEmpty_const(const std::vector>* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLstd_vectorLcv_MatGG_capacity_const(const std::vector>* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLstd_vectorLcv_MatGG_shrinkToFit(std::vector>* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLstd_vectorLcv_MatGG_reserve_size_t(std::vector>* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLstd_vectorLcv_MatGG_remove_size_t(std::vector>* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLstd_vectorLcv_MatGG_swap_size_t_size_t(std::vector>* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLstd_vectorLcv_MatGG_clear(std::vector>* instance) { + instance->clear(); + } + + void std_vectorLstd_vectorLcv_MatGG_push_const_vectorLMatG(std::vector>* instance, const std::vector* val) { + instance->push_back(*val); + } + + void std_vectorLstd_vectorLcv_MatGG_insert_size_t_const_vectorLMatG(std::vector>* instance, size_t index, const std::vector* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLstd_vectorLcv_MatGG_get_const_size_t(const std::vector>* instance, size_t index, std::vector** ocvrs_return) { + std::vector ret = (*instance)[index]; + *ocvrs_return = new std::vector(ret); + } + + void std_vectorLstd_vectorLcv_MatGG_set_size_t_const_vectorLMatG(std::vector>* instance, size_t index, const std::vector* val) { + (*instance)[index] = *val; + } + +} + + +extern "C" { + std::vector>* std_vectorLstd_vectorLcv_PointGG_new_const() { + std::vector>* ret = new std::vector>(); + return ret; + } + + void std_vectorLstd_vectorLcv_PointGG_delete(std::vector>* instance) { + delete instance; + } + + size_t std_vectorLstd_vectorLcv_PointGG_len_const(const std::vector>* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLstd_vectorLcv_PointGG_isEmpty_const(const std::vector>* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLstd_vectorLcv_PointGG_capacity_const(const std::vector>* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLstd_vectorLcv_PointGG_shrinkToFit(std::vector>* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLstd_vectorLcv_PointGG_reserve_size_t(std::vector>* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLstd_vectorLcv_PointGG_remove_size_t(std::vector>* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLstd_vectorLcv_PointGG_swap_size_t_size_t(std::vector>* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLstd_vectorLcv_PointGG_clear(std::vector>* instance) { + instance->clear(); + } + + void std_vectorLstd_vectorLcv_PointGG_push_const_vectorLPointG(std::vector>* instance, const std::vector* val) { + instance->push_back(*val); + } + + void std_vectorLstd_vectorLcv_PointGG_insert_size_t_const_vectorLPointG(std::vector>* instance, size_t index, const std::vector* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLstd_vectorLcv_PointGG_get_const_size_t(const std::vector>* instance, size_t index, std::vector** ocvrs_return) { + std::vector ret = (*instance)[index]; + *ocvrs_return = new std::vector(ret); + } + + void std_vectorLstd_vectorLcv_PointGG_set_size_t_const_vectorLPointG(std::vector>* instance, size_t index, const std::vector* val) { + (*instance)[index] = *val; + } + + void std_vectorLstd_vectorLcv_PointGG_inputArray_const(const std::vector>* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLstd_vectorLcv_PointGG_outputArray(std::vector>* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLstd_vectorLcv_PointGG_inputOutputArray(std::vector>* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector>* std_vectorLstd_vectorLcv_Point2fGG_new_const() { + std::vector>* ret = new std::vector>(); + return ret; + } + + void std_vectorLstd_vectorLcv_Point2fGG_delete(std::vector>* instance) { + delete instance; + } + + size_t std_vectorLstd_vectorLcv_Point2fGG_len_const(const std::vector>* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLstd_vectorLcv_Point2fGG_isEmpty_const(const std::vector>* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLstd_vectorLcv_Point2fGG_capacity_const(const std::vector>* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLstd_vectorLcv_Point2fGG_shrinkToFit(std::vector>* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLstd_vectorLcv_Point2fGG_reserve_size_t(std::vector>* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLstd_vectorLcv_Point2fGG_remove_size_t(std::vector>* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLstd_vectorLcv_Point2fGG_swap_size_t_size_t(std::vector>* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLstd_vectorLcv_Point2fGG_clear(std::vector>* instance) { + instance->clear(); + } + + void std_vectorLstd_vectorLcv_Point2fGG_push_const_vectorLPoint2fG(std::vector>* instance, const std::vector* val) { + instance->push_back(*val); + } + + void std_vectorLstd_vectorLcv_Point2fGG_insert_size_t_const_vectorLPoint2fG(std::vector>* instance, size_t index, const std::vector* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLstd_vectorLcv_Point2fGG_get_const_size_t(const std::vector>* instance, size_t index, std::vector** ocvrs_return) { + std::vector ret = (*instance)[index]; + *ocvrs_return = new std::vector(ret); + } + + void std_vectorLstd_vectorLcv_Point2fGG_set_size_t_const_vectorLPoint2fG(std::vector>* instance, size_t index, const std::vector* val) { + (*instance)[index] = *val; + } + + void std_vectorLstd_vectorLcv_Point2fGG_inputArray_const(const std::vector>* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLstd_vectorLcv_Point2fGG_outputArray(std::vector>* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLstd_vectorLcv_Point2fGG_inputOutputArray(std::vector>* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector>* std_vectorLstd_vectorLcv_RangeGG_new_const() { + std::vector>* ret = new std::vector>(); + return ret; + } + + void std_vectorLstd_vectorLcv_RangeGG_delete(std::vector>* instance) { + delete instance; + } + + size_t std_vectorLstd_vectorLcv_RangeGG_len_const(const std::vector>* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLstd_vectorLcv_RangeGG_isEmpty_const(const std::vector>* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLstd_vectorLcv_RangeGG_capacity_const(const std::vector>* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLstd_vectorLcv_RangeGG_shrinkToFit(std::vector>* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLstd_vectorLcv_RangeGG_reserve_size_t(std::vector>* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLstd_vectorLcv_RangeGG_remove_size_t(std::vector>* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLstd_vectorLcv_RangeGG_swap_size_t_size_t(std::vector>* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLstd_vectorLcv_RangeGG_clear(std::vector>* instance) { + instance->clear(); + } + + void std_vectorLstd_vectorLcv_RangeGG_push_const_vectorLRangeG(std::vector>* instance, const std::vector* val) { + instance->push_back(*val); + } + + void std_vectorLstd_vectorLcv_RangeGG_insert_size_t_const_vectorLRangeG(std::vector>* instance, size_t index, const std::vector* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLstd_vectorLcv_RangeGG_get_const_size_t(const std::vector>* instance, size_t index, std::vector** ocvrs_return) { + std::vector ret = (*instance)[index]; + *ocvrs_return = new std::vector(ret); + } + + void std_vectorLstd_vectorLcv_RangeGG_set_size_t_const_vectorLRangeG(std::vector>* instance, size_t index, const std::vector* val) { + (*instance)[index] = *val; + } + +} + + +extern "C" { + std::vector>* std_vectorLstd_vectorLintGG_new_const() { + std::vector>* ret = new std::vector>(); + return ret; + } + + void std_vectorLstd_vectorLintGG_delete(std::vector>* instance) { + delete instance; + } + + size_t std_vectorLstd_vectorLintGG_len_const(const std::vector>* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLstd_vectorLintGG_isEmpty_const(const std::vector>* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLstd_vectorLintGG_capacity_const(const std::vector>* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLstd_vectorLintGG_shrinkToFit(std::vector>* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLstd_vectorLintGG_reserve_size_t(std::vector>* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLstd_vectorLintGG_remove_size_t(std::vector>* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLstd_vectorLintGG_swap_size_t_size_t(std::vector>* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLstd_vectorLintGG_clear(std::vector>* instance) { + instance->clear(); + } + + void std_vectorLstd_vectorLintGG_push_const_vectorLintG(std::vector>* instance, const std::vector* val) { + instance->push_back(*val); + } + + void std_vectorLstd_vectorLintGG_insert_size_t_const_vectorLintG(std::vector>* instance, size_t index, const std::vector* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLstd_vectorLintGG_get_const_size_t(const std::vector>* instance, size_t index, std::vector** ocvrs_return) { + std::vector ret = (*instance)[index]; + *ocvrs_return = new std::vector(ret); + } + + void std_vectorLstd_vectorLintGG_set_size_t_const_vectorLintG(std::vector>* instance, size_t index, const std::vector* val) { + (*instance)[index] = *val; + } + + void std_vectorLstd_vectorLintGG_inputArray_const(const std::vector>* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLstd_vectorLintGG_outputArray(std::vector>* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLstd_vectorLintGG_inputOutputArray(std::vector>* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLboolG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLboolG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLboolG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLboolG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLboolG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLboolG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLboolG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLboolG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLboolG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + instance->swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLboolG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLboolG_push_const_bool(std::vector* instance, const bool val) { + instance->push_back(val); + } + + void std_vectorLboolG_insert_size_t_const_bool(std::vector* instance, size_t index, const bool val) { + instance->insert(instance->begin() + index, val); + } + + void std_vectorLboolG_get_const_size_t(const std::vector* instance, size_t index, bool* ocvrs_return) { + bool ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLboolG_set_size_t_const_bool(std::vector* instance, size_t index, const bool val) { + (*instance)[index] = val; + } + + void std_vectorLboolG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { +} + + +extern "C" { + std::vector* std_vectorLfloatG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLfloatG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLfloatG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLfloatG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLfloatG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLfloatG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLfloatG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLfloatG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLfloatG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLfloatG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLfloatG_push_const_float(std::vector* instance, const float val) { + instance->push_back(val); + } + + void std_vectorLfloatG_insert_size_t_const_float(std::vector* instance, size_t index, const float val) { + instance->insert(instance->begin() + index, val); + } + + void std_vectorLfloatG_get_const_size_t(const std::vector* instance, size_t index, float* ocvrs_return) { + float ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLfloatG_set_size_t_const_float(std::vector* instance, size_t index, const float val) { + (*instance)[index] = val; + } + + std::vector* std_vectorLfloatG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const float* std_vectorLfloatG_data_const(const std::vector* instance) { + const float* ret = instance->data(); + return ret; + } + + float* std_vectorLfloatG_dataMut(std::vector* instance) { + float* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_floatX_size_t(const float* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLfloatG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLfloatG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLfloatG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLdoubleG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLdoubleG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLdoubleG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLdoubleG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLdoubleG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLdoubleG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLdoubleG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLdoubleG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLdoubleG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLdoubleG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLdoubleG_push_const_double(std::vector* instance, const double val) { + instance->push_back(val); + } + + void std_vectorLdoubleG_insert_size_t_const_double(std::vector* instance, size_t index, const double val) { + instance->insert(instance->begin() + index, val); + } + + void std_vectorLdoubleG_get_const_size_t(const std::vector* instance, size_t index, double* ocvrs_return) { + double ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLdoubleG_set_size_t_const_double(std::vector* instance, size_t index, const double val) { + (*instance)[index] = val; + } + + std::vector* std_vectorLdoubleG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const double* std_vectorLdoubleG_data_const(const std::vector* instance) { + const double* ret = instance->data(); + return ret; + } + + double* std_vectorLdoubleG_dataMut(std::vector* instance) { + double* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_doubleX_size_t(const double* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLdoubleG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLdoubleG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLdoubleG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLintG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLintG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLintG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLintG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLintG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLintG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLintG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLintG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLintG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLintG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLintG_push_const_int(std::vector* instance, const int val) { + instance->push_back(val); + } + + void std_vectorLintG_insert_size_t_const_int(std::vector* instance, size_t index, const int val) { + instance->insert(instance->begin() + index, val); + } + + void std_vectorLintG_get_const_size_t(const std::vector* instance, size_t index, int* ocvrs_return) { + int ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLintG_set_size_t_const_int(std::vector* instance, size_t index, const int val) { + (*instance)[index] = val; + } + + std::vector* std_vectorLintG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const int* std_vectorLintG_data_const(const std::vector* instance) { + const int* ret = instance->data(); + return ret; + } + + int* std_vectorLintG_dataMut(std::vector* instance) { + int* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_intX_size_t(const int* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLintG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLintG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLintG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector* std_vectorLsigned_charG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLsigned_charG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLsigned_charG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLsigned_charG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLsigned_charG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLsigned_charG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLsigned_charG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLsigned_charG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLsigned_charG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLsigned_charG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLsigned_charG_push_const_signed_char(std::vector* instance, const signed char val) { + instance->push_back(val); + } + + void std_vectorLsigned_charG_insert_size_t_const_signed_char(std::vector* instance, size_t index, const signed char val) { + instance->insert(instance->begin() + index, val); + } + + void std_vectorLsigned_charG_get_const_size_t(const std::vector* instance, size_t index, signed char* ocvrs_return) { + signed char ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLsigned_charG_set_size_t_const_signed_char(std::vector* instance, size_t index, const signed char val) { + (*instance)[index] = val; + } + + std::vector* std_vectorLsigned_charG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const signed char* std_vectorLsigned_charG_data_const(const std::vector* instance) { + const signed char* ret = instance->data(); + return ret; + } + + signed char* std_vectorLsigned_charG_dataMut(std::vector* instance) { + signed char* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_signed_charX_size_t(const signed char* data, size_t len) { + return new std::vector(data, data + len); + } + +} + + +extern "C" { + std::vector* std_vectorLsize_tG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLsize_tG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLsize_tG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLsize_tG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLsize_tG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLsize_tG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLsize_tG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLsize_tG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLsize_tG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLsize_tG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLsize_tG_push_const_size_t(std::vector* instance, const size_t val) { + instance->push_back(val); + } + + void std_vectorLsize_tG_insert_size_t_const_size_t(std::vector* instance, size_t index, const size_t val) { + instance->insert(instance->begin() + index, val); + } + + void std_vectorLsize_tG_get_const_size_t(const std::vector* instance, size_t index, size_t* ocvrs_return) { + size_t ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLsize_tG_set_size_t_const_size_t(std::vector* instance, size_t index, const size_t val) { + (*instance)[index] = val; + } + + std::vector* std_vectorLsize_tG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const size_t* std_vectorLsize_tG_data_const(const std::vector* instance) { + const size_t* ret = instance->data(); + return ret; + } + + size_t* std_vectorLsize_tG_dataMut(std::vector* instance) { + size_t* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_size_tX_size_t(const size_t* data, size_t len) { + return new std::vector(data, data + len); + } + +} + + +extern "C" { + std::vector* std_vectorLunsigned_charG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLunsigned_charG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLunsigned_charG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLunsigned_charG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLunsigned_charG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLunsigned_charG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLunsigned_charG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLunsigned_charG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLunsigned_charG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLunsigned_charG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLunsigned_charG_push_const_unsigned_char(std::vector* instance, const unsigned char val) { + instance->push_back(val); + } + + void std_vectorLunsigned_charG_insert_size_t_const_unsigned_char(std::vector* instance, size_t index, const unsigned char val) { + instance->insert(instance->begin() + index, val); + } + + void std_vectorLunsigned_charG_get_const_size_t(const std::vector* instance, size_t index, unsigned char* ocvrs_return) { + unsigned char ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLunsigned_charG_set_size_t_const_unsigned_char(std::vector* instance, size_t index, const unsigned char val) { + (*instance)[index] = val; + } + + std::vector* std_vectorLunsigned_charG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const unsigned char* std_vectorLunsigned_charG_data_const(const std::vector* instance) { + const unsigned char* ret = instance->data(); + return ret; + } + + unsigned char* std_vectorLunsigned_charG_dataMut(std::vector* instance) { + unsigned char* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_unsigned_charX_size_t(const unsigned char* data, size_t len) { + return new std::vector(data, data + len); + } + + void std_vectorLunsigned_charG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLunsigned_charG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLunsigned_charG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + diff --git a/target/debug/build/opencv-4e105b5546afc119/out/dnn.cpp b/target/debug/build/opencv-4e105b5546afc119/out/dnn.cpp new file mode 100644 index 0000000..36b1811 --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/dnn.cpp @@ -0,0 +1,7342 @@ +#include "dnn.hpp" +#include "dnn_types.hpp" + +extern "C" { +void cv_dnn_NMSBoxesBatched_const_vectorLRect2dGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR(const std::vector* bboxes, const std::vector* scores, const std::vector* class_ids, const float score_threshold, const float nms_threshold, std::vector* indices, ResultVoid* ocvrs_return) { + try { + cv::dnn::NMSBoxesBatched(*bboxes, *scores, *class_ids, score_threshold, nms_threshold, *indices); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_NMSBoxesBatched_const_vectorLRect2dGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR_const_float_const_int(const std::vector* bboxes, const std::vector* scores, const std::vector* class_ids, const float score_threshold, const float nms_threshold, std::vector* indices, const float eta, const int top_k, ResultVoid* ocvrs_return) { + try { + cv::dnn::NMSBoxesBatched(*bboxes, *scores, *class_ids, score_threshold, nms_threshold, *indices, eta, top_k); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_NMSBoxesBatched_const_vectorLRectGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR(const std::vector* bboxes, const std::vector* scores, const std::vector* class_ids, const float score_threshold, const float nms_threshold, std::vector* indices, ResultVoid* ocvrs_return) { + try { + cv::dnn::NMSBoxesBatched(*bboxes, *scores, *class_ids, score_threshold, nms_threshold, *indices); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_NMSBoxesBatched_const_vectorLRectGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR_const_float_const_int(const std::vector* bboxes, const std::vector* scores, const std::vector* class_ids, const float score_threshold, const float nms_threshold, std::vector* indices, const float eta, const int top_k, ResultVoid* ocvrs_return) { + try { + cv::dnn::NMSBoxesBatched(*bboxes, *scores, *class_ids, score_threshold, nms_threshold, *indices, eta, top_k); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_NMSBoxes_const_vectorLRect2dGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR(const std::vector* bboxes, const std::vector* scores, const float score_threshold, const float nms_threshold, std::vector* indices, ResultVoid* ocvrs_return) { + try { + cv::dnn::NMSBoxes(*bboxes, *scores, score_threshold, nms_threshold, *indices); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_NMSBoxes_const_vectorLRect2dGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR_const_float_const_int(const std::vector* bboxes, const std::vector* scores, const float score_threshold, const float nms_threshold, std::vector* indices, const float eta, const int top_k, ResultVoid* ocvrs_return) { + try { + cv::dnn::NMSBoxes(*bboxes, *scores, score_threshold, nms_threshold, *indices, eta, top_k); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_NMSBoxes_const_vectorLRectGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR(const std::vector* bboxes, const std::vector* scores, const float score_threshold, const float nms_threshold, std::vector* indices, ResultVoid* ocvrs_return) { + try { + cv::dnn::NMSBoxes(*bboxes, *scores, score_threshold, nms_threshold, *indices); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_NMSBoxes_const_vectorLRectGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR_const_float_const_int(const std::vector* bboxes, const std::vector* scores, const float score_threshold, const float nms_threshold, std::vector* indices, const float eta, const int top_k, ResultVoid* ocvrs_return) { + try { + cv::dnn::NMSBoxes(*bboxes, *scores, score_threshold, nms_threshold, *indices, eta, top_k); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_NMSBoxes_const_vectorLRotatedRectGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR(const std::vector* bboxes, const std::vector* scores, const float score_threshold, const float nms_threshold, std::vector* indices, ResultVoid* ocvrs_return) { + try { + cv::dnn::NMSBoxes(*bboxes, *scores, score_threshold, nms_threshold, *indices); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_NMSBoxes_const_vectorLRotatedRectGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR_const_float_const_int(const std::vector* bboxes, const std::vector* scores, const float score_threshold, const float nms_threshold, std::vector* indices, const float eta, const int top_k, ResultVoid* ocvrs_return) { + try { + cv::dnn::NMSBoxes(*bboxes, *scores, score_threshold, nms_threshold, *indices, eta, top_k); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImageWithParams_const__InputArrayR(const cv::_InputArray* image, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::blobFromImageWithParams(*image); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImageWithParams_const__InputArrayR_const_Image2BlobParamsR(const cv::_InputArray* image, const cv::dnn::Image2BlobParams* param, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::blobFromImageWithParams(*image, *param); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImageWithParams_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* image, const cv::_OutputArray* blob, ResultVoid* ocvrs_return) { + try { + cv::dnn::blobFromImageWithParams(*image, *blob); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImageWithParams_const__InputArrayR_const__OutputArrayR_const_Image2BlobParamsR(const cv::_InputArray* image, const cv::_OutputArray* blob, const cv::dnn::Image2BlobParams* param, ResultVoid* ocvrs_return) { + try { + cv::dnn::blobFromImageWithParams(*image, *blob, *param); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImage_const__InputArrayR(const cv::_InputArray* image, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::blobFromImage(*image); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImage_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* image, const cv::_OutputArray* blob, ResultVoid* ocvrs_return) { + try { + cv::dnn::blobFromImage(*image, *blob); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImage_const__InputArrayR_const__OutputArrayR_double_const_SizeR_const_ScalarR_bool_bool_int(const cv::_InputArray* image, const cv::_OutputArray* blob, double scalefactor, const cv::Size* size, const cv::Scalar* mean, bool swapRB, bool crop, int ddepth, ResultVoid* ocvrs_return) { + try { + cv::dnn::blobFromImage(*image, *blob, scalefactor, *size, *mean, swapRB, crop, ddepth); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImage_const__InputArrayR_double_const_SizeR_const_ScalarR_bool_bool_int(const cv::_InputArray* image, double scalefactor, const cv::Size* size, const cv::Scalar* mean, bool swapRB, bool crop, int ddepth, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::blobFromImage(*image, scalefactor, *size, *mean, swapRB, crop, ddepth); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImagesWithParams_const__InputArrayR(const cv::_InputArray* images, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::blobFromImagesWithParams(*images); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImagesWithParams_const__InputArrayR_const_Image2BlobParamsR(const cv::_InputArray* images, const cv::dnn::Image2BlobParams* param, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::blobFromImagesWithParams(*images, *param); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImagesWithParams_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* images, const cv::_OutputArray* blob, ResultVoid* ocvrs_return) { + try { + cv::dnn::blobFromImagesWithParams(*images, *blob); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImagesWithParams_const__InputArrayR_const__OutputArrayR_const_Image2BlobParamsR(const cv::_InputArray* images, const cv::_OutputArray* blob, const cv::dnn::Image2BlobParams* param, ResultVoid* ocvrs_return) { + try { + cv::dnn::blobFromImagesWithParams(*images, *blob, *param); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImages_const__InputArrayR(const cv::_InputArray* images, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::blobFromImages(*images); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImages_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* images, const cv::_OutputArray* blob, ResultVoid* ocvrs_return) { + try { + cv::dnn::blobFromImages(*images, *blob); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImages_const__InputArrayR_const__OutputArrayR_double_Size_const_ScalarR_bool_bool_int(const cv::_InputArray* images, const cv::_OutputArray* blob, double scalefactor, cv::Size* size, const cv::Scalar* mean, bool swapRB, bool crop, int ddepth, ResultVoid* ocvrs_return) { + try { + cv::dnn::blobFromImages(*images, *blob, scalefactor, *size, *mean, swapRB, crop, ddepth); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_blobFromImages_const__InputArrayR_double_Size_const_ScalarR_bool_bool_int(const cv::_InputArray* images, double scalefactor, cv::Size* size, const cv::Scalar* mean, bool swapRB, bool crop, int ddepth, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::blobFromImages(*images, scalefactor, *size, *mean, swapRB, crop, ddepth); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_concat_const_MatShapeR_const_MatShapeR(const cv::dnn::MatShape* a, const cv::dnn::MatShape* b, Result* ocvrs_return) { + try { + cv::dnn::MatShape ret = cv::dnn::concat(*a, *b); + Ok(new cv::dnn::MatShape(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_enableModelDiagnostics_bool(bool isDiagnosticsMode, ResultVoid* ocvrs_return) { + try { + cv::dnn::enableModelDiagnostics(isDiagnosticsMode); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_getAvailableBackends(Result>*>* ocvrs_return) { + try { + std::vector> ret = cv::dnn::getAvailableBackends(); + Ok(new std::vector>(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_getAvailableTargets_Backend(cv::dnn::Backend be, Result*>* ocvrs_return) { + try { + std::vector ret = cv::dnn::getAvailableTargets(be); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_getInferenceEngineBackendType(Result* ocvrs_return) { + try { + cv::String ret = cv::dnn::getInferenceEngineBackendType(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_getInferenceEngineCPUType(Result* ocvrs_return) { + try { + cv::String ret = cv::dnn::getInferenceEngineCPUType(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_getInferenceEngineVPUType(Result* ocvrs_return) { + try { + cv::String ret = cv::dnn::getInferenceEngineVPUType(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_getPlane_const_MatR_int_int(const cv::Mat* m, int n, int cn, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::getPlane(*m, n, cn); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_imagesFromBlob_const_MatR_const__OutputArrayR(const cv::Mat* blob_, const cv::_OutputArray* images_, ResultVoid* ocvrs_return) { + try { + cv::dnn::imagesFromBlob(*blob_, *images_); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromCaffe_const_StringR(const char* prototxt, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromCaffe(std::string(prototxt)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromCaffe_const_StringR_const_StringR(const char* prototxt, const char* caffeModel, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromCaffe(std::string(prototxt), std::string(caffeModel)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromCaffe_const_charX_size_t(const char* bufferProto, size_t lenProto, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromCaffe(bufferProto, lenProto); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromCaffe_const_charX_size_t_const_charX_size_t(const char* bufferProto, size_t lenProto, const char* bufferModel, size_t lenModel, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromCaffe(bufferProto, lenProto, bufferModel, lenModel); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromCaffe_const_vectorLunsigned_charGR(const std::vector* bufferProto, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromCaffe(*bufferProto); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromCaffe_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(const std::vector* bufferProto, const std::vector* bufferModel, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromCaffe(*bufferProto, *bufferModel); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromDarknet_const_StringR(const char* cfgFile, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromDarknet(std::string(cfgFile)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromDarknet_const_StringR_const_StringR(const char* cfgFile, const char* darknetModel, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromDarknet(std::string(cfgFile), std::string(darknetModel)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromDarknet_const_charX_size_t(const char* bufferCfg, size_t lenCfg, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromDarknet(bufferCfg, lenCfg); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromDarknet_const_charX_size_t_const_charX_size_t(const char* bufferCfg, size_t lenCfg, const char* bufferModel, size_t lenModel, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromDarknet(bufferCfg, lenCfg, bufferModel, lenModel); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromDarknet_const_vectorLunsigned_charGR(const std::vector* bufferCfg, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromDarknet(*bufferCfg); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromDarknet_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(const std::vector* bufferCfg, const std::vector* bufferModel, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromDarknet(*bufferCfg, *bufferModel); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromModelOptimizer_const_StringR(const char* xml, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromModelOptimizer(std::string(xml)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromModelOptimizer_const_StringR_const_StringR(const char* xml, const char* bin, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromModelOptimizer(std::string(xml), std::string(bin)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromModelOptimizer_const_unsigned_charX_size_t_const_unsigned_charX_size_t(const unsigned char* bufferModelConfigPtr, size_t bufferModelConfigSize, const unsigned char* bufferWeightsPtr, size_t bufferWeightsSize, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromModelOptimizer(bufferModelConfigPtr, bufferModelConfigSize, bufferWeightsPtr, bufferWeightsSize); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromModelOptimizer_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(const std::vector* bufferModelConfig, const std::vector* bufferWeights, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromModelOptimizer(*bufferModelConfig, *bufferWeights); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromONNX_const_StringR(const char* onnxFile, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromONNX(std::string(onnxFile)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromONNX_const_charX_size_t(const char* buffer, size_t sizeBuffer, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromONNX(buffer, sizeBuffer); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromONNX_const_vectorLunsigned_charGR(const std::vector* buffer, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromONNX(*buffer); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromTFLite_const_StringR(const char* model, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromTFLite(std::string(model)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromTFLite_const_charX_size_t(const char* bufferModel, size_t lenModel, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromTFLite(bufferModel, lenModel); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromTFLite_const_vectorLunsigned_charGR(const std::vector* bufferModel, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromTFLite(*bufferModel); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromTensorflow_const_StringR(const char* model, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromTensorflow(std::string(model)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromTensorflow_const_StringR_const_StringR(const char* model, const char* config, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromTensorflow(std::string(model), std::string(config)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromTensorflow_const_charX_size_t(const char* bufferModel, size_t lenModel, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromTensorflow(bufferModel, lenModel); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromTensorflow_const_charX_size_t_const_charX_size_t(const char* bufferModel, size_t lenModel, const char* bufferConfig, size_t lenConfig, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromTensorflow(bufferModel, lenModel, bufferConfig, lenConfig); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromTensorflow_const_vectorLunsigned_charGR(const std::vector* bufferModel, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromTensorflow(*bufferModel); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromTensorflow_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(const std::vector* bufferModel, const std::vector* bufferConfig, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromTensorflow(*bufferModel, *bufferConfig); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromTorch_const_StringR(const char* model, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromTorch(std::string(model)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNetFromTorch_const_StringR_bool_bool(const char* model, bool isBinary, bool evaluate, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNetFromTorch(std::string(model), isBinary, evaluate); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNet_const_StringR(const char* model, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNet(std::string(model)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNet_const_StringR_const_StringR_const_StringR(const char* model, const char* config, const char* framework, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNet(std::string(model), std::string(config), std::string(framework)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNet_const_StringR_const_vectorLunsigned_charGR(const char* framework, const std::vector* bufferModel, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNet(std::string(framework), *bufferModel); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readNet_const_StringR_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(const char* framework, const std::vector* bufferModel, const std::vector* bufferConfig, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::readNet(std::string(framework), *bufferModel, *bufferConfig); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readTensorFromONNX_const_StringR(const char* path, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::readTensorFromONNX(std::string(path)); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readTorchBlob_const_StringR(const char* filename, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::readTorchBlob(std::string(filename)); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_readTorchBlob_const_StringR_bool(const char* filename, bool isBinary, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::readTorchBlob(std::string(filename), isBinary); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_releaseHDDLPlugin(ResultVoid* ocvrs_return) { + try { + cv::dnn::releaseHDDLPlugin(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_resetMyriadDevice(ResultVoid* ocvrs_return) { + try { + cv::dnn::resetMyriadDevice(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_setInferenceEngineBackendType_const_StringR(const char* newBackendType, Result* ocvrs_return) { + try { + cv::String ret = cv::dnn::setInferenceEngineBackendType(std::string(newBackendType)); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_shape_const_MatR(const cv::Mat* mat, Result* ocvrs_return) { + try { + cv::dnn::MatShape ret = cv::dnn::shape(*mat); + Ok(new cv::dnn::MatShape(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_shape_const_MatSizeR(const cv::MatSize* sz, Result* ocvrs_return) { + try { + cv::dnn::MatShape ret = cv::dnn::shape(*sz); + Ok(new cv::dnn::MatShape(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_shape_const_UMatR(const cv::UMat* mat, Result* ocvrs_return) { + try { + cv::dnn::MatShape ret = cv::dnn::shape(*mat); + Ok(new cv::dnn::MatShape(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_shape_const_intX_const_int(const int* dims, const int n, Result* ocvrs_return) { + try { + cv::dnn::MatShape ret = cv::dnn::shape(dims, n); + Ok(new cv::dnn::MatShape(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_shape_int(int a0, Result* ocvrs_return) { + try { + cv::dnn::MatShape ret = cv::dnn::shape(a0); + Ok(new cv::dnn::MatShape(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_shape_int_int_int_int(int a0, int a1, int a2, int a3, Result* ocvrs_return) { + try { + cv::dnn::MatShape ret = cv::dnn::shape(a0, a1, a2, a3); + Ok(new cv::dnn::MatShape(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_shrinkCaffeModel_const_StringR_const_StringR(const char* src, const char* dst, ResultVoid* ocvrs_return) { + try { + cv::dnn::shrinkCaffeModel(std::string(src), std::string(dst)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_shrinkCaffeModel_const_StringR_const_StringR_const_vectorLStringGR(const char* src, const char* dst, const std::vector* layersTypes, ResultVoid* ocvrs_return) { + try { + cv::dnn::shrinkCaffeModel(std::string(src), std::string(dst), *layersTypes); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_slice_const_MatR_const__RangeR(const cv::Mat* m, const cv::dnn::_Range* r0, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::slice(*m, *r0); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_slice_const_MatR_const__RangeR_const__RangeR(const cv::Mat* m, const cv::dnn::_Range* r0, const cv::dnn::_Range* r1, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::slice(*m, *r0, *r1); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_slice_const_MatR_const__RangeR_const__RangeR_const__RangeR(const cv::Mat* m, const cv::dnn::_Range* r0, const cv::dnn::_Range* r1, const cv::dnn::_Range* r2, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::slice(*m, *r0, *r1, *r2); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_slice_const_MatR_const__RangeR_const__RangeR_const__RangeR_const__RangeR(const cv::Mat* m, const cv::dnn::_Range* r0, const cv::dnn::_Range* r1, const cv::dnn::_Range* r2, const cv::dnn::_Range* r3, Result* ocvrs_return) { + try { + cv::Mat ret = cv::dnn::slice(*m, *r0, *r1, *r2, *r3); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_softNMSBoxes_const_vectorLRectGR_const_vectorLfloatGR_vectorLfloatGR_const_float_const_float_vectorLintGR(const std::vector* bboxes, const std::vector* scores, std::vector* updated_scores, const float score_threshold, const float nms_threshold, std::vector* indices, ResultVoid* ocvrs_return) { + try { + cv::dnn::softNMSBoxes(*bboxes, *scores, *updated_scores, score_threshold, nms_threshold, *indices); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_softNMSBoxes_const_vectorLRectGR_const_vectorLfloatGR_vectorLfloatGR_const_float_const_float_vectorLintGR_size_t_const_float_SoftNMSMethod(const std::vector* bboxes, const std::vector* scores, std::vector* updated_scores, const float score_threshold, const float nms_threshold, std::vector* indices, size_t top_k, const float sigma, cv::dnn::SoftNMSMethod method, ResultVoid* ocvrs_return) { + try { + cv::dnn::softNMSBoxes(*bboxes, *scores, *updated_scores, score_threshold, nms_threshold, *indices, top_k, sigma, method); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_total_const_MatR(const cv::Mat* mat, Result* ocvrs_return) { + try { + int ret = cv::dnn::total(*mat); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_total_const_MatR_int_int(const cv::Mat* mat, int start, int end, Result* ocvrs_return) { + try { + int ret = cv::dnn::total(*mat, start, end); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_total_const_MatShapeR(const cv::dnn::MatShape* shape, Result* ocvrs_return) { + try { + int ret = cv::dnn::total(*shape); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_total_const_MatShapeR_int_int(const cv::dnn::MatShape* shape, int start, int end, Result* ocvrs_return) { + try { + int ret = cv::dnn::total(*shape, start, end); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_writeTextGraph_const_StringR_const_StringR(const char* model, const char* output, ResultVoid* ocvrs_return) { + try { + cv::dnn::writeTextGraph(std::string(model), std::string(output)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_AbsLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::AbsLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::AbsLayer* cv_dnn_AbsLayer_defaultNew_const() { + cv::dnn::AbsLayer* ret = new cv::dnn::AbsLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_AbsLayer_to_ActivationLayer(cv::dnn::AbsLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_AbsLayer_to_Algorithm(cv::dnn::AbsLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_AbsLayer_to_Layer(cv::dnn::AbsLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_AbsLayer_delete(cv::dnn::AbsLayer* instance) { + delete instance; +} + +void cv_dnn_AccumLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::AccumLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::AccumLayer* cv_dnn_AccumLayer_defaultNew_const() { + cv::dnn::AccumLayer* ret = new cv::dnn::AccumLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_AccumLayer_to_Algorithm(cv::dnn::AccumLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_AccumLayer_to_Layer(cv::dnn::AccumLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_AccumLayer_delete(cv::dnn::AccumLayer* instance) { + delete instance; +} + +void cv_dnn_AcosLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::AcosLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::AcosLayer* cv_dnn_AcosLayer_defaultNew_const() { + cv::dnn::AcosLayer* ret = new cv::dnn::AcosLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_AcosLayer_to_ActivationLayer(cv::dnn::AcosLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_AcosLayer_to_Algorithm(cv::dnn::AcosLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_AcosLayer_to_Layer(cv::dnn::AcosLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_AcosLayer_delete(cv::dnn::AcosLayer* instance) { + delete instance; +} + +void cv_dnn_AcoshLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::AcoshLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::AcoshLayer* cv_dnn_AcoshLayer_defaultNew_const() { + cv::dnn::AcoshLayer* ret = new cv::dnn::AcoshLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_AcoshLayer_to_ActivationLayer(cv::dnn::AcoshLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_AcoshLayer_to_Algorithm(cv::dnn::AcoshLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_AcoshLayer_to_Layer(cv::dnn::AcoshLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_AcoshLayer_delete(cv::dnn::AcoshLayer* instance) { + delete instance; +} + +void cv_dnn_ActivationLayer_forwardSlice_const_const_floatX_floatX_int_size_t_int_int(const cv::dnn::ActivationLayer* instance, const float* src, float* dst, int len, size_t outPlaneSize, int cn0, int cn1, ResultVoid* ocvrs_return) { + try { + instance->forwardSlice(src, dst, len, outPlaneSize, cn0, cn1); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_ActivationLayer_forwardSlice_const_const_intX_const_intX_intX_int_size_t_int_int(const cv::dnn::ActivationLayer* instance, const int* src, const int* lut, int* dst, int len, size_t outPlaneSize, int cn0, int cn1, ResultVoid* ocvrs_return) { + try { + instance->forwardSlice(src, lut, dst, len, outPlaneSize, cn0, cn1); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ActivationLayer* cv_dnn_ActivationLayer_defaultNew_const() { + cv::dnn::ActivationLayer* ret = new cv::dnn::ActivationLayer(); + return ret; +} + +cv::dnn::AbsLayer* cv_dnn_ActivationLayer_to_AbsLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AcosLayer* cv_dnn_ActivationLayer_to_AcosLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AcoshLayer* cv_dnn_ActivationLayer_to_AcoshLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ActivationLayerInt8* cv_dnn_ActivationLayer_to_ActivationLayerInt8(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AsinLayer* cv_dnn_ActivationLayer_to_AsinLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AsinhLayer* cv_dnn_ActivationLayer_to_AsinhLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AtanLayer* cv_dnn_ActivationLayer_to_AtanLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AtanhLayer* cv_dnn_ActivationLayer_to_AtanhLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::BNLLLayer* cv_dnn_ActivationLayer_to_BNLLLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::BatchNormLayer* cv_dnn_ActivationLayer_to_BatchNormLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::BatchNormLayerInt8* cv_dnn_ActivationLayer_to_BatchNormLayerInt8(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::CeilLayer* cv_dnn_ActivationLayer_to_CeilLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::CeluLayer* cv_dnn_ActivationLayer_to_CeluLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ChannelsPReLULayer* cv_dnn_ActivationLayer_to_ChannelsPReLULayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::CosLayer* cv_dnn_ActivationLayer_to_CosLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::CoshLayer* cv_dnn_ActivationLayer_to_CoshLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ELULayer* cv_dnn_ActivationLayer_to_ELULayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ErfLayer* cv_dnn_ActivationLayer_to_ErfLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ExpLayer* cv_dnn_ActivationLayer_to_ExpLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::FloorLayer* cv_dnn_ActivationLayer_to_FloorLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::GeluApproximationLayer* cv_dnn_ActivationLayer_to_GeluApproximationLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::GeluLayer* cv_dnn_ActivationLayer_to_GeluLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::HardSigmoidLayer* cv_dnn_ActivationLayer_to_HardSigmoidLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::HardSwishLayer* cv_dnn_ActivationLayer_to_HardSwishLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::LogLayer* cv_dnn_ActivationLayer_to_LogLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::MishLayer* cv_dnn_ActivationLayer_to_MishLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::NotLayer* cv_dnn_ActivationLayer_to_NotLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::PowerLayer* cv_dnn_ActivationLayer_to_PowerLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ReLU6Layer* cv_dnn_ActivationLayer_to_ReLU6Layer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ReLULayer* cv_dnn_ActivationLayer_to_ReLULayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ReciprocalLayer* cv_dnn_ActivationLayer_to_ReciprocalLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::RoundLayer* cv_dnn_ActivationLayer_to_RoundLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SeluLayer* cv_dnn_ActivationLayer_to_SeluLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ShrinkLayer* cv_dnn_ActivationLayer_to_ShrinkLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SigmoidLayer* cv_dnn_ActivationLayer_to_SigmoidLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SignLayer* cv_dnn_ActivationLayer_to_SignLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SinLayer* cv_dnn_ActivationLayer_to_SinLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SinhLayer* cv_dnn_ActivationLayer_to_SinhLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SoftplusLayer* cv_dnn_ActivationLayer_to_SoftplusLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SoftsignLayer* cv_dnn_ActivationLayer_to_SoftsignLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SqrtLayer* cv_dnn_ActivationLayer_to_SqrtLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SwishLayer* cv_dnn_ActivationLayer_to_SwishLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::TanHLayer* cv_dnn_ActivationLayer_to_TanHLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::TanLayer* cv_dnn_ActivationLayer_to_TanLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ThresholdedReluLayer* cv_dnn_ActivationLayer_to_ThresholdedReluLayer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_ActivationLayer_to_Algorithm(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ActivationLayer_to_Layer(cv::dnn::ActivationLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ActivationLayer_delete(cv::dnn::ActivationLayer* instance) { + delete instance; +} + +void cv_dnn_ActivationLayerInt8_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ActivationLayerInt8::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ActivationLayerInt8* cv_dnn_ActivationLayerInt8_defaultNew_const() { + cv::dnn::ActivationLayerInt8* ret = new cv::dnn::ActivationLayerInt8(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_ActivationLayerInt8_to_ActivationLayer(cv::dnn::ActivationLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_ActivationLayerInt8_to_Algorithm(cv::dnn::ActivationLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ActivationLayerInt8_to_Layer(cv::dnn::ActivationLayerInt8* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ActivationLayerInt8_delete(cv::dnn::ActivationLayerInt8* instance) { + delete instance; +} + +void cv_dnn_ArgLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ArgLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ArgLayer* cv_dnn_ArgLayer_defaultNew_const() { + cv::dnn::ArgLayer* ret = new cv::dnn::ArgLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_ArgLayer_to_Algorithm(cv::dnn::ArgLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ArgLayer_to_Layer(cv::dnn::ArgLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ArgLayer_delete(cv::dnn::ArgLayer* instance) { + delete instance; +} + +void cv_dnn_AsinLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::AsinLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::AsinLayer* cv_dnn_AsinLayer_defaultNew_const() { + cv::dnn::AsinLayer* ret = new cv::dnn::AsinLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_AsinLayer_to_ActivationLayer(cv::dnn::AsinLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_AsinLayer_to_Algorithm(cv::dnn::AsinLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_AsinLayer_to_Layer(cv::dnn::AsinLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_AsinLayer_delete(cv::dnn::AsinLayer* instance) { + delete instance; +} + +void cv_dnn_AsinhLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::AsinhLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::AsinhLayer* cv_dnn_AsinhLayer_defaultNew_const() { + cv::dnn::AsinhLayer* ret = new cv::dnn::AsinhLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_AsinhLayer_to_ActivationLayer(cv::dnn::AsinhLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_AsinhLayer_to_Algorithm(cv::dnn::AsinhLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_AsinhLayer_to_Layer(cv::dnn::AsinhLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_AsinhLayer_delete(cv::dnn::AsinhLayer* instance) { + delete instance; +} + +void cv_dnn_AtanLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::AtanLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::AtanLayer* cv_dnn_AtanLayer_defaultNew_const() { + cv::dnn::AtanLayer* ret = new cv::dnn::AtanLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_AtanLayer_to_ActivationLayer(cv::dnn::AtanLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_AtanLayer_to_Algorithm(cv::dnn::AtanLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_AtanLayer_to_Layer(cv::dnn::AtanLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_AtanLayer_delete(cv::dnn::AtanLayer* instance) { + delete instance; +} + +void cv_dnn_AtanhLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::AtanhLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::AtanhLayer* cv_dnn_AtanhLayer_defaultNew_const() { + cv::dnn::AtanhLayer* ret = new cv::dnn::AtanhLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_AtanhLayer_to_ActivationLayer(cv::dnn::AtanhLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_AtanhLayer_to_Algorithm(cv::dnn::AtanhLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_AtanhLayer_to_Layer(cv::dnn::AtanhLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_AtanhLayer_delete(cv::dnn::AtanhLayer* instance) { + delete instance; +} + +void cv_dnn_AttentionLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::AttentionLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::AttentionLayer* cv_dnn_AttentionLayer_defaultNew_const() { + cv::dnn::AttentionLayer* ret = new cv::dnn::AttentionLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_AttentionLayer_to_Algorithm(cv::dnn::AttentionLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_AttentionLayer_to_Layer(cv::dnn::AttentionLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_AttentionLayer_delete(cv::dnn::AttentionLayer* instance) { + delete instance; +} + +void cv_dnn_BNLLLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::BNLLLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::BNLLLayer* cv_dnn_BNLLLayer_defaultNew_const() { + cv::dnn::BNLLLayer* ret = new cv::dnn::BNLLLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_BNLLLayer_to_ActivationLayer(cv::dnn::BNLLLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_BNLLLayer_to_Algorithm(cv::dnn::BNLLLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_BNLLLayer_to_Layer(cv::dnn::BNLLLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_BNLLLayer_delete(cv::dnn::BNLLLayer* instance) { + delete instance; +} + +int cv_dnn_BackendNode_propBackendId_const(const cv::dnn::BackendNode* instance) { + int ret = instance->backendId; + return ret; +} + +void cv_dnn_BackendNode_propBackendId_const_int(cv::dnn::BackendNode* instance, const int val) { + instance->backendId = val; +} + +void cv_dnn_BackendNode_delete(cv::dnn::BackendNode* instance) { + delete instance; +} + +void cv_dnn_BackendWrapper_copyToHost(cv::dnn::BackendWrapper* instance, ResultVoid* ocvrs_return) { + try { + instance->copyToHost(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_BackendWrapper_setHostDirty(cv::dnn::BackendWrapper* instance, ResultVoid* ocvrs_return) { + try { + instance->setHostDirty(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +int cv_dnn_BackendWrapper_propBackendId_const(const cv::dnn::BackendWrapper* instance) { + int ret = instance->backendId; + return ret; +} + +void cv_dnn_BackendWrapper_propBackendId_const_int(cv::dnn::BackendWrapper* instance, const int val) { + instance->backendId = val; +} + +int cv_dnn_BackendWrapper_propTargetId_const(const cv::dnn::BackendWrapper* instance) { + int ret = instance->targetId; + return ret; +} + +void cv_dnn_BackendWrapper_propTargetId_const_int(cv::dnn::BackendWrapper* instance, const int val) { + instance->targetId = val; +} + +void cv_dnn_BackendWrapper_delete(cv::dnn::BackendWrapper* instance) { + delete instance; +} + +cv::dnn::BaseConvolutionLayer* cv_dnn_BaseConvolutionLayer_defaultNew_const() { + cv::dnn::BaseConvolutionLayer* ret = new cv::dnn::BaseConvolutionLayer(); + return ret; +} + +void cv_dnn_BaseConvolutionLayer_propKernel_const(const cv::dnn::BaseConvolutionLayer* instance, cv::Size* ocvrs_return) { + cv::Size ret = instance->kernel; + *ocvrs_return = ret; +} + +void cv_dnn_BaseConvolutionLayer_propKernel_const_Size(cv::dnn::BaseConvolutionLayer* instance, const cv::Size* val) { + instance->kernel = *val; +} + +void cv_dnn_BaseConvolutionLayer_propStride_const(const cv::dnn::BaseConvolutionLayer* instance, cv::Size* ocvrs_return) { + cv::Size ret = instance->stride; + *ocvrs_return = ret; +} + +void cv_dnn_BaseConvolutionLayer_propStride_const_Size(cv::dnn::BaseConvolutionLayer* instance, const cv::Size* val) { + instance->stride = *val; +} + +void cv_dnn_BaseConvolutionLayer_propPad_const(const cv::dnn::BaseConvolutionLayer* instance, cv::Size* ocvrs_return) { + cv::Size ret = instance->pad; + *ocvrs_return = ret; +} + +void cv_dnn_BaseConvolutionLayer_propPad_const_Size(cv::dnn::BaseConvolutionLayer* instance, const cv::Size* val) { + instance->pad = *val; +} + +void cv_dnn_BaseConvolutionLayer_propDilation_const(const cv::dnn::BaseConvolutionLayer* instance, cv::Size* ocvrs_return) { + cv::Size ret = instance->dilation; + *ocvrs_return = ret; +} + +void cv_dnn_BaseConvolutionLayer_propDilation_const_Size(cv::dnn::BaseConvolutionLayer* instance, const cv::Size* val) { + instance->dilation = *val; +} + +void cv_dnn_BaseConvolutionLayer_propAdjustPad_const(const cv::dnn::BaseConvolutionLayer* instance, cv::Size* ocvrs_return) { + cv::Size ret = instance->adjustPad; + *ocvrs_return = ret; +} + +void cv_dnn_BaseConvolutionLayer_propAdjustPad_const_Size(cv::dnn::BaseConvolutionLayer* instance, const cv::Size* val) { + instance->adjustPad = *val; +} + +std::vector* cv_dnn_BaseConvolutionLayer_propAdjust_pads_const(const cv::dnn::BaseConvolutionLayer* instance) { + std::vector ret = instance->adjust_pads; + return new std::vector(ret); +} + +void cv_dnn_BaseConvolutionLayer_propAdjust_pads_const_vectorLsize_tG(cv::dnn::BaseConvolutionLayer* instance, const std::vector* val) { + instance->adjust_pads = *val; +} + +std::vector* cv_dnn_BaseConvolutionLayer_propKernel_size_const(const cv::dnn::BaseConvolutionLayer* instance) { + std::vector ret = instance->kernel_size; + return new std::vector(ret); +} + +void cv_dnn_BaseConvolutionLayer_propKernel_size_const_vectorLsize_tG(cv::dnn::BaseConvolutionLayer* instance, const std::vector* val) { + instance->kernel_size = *val; +} + +std::vector* cv_dnn_BaseConvolutionLayer_propStrides_const(const cv::dnn::BaseConvolutionLayer* instance) { + std::vector ret = instance->strides; + return new std::vector(ret); +} + +void cv_dnn_BaseConvolutionLayer_propStrides_const_vectorLsize_tG(cv::dnn::BaseConvolutionLayer* instance, const std::vector* val) { + instance->strides = *val; +} + +std::vector* cv_dnn_BaseConvolutionLayer_propDilations_const(const cv::dnn::BaseConvolutionLayer* instance) { + std::vector ret = instance->dilations; + return new std::vector(ret); +} + +void cv_dnn_BaseConvolutionLayer_propDilations_const_vectorLsize_tG(cv::dnn::BaseConvolutionLayer* instance, const std::vector* val) { + instance->dilations = *val; +} + +std::vector* cv_dnn_BaseConvolutionLayer_propPads_begin_const(const cv::dnn::BaseConvolutionLayer* instance) { + std::vector ret = instance->pads_begin; + return new std::vector(ret); +} + +void cv_dnn_BaseConvolutionLayer_propPads_begin_const_vectorLsize_tG(cv::dnn::BaseConvolutionLayer* instance, const std::vector* val) { + instance->pads_begin = *val; +} + +std::vector* cv_dnn_BaseConvolutionLayer_propPads_end_const(const cv::dnn::BaseConvolutionLayer* instance) { + std::vector ret = instance->pads_end; + return new std::vector(ret); +} + +void cv_dnn_BaseConvolutionLayer_propPads_end_const_vectorLsize_tG(cv::dnn::BaseConvolutionLayer* instance, const std::vector* val) { + instance->pads_end = *val; +} + +void* cv_dnn_BaseConvolutionLayer_propPadMode_const(const cv::dnn::BaseConvolutionLayer* instance) { + cv::String ret = instance->padMode; + return ocvrs_create_string(ret.c_str()); +} + +void cv_dnn_BaseConvolutionLayer_propPadMode_const_String(cv::dnn::BaseConvolutionLayer* instance, const char* val) { + instance->padMode = std::string(val); +} + +int cv_dnn_BaseConvolutionLayer_propNumOutput_const(const cv::dnn::BaseConvolutionLayer* instance) { + int ret = instance->numOutput; + return ret; +} + +void cv_dnn_BaseConvolutionLayer_propNumOutput_const_int(cv::dnn::BaseConvolutionLayer* instance, const int val) { + instance->numOutput = val; +} + +cv::Algorithm* cv_dnn_BaseConvolutionLayer_to_Algorithm(cv::dnn::BaseConvolutionLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_BaseConvolutionLayer_to_Layer(cv::dnn::BaseConvolutionLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_BaseConvolutionLayer_delete(cv::dnn::BaseConvolutionLayer* instance) { + delete instance; +} + +void cv_dnn_BatchNormLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::BatchNormLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::BatchNormLayer* cv_dnn_BatchNormLayer_defaultNew_const() { + cv::dnn::BatchNormLayer* ret = new cv::dnn::BatchNormLayer(); + return ret; +} + +bool cv_dnn_BatchNormLayer_propHasWeights_const(const cv::dnn::BatchNormLayer* instance) { + bool ret = instance->hasWeights; + return ret; +} + +void cv_dnn_BatchNormLayer_propHasWeights_const_bool(cv::dnn::BatchNormLayer* instance, const bool val) { + instance->hasWeights = val; +} + +bool cv_dnn_BatchNormLayer_propHasBias_const(const cv::dnn::BatchNormLayer* instance) { + bool ret = instance->hasBias; + return ret; +} + +void cv_dnn_BatchNormLayer_propHasBias_const_bool(cv::dnn::BatchNormLayer* instance, const bool val) { + instance->hasBias = val; +} + +float cv_dnn_BatchNormLayer_propEpsilon_const(const cv::dnn::BatchNormLayer* instance) { + float ret = instance->epsilon; + return ret; +} + +void cv_dnn_BatchNormLayer_propEpsilon_const_float(cv::dnn::BatchNormLayer* instance, const float val) { + instance->epsilon = val; +} + +cv::dnn::ActivationLayer* cv_dnn_BatchNormLayer_to_ActivationLayer(cv::dnn::BatchNormLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_BatchNormLayer_to_Algorithm(cv::dnn::BatchNormLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_BatchNormLayer_to_Layer(cv::dnn::BatchNormLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_BatchNormLayer_delete(cv::dnn::BatchNormLayer* instance) { + delete instance; +} + +void cv_dnn_BatchNormLayerInt8_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::BatchNormLayerInt8::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::BatchNormLayerInt8* cv_dnn_BatchNormLayerInt8_defaultNew_const() { + cv::dnn::BatchNormLayerInt8* ret = new cv::dnn::BatchNormLayerInt8(); + return ret; +} + +float cv_dnn_BatchNormLayerInt8_propInput_sc_const(const cv::dnn::BatchNormLayerInt8* instance) { + float ret = instance->input_sc; + return ret; +} + +void cv_dnn_BatchNormLayerInt8_propInput_sc_const_float(cv::dnn::BatchNormLayerInt8* instance, const float val) { + instance->input_sc = val; +} + +float cv_dnn_BatchNormLayerInt8_propOutput_sc_const(const cv::dnn::BatchNormLayerInt8* instance) { + float ret = instance->output_sc; + return ret; +} + +void cv_dnn_BatchNormLayerInt8_propOutput_sc_const_float(cv::dnn::BatchNormLayerInt8* instance, const float val) { + instance->output_sc = val; +} + +int cv_dnn_BatchNormLayerInt8_propInput_zp_const(const cv::dnn::BatchNormLayerInt8* instance) { + int ret = instance->input_zp; + return ret; +} + +void cv_dnn_BatchNormLayerInt8_propInput_zp_const_int(cv::dnn::BatchNormLayerInt8* instance, const int val) { + instance->input_zp = val; +} + +int cv_dnn_BatchNormLayerInt8_propOutput_zp_const(const cv::dnn::BatchNormLayerInt8* instance) { + int ret = instance->output_zp; + return ret; +} + +void cv_dnn_BatchNormLayerInt8_propOutput_zp_const_int(cv::dnn::BatchNormLayerInt8* instance, const int val) { + instance->output_zp = val; +} + +cv::dnn::ActivationLayer* cv_dnn_BatchNormLayerInt8_to_ActivationLayer(cv::dnn::BatchNormLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_BatchNormLayerInt8_to_Algorithm(cv::dnn::BatchNormLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::BatchNormLayer* cv_dnn_BatchNormLayerInt8_to_BatchNormLayer(cv::dnn::BatchNormLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_BatchNormLayerInt8_to_Layer(cv::dnn::BatchNormLayerInt8* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_BatchNormLayerInt8_delete(cv::dnn::BatchNormLayerInt8* instance) { + delete instance; +} + +void cv_dnn_BlankLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::BlankLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::BlankLayer* cv_dnn_BlankLayer_defaultNew_const() { + cv::dnn::BlankLayer* ret = new cv::dnn::BlankLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_BlankLayer_to_Algorithm(cv::dnn::BlankLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_BlankLayer_to_Layer(cv::dnn::BlankLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_BlankLayer_delete(cv::dnn::BlankLayer* instance) { + delete instance; +} + +void cv_dnn_CeilLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::CeilLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::CeilLayer* cv_dnn_CeilLayer_defaultNew_const() { + cv::dnn::CeilLayer* ret = new cv::dnn::CeilLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_CeilLayer_to_ActivationLayer(cv::dnn::CeilLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_CeilLayer_to_Algorithm(cv::dnn::CeilLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_CeilLayer_to_Layer(cv::dnn::CeilLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_CeilLayer_delete(cv::dnn::CeilLayer* instance) { + delete instance; +} + +void cv_dnn_CeluLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::CeluLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::CeluLayer* cv_dnn_CeluLayer_defaultNew_const() { + cv::dnn::CeluLayer* ret = new cv::dnn::CeluLayer(); + return ret; +} + +float cv_dnn_CeluLayer_propAlpha_const(const cv::dnn::CeluLayer* instance) { + float ret = instance->alpha; + return ret; +} + +void cv_dnn_CeluLayer_propAlpha_const_float(cv::dnn::CeluLayer* instance, const float val) { + instance->alpha = val; +} + +cv::dnn::ActivationLayer* cv_dnn_CeluLayer_to_ActivationLayer(cv::dnn::CeluLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_CeluLayer_to_Algorithm(cv::dnn::CeluLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_CeluLayer_to_Layer(cv::dnn::CeluLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_CeluLayer_delete(cv::dnn::CeluLayer* instance) { + delete instance; +} + +void cv_dnn_ChannelsPReLULayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ChannelsPReLULayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ChannelsPReLULayer* cv_dnn_ChannelsPReLULayer_defaultNew_const() { + cv::dnn::ChannelsPReLULayer* ret = new cv::dnn::ChannelsPReLULayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_ChannelsPReLULayer_to_ActivationLayer(cv::dnn::ChannelsPReLULayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_ChannelsPReLULayer_to_Algorithm(cv::dnn::ChannelsPReLULayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ChannelsPReLULayer_to_Layer(cv::dnn::ChannelsPReLULayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ChannelsPReLULayer_delete(cv::dnn::ChannelsPReLULayer* instance) { + delete instance; +} + +void cv_dnn_ClassificationModel_ClassificationModel(Result* ocvrs_return) { + try { + cv::dnn::ClassificationModel* ret = new cv::dnn::ClassificationModel(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_ClassificationModel_ClassificationModel_const_StringR_const_StringR(const char* model, const char* config, Result* ocvrs_return) { + try { + cv::dnn::ClassificationModel* ret = new cv::dnn::ClassificationModel(std::string(model), std::string(config)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_ClassificationModel_ClassificationModel_const_StringR(const char* model, Result* ocvrs_return) { + try { + cv::dnn::ClassificationModel* ret = new cv::dnn::ClassificationModel(std::string(model)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_ClassificationModel_ClassificationModel_const_NetR(const cv::dnn::Net* network, Result* ocvrs_return) { + try { + cv::dnn::ClassificationModel* ret = new cv::dnn::ClassificationModel(*network); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_ClassificationModel_setEnableSoftmaxPostProcessing_bool(cv::dnn::ClassificationModel* instance, bool enable, Result* ocvrs_return) { + try { + cv::dnn::ClassificationModel ret = instance->setEnableSoftmaxPostProcessing(enable); + Ok(new cv::dnn::ClassificationModel(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_ClassificationModel_getEnableSoftmaxPostProcessing_const(const cv::dnn::ClassificationModel* instance, Result* ocvrs_return) { + try { + bool ret = instance->getEnableSoftmaxPostProcessing(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_ClassificationModel_classify_const__InputArrayR(cv::dnn::ClassificationModel* instance, const cv::_InputArray* frame, Result*>* ocvrs_return) { + try { + std::pair ret = instance->classify(*frame); + Ok(new std::pair(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_ClassificationModel_classify_const__InputArrayR_intR_floatR(cv::dnn::ClassificationModel* instance, const cv::_InputArray* frame, int* classId, float* conf, ResultVoid* ocvrs_return) { + try { + instance->classify(*frame, *classId, *conf); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ClassificationModel* cv_dnn_ClassificationModel_implicitClone_const(const cv::dnn::ClassificationModel* instance) { + return new cv::dnn::ClassificationModel(*instance); +} + +cv::dnn::Model* cv_dnn_ClassificationModel_to_Model(cv::dnn::ClassificationModel* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ClassificationModel_delete(cv::dnn::ClassificationModel* instance) { + delete instance; +} + +void cv_dnn_CompareLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::CompareLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::CompareLayer* cv_dnn_CompareLayer_defaultNew_const() { + cv::dnn::CompareLayer* ret = new cv::dnn::CompareLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_CompareLayer_to_Algorithm(cv::dnn::CompareLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_CompareLayer_to_Layer(cv::dnn::CompareLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_CompareLayer_delete(cv::dnn::CompareLayer* instance) { + delete instance; +} + +void cv_dnn_ConcatLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ConcatLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ConcatLayer* cv_dnn_ConcatLayer_defaultNew_const() { + cv::dnn::ConcatLayer* ret = new cv::dnn::ConcatLayer(); + return ret; +} + +int cv_dnn_ConcatLayer_propAxis_const(const cv::dnn::ConcatLayer* instance) { + int ret = instance->axis; + return ret; +} + +void cv_dnn_ConcatLayer_propAxis_const_int(cv::dnn::ConcatLayer* instance, const int val) { + instance->axis = val; +} + +bool cv_dnn_ConcatLayer_propPadding_const(const cv::dnn::ConcatLayer* instance) { + bool ret = instance->padding; + return ret; +} + +void cv_dnn_ConcatLayer_propPadding_const_bool(cv::dnn::ConcatLayer* instance, const bool val) { + instance->padding = val; +} + +int cv_dnn_ConcatLayer_propPaddingValue_const(const cv::dnn::ConcatLayer* instance) { + int ret = instance->paddingValue; + return ret; +} + +void cv_dnn_ConcatLayer_propPaddingValue_const_int(cv::dnn::ConcatLayer* instance, const int val) { + instance->paddingValue = val; +} + +cv::Algorithm* cv_dnn_ConcatLayer_to_Algorithm(cv::dnn::ConcatLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ConcatLayer_to_Layer(cv::dnn::ConcatLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ConcatLayer_delete(cv::dnn::ConcatLayer* instance) { + delete instance; +} + +void cv_dnn_ConstLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ConstLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ConstLayer* cv_dnn_ConstLayer_defaultNew_const() { + cv::dnn::ConstLayer* ret = new cv::dnn::ConstLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_ConstLayer_to_Algorithm(cv::dnn::ConstLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ConstLayer_to_Layer(cv::dnn::ConstLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ConstLayer_delete(cv::dnn::ConstLayer* instance) { + delete instance; +} + +void cv_dnn_ConvolutionLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ConvolutionLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ConvolutionLayer* cv_dnn_ConvolutionLayer_defaultNew_const() { + cv::dnn::ConvolutionLayer* ret = new cv::dnn::ConvolutionLayer(); + return ret; +} + +bool cv_dnn_ConvolutionLayer_propFusedActivation_const(const cv::dnn::ConvolutionLayer* instance) { + bool ret = instance->fusedActivation; + return ret; +} + +void cv_dnn_ConvolutionLayer_propFusedActivation_const_bool(cv::dnn::ConvolutionLayer* instance, const bool val) { + instance->fusedActivation = val; +} + +bool cv_dnn_ConvolutionLayer_propFusedAdd_const(const cv::dnn::ConvolutionLayer* instance) { + bool ret = instance->fusedAdd; + return ret; +} + +void cv_dnn_ConvolutionLayer_propFusedAdd_const_bool(cv::dnn::ConvolutionLayer* instance, const bool val) { + instance->fusedAdd = val; +} + +bool cv_dnn_ConvolutionLayer_propUseWinograd_const(const cv::dnn::ConvolutionLayer* instance) { + bool ret = instance->useWinograd; + return ret; +} + +void cv_dnn_ConvolutionLayer_propUseWinograd_const_bool(cv::dnn::ConvolutionLayer* instance, const bool val) { + instance->useWinograd = val; +} + +cv::Algorithm* cv_dnn_ConvolutionLayer_to_Algorithm(cv::dnn::ConvolutionLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::BaseConvolutionLayer* cv_dnn_ConvolutionLayer_to_BaseConvolutionLayer(cv::dnn::ConvolutionLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ConvolutionLayer_to_Layer(cv::dnn::ConvolutionLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ConvolutionLayer_delete(cv::dnn::ConvolutionLayer* instance) { + delete instance; +} + +void cv_dnn_ConvolutionLayerInt8_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ConvolutionLayerInt8::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ConvolutionLayerInt8* cv_dnn_ConvolutionLayerInt8_defaultNew_const() { + cv::dnn::ConvolutionLayerInt8* ret = new cv::dnn::ConvolutionLayerInt8(); + return ret; +} + +int cv_dnn_ConvolutionLayerInt8_propInput_zp_const(const cv::dnn::ConvolutionLayerInt8* instance) { + int ret = instance->input_zp; + return ret; +} + +void cv_dnn_ConvolutionLayerInt8_propInput_zp_const_int(cv::dnn::ConvolutionLayerInt8* instance, const int val) { + instance->input_zp = val; +} + +int cv_dnn_ConvolutionLayerInt8_propOutput_zp_const(const cv::dnn::ConvolutionLayerInt8* instance) { + int ret = instance->output_zp; + return ret; +} + +void cv_dnn_ConvolutionLayerInt8_propOutput_zp_const_int(cv::dnn::ConvolutionLayerInt8* instance, const int val) { + instance->output_zp = val; +} + +float cv_dnn_ConvolutionLayerInt8_propInput_sc_const(const cv::dnn::ConvolutionLayerInt8* instance) { + float ret = instance->input_sc; + return ret; +} + +void cv_dnn_ConvolutionLayerInt8_propInput_sc_const_float(cv::dnn::ConvolutionLayerInt8* instance, const float val) { + instance->input_sc = val; +} + +float cv_dnn_ConvolutionLayerInt8_propOutput_sc_const(const cv::dnn::ConvolutionLayerInt8* instance) { + float ret = instance->output_sc; + return ret; +} + +void cv_dnn_ConvolutionLayerInt8_propOutput_sc_const_float(cv::dnn::ConvolutionLayerInt8* instance, const float val) { + instance->output_sc = val; +} + +bool cv_dnn_ConvolutionLayerInt8_propPer_channel_const(const cv::dnn::ConvolutionLayerInt8* instance) { + bool ret = instance->per_channel; + return ret; +} + +void cv_dnn_ConvolutionLayerInt8_propPer_channel_const_bool(cv::dnn::ConvolutionLayerInt8* instance, const bool val) { + instance->per_channel = val; +} + +bool cv_dnn_ConvolutionLayerInt8_propUseWinograd_const(const cv::dnn::ConvolutionLayerInt8* instance) { + bool ret = instance->useWinograd; + return ret; +} + +void cv_dnn_ConvolutionLayerInt8_propUseWinograd_const_bool(cv::dnn::ConvolutionLayerInt8* instance, const bool val) { + instance->useWinograd = val; +} + +cv::Algorithm* cv_dnn_ConvolutionLayerInt8_to_Algorithm(cv::dnn::ConvolutionLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::BaseConvolutionLayer* cv_dnn_ConvolutionLayerInt8_to_BaseConvolutionLayer(cv::dnn::ConvolutionLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ConvolutionLayerInt8_to_Layer(cv::dnn::ConvolutionLayerInt8* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ConvolutionLayerInt8_delete(cv::dnn::ConvolutionLayerInt8* instance) { + delete instance; +} + +void cv_dnn_CorrelationLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::CorrelationLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::CorrelationLayer* cv_dnn_CorrelationLayer_defaultNew_const() { + cv::dnn::CorrelationLayer* ret = new cv::dnn::CorrelationLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_CorrelationLayer_to_Algorithm(cv::dnn::CorrelationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_CorrelationLayer_to_Layer(cv::dnn::CorrelationLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_CorrelationLayer_delete(cv::dnn::CorrelationLayer* instance) { + delete instance; +} + +void cv_dnn_CosLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::CosLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::CosLayer* cv_dnn_CosLayer_defaultNew_const() { + cv::dnn::CosLayer* ret = new cv::dnn::CosLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_CosLayer_to_ActivationLayer(cv::dnn::CosLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_CosLayer_to_Algorithm(cv::dnn::CosLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_CosLayer_to_Layer(cv::dnn::CosLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_CosLayer_delete(cv::dnn::CosLayer* instance) { + delete instance; +} + +void cv_dnn_CoshLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::CoshLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::CoshLayer* cv_dnn_CoshLayer_defaultNew_const() { + cv::dnn::CoshLayer* ret = new cv::dnn::CoshLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_CoshLayer_to_ActivationLayer(cv::dnn::CoshLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_CoshLayer_to_Algorithm(cv::dnn::CoshLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_CoshLayer_to_Layer(cv::dnn::CoshLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_CoshLayer_delete(cv::dnn::CoshLayer* instance) { + delete instance; +} + +void cv_dnn_CropAndResizeLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::CropAndResizeLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::CropAndResizeLayer* cv_dnn_CropAndResizeLayer_defaultNew_const() { + cv::dnn::CropAndResizeLayer* ret = new cv::dnn::CropAndResizeLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_CropAndResizeLayer_to_Algorithm(cv::dnn::CropAndResizeLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_CropAndResizeLayer_to_Layer(cv::dnn::CropAndResizeLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_CropAndResizeLayer_delete(cv::dnn::CropAndResizeLayer* instance) { + delete instance; +} + +void cv_dnn_CropLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::CropLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::CropLayer* cv_dnn_CropLayer_defaultNew_const() { + cv::dnn::CropLayer* ret = new cv::dnn::CropLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_CropLayer_to_Algorithm(cv::dnn::CropLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_CropLayer_to_Layer(cv::dnn::CropLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_CropLayer_delete(cv::dnn::CropLayer* instance) { + delete instance; +} + +void cv_dnn_CumSumLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::CumSumLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::CumSumLayer* cv_dnn_CumSumLayer_defaultNew_const() { + cv::dnn::CumSumLayer* ret = new cv::dnn::CumSumLayer(); + return ret; +} + +int cv_dnn_CumSumLayer_propExclusive_const(const cv::dnn::CumSumLayer* instance) { + int ret = instance->exclusive; + return ret; +} + +void cv_dnn_CumSumLayer_propExclusive_const_int(cv::dnn::CumSumLayer* instance, const int val) { + instance->exclusive = val; +} + +int cv_dnn_CumSumLayer_propReverse_const(const cv::dnn::CumSumLayer* instance) { + int ret = instance->reverse; + return ret; +} + +void cv_dnn_CumSumLayer_propReverse_const_int(cv::dnn::CumSumLayer* instance, const int val) { + instance->reverse = val; +} + +cv::Algorithm* cv_dnn_CumSumLayer_to_Algorithm(cv::dnn::CumSumLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_CumSumLayer_to_Layer(cv::dnn::CumSumLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_CumSumLayer_delete(cv::dnn::CumSumLayer* instance) { + delete instance; +} + +void cv_dnn_DataAugmentationLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::DataAugmentationLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::DataAugmentationLayer* cv_dnn_DataAugmentationLayer_defaultNew_const() { + cv::dnn::DataAugmentationLayer* ret = new cv::dnn::DataAugmentationLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_DataAugmentationLayer_to_Algorithm(cv::dnn::DataAugmentationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_DataAugmentationLayer_to_Layer(cv::dnn::DataAugmentationLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_DataAugmentationLayer_delete(cv::dnn::DataAugmentationLayer* instance) { + delete instance; +} + +void cv_dnn_DeconvolutionLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::DeconvolutionLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::DeconvolutionLayer* cv_dnn_DeconvolutionLayer_defaultNew_const() { + cv::dnn::DeconvolutionLayer* ret = new cv::dnn::DeconvolutionLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_DeconvolutionLayer_to_Algorithm(cv::dnn::DeconvolutionLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::BaseConvolutionLayer* cv_dnn_DeconvolutionLayer_to_BaseConvolutionLayer(cv::dnn::DeconvolutionLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_DeconvolutionLayer_to_Layer(cv::dnn::DeconvolutionLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_DeconvolutionLayer_delete(cv::dnn::DeconvolutionLayer* instance) { + delete instance; +} + +void cv_dnn_DepthToSpaceLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::DepthToSpaceLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::DepthToSpaceLayer* cv_dnn_DepthToSpaceLayer_defaultNew_const() { + cv::dnn::DepthToSpaceLayer* ret = new cv::dnn::DepthToSpaceLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_DepthToSpaceLayer_to_Algorithm(cv::dnn::DepthToSpaceLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_DepthToSpaceLayer_to_Layer(cv::dnn::DepthToSpaceLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_DepthToSpaceLayer_delete(cv::dnn::DepthToSpaceLayer* instance) { + delete instance; +} + +void cv_dnn_DequantizeLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::DequantizeLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::DequantizeLayer* cv_dnn_DequantizeLayer_defaultNew_const() { + cv::dnn::DequantizeLayer* ret = new cv::dnn::DequantizeLayer(); + return ret; +} + +std::vector* cv_dnn_DequantizeLayer_propScales_const(const cv::dnn::DequantizeLayer* instance) { + std::vector ret = instance->scales; + return new std::vector(ret); +} + +void cv_dnn_DequantizeLayer_propScales_const_vectorLfloatG(cv::dnn::DequantizeLayer* instance, const std::vector* val) { + instance->scales = *val; +} + +std::vector* cv_dnn_DequantizeLayer_propZeropoints_const(const cv::dnn::DequantizeLayer* instance) { + std::vector ret = instance->zeropoints; + return new std::vector(ret); +} + +void cv_dnn_DequantizeLayer_propZeropoints_const_vectorLintG(cv::dnn::DequantizeLayer* instance, const std::vector* val) { + instance->zeropoints = *val; +} + +cv::Algorithm* cv_dnn_DequantizeLayer_to_Algorithm(cv::dnn::DequantizeLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_DequantizeLayer_to_Layer(cv::dnn::DequantizeLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_DequantizeLayer_delete(cv::dnn::DequantizeLayer* instance) { + delete instance; +} + +void cv_dnn_DetectionModel_DetectionModel_const_StringR_const_StringR(const char* model, const char* config, Result* ocvrs_return) { + try { + cv::dnn::DetectionModel* ret = new cv::dnn::DetectionModel(std::string(model), std::string(config)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DetectionModel_DetectionModel_const_StringR(const char* model, Result* ocvrs_return) { + try { + cv::dnn::DetectionModel* ret = new cv::dnn::DetectionModel(std::string(model)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DetectionModel_DetectionModel_const_NetR(const cv::dnn::Net* network, Result* ocvrs_return) { + try { + cv::dnn::DetectionModel* ret = new cv::dnn::DetectionModel(*network); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DetectionModel_DetectionModel(Result* ocvrs_return) { + try { + cv::dnn::DetectionModel* ret = new cv::dnn::DetectionModel(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DetectionModel_setNmsAcrossClasses_bool(cv::dnn::DetectionModel* instance, bool value, Result* ocvrs_return) { + try { + cv::dnn::DetectionModel ret = instance->setNmsAcrossClasses(value); + Ok(new cv::dnn::DetectionModel(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DetectionModel_getNmsAcrossClasses(cv::dnn::DetectionModel* instance, Result* ocvrs_return) { + try { + bool ret = instance->getNmsAcrossClasses(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DetectionModel_detect_const__InputArrayR_vectorLintGR_vectorLfloatGR_vectorLRectGR_float_float(cv::dnn::DetectionModel* instance, const cv::_InputArray* frame, std::vector* classIds, std::vector* confidences, std::vector* boxes, float confThreshold, float nmsThreshold, ResultVoid* ocvrs_return) { + try { + instance->detect(*frame, *classIds, *confidences, *boxes, confThreshold, nmsThreshold); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DetectionModel_detect_const__InputArrayR_vectorLintGR_vectorLfloatGR_vectorLRectGR(cv::dnn::DetectionModel* instance, const cv::_InputArray* frame, std::vector* classIds, std::vector* confidences, std::vector* boxes, ResultVoid* ocvrs_return) { + try { + instance->detect(*frame, *classIds, *confidences, *boxes); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::DetectionModel* cv_dnn_DetectionModel_implicitClone_const(const cv::dnn::DetectionModel* instance) { + return new cv::dnn::DetectionModel(*instance); +} + +cv::dnn::Model* cv_dnn_DetectionModel_to_Model(cv::dnn::DetectionModel* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_DetectionModel_delete(cv::dnn::DetectionModel* instance) { + delete instance; +} + +void cv_dnn_DetectionOutputLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::DetectionOutputLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::DetectionOutputLayer* cv_dnn_DetectionOutputLayer_defaultNew_const() { + cv::dnn::DetectionOutputLayer* ret = new cv::dnn::DetectionOutputLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_DetectionOutputLayer_to_Algorithm(cv::dnn::DetectionOutputLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_DetectionOutputLayer_to_Layer(cv::dnn::DetectionOutputLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_DetectionOutputLayer_delete(cv::dnn::DetectionOutputLayer* instance) { + delete instance; +} + +void cv_dnn_Dict_has_const_const_StringR(const cv::dnn::Dict* instance, const char* key, Result* ocvrs_return) { + try { + bool ret = instance->has(std::string(key)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Dict_ptr_const_StringR(cv::dnn::Dict* instance, const char* key, Result* ocvrs_return) { + try { + cv::dnn::DictValue* ret = instance->ptr(std::string(key)); + Ok(new cv::dnn::DictValue(*ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Dict_ptr_const_const_StringR(const cv::dnn::Dict* instance, const char* key, Result* ocvrs_return) { + try { + const cv::dnn::DictValue* ret = instance->ptr(std::string(key)); + Ok(new const cv::dnn::DictValue(*ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Dict_get_const_const_StringR(const cv::dnn::Dict* instance, const char* key, Result* ocvrs_return) { + try { + const cv::dnn::DictValue ret = instance->get(std::string(key)); + Ok(new const cv::dnn::DictValue(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Dict_set_const_cv_String_const_StringR_const_StringR(cv::dnn::Dict* instance, const char* key, const char* value, Result* ocvrs_return) { + try { + const cv::String ret = instance->set(std::string(key), cv::String(value)); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Dict_set_const_cv_dnn_DictValue_const_StringR_const_DictValueR(cv::dnn::Dict* instance, const char* key, const cv::dnn::DictValue* value, Result* ocvrs_return) { + try { + const cv::dnn::DictValue ret = instance->set(std::string(key), *value); + Ok(new const cv::dnn::DictValue(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Dict_set_const_double_const_StringR_const_doubleR(cv::dnn::Dict* instance, const char* key, const double* value, Result* ocvrs_return) { + try { + const double ret = instance->set(std::string(key), *value); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Dict_set_const_int64_t_const_StringR_const_int64_tR(cv::dnn::Dict* instance, const char* key, const int64_t* value, Result* ocvrs_return) { + try { + const int64_t ret = instance->set(std::string(key), *value); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Dict_erase_const_StringR(cv::dnn::Dict* instance, const char* key, ResultVoid* ocvrs_return) { + try { + instance->erase(std::string(key)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::Dict* cv_dnn_Dict_defaultNew_const() { + cv::dnn::Dict* ret = new cv::dnn::Dict(); + return ret; +} + +void cv_dnn_Dict_delete(cv::dnn::Dict* instance) { + delete instance; +} + +void cv_dnn_DictValue_DictValue_const_DictValueR(const cv::dnn::DictValue* r, Result* ocvrs_return) { + try { + cv::dnn::DictValue* ret = new cv::dnn::DictValue(*r); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_DictValue_bool(bool i, Result* ocvrs_return) { + try { + cv::dnn::DictValue* ret = new cv::dnn::DictValue(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_DictValue_int64_t(int64_t i, Result* ocvrs_return) { + try { + cv::dnn::DictValue* ret = new cv::dnn::DictValue(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_DictValue(Result* ocvrs_return) { + try { + cv::dnn::DictValue* ret = new cv::dnn::DictValue(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_DictValue_int(int i, Result* ocvrs_return) { + try { + cv::dnn::DictValue* ret = new cv::dnn::DictValue(i); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_DictValue_unsigned_int(unsigned int p, Result* ocvrs_return) { + try { + cv::dnn::DictValue* ret = new cv::dnn::DictValue(p); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_DictValue_double(double p, Result* ocvrs_return) { + try { + cv::dnn::DictValue* ret = new cv::dnn::DictValue(p); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_DictValue_const_charX(const char* s, Result* ocvrs_return) { + try { + cv::dnn::DictValue* ret = new cv::dnn::DictValue(s); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_get_cv_String_const_int(const cv::dnn::DictValue* instance, int idx, Result* ocvrs_return) { + try { + cv::String ret = instance->get(idx); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_get_cv_String_const(const cv::dnn::DictValue* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->get(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_get_double_const_int(const cv::dnn::DictValue* instance, int idx, Result* ocvrs_return) { + try { + double ret = instance->get(idx); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_get_double_const(const cv::dnn::DictValue* instance, Result* ocvrs_return) { + try { + double ret = instance->get(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_get_int_const_int(const cv::dnn::DictValue* instance, int idx, Result* ocvrs_return) { + try { + int ret = instance->get(idx); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_get_int_const(const cv::dnn::DictValue* instance, Result* ocvrs_return) { + try { + int ret = instance->get(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_get_int64_t_const_int(const cv::dnn::DictValue* instance, int idx, Result* ocvrs_return) { + try { + int64_t ret = instance->get(idx); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_get_int64_t_const(const cv::dnn::DictValue* instance, Result* ocvrs_return) { + try { + int64_t ret = instance->get(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_size_const(const cv::dnn::DictValue* instance, Result* ocvrs_return) { + try { + int ret = instance->size(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_isInt_const(const cv::dnn::DictValue* instance, Result* ocvrs_return) { + try { + bool ret = instance->isInt(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_isString_const(const cv::dnn::DictValue* instance, Result* ocvrs_return) { + try { + bool ret = instance->isString(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_isReal_const(const cv::dnn::DictValue* instance, Result* ocvrs_return) { + try { + bool ret = instance->isReal(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_getIntValue_const_int(const cv::dnn::DictValue* instance, int idx, Result* ocvrs_return) { + try { + int ret = instance->getIntValue(idx); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_getIntValue_const(const cv::dnn::DictValue* instance, Result* ocvrs_return) { + try { + int ret = instance->getIntValue(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_getRealValue_const_int(const cv::dnn::DictValue* instance, int idx, Result* ocvrs_return) { + try { + double ret = instance->getRealValue(idx); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_getRealValue_const(const cv::dnn::DictValue* instance, Result* ocvrs_return) { + try { + double ret = instance->getRealValue(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_getStringValue_const_int(const cv::dnn::DictValue* instance, int idx, Result* ocvrs_return) { + try { + cv::String ret = instance->getStringValue(idx); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_getStringValue_const(const cv::dnn::DictValue* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->getStringValue(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_operatorST_const_DictValueR(cv::dnn::DictValue* instance, const cv::dnn::DictValue* r, ResultVoid* ocvrs_return) { + try { + instance->operator=(*r); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_DictValue_delete(cv::dnn::DictValue* instance) { + delete instance; +} + +void cv_dnn_ELULayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ELULayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ELULayer* cv_dnn_ELULayer_defaultNew_const() { + cv::dnn::ELULayer* ret = new cv::dnn::ELULayer(); + return ret; +} + +float cv_dnn_ELULayer_propAlpha_const(const cv::dnn::ELULayer* instance) { + float ret = instance->alpha; + return ret; +} + +void cv_dnn_ELULayer_propAlpha_const_float(cv::dnn::ELULayer* instance, const float val) { + instance->alpha = val; +} + +cv::dnn::ActivationLayer* cv_dnn_ELULayer_to_ActivationLayer(cv::dnn::ELULayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_ELULayer_to_Algorithm(cv::dnn::ELULayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ELULayer_to_Layer(cv::dnn::ELULayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ELULayer_delete(cv::dnn::ELULayer* instance) { + delete instance; +} + +void cv_dnn_EinsumLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::EinsumLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::EinsumLayer* cv_dnn_EinsumLayer_defaultNew_const() { + cv::dnn::EinsumLayer* ret = new cv::dnn::EinsumLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_EinsumLayer_to_Algorithm(cv::dnn::EinsumLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_EinsumLayer_to_Layer(cv::dnn::EinsumLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_EinsumLayer_delete(cv::dnn::EinsumLayer* instance) { + delete instance; +} + +void cv_dnn_EltwiseLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::EltwiseLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::EltwiseLayer* cv_dnn_EltwiseLayer_defaultNew_const() { + cv::dnn::EltwiseLayer* ret = new cv::dnn::EltwiseLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_EltwiseLayer_to_Algorithm(cv::dnn::EltwiseLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_EltwiseLayer_to_Layer(cv::dnn::EltwiseLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_EltwiseLayer_delete(cv::dnn::EltwiseLayer* instance) { + delete instance; +} + +void cv_dnn_EltwiseLayerInt8_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::EltwiseLayerInt8::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::EltwiseLayerInt8* cv_dnn_EltwiseLayerInt8_defaultNew_const() { + cv::dnn::EltwiseLayerInt8* ret = new cv::dnn::EltwiseLayerInt8(); + return ret; +} + +cv::Algorithm* cv_dnn_EltwiseLayerInt8_to_Algorithm(cv::dnn::EltwiseLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_EltwiseLayerInt8_to_Layer(cv::dnn::EltwiseLayerInt8* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_EltwiseLayerInt8_delete(cv::dnn::EltwiseLayerInt8* instance) { + delete instance; +} + +void cv_dnn_ErfLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ErfLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ErfLayer* cv_dnn_ErfLayer_defaultNew_const() { + cv::dnn::ErfLayer* ret = new cv::dnn::ErfLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_ErfLayer_to_ActivationLayer(cv::dnn::ErfLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_ErfLayer_to_Algorithm(cv::dnn::ErfLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ErfLayer_to_Layer(cv::dnn::ErfLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ErfLayer_delete(cv::dnn::ErfLayer* instance) { + delete instance; +} + +void cv_dnn_ExpLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ExpLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ExpLayer* cv_dnn_ExpLayer_defaultNew_const() { + cv::dnn::ExpLayer* ret = new cv::dnn::ExpLayer(); + return ret; +} + +float cv_dnn_ExpLayer_propBase_const(const cv::dnn::ExpLayer* instance) { + float ret = instance->base; + return ret; +} + +void cv_dnn_ExpLayer_propBase_const_float(cv::dnn::ExpLayer* instance, const float val) { + instance->base = val; +} + +float cv_dnn_ExpLayer_propScale_const(const cv::dnn::ExpLayer* instance) { + float ret = instance->scale; + return ret; +} + +void cv_dnn_ExpLayer_propScale_const_float(cv::dnn::ExpLayer* instance, const float val) { + instance->scale = val; +} + +float cv_dnn_ExpLayer_propShift_const(const cv::dnn::ExpLayer* instance) { + float ret = instance->shift; + return ret; +} + +void cv_dnn_ExpLayer_propShift_const_float(cv::dnn::ExpLayer* instance, const float val) { + instance->shift = val; +} + +cv::dnn::ActivationLayer* cv_dnn_ExpLayer_to_ActivationLayer(cv::dnn::ExpLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_ExpLayer_to_Algorithm(cv::dnn::ExpLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ExpLayer_to_Layer(cv::dnn::ExpLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ExpLayer_delete(cv::dnn::ExpLayer* instance) { + delete instance; +} + +void cv_dnn_ExpandLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ExpandLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ExpandLayer* cv_dnn_ExpandLayer_defaultNew_const() { + cv::dnn::ExpandLayer* ret = new cv::dnn::ExpandLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_ExpandLayer_to_Algorithm(cv::dnn::ExpandLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ExpandLayer_to_Layer(cv::dnn::ExpandLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ExpandLayer_delete(cv::dnn::ExpandLayer* instance) { + delete instance; +} + +void cv_dnn_FlattenLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::FlattenLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::FlattenLayer* cv_dnn_FlattenLayer_defaultNew_const() { + cv::dnn::FlattenLayer* ret = new cv::dnn::FlattenLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_FlattenLayer_to_Algorithm(cv::dnn::FlattenLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_FlattenLayer_to_Layer(cv::dnn::FlattenLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_FlattenLayer_delete(cv::dnn::FlattenLayer* instance) { + delete instance; +} + +void cv_dnn_FloorLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::FloorLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::FloorLayer* cv_dnn_FloorLayer_defaultNew_const() { + cv::dnn::FloorLayer* ret = new cv::dnn::FloorLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_FloorLayer_to_ActivationLayer(cv::dnn::FloorLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_FloorLayer_to_Algorithm(cv::dnn::FloorLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_FloorLayer_to_Layer(cv::dnn::FloorLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_FloorLayer_delete(cv::dnn::FloorLayer* instance) { + delete instance; +} + +void cv_dnn_FlowWarpLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::FlowWarpLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::FlowWarpLayer* cv_dnn_FlowWarpLayer_defaultNew_const() { + cv::dnn::FlowWarpLayer* ret = new cv::dnn::FlowWarpLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_FlowWarpLayer_to_Algorithm(cv::dnn::FlowWarpLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_FlowWarpLayer_to_Layer(cv::dnn::FlowWarpLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_FlowWarpLayer_delete(cv::dnn::FlowWarpLayer* instance) { + delete instance; +} + +void cv_dnn_GRULayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::GRULayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::GRULayer* cv_dnn_GRULayer_defaultNew_const() { + cv::dnn::GRULayer* ret = new cv::dnn::GRULayer(); + return ret; +} + +cv::Algorithm* cv_dnn_GRULayer_to_Algorithm(cv::dnn::GRULayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_GRULayer_to_Layer(cv::dnn::GRULayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_GRULayer_delete(cv::dnn::GRULayer* instance) { + delete instance; +} + +void cv_dnn_GatherElementsLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::GatherElementsLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::GatherElementsLayer* cv_dnn_GatherElementsLayer_defaultNew_const() { + cv::dnn::GatherElementsLayer* ret = new cv::dnn::GatherElementsLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_GatherElementsLayer_to_Algorithm(cv::dnn::GatherElementsLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_GatherElementsLayer_to_Layer(cv::dnn::GatherElementsLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_GatherElementsLayer_delete(cv::dnn::GatherElementsLayer* instance) { + delete instance; +} + +void cv_dnn_GatherLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::GatherLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::GatherLayer* cv_dnn_GatherLayer_defaultNew_const() { + cv::dnn::GatherLayer* ret = new cv::dnn::GatherLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_GatherLayer_to_Algorithm(cv::dnn::GatherLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_GatherLayer_to_Layer(cv::dnn::GatherLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_GatherLayer_delete(cv::dnn::GatherLayer* instance) { + delete instance; +} + +void cv_dnn_GeluApproximationLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::GeluApproximationLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::GeluApproximationLayer* cv_dnn_GeluApproximationLayer_defaultNew_const() { + cv::dnn::GeluApproximationLayer* ret = new cv::dnn::GeluApproximationLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_GeluApproximationLayer_to_ActivationLayer(cv::dnn::GeluApproximationLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_GeluApproximationLayer_to_Algorithm(cv::dnn::GeluApproximationLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_GeluApproximationLayer_to_Layer(cv::dnn::GeluApproximationLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_GeluApproximationLayer_delete(cv::dnn::GeluApproximationLayer* instance) { + delete instance; +} + +void cv_dnn_GeluLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::GeluLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::GeluLayer* cv_dnn_GeluLayer_defaultNew_const() { + cv::dnn::GeluLayer* ret = new cv::dnn::GeluLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_GeluLayer_to_ActivationLayer(cv::dnn::GeluLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_GeluLayer_to_Algorithm(cv::dnn::GeluLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_GeluLayer_to_Layer(cv::dnn::GeluLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_GeluLayer_delete(cv::dnn::GeluLayer* instance) { + delete instance; +} + +void cv_dnn_GemmLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::GemmLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::GemmLayer* cv_dnn_GemmLayer_defaultNew_const() { + cv::dnn::GemmLayer* ret = new cv::dnn::GemmLayer(); + return ret; +} + +bool cv_dnn_GemmLayer_propTrans_a_const(const cv::dnn::GemmLayer* instance) { + bool ret = instance->trans_a; + return ret; +} + +void cv_dnn_GemmLayer_propTrans_a_const_bool(cv::dnn::GemmLayer* instance, const bool val) { + instance->trans_a = val; +} + +bool cv_dnn_GemmLayer_propTrans_b_const(const cv::dnn::GemmLayer* instance) { + bool ret = instance->trans_b; + return ret; +} + +void cv_dnn_GemmLayer_propTrans_b_const_bool(cv::dnn::GemmLayer* instance, const bool val) { + instance->trans_b = val; +} + +float cv_dnn_GemmLayer_propAlpha_const(const cv::dnn::GemmLayer* instance) { + float ret = instance->alpha; + return ret; +} + +void cv_dnn_GemmLayer_propAlpha_const_float(cv::dnn::GemmLayer* instance, const float val) { + instance->alpha = val; +} + +float cv_dnn_GemmLayer_propBeta_const(const cv::dnn::GemmLayer* instance) { + float ret = instance->beta; + return ret; +} + +void cv_dnn_GemmLayer_propBeta_const_float(cv::dnn::GemmLayer* instance, const float val) { + instance->beta = val; +} + +cv::Algorithm* cv_dnn_GemmLayer_to_Algorithm(cv::dnn::GemmLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_GemmLayer_to_Layer(cv::dnn::GemmLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_GemmLayer_delete(cv::dnn::GemmLayer* instance) { + delete instance; +} + +void cv_dnn_GroupNormLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::GroupNormLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::GroupNormLayer* cv_dnn_GroupNormLayer_defaultNew_const() { + cv::dnn::GroupNormLayer* ret = new cv::dnn::GroupNormLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_GroupNormLayer_to_Algorithm(cv::dnn::GroupNormLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_GroupNormLayer_to_Layer(cv::dnn::GroupNormLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_GroupNormLayer_delete(cv::dnn::GroupNormLayer* instance) { + delete instance; +} + +void cv_dnn_HardSigmoidLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::HardSigmoidLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::HardSigmoidLayer* cv_dnn_HardSigmoidLayer_defaultNew_const() { + cv::dnn::HardSigmoidLayer* ret = new cv::dnn::HardSigmoidLayer(); + return ret; +} + +float cv_dnn_HardSigmoidLayer_propAlpha_const(const cv::dnn::HardSigmoidLayer* instance) { + float ret = instance->alpha; + return ret; +} + +void cv_dnn_HardSigmoidLayer_propAlpha_const_float(cv::dnn::HardSigmoidLayer* instance, const float val) { + instance->alpha = val; +} + +float cv_dnn_HardSigmoidLayer_propBeta_const(const cv::dnn::HardSigmoidLayer* instance) { + float ret = instance->beta; + return ret; +} + +void cv_dnn_HardSigmoidLayer_propBeta_const_float(cv::dnn::HardSigmoidLayer* instance, const float val) { + instance->beta = val; +} + +cv::dnn::ActivationLayer* cv_dnn_HardSigmoidLayer_to_ActivationLayer(cv::dnn::HardSigmoidLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_HardSigmoidLayer_to_Algorithm(cv::dnn::HardSigmoidLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_HardSigmoidLayer_to_Layer(cv::dnn::HardSigmoidLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_HardSigmoidLayer_delete(cv::dnn::HardSigmoidLayer* instance) { + delete instance; +} + +void cv_dnn_HardSwishLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::HardSwishLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::HardSwishLayer* cv_dnn_HardSwishLayer_defaultNew_const() { + cv::dnn::HardSwishLayer* ret = new cv::dnn::HardSwishLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_HardSwishLayer_to_ActivationLayer(cv::dnn::HardSwishLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_HardSwishLayer_to_Algorithm(cv::dnn::HardSwishLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_HardSwishLayer_to_Layer(cv::dnn::HardSwishLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_HardSwishLayer_delete(cv::dnn::HardSwishLayer* instance) { + delete instance; +} + +void cv_dnn_Image2BlobParams_Image2BlobParams(Result* ocvrs_return) { + try { + cv::dnn::Image2BlobParams ret; + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Image2BlobParams_Image2BlobParams_const_ScalarR_const_SizeR_const_ScalarR_bool_int_DataLayout_ImagePaddingMode_Scalar(const cv::Scalar* scalefactor, const cv::Size* size, const cv::Scalar* mean, bool swapRB, int ddepth, cv::dnn::DataLayout datalayout, cv::dnn::ImagePaddingMode mode, cv::Scalar* borderValue, Result* ocvrs_return) { + try { + cv::dnn::Image2BlobParams ret(*scalefactor, *size, *mean, swapRB, ddepth, datalayout, mode, *borderValue); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Image2BlobParams_Image2BlobParams_const_ScalarR(const cv::Scalar* scalefactor, Result* ocvrs_return) { + try { + cv::dnn::Image2BlobParams ret(*scalefactor); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Image2BlobParams_blobRectToImageRect_const_RectR_const_SizeR(cv::dnn::Image2BlobParams* instance, const cv::Rect* rBlob, const cv::Size* size, Result* ocvrs_return) { + try { + cv::Rect ret = instance->blobRectToImageRect(*rBlob, *size); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Image2BlobParams_blobRectsToImageRects_const_vectorLRectGR_vectorLRectGR_const_SizeR(cv::dnn::Image2BlobParams* instance, const std::vector* rBlob, std::vector* rImg, const cv::Size* size, ResultVoid* ocvrs_return) { + try { + instance->blobRectsToImageRects(*rBlob, *rImg, *size); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_InnerProductLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::InnerProductLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::InnerProductLayer* cv_dnn_InnerProductLayer_defaultNew_const() { + cv::dnn::InnerProductLayer* ret = new cv::dnn::InnerProductLayer(); + return ret; +} + +int cv_dnn_InnerProductLayer_propAxis_const(const cv::dnn::InnerProductLayer* instance) { + int ret = instance->axis; + return ret; +} + +void cv_dnn_InnerProductLayer_propAxis_const_int(cv::dnn::InnerProductLayer* instance, const int val) { + instance->axis = val; +} + +cv::Algorithm* cv_dnn_InnerProductLayer_to_Algorithm(cv::dnn::InnerProductLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_InnerProductLayer_to_Layer(cv::dnn::InnerProductLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_InnerProductLayer_delete(cv::dnn::InnerProductLayer* instance) { + delete instance; +} + +void cv_dnn_InnerProductLayerInt8_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::InnerProductLayerInt8::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::InnerProductLayerInt8* cv_dnn_InnerProductLayerInt8_defaultNew_const() { + cv::dnn::InnerProductLayerInt8* ret = new cv::dnn::InnerProductLayerInt8(); + return ret; +} + +int cv_dnn_InnerProductLayerInt8_propInput_zp_const(const cv::dnn::InnerProductLayerInt8* instance) { + int ret = instance->input_zp; + return ret; +} + +void cv_dnn_InnerProductLayerInt8_propInput_zp_const_int(cv::dnn::InnerProductLayerInt8* instance, const int val) { + instance->input_zp = val; +} + +int cv_dnn_InnerProductLayerInt8_propOutput_zp_const(const cv::dnn::InnerProductLayerInt8* instance) { + int ret = instance->output_zp; + return ret; +} + +void cv_dnn_InnerProductLayerInt8_propOutput_zp_const_int(cv::dnn::InnerProductLayerInt8* instance, const int val) { + instance->output_zp = val; +} + +float cv_dnn_InnerProductLayerInt8_propInput_sc_const(const cv::dnn::InnerProductLayerInt8* instance) { + float ret = instance->input_sc; + return ret; +} + +void cv_dnn_InnerProductLayerInt8_propInput_sc_const_float(cv::dnn::InnerProductLayerInt8* instance, const float val) { + instance->input_sc = val; +} + +float cv_dnn_InnerProductLayerInt8_propOutput_sc_const(const cv::dnn::InnerProductLayerInt8* instance) { + float ret = instance->output_sc; + return ret; +} + +void cv_dnn_InnerProductLayerInt8_propOutput_sc_const_float(cv::dnn::InnerProductLayerInt8* instance, const float val) { + instance->output_sc = val; +} + +bool cv_dnn_InnerProductLayerInt8_propPer_channel_const(const cv::dnn::InnerProductLayerInt8* instance) { + bool ret = instance->per_channel; + return ret; +} + +void cv_dnn_InnerProductLayerInt8_propPer_channel_const_bool(cv::dnn::InnerProductLayerInt8* instance, const bool val) { + instance->per_channel = val; +} + +cv::Algorithm* cv_dnn_InnerProductLayerInt8_to_Algorithm(cv::dnn::InnerProductLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::InnerProductLayer* cv_dnn_InnerProductLayerInt8_to_InnerProductLayer(cv::dnn::InnerProductLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_InnerProductLayerInt8_to_Layer(cv::dnn::InnerProductLayerInt8* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_InnerProductLayerInt8_delete(cv::dnn::InnerProductLayerInt8* instance) { + delete instance; +} + +void cv_dnn_InstanceNormLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::InstanceNormLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::InstanceNormLayer* cv_dnn_InstanceNormLayer_defaultNew_const() { + cv::dnn::InstanceNormLayer* ret = new cv::dnn::InstanceNormLayer(); + return ret; +} + +float cv_dnn_InstanceNormLayer_propEpsilon_const(const cv::dnn::InstanceNormLayer* instance) { + float ret = instance->epsilon; + return ret; +} + +void cv_dnn_InstanceNormLayer_propEpsilon_const_float(cv::dnn::InstanceNormLayer* instance, const float val) { + instance->epsilon = val; +} + +cv::Algorithm* cv_dnn_InstanceNormLayer_to_Algorithm(cv::dnn::InstanceNormLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_InstanceNormLayer_to_Layer(cv::dnn::InstanceNormLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_InstanceNormLayer_delete(cv::dnn::InstanceNormLayer* instance) { + delete instance; +} + +void cv_dnn_InterpLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::InterpLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::InterpLayer* cv_dnn_InterpLayer_defaultNew_const() { + cv::dnn::InterpLayer* ret = new cv::dnn::InterpLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_InterpLayer_to_Algorithm(cv::dnn::InterpLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_InterpLayer_to_Layer(cv::dnn::InterpLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_InterpLayer_delete(cv::dnn::InterpLayer* instance) { + delete instance; +} + +void cv_dnn_KeypointsModel_KeypointsModel_const_StringR_const_StringR(const char* model, const char* config, Result* ocvrs_return) { + try { + cv::dnn::KeypointsModel* ret = new cv::dnn::KeypointsModel(std::string(model), std::string(config)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_KeypointsModel_KeypointsModel_const_StringR(const char* model, Result* ocvrs_return) { + try { + cv::dnn::KeypointsModel* ret = new cv::dnn::KeypointsModel(std::string(model)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_KeypointsModel_KeypointsModel_const_NetR(const cv::dnn::Net* network, Result* ocvrs_return) { + try { + cv::dnn::KeypointsModel* ret = new cv::dnn::KeypointsModel(*network); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_KeypointsModel_estimate_const__InputArrayR_float(cv::dnn::KeypointsModel* instance, const cv::_InputArray* frame, float thresh, Result*>* ocvrs_return) { + try { + std::vector ret = instance->estimate(*frame, thresh); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_KeypointsModel_estimate_const__InputArrayR(cv::dnn::KeypointsModel* instance, const cv::_InputArray* frame, Result*>* ocvrs_return) { + try { + std::vector ret = instance->estimate(*frame); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::KeypointsModel* cv_dnn_KeypointsModel_implicitClone_const(const cv::dnn::KeypointsModel* instance) { + return new cv::dnn::KeypointsModel(*instance); +} + +cv::dnn::Model* cv_dnn_KeypointsModel_to_Model(cv::dnn::KeypointsModel* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_KeypointsModel_delete(cv::dnn::KeypointsModel* instance) { + delete instance; +} + +void cv_dnn_LRNLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::LRNLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::LRNLayer* cv_dnn_LRNLayer_defaultNew_const() { + cv::dnn::LRNLayer* ret = new cv::dnn::LRNLayer(); + return ret; +} + +int cv_dnn_LRNLayer_propType_const(const cv::dnn::LRNLayer* instance) { + int ret = instance->type; + return ret; +} + +void cv_dnn_LRNLayer_propType_const_int(cv::dnn::LRNLayer* instance, const int val) { + instance->type = val; +} + +int cv_dnn_LRNLayer_propSize_const(const cv::dnn::LRNLayer* instance) { + int ret = instance->size; + return ret; +} + +void cv_dnn_LRNLayer_propSize_const_int(cv::dnn::LRNLayer* instance, const int val) { + instance->size = val; +} + +float cv_dnn_LRNLayer_propAlpha_const(const cv::dnn::LRNLayer* instance) { + float ret = instance->alpha; + return ret; +} + +void cv_dnn_LRNLayer_propAlpha_const_float(cv::dnn::LRNLayer* instance, const float val) { + instance->alpha = val; +} + +float cv_dnn_LRNLayer_propBeta_const(const cv::dnn::LRNLayer* instance) { + float ret = instance->beta; + return ret; +} + +void cv_dnn_LRNLayer_propBeta_const_float(cv::dnn::LRNLayer* instance, const float val) { + instance->beta = val; +} + +float cv_dnn_LRNLayer_propBias_const(const cv::dnn::LRNLayer* instance) { + float ret = instance->bias; + return ret; +} + +void cv_dnn_LRNLayer_propBias_const_float(cv::dnn::LRNLayer* instance, const float val) { + instance->bias = val; +} + +bool cv_dnn_LRNLayer_propNormBySize_const(const cv::dnn::LRNLayer* instance) { + bool ret = instance->normBySize; + return ret; +} + +void cv_dnn_LRNLayer_propNormBySize_const_bool(cv::dnn::LRNLayer* instance, const bool val) { + instance->normBySize = val; +} + +cv::Algorithm* cv_dnn_LRNLayer_to_Algorithm(cv::dnn::LRNLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_LRNLayer_to_Layer(cv::dnn::LRNLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_LRNLayer_delete(cv::dnn::LRNLayer* instance) { + delete instance; +} + +void cv_dnn_LSTMLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::LSTMLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_LSTMLayer_setWeights_const_MatR_const_MatR_const_MatR(cv::dnn::LSTMLayer* instance, const cv::Mat* Wh, const cv::Mat* Wx, const cv::Mat* b, ResultVoid* ocvrs_return) { + try { + instance->setWeights(*Wh, *Wx, *b); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_LSTMLayer_setOutShape_const_MatShapeR(cv::dnn::LSTMLayer* instance, const cv::dnn::MatShape* outTailShape, ResultVoid* ocvrs_return) { + try { + instance->setOutShape(*outTailShape); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_LSTMLayer_setOutShape(cv::dnn::LSTMLayer* instance, ResultVoid* ocvrs_return) { + try { + instance->setOutShape(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_LSTMLayer_setUseTimstampsDim_bool(cv::dnn::LSTMLayer* instance, bool use, ResultVoid* ocvrs_return) { + try { + instance->setUseTimstampsDim(use); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_LSTMLayer_setUseTimstampsDim(cv::dnn::LSTMLayer* instance, ResultVoid* ocvrs_return) { + try { + instance->setUseTimstampsDim(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_LSTMLayer_setProduceCellOutput_bool(cv::dnn::LSTMLayer* instance, bool produce, ResultVoid* ocvrs_return) { + try { + instance->setProduceCellOutput(produce); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_LSTMLayer_setProduceCellOutput(cv::dnn::LSTMLayer* instance, ResultVoid* ocvrs_return) { + try { + instance->setProduceCellOutput(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_LSTMLayer_inputNameToIndex_String(cv::dnn::LSTMLayer* instance, const char* inputName, Result* ocvrs_return) { + try { + int ret = instance->inputNameToIndex(std::string(inputName)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_LSTMLayer_outputNameToIndex_const_StringR(cv::dnn::LSTMLayer* instance, const char* outputName, Result* ocvrs_return) { + try { + int ret = instance->outputNameToIndex(std::string(outputName)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::Algorithm* cv_dnn_LSTMLayer_to_Algorithm(cv::dnn::LSTMLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_LSTMLayer_to_Layer(cv::dnn::LSTMLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_LSTMLayer_delete(cv::dnn::LSTMLayer* instance) { + delete instance; +} + +void cv_dnn_Layer_finalize_const__InputArrayR_const__OutputArrayR(cv::dnn::Layer* instance, const cv::_InputArray* inputs, const cv::_OutputArray* outputs, ResultVoid* ocvrs_return) { + try { + instance->finalize(*inputs, *outputs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_forward_vectorLMatXGR_vectorLMatGR_vectorLMatGR(cv::dnn::Layer* instance, std::vector* input, std::vector* output, std::vector* internals, ResultVoid* ocvrs_return) { + try { + instance->forward(*input, *output, *internals); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_forward_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(cv::dnn::Layer* instance, const cv::_InputArray* inputs, const cv::_OutputArray* outputs, const cv::_OutputArray* internals, ResultVoid* ocvrs_return) { + try { + instance->forward(*inputs, *outputs, *internals); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_tryQuantize_const_vectorLvectorLfloatGGR_const_vectorLvectorLintGGR_LayerParamsR(cv::dnn::Layer* instance, const std::vector>* scales, const std::vector>* zeropoints, cv::dnn::LayerParams* params, Result* ocvrs_return) { + try { + bool ret = instance->tryQuantize(*scales, *zeropoints, *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_forward_fallback_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(cv::dnn::Layer* instance, const cv::_InputArray* inputs, const cv::_OutputArray* outputs, const cv::_OutputArray* internals, ResultVoid* ocvrs_return) { + try { + instance->forward_fallback(*inputs, *outputs, *internals); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_finalize_const_vectorLMatGR_vectorLMatGR(cv::dnn::Layer* instance, const std::vector* inputs, std::vector* outputs, ResultVoid* ocvrs_return) { + try { + instance->finalize(*inputs, *outputs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_finalize_const_vectorLMatGR(cv::dnn::Layer* instance, const std::vector* inputs, Result*>* ocvrs_return) { + try { + std::vector ret = instance->finalize(*inputs); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_run_const_vectorLMatGR_vectorLMatGR_vectorLMatGR(cv::dnn::Layer* instance, const std::vector* inputs, std::vector* outputs, std::vector* internals, ResultVoid* ocvrs_return) { + try { + instance->run(*inputs, *outputs, *internals); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_inputNameToIndex_String(cv::dnn::Layer* instance, const char* inputName, Result* ocvrs_return) { + try { + int ret = instance->inputNameToIndex(std::string(inputName)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_outputNameToIndex_const_StringR(cv::dnn::Layer* instance, const char* outputName, Result* ocvrs_return) { + try { + int ret = instance->outputNameToIndex(std::string(outputName)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_supportBackend_int(cv::dnn::Layer* instance, int backendId, Result* ocvrs_return) { + try { + bool ret = instance->supportBackend(backendId); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_initHalide_const_vectorLPtrLBackendWrapperGGR(cv::dnn::Layer* instance, const std::vector>* inputs, Result*>* ocvrs_return) { + try { + cv::Ptr ret = instance->initHalide(*inputs); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_initNgraph_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendNodeGGR(cv::dnn::Layer* instance, const std::vector>* inputs, const std::vector>* nodes, Result*>* ocvrs_return) { + try { + cv::Ptr ret = instance->initNgraph(*inputs, *nodes); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_initVkCom_const_vectorLPtrLBackendWrapperGGR_vectorLPtrLBackendWrapperGGR(cv::dnn::Layer* instance, const std::vector>* inputs, std::vector>* outputs, Result*>* ocvrs_return) { + try { + cv::Ptr ret = instance->initVkCom(*inputs, *outputs); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_initWebnn_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendNodeGGR(cv::dnn::Layer* instance, const std::vector>* inputs, const std::vector>* nodes, Result*>* ocvrs_return) { + try { + cv::Ptr ret = instance->initWebnn(*inputs, *nodes); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_initCUDA_voidX_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendWrapperGGR(cv::dnn::Layer* instance, void* context, const std::vector>* inputs, const std::vector>* outputs, Result*>* ocvrs_return) { + try { + cv::Ptr ret = instance->initCUDA(context, *inputs, *outputs); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_initTimVX_voidX_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendWrapperGGR_bool(cv::dnn::Layer* instance, void* timVxInfo, const std::vector>* inputsWrapper, const std::vector>* outputsWrapper, bool isLast, Result*>* ocvrs_return) { + try { + cv::Ptr ret = instance->initTimVX(timVxInfo, *inputsWrapper, *outputsWrapper, isLast); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_initCann_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendNodeGGR(cv::dnn::Layer* instance, const std::vector>* inputs, const std::vector>* outputs, const std::vector>* nodes, Result*>* ocvrs_return) { + try { + cv::Ptr ret = instance->initCann(*inputs, *outputs, *nodes); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_applyHalideScheduler_const_PtrLBackendNodeGR_const_vectorLMatXGR_const_vectorLMatGR_int(const cv::dnn::Layer* instance, cv::Ptr* node, const std::vector* inputs, const std::vector* outputs, int targetId, ResultVoid* ocvrs_return) { + try { + instance->applyHalideScheduler(*node, *inputs, *outputs, targetId); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_tryAttach_const_PtrLBackendNodeGR(cv::dnn::Layer* instance, const cv::Ptr* node, Result*>* ocvrs_return) { + try { + cv::Ptr ret = instance->tryAttach(*node); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_tryFuse_PtrLLayerGR(cv::dnn::Layer* instance, cv::Ptr* top, Result* ocvrs_return) { + try { + bool ret = instance->tryFuse(*top); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_getScaleShift_const_MatR_MatR(const cv::dnn::Layer* instance, cv::Mat* scale, cv::Mat* shift, ResultVoid* ocvrs_return) { + try { + instance->getScaleShift(*scale, *shift); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_getScaleZeropoint_const_floatR_intR(const cv::dnn::Layer* instance, float* scale, int* zeropoint, ResultVoid* ocvrs_return) { + try { + instance->getScaleZeropoint(*scale, *zeropoint); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_unsetAttached(cv::dnn::Layer* instance, ResultVoid* ocvrs_return) { + try { + instance->unsetAttached(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_getMemoryShapes_const_const_vectorLMatShapeGR_const_int_vectorLMatShapeGR_vectorLMatShapeGR(const cv::dnn::Layer* instance, const std::vector* inputs, const int requiredOutputs, std::vector* outputs, std::vector* internals, Result* ocvrs_return) { + try { + bool ret = instance->getMemoryShapes(*inputs, requiredOutputs, *outputs, *internals); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_getFLOPS_const_const_vectorLMatShapeGR_const_vectorLMatShapeGR(const cv::dnn::Layer* instance, const std::vector* inputs, const std::vector* outputs, Result* ocvrs_return) { + try { + int64_t ret = instance->getFLOPS(*inputs, *outputs); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_updateMemoryShapes_const_vectorLMatShapeGR(cv::dnn::Layer* instance, const std::vector* inputs, Result* ocvrs_return) { + try { + bool ret = instance->updateMemoryShapes(*inputs); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_Layer(Result* ocvrs_return) { + try { + cv::dnn::Layer* ret = new cv::dnn::Layer(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_Layer_const_LayerParamsR(const cv::dnn::LayerParams* params, Result* ocvrs_return) { + try { + cv::dnn::Layer* ret = new cv::dnn::Layer(*params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Layer_setParamsFrom_const_LayerParamsR(cv::dnn::Layer* instance, const cv::dnn::LayerParams* params, ResultVoid* ocvrs_return) { + try { + instance->setParamsFrom(*params); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +std::vector* cv_dnn_Layer_propBlobs_const(const cv::dnn::Layer* instance) { + std::vector ret = instance->blobs; + return new std::vector(ret); +} + +void cv_dnn_Layer_propBlobs_const_vectorLMatG(cv::dnn::Layer* instance, const std::vector* val) { + instance->blobs = *val; +} + +void* cv_dnn_Layer_propName_const(const cv::dnn::Layer* instance) { + cv::String ret = instance->name; + return ocvrs_create_string(ret.c_str()); +} + +void cv_dnn_Layer_propName_const_String(cv::dnn::Layer* instance, const char* val) { + instance->name = std::string(val); +} + +void* cv_dnn_Layer_propType_const(const cv::dnn::Layer* instance) { + cv::String ret = instance->type; + return ocvrs_create_string(ret.c_str()); +} + +void cv_dnn_Layer_propType_const_String(cv::dnn::Layer* instance, const char* val) { + instance->type = std::string(val); +} + +int cv_dnn_Layer_propPreferableTarget_const(const cv::dnn::Layer* instance) { + int ret = instance->preferableTarget; + return ret; +} + +void cv_dnn_Layer_propPreferableTarget_const_int(cv::dnn::Layer* instance, const int val) { + instance->preferableTarget = val; +} + +cv::dnn::AbsLayer* cv_dnn_Layer_to_AbsLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AccumLayer* cv_dnn_Layer_to_AccumLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AcosLayer* cv_dnn_Layer_to_AcosLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AcoshLayer* cv_dnn_Layer_to_AcoshLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ActivationLayer* cv_dnn_Layer_to_ActivationLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ActivationLayerInt8* cv_dnn_Layer_to_ActivationLayerInt8(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ArgLayer* cv_dnn_Layer_to_ArgLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AsinLayer* cv_dnn_Layer_to_AsinLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AsinhLayer* cv_dnn_Layer_to_AsinhLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AtanLayer* cv_dnn_Layer_to_AtanLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AtanhLayer* cv_dnn_Layer_to_AtanhLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::AttentionLayer* cv_dnn_Layer_to_AttentionLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::BNLLLayer* cv_dnn_Layer_to_BNLLLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::BaseConvolutionLayer* cv_dnn_Layer_to_BaseConvolutionLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::BatchNormLayer* cv_dnn_Layer_to_BatchNormLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::BatchNormLayerInt8* cv_dnn_Layer_to_BatchNormLayerInt8(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::BlankLayer* cv_dnn_Layer_to_BlankLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::CeilLayer* cv_dnn_Layer_to_CeilLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::CeluLayer* cv_dnn_Layer_to_CeluLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ChannelsPReLULayer* cv_dnn_Layer_to_ChannelsPReLULayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::CompareLayer* cv_dnn_Layer_to_CompareLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ConcatLayer* cv_dnn_Layer_to_ConcatLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ConstLayer* cv_dnn_Layer_to_ConstLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ConvolutionLayer* cv_dnn_Layer_to_ConvolutionLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ConvolutionLayerInt8* cv_dnn_Layer_to_ConvolutionLayerInt8(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::CorrelationLayer* cv_dnn_Layer_to_CorrelationLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::CosLayer* cv_dnn_Layer_to_CosLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::CoshLayer* cv_dnn_Layer_to_CoshLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::CropAndResizeLayer* cv_dnn_Layer_to_CropAndResizeLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::CropLayer* cv_dnn_Layer_to_CropLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::CumSumLayer* cv_dnn_Layer_to_CumSumLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::DataAugmentationLayer* cv_dnn_Layer_to_DataAugmentationLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::DeconvolutionLayer* cv_dnn_Layer_to_DeconvolutionLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::DepthToSpaceLayer* cv_dnn_Layer_to_DepthToSpaceLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::DequantizeLayer* cv_dnn_Layer_to_DequantizeLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::DetectionOutputLayer* cv_dnn_Layer_to_DetectionOutputLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ELULayer* cv_dnn_Layer_to_ELULayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::EinsumLayer* cv_dnn_Layer_to_EinsumLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::EltwiseLayer* cv_dnn_Layer_to_EltwiseLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::EltwiseLayerInt8* cv_dnn_Layer_to_EltwiseLayerInt8(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ErfLayer* cv_dnn_Layer_to_ErfLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ExpLayer* cv_dnn_Layer_to_ExpLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ExpandLayer* cv_dnn_Layer_to_ExpandLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::FlattenLayer* cv_dnn_Layer_to_FlattenLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::FloorLayer* cv_dnn_Layer_to_FloorLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::FlowWarpLayer* cv_dnn_Layer_to_FlowWarpLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::GRULayer* cv_dnn_Layer_to_GRULayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::GatherElementsLayer* cv_dnn_Layer_to_GatherElementsLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::GatherLayer* cv_dnn_Layer_to_GatherLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::GeluApproximationLayer* cv_dnn_Layer_to_GeluApproximationLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::GeluLayer* cv_dnn_Layer_to_GeluLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::GemmLayer* cv_dnn_Layer_to_GemmLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::GroupNormLayer* cv_dnn_Layer_to_GroupNormLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::HardSigmoidLayer* cv_dnn_Layer_to_HardSigmoidLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::HardSwishLayer* cv_dnn_Layer_to_HardSwishLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::InnerProductLayer* cv_dnn_Layer_to_InnerProductLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::InnerProductLayerInt8* cv_dnn_Layer_to_InnerProductLayerInt8(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::InstanceNormLayer* cv_dnn_Layer_to_InstanceNormLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::InterpLayer* cv_dnn_Layer_to_InterpLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::LRNLayer* cv_dnn_Layer_to_LRNLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::LSTMLayer* cv_dnn_Layer_to_LSTMLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::LayerNormLayer* cv_dnn_Layer_to_LayerNormLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::LogLayer* cv_dnn_Layer_to_LogLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::MVNLayer* cv_dnn_Layer_to_MVNLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::MatMulLayer* cv_dnn_Layer_to_MatMulLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::MaxUnpoolLayer* cv_dnn_Layer_to_MaxUnpoolLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::MishLayer* cv_dnn_Layer_to_MishLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::NaryEltwiseLayer* cv_dnn_Layer_to_NaryEltwiseLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::NormalizeBBoxLayer* cv_dnn_Layer_to_NormalizeBBoxLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::NotLayer* cv_dnn_Layer_to_NotLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::PaddingLayer* cv_dnn_Layer_to_PaddingLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::PermuteLayer* cv_dnn_Layer_to_PermuteLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::PoolingLayer* cv_dnn_Layer_to_PoolingLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::PoolingLayerInt8* cv_dnn_Layer_to_PoolingLayerInt8(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::PowerLayer* cv_dnn_Layer_to_PowerLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::PriorBoxLayer* cv_dnn_Layer_to_PriorBoxLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ProposalLayer* cv_dnn_Layer_to_ProposalLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::QuantizeLayer* cv_dnn_Layer_to_QuantizeLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::RNNLayer* cv_dnn_Layer_to_RNNLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ReLU6Layer* cv_dnn_Layer_to_ReLU6Layer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ReLULayer* cv_dnn_Layer_to_ReLULayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ReciprocalLayer* cv_dnn_Layer_to_ReciprocalLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ReduceLayer* cv_dnn_Layer_to_ReduceLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::RegionLayer* cv_dnn_Layer_to_RegionLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ReorgLayer* cv_dnn_Layer_to_ReorgLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::RequantizeLayer* cv_dnn_Layer_to_RequantizeLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ReshapeLayer* cv_dnn_Layer_to_ReshapeLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ResizeLayer* cv_dnn_Layer_to_ResizeLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::RoundLayer* cv_dnn_Layer_to_RoundLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ScaleLayer* cv_dnn_Layer_to_ScaleLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ScaleLayerInt8* cv_dnn_Layer_to_ScaleLayerInt8(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ScatterLayer* cv_dnn_Layer_to_ScatterLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ScatterNDLayer* cv_dnn_Layer_to_ScatterNDLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SeluLayer* cv_dnn_Layer_to_SeluLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ShiftLayer* cv_dnn_Layer_to_ShiftLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ShiftLayerInt8* cv_dnn_Layer_to_ShiftLayerInt8(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ShrinkLayer* cv_dnn_Layer_to_ShrinkLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ShuffleChannelLayer* cv_dnn_Layer_to_ShuffleChannelLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SigmoidLayer* cv_dnn_Layer_to_SigmoidLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SignLayer* cv_dnn_Layer_to_SignLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SinLayer* cv_dnn_Layer_to_SinLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SinhLayer* cv_dnn_Layer_to_SinhLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SliceLayer* cv_dnn_Layer_to_SliceLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SoftmaxLayer* cv_dnn_Layer_to_SoftmaxLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SoftmaxLayerInt8* cv_dnn_Layer_to_SoftmaxLayerInt8(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SoftplusLayer* cv_dnn_Layer_to_SoftplusLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SoftsignLayer* cv_dnn_Layer_to_SoftsignLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SpaceToDepthLayer* cv_dnn_Layer_to_SpaceToDepthLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SplitLayer* cv_dnn_Layer_to_SplitLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SqrtLayer* cv_dnn_Layer_to_SqrtLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SwishLayer* cv_dnn_Layer_to_SwishLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::TanHLayer* cv_dnn_Layer_to_TanHLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::TanLayer* cv_dnn_Layer_to_TanLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ThresholdedReluLayer* cv_dnn_Layer_to_ThresholdedReluLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::TileLayer* cv_dnn_Layer_to_TileLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::TopKLayer* cv_dnn_Layer_to_TopKLayer(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_Layer_to_Algorithm(cv::dnn::Layer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_Layer_delete(cv::dnn::Layer* instance) { + delete instance; +} + +void cv_dnn_LayerFactory_registerLayer_const_StringR_Constructor(const char* type, cv::dnn::LayerFactory::Constructor constructor, ResultVoid* ocvrs_return) { + try { + cv::dnn::LayerFactory::registerLayer(std::string(type), constructor); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_LayerFactory_unregisterLayer_const_StringR(const char* type, ResultVoid* ocvrs_return) { + try { + cv::dnn::LayerFactory::unregisterLayer(std::string(type)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_LayerFactory_isLayerRegistered_const_stringR(const char* type, Result* ocvrs_return) { + try { + bool ret = cv::dnn::LayerFactory::isLayerRegistered(std::string(type)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_LayerFactory_createLayerInstance_const_StringR_LayerParamsR(const char* type, cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::LayerFactory::createLayerInstance(std::string(type), *params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_LayerFactory_delete(cv::dnn::LayerFactory* instance) { + delete instance; +} + +void cv_dnn_LayerNormLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::LayerNormLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::LayerNormLayer* cv_dnn_LayerNormLayer_defaultNew_const() { + cv::dnn::LayerNormLayer* ret = new cv::dnn::LayerNormLayer(); + return ret; +} + +bool cv_dnn_LayerNormLayer_propHasBias_const(const cv::dnn::LayerNormLayer* instance) { + bool ret = instance->hasBias; + return ret; +} + +void cv_dnn_LayerNormLayer_propHasBias_const_bool(cv::dnn::LayerNormLayer* instance, const bool val) { + instance->hasBias = val; +} + +int cv_dnn_LayerNormLayer_propAxis_const(const cv::dnn::LayerNormLayer* instance) { + int ret = instance->axis; + return ret; +} + +void cv_dnn_LayerNormLayer_propAxis_const_int(cv::dnn::LayerNormLayer* instance, const int val) { + instance->axis = val; +} + +float cv_dnn_LayerNormLayer_propEpsilon_const(const cv::dnn::LayerNormLayer* instance) { + float ret = instance->epsilon; + return ret; +} + +void cv_dnn_LayerNormLayer_propEpsilon_const_float(cv::dnn::LayerNormLayer* instance, const float val) { + instance->epsilon = val; +} + +cv::Algorithm* cv_dnn_LayerNormLayer_to_Algorithm(cv::dnn::LayerNormLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_LayerNormLayer_to_Layer(cv::dnn::LayerNormLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_LayerNormLayer_delete(cv::dnn::LayerNormLayer* instance) { + delete instance; +} + +cv::dnn::LayerParams* cv_dnn_LayerParams_defaultNew_const() { + cv::dnn::LayerParams* ret = new cv::dnn::LayerParams(); + return ret; +} + +std::vector* cv_dnn_LayerParams_propBlobs_const(const cv::dnn::LayerParams* instance) { + std::vector ret = instance->blobs; + return new std::vector(ret); +} + +void cv_dnn_LayerParams_propBlobs_const_vectorLMatG(cv::dnn::LayerParams* instance, const std::vector* val) { + instance->blobs = *val; +} + +void* cv_dnn_LayerParams_propName_const(const cv::dnn::LayerParams* instance) { + cv::String ret = instance->name; + return ocvrs_create_string(ret.c_str()); +} + +void cv_dnn_LayerParams_propName_const_String(cv::dnn::LayerParams* instance, const char* val) { + instance->name = std::string(val); +} + +void* cv_dnn_LayerParams_propType_const(const cv::dnn::LayerParams* instance) { + cv::String ret = instance->type; + return ocvrs_create_string(ret.c_str()); +} + +void cv_dnn_LayerParams_propType_const_String(cv::dnn::LayerParams* instance, const char* val) { + instance->type = std::string(val); +} + +cv::dnn::Dict* cv_dnn_LayerParams_to_Dict(cv::dnn::LayerParams* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_LayerParams_delete(cv::dnn::LayerParams* instance) { + delete instance; +} + +void cv_dnn_LogLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::LogLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::LogLayer* cv_dnn_LogLayer_defaultNew_const() { + cv::dnn::LogLayer* ret = new cv::dnn::LogLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_LogLayer_to_ActivationLayer(cv::dnn::LogLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_LogLayer_to_Algorithm(cv::dnn::LogLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_LogLayer_to_Layer(cv::dnn::LogLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_LogLayer_delete(cv::dnn::LogLayer* instance) { + delete instance; +} + +void cv_dnn_MVNLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::MVNLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::MVNLayer* cv_dnn_MVNLayer_defaultNew_const() { + cv::dnn::MVNLayer* ret = new cv::dnn::MVNLayer(); + return ret; +} + +float cv_dnn_MVNLayer_propEps_const(const cv::dnn::MVNLayer* instance) { + float ret = instance->eps; + return ret; +} + +void cv_dnn_MVNLayer_propEps_const_float(cv::dnn::MVNLayer* instance, const float val) { + instance->eps = val; +} + +bool cv_dnn_MVNLayer_propNormVariance_const(const cv::dnn::MVNLayer* instance) { + bool ret = instance->normVariance; + return ret; +} + +void cv_dnn_MVNLayer_propNormVariance_const_bool(cv::dnn::MVNLayer* instance, const bool val) { + instance->normVariance = val; +} + +bool cv_dnn_MVNLayer_propAcrossChannels_const(const cv::dnn::MVNLayer* instance) { + bool ret = instance->acrossChannels; + return ret; +} + +void cv_dnn_MVNLayer_propAcrossChannels_const_bool(cv::dnn::MVNLayer* instance, const bool val) { + instance->acrossChannels = val; +} + +cv::Algorithm* cv_dnn_MVNLayer_to_Algorithm(cv::dnn::MVNLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_MVNLayer_to_Layer(cv::dnn::MVNLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_MVNLayer_delete(cv::dnn::MVNLayer* instance) { + delete instance; +} + +void cv_dnn_MatMulLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::MatMulLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::MatMulLayer* cv_dnn_MatMulLayer_defaultNew_const() { + cv::dnn::MatMulLayer* ret = new cv::dnn::MatMulLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_MatMulLayer_to_Algorithm(cv::dnn::MatMulLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_MatMulLayer_to_Layer(cv::dnn::MatMulLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_MatMulLayer_delete(cv::dnn::MatMulLayer* instance) { + delete instance; +} + +void cv_dnn_MaxUnpoolLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::MaxUnpoolLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::MaxUnpoolLayer* cv_dnn_MaxUnpoolLayer_defaultNew_const() { + cv::dnn::MaxUnpoolLayer* ret = new cv::dnn::MaxUnpoolLayer(); + return ret; +} + +void cv_dnn_MaxUnpoolLayer_propPoolKernel_const(const cv::dnn::MaxUnpoolLayer* instance, cv::Size* ocvrs_return) { + cv::Size ret = instance->poolKernel; + *ocvrs_return = ret; +} + +void cv_dnn_MaxUnpoolLayer_propPoolKernel_const_Size(cv::dnn::MaxUnpoolLayer* instance, const cv::Size* val) { + instance->poolKernel = *val; +} + +void cv_dnn_MaxUnpoolLayer_propPoolPad_const(const cv::dnn::MaxUnpoolLayer* instance, cv::Size* ocvrs_return) { + cv::Size ret = instance->poolPad; + *ocvrs_return = ret; +} + +void cv_dnn_MaxUnpoolLayer_propPoolPad_const_Size(cv::dnn::MaxUnpoolLayer* instance, const cv::Size* val) { + instance->poolPad = *val; +} + +void cv_dnn_MaxUnpoolLayer_propPoolStride_const(const cv::dnn::MaxUnpoolLayer* instance, cv::Size* ocvrs_return) { + cv::Size ret = instance->poolStride; + *ocvrs_return = ret; +} + +void cv_dnn_MaxUnpoolLayer_propPoolStride_const_Size(cv::dnn::MaxUnpoolLayer* instance, const cv::Size* val) { + instance->poolStride = *val; +} + +cv::Algorithm* cv_dnn_MaxUnpoolLayer_to_Algorithm(cv::dnn::MaxUnpoolLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_MaxUnpoolLayer_to_Layer(cv::dnn::MaxUnpoolLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_MaxUnpoolLayer_delete(cv::dnn::MaxUnpoolLayer* instance) { + delete instance; +} + +void cv_dnn_MishLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::MishLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::MishLayer* cv_dnn_MishLayer_defaultNew_const() { + cv::dnn::MishLayer* ret = new cv::dnn::MishLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_MishLayer_to_ActivationLayer(cv::dnn::MishLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_MishLayer_to_Algorithm(cv::dnn::MishLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_MishLayer_to_Layer(cv::dnn::MishLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_MishLayer_delete(cv::dnn::MishLayer* instance) { + delete instance; +} + +void cv_dnn_Model_Model(Result* ocvrs_return) { + try { + cv::dnn::Model* ret = new cv::dnn::Model(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::Model* cv_dnn_Model_Model_const_ModelR(const cv::dnn::Model* unnamed) { + cv::dnn::Model* ret = new cv::dnn::Model(*unnamed); + return ret; +} + +cv::dnn::Model* cv_dnn_Model_Model_ModelRR(cv::dnn::Model* unnamed) { + cv::dnn::Model* ret = new cv::dnn::Model(std::move(*unnamed)); + return ret; +} + +void cv_dnn_Model_operatorST_const_ModelR(cv::dnn::Model* instance, const cv::dnn::Model* unnamed) { + instance->operator=(*unnamed); +} + +void cv_dnn_Model_operatorST_ModelRR(cv::dnn::Model* instance, cv::dnn::Model* unnamed) { + instance->operator=(std::move(*unnamed)); +} + +void cv_dnn_Model_Model_const_StringR_const_StringR(const char* model, const char* config, Result* ocvrs_return) { + try { + cv::dnn::Model* ret = new cv::dnn::Model(std::string(model), std::string(config)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_Model_const_StringR(const char* model, Result* ocvrs_return) { + try { + cv::dnn::Model* ret = new cv::dnn::Model(std::string(model)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_Model_const_NetR(const cv::dnn::Net* network, Result* ocvrs_return) { + try { + cv::dnn::Model* ret = new cv::dnn::Model(*network); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_setInputSize_const_SizeR(cv::dnn::Model* instance, const cv::Size* size, Result* ocvrs_return) { + try { + cv::dnn::Model ret = instance->setInputSize(*size); + Ok(new cv::dnn::Model(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_setInputSize_int_int(cv::dnn::Model* instance, int width, int height, Result* ocvrs_return) { + try { + cv::dnn::Model ret = instance->setInputSize(width, height); + Ok(new cv::dnn::Model(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_setInputMean_const_ScalarR(cv::dnn::Model* instance, const cv::Scalar* mean, Result* ocvrs_return) { + try { + cv::dnn::Model ret = instance->setInputMean(*mean); + Ok(new cv::dnn::Model(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_setInputScale_const_ScalarR(cv::dnn::Model* instance, const cv::Scalar* scale, Result* ocvrs_return) { + try { + cv::dnn::Model ret = instance->setInputScale(*scale); + Ok(new cv::dnn::Model(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_setInputCrop_bool(cv::dnn::Model* instance, bool crop, Result* ocvrs_return) { + try { + cv::dnn::Model ret = instance->setInputCrop(crop); + Ok(new cv::dnn::Model(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_setInputSwapRB_bool(cv::dnn::Model* instance, bool swapRB, Result* ocvrs_return) { + try { + cv::dnn::Model ret = instance->setInputSwapRB(swapRB); + Ok(new cv::dnn::Model(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_setOutputNames_const_vectorLStringGR(cv::dnn::Model* instance, const std::vector* outNames, Result* ocvrs_return) { + try { + cv::dnn::Model ret = instance->setOutputNames(*outNames); + Ok(new cv::dnn::Model(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_setInputParams_double_const_SizeR_const_ScalarR_bool_bool(cv::dnn::Model* instance, double scale, const cv::Size* size, const cv::Scalar* mean, bool swapRB, bool crop, ResultVoid* ocvrs_return) { + try { + instance->setInputParams(scale, *size, *mean, swapRB, crop); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_setInputParams(cv::dnn::Model* instance, ResultVoid* ocvrs_return) { + try { + instance->setInputParams(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_predict_const_const__InputArrayR_const__OutputArrayR(const cv::dnn::Model* instance, const cv::_InputArray* frame, const cv::_OutputArray* outs, ResultVoid* ocvrs_return) { + try { + instance->predict(*frame, *outs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_setPreferableBackend_Backend(cv::dnn::Model* instance, cv::dnn::Backend backendId, Result* ocvrs_return) { + try { + cv::dnn::Model ret = instance->setPreferableBackend(backendId); + Ok(new cv::dnn::Model(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_setPreferableTarget_Target(cv::dnn::Model* instance, cv::dnn::Target targetId, Result* ocvrs_return) { + try { + cv::dnn::Model ret = instance->setPreferableTarget(targetId); + Ok(new cv::dnn::Model(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_enableWinograd_bool(cv::dnn::Model* instance, bool useWinograd, Result* ocvrs_return) { + try { + cv::dnn::Model ret = instance->enableWinograd(useWinograd); + Ok(new cv::dnn::Model(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_getNetwork__const(const cv::dnn::Model* instance, Result* ocvrs_return) { + try { + cv::dnn::Net ret = instance->getNetwork_(); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Model_getNetwork_(cv::dnn::Model* instance, Result* ocvrs_return) { + try { + cv::dnn::Net ret = instance->getNetwork_(); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::Model* cv_dnn_Model_implicitClone_const(const cv::dnn::Model* instance) { + return new cv::dnn::Model(*instance); +} + +void cv_dnn_Model_delete(cv::dnn::Model* instance) { + delete instance; +} + +void cv_dnn_NaryEltwiseLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::NaryEltwiseLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::NaryEltwiseLayer* cv_dnn_NaryEltwiseLayer_defaultNew_const() { + cv::dnn::NaryEltwiseLayer* ret = new cv::dnn::NaryEltwiseLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_NaryEltwiseLayer_to_Algorithm(cv::dnn::NaryEltwiseLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_NaryEltwiseLayer_to_Layer(cv::dnn::NaryEltwiseLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_NaryEltwiseLayer_delete(cv::dnn::NaryEltwiseLayer* instance) { + delete instance; +} + +void cv_dnn_Net_Net(Result* ocvrs_return) { + try { + cv::dnn::Net* ret = new cv::dnn::Net(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_readFromModelOptimizer_const_StringR_const_StringR(const char* xml, const char* bin, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::Net::readFromModelOptimizer(std::string(xml), std::string(bin)); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_readFromModelOptimizer_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(const std::vector* bufferModelConfig, const std::vector* bufferWeights, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::Net::readFromModelOptimizer(*bufferModelConfig, *bufferWeights); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_readFromModelOptimizer_const_unsigned_charX_size_t_const_unsigned_charX_size_t(const unsigned char* bufferModelConfigPtr, size_t bufferModelConfigSize, const unsigned char* bufferWeightsPtr, size_t bufferWeightsSize, Result* ocvrs_return) { + try { + cv::dnn::Net ret = cv::dnn::Net::readFromModelOptimizer(bufferModelConfigPtr, bufferModelConfigSize, bufferWeightsPtr, bufferWeightsSize); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_empty_const(const cv::dnn::Net* instance, Result* ocvrs_return) { + try { + bool ret = instance->empty(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_dump(cv::dnn::Net* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->dump(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_dumpToFile_const_StringR(cv::dnn::Net* instance, const char* path, ResultVoid* ocvrs_return) { + try { + instance->dumpToFile(std::string(path)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_dumpToPbtxt_const_StringR(cv::dnn::Net* instance, const char* path, ResultVoid* ocvrs_return) { + try { + instance->dumpToPbtxt(std::string(path)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_addLayer_const_StringR_const_StringR_const_intR_LayerParamsR(cv::dnn::Net* instance, const char* name, const char* type, const int* dtype, cv::dnn::LayerParams* params, Result* ocvrs_return) { + try { + int ret = instance->addLayer(std::string(name), std::string(type), *dtype, *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_addLayer_const_StringR_const_StringR_LayerParamsR(cv::dnn::Net* instance, const char* name, const char* type, cv::dnn::LayerParams* params, Result* ocvrs_return) { + try { + int ret = instance->addLayer(std::string(name), std::string(type), *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_addLayerToPrev_const_StringR_const_StringR_const_intR_LayerParamsR(cv::dnn::Net* instance, const char* name, const char* type, const int* dtype, cv::dnn::LayerParams* params, Result* ocvrs_return) { + try { + int ret = instance->addLayerToPrev(std::string(name), std::string(type), *dtype, *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_addLayerToPrev_const_StringR_const_StringR_LayerParamsR(cv::dnn::Net* instance, const char* name, const char* type, cv::dnn::LayerParams* params, Result* ocvrs_return) { + try { + int ret = instance->addLayerToPrev(std::string(name), std::string(type), *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getLayerId_const_const_StringR(const cv::dnn::Net* instance, const char* layer, Result* ocvrs_return) { + try { + int ret = instance->getLayerId(std::string(layer)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getLayerNames_const(const cv::dnn::Net* instance, Result*>* ocvrs_return) { + try { + std::vector ret = instance->getLayerNames(); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getLayer_const_int(const cv::dnn::Net* instance, int layerId, Result*>* ocvrs_return) { + try { + cv::Ptr ret = instance->getLayer(layerId); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getLayer_const_const_StringR(const cv::dnn::Net* instance, const char* layerName, Result*>* ocvrs_return) { + try { + cv::Ptr ret = instance->getLayer(std::string(layerName)); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getLayer_const_const_LayerIdR(const cv::dnn::Net* instance, const cv::dnn::Net::LayerId* layerId, Result*>* ocvrs_return) { + try { + cv::Ptr ret = instance->getLayer(*layerId); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getLayerInputs_const_int(const cv::dnn::Net* instance, int layerId, Result>*>* ocvrs_return) { + try { + std::vector> ret = instance->getLayerInputs(layerId); + Ok(new std::vector>(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_connect_String_String(cv::dnn::Net* instance, const char* outPin, const char* inpPin, ResultVoid* ocvrs_return) { + try { + instance->connect(std::string(outPin), std::string(inpPin)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_connect_int_int_int_int(cv::dnn::Net* instance, int outLayerId, int outNum, int inpLayerId, int inpNum, ResultVoid* ocvrs_return) { + try { + instance->connect(outLayerId, outNum, inpLayerId, inpNum); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_registerOutput_const_stringR_int_int(cv::dnn::Net* instance, const char* outputName, int layerId, int outputPort, Result* ocvrs_return) { + try { + int ret = instance->registerOutput(std::string(outputName), layerId, outputPort); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_setInputsNames_const_vectorLStringGR(cv::dnn::Net* instance, const std::vector* inputBlobNames, ResultVoid* ocvrs_return) { + try { + instance->setInputsNames(*inputBlobNames); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_setInputShape_const_StringR_const_MatShapeR(cv::dnn::Net* instance, const char* inputName, const cv::dnn::MatShape* shape, ResultVoid* ocvrs_return) { + try { + instance->setInputShape(std::string(inputName), *shape); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_forward_const_StringR(cv::dnn::Net* instance, const char* outputName, Result* ocvrs_return) { + try { + cv::Mat ret = instance->forward(std::string(outputName)); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_forward(cv::dnn::Net* instance, Result* ocvrs_return) { + try { + cv::Mat ret = instance->forward(); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_forwardAsync_const_StringR(cv::dnn::Net* instance, const char* outputName, Result* ocvrs_return) { + try { + cv::AsyncArray ret = instance->forwardAsync(std::string(outputName)); + Ok(new cv::AsyncArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_forwardAsync(cv::dnn::Net* instance, Result* ocvrs_return) { + try { + cv::AsyncArray ret = instance->forwardAsync(); + Ok(new cv::AsyncArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_forward_const__OutputArrayR_const_StringR(cv::dnn::Net* instance, const cv::_OutputArray* outputBlobs, const char* outputName, ResultVoid* ocvrs_return) { + try { + instance->forward(*outputBlobs, std::string(outputName)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_forward_const__OutputArrayR(cv::dnn::Net* instance, const cv::_OutputArray* outputBlobs, ResultVoid* ocvrs_return) { + try { + instance->forward(*outputBlobs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_forward_const__OutputArrayR_const_vectorLStringGR(cv::dnn::Net* instance, const cv::_OutputArray* outputBlobs, const std::vector* outBlobNames, ResultVoid* ocvrs_return) { + try { + instance->forward(*outputBlobs, *outBlobNames); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_forward_vectorLvectorLMatGGR_const_vectorLStringGR(cv::dnn::Net* instance, std::vector>* outputBlobs, const std::vector* outBlobNames, ResultVoid* ocvrs_return) { + try { + instance->forward(*outputBlobs, *outBlobNames); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_quantize_const__InputArrayR_int_int_bool(cv::dnn::Net* instance, const cv::_InputArray* calibData, int inputsDtype, int outputsDtype, bool perChannel, Result* ocvrs_return) { + try { + cv::dnn::Net ret = instance->quantize(*calibData, inputsDtype, outputsDtype, perChannel); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_quantize_const__InputArrayR_int_int(cv::dnn::Net* instance, const cv::_InputArray* calibData, int inputsDtype, int outputsDtype, Result* ocvrs_return) { + try { + cv::dnn::Net ret = instance->quantize(*calibData, inputsDtype, outputsDtype); + Ok(new cv::dnn::Net(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getInputDetails_const_vectorLfloatGR_vectorLintGR(const cv::dnn::Net* instance, std::vector* scales, std::vector* zeropoints, ResultVoid* ocvrs_return) { + try { + instance->getInputDetails(*scales, *zeropoints); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getOutputDetails_const_vectorLfloatGR_vectorLintGR(const cv::dnn::Net* instance, std::vector* scales, std::vector* zeropoints, ResultVoid* ocvrs_return) { + try { + instance->getOutputDetails(*scales, *zeropoints); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_setHalideScheduler_const_StringR(cv::dnn::Net* instance, const char* scheduler, ResultVoid* ocvrs_return) { + try { + instance->setHalideScheduler(std::string(scheduler)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_setPreferableBackend_int(cv::dnn::Net* instance, int backendId, ResultVoid* ocvrs_return) { + try { + instance->setPreferableBackend(backendId); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_setPreferableTarget_int(cv::dnn::Net* instance, int targetId, ResultVoid* ocvrs_return) { + try { + instance->setPreferableTarget(targetId); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_setInput_const__InputArrayR_const_StringR_double_const_ScalarR(cv::dnn::Net* instance, const cv::_InputArray* blob, const char* name, double scalefactor, const cv::Scalar* mean, ResultVoid* ocvrs_return) { + try { + instance->setInput(*blob, std::string(name), scalefactor, *mean); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_setInput_const__InputArrayR(cv::dnn::Net* instance, const cv::_InputArray* blob, ResultVoid* ocvrs_return) { + try { + instance->setInput(*blob); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_setParam_int_int_const_MatR(cv::dnn::Net* instance, int layer, int numParam, const cv::Mat* blob, ResultVoid* ocvrs_return) { + try { + instance->setParam(layer, numParam, *blob); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_setParam_const_StringR_int_const_MatR(cv::dnn::Net* instance, const char* layerName, int numParam, const cv::Mat* blob, ResultVoid* ocvrs_return) { + try { + instance->setParam(std::string(layerName), numParam, *blob); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getParam_const_int_int(const cv::dnn::Net* instance, int layer, int numParam, Result* ocvrs_return) { + try { + cv::Mat ret = instance->getParam(layer, numParam); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getParam_const_int(const cv::dnn::Net* instance, int layer, Result* ocvrs_return) { + try { + cv::Mat ret = instance->getParam(layer); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getParam_const_const_StringR_int(const cv::dnn::Net* instance, const char* layerName, int numParam, Result* ocvrs_return) { + try { + cv::Mat ret = instance->getParam(std::string(layerName), numParam); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getParam_const_const_StringR(const cv::dnn::Net* instance, const char* layerName, Result* ocvrs_return) { + try { + cv::Mat ret = instance->getParam(std::string(layerName)); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getUnconnectedOutLayers_const(const cv::dnn::Net* instance, Result*>* ocvrs_return) { + try { + std::vector ret = instance->getUnconnectedOutLayers(); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getUnconnectedOutLayersNames_const(const cv::dnn::Net* instance, Result*>* ocvrs_return) { + try { + std::vector ret = instance->getUnconnectedOutLayersNames(); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getLayersShapes_const_const_vectorLMatShapeGR_vectorLintGR_vectorLvectorLMatShapeGGR_vectorLvectorLMatShapeGGR(const cv::dnn::Net* instance, const std::vector* netInputShapes, std::vector* layersIds, std::vector>* inLayersShapes, std::vector>* outLayersShapes, ResultVoid* ocvrs_return) { + try { + instance->getLayersShapes(*netInputShapes, *layersIds, *inLayersShapes, *outLayersShapes); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getLayersShapes_const_const_MatShapeR_vectorLintGR_vectorLvectorLMatShapeGGR_vectorLvectorLMatShapeGGR(const cv::dnn::Net* instance, const cv::dnn::MatShape* netInputShape, std::vector* layersIds, std::vector>* inLayersShapes, std::vector>* outLayersShapes, ResultVoid* ocvrs_return) { + try { + instance->getLayersShapes(*netInputShape, *layersIds, *inLayersShapes, *outLayersShapes); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getLayerShapes_const_const_MatShapeR_const_int_vectorLMatShapeGR_vectorLMatShapeGR(const cv::dnn::Net* instance, const cv::dnn::MatShape* netInputShape, const int layerId, std::vector* inLayerShapes, std::vector* outLayerShapes, ResultVoid* ocvrs_return) { + try { + instance->getLayerShapes(*netInputShape, layerId, *inLayerShapes, *outLayerShapes); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getLayerShapes_const_const_vectorLMatShapeGR_const_int_vectorLMatShapeGR_vectorLMatShapeGR(const cv::dnn::Net* instance, const std::vector* netInputShapes, const int layerId, std::vector* inLayerShapes, std::vector* outLayerShapes, ResultVoid* ocvrs_return) { + try { + instance->getLayerShapes(*netInputShapes, layerId, *inLayerShapes, *outLayerShapes); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getFLOPS_const_const_vectorLMatShapeGR(const cv::dnn::Net* instance, const std::vector* netInputShapes, Result* ocvrs_return) { + try { + int64_t ret = instance->getFLOPS(*netInputShapes); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getFLOPS_const_const_MatShapeR(const cv::dnn::Net* instance, const cv::dnn::MatShape* netInputShape, Result* ocvrs_return) { + try { + int64_t ret = instance->getFLOPS(*netInputShape); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getFLOPS_const_const_int_const_vectorLMatShapeGR(const cv::dnn::Net* instance, const int layerId, const std::vector* netInputShapes, Result* ocvrs_return) { + try { + int64_t ret = instance->getFLOPS(layerId, *netInputShapes); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getFLOPS_const_const_int_const_MatShapeR(const cv::dnn::Net* instance, const int layerId, const cv::dnn::MatShape* netInputShape, Result* ocvrs_return) { + try { + int64_t ret = instance->getFLOPS(layerId, *netInputShape); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getLayerTypes_const_vectorLStringGR(const cv::dnn::Net* instance, std::vector* layersTypes, ResultVoid* ocvrs_return) { + try { + instance->getLayerTypes(*layersTypes); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getLayersCount_const_const_StringR(const cv::dnn::Net* instance, const char* layerType, Result* ocvrs_return) { + try { + int ret = instance->getLayersCount(std::string(layerType)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getMemoryConsumption_const_const_vectorLMatShapeGR_size_tR_size_tR(const cv::dnn::Net* instance, const std::vector* netInputShapes, size_t* weights, size_t* blobs, ResultVoid* ocvrs_return) { + try { + instance->getMemoryConsumption(*netInputShapes, *weights, *blobs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getMemoryConsumption_const_const_MatShapeR_size_tR_size_tR(const cv::dnn::Net* instance, const cv::dnn::MatShape* netInputShape, size_t* weights, size_t* blobs, ResultVoid* ocvrs_return) { + try { + instance->getMemoryConsumption(*netInputShape, *weights, *blobs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getMemoryConsumption_const_const_int_const_vectorLMatShapeGR_size_tR_size_tR(const cv::dnn::Net* instance, const int layerId, const std::vector* netInputShapes, size_t* weights, size_t* blobs, ResultVoid* ocvrs_return) { + try { + instance->getMemoryConsumption(layerId, *netInputShapes, *weights, *blobs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getMemoryConsumption_const_const_int_const_MatShapeR_size_tR_size_tR(const cv::dnn::Net* instance, const int layerId, const cv::dnn::MatShape* netInputShape, size_t* weights, size_t* blobs, ResultVoid* ocvrs_return) { + try { + instance->getMemoryConsumption(layerId, *netInputShape, *weights, *blobs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getMemoryConsumption_const_const_vectorLMatShapeGR_vectorLintGR_vectorLsize_tGR_vectorLsize_tGR(const cv::dnn::Net* instance, const std::vector* netInputShapes, std::vector* layerIds, std::vector* weights, std::vector* blobs, ResultVoid* ocvrs_return) { + try { + instance->getMemoryConsumption(*netInputShapes, *layerIds, *weights, *blobs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getMemoryConsumption_const_const_MatShapeR_vectorLintGR_vectorLsize_tGR_vectorLsize_tGR(const cv::dnn::Net* instance, const cv::dnn::MatShape* netInputShape, std::vector* layerIds, std::vector* weights, std::vector* blobs, ResultVoid* ocvrs_return) { + try { + instance->getMemoryConsumption(*netInputShape, *layerIds, *weights, *blobs); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_enableFusion_bool(cv::dnn::Net* instance, bool fusion, ResultVoid* ocvrs_return) { + try { + instance->enableFusion(fusion); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_enableWinograd_bool(cv::dnn::Net* instance, bool useWinograd, ResultVoid* ocvrs_return) { + try { + instance->enableWinograd(useWinograd); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_Net_getPerfProfile_vectorLdoubleGR(cv::dnn::Net* instance, std::vector* timings, Result* ocvrs_return) { + try { + int64_t ret = instance->getPerfProfile(*timings); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::Net* cv_dnn_Net_implicitClone_const(const cv::dnn::Net* instance) { + return new cv::dnn::Net(*instance); +} + +void cv_dnn_Net_delete(cv::dnn::Net* instance) { + delete instance; +} + +void cv_dnn_NormalizeBBoxLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::NormalizeBBoxLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::NormalizeBBoxLayer* cv_dnn_NormalizeBBoxLayer_defaultNew_const() { + cv::dnn::NormalizeBBoxLayer* ret = new cv::dnn::NormalizeBBoxLayer(); + return ret; +} + +float cv_dnn_NormalizeBBoxLayer_propPnorm_const(const cv::dnn::NormalizeBBoxLayer* instance) { + float ret = instance->pnorm; + return ret; +} + +void cv_dnn_NormalizeBBoxLayer_propPnorm_const_float(cv::dnn::NormalizeBBoxLayer* instance, const float val) { + instance->pnorm = val; +} + +float cv_dnn_NormalizeBBoxLayer_propEpsilon_const(const cv::dnn::NormalizeBBoxLayer* instance) { + float ret = instance->epsilon; + return ret; +} + +void cv_dnn_NormalizeBBoxLayer_propEpsilon_const_float(cv::dnn::NormalizeBBoxLayer* instance, const float val) { + instance->epsilon = val; +} + +bool cv_dnn_NormalizeBBoxLayer_propAcrossSpatial_const(const cv::dnn::NormalizeBBoxLayer* instance) { + bool ret = instance->acrossSpatial; + return ret; +} + +void cv_dnn_NormalizeBBoxLayer_propAcrossSpatial_const_bool(cv::dnn::NormalizeBBoxLayer* instance, const bool val) { + instance->acrossSpatial = val; +} + +cv::Algorithm* cv_dnn_NormalizeBBoxLayer_to_Algorithm(cv::dnn::NormalizeBBoxLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_NormalizeBBoxLayer_to_Layer(cv::dnn::NormalizeBBoxLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_NormalizeBBoxLayer_delete(cv::dnn::NormalizeBBoxLayer* instance) { + delete instance; +} + +void cv_dnn_NotLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::NotLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::NotLayer* cv_dnn_NotLayer_defaultNew_const() { + cv::dnn::NotLayer* ret = new cv::dnn::NotLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_NotLayer_to_ActivationLayer(cv::dnn::NotLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_NotLayer_to_Algorithm(cv::dnn::NotLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_NotLayer_to_Layer(cv::dnn::NotLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_NotLayer_delete(cv::dnn::NotLayer* instance) { + delete instance; +} + +void cv_dnn_PaddingLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::PaddingLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::PaddingLayer* cv_dnn_PaddingLayer_defaultNew_const() { + cv::dnn::PaddingLayer* ret = new cv::dnn::PaddingLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_PaddingLayer_to_Algorithm(cv::dnn::PaddingLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_PaddingLayer_to_Layer(cv::dnn::PaddingLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_PaddingLayer_delete(cv::dnn::PaddingLayer* instance) { + delete instance; +} + +void cv_dnn_PermuteLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::PermuteLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::PermuteLayer* cv_dnn_PermuteLayer_defaultNew_const() { + cv::dnn::PermuteLayer* ret = new cv::dnn::PermuteLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_PermuteLayer_to_Algorithm(cv::dnn::PermuteLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_PermuteLayer_to_Layer(cv::dnn::PermuteLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_PermuteLayer_delete(cv::dnn::PermuteLayer* instance) { + delete instance; +} + +void cv_dnn_PoolingLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::PoolingLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::PoolingLayer* cv_dnn_PoolingLayer_defaultNew_const() { + cv::dnn::PoolingLayer* ret = new cv::dnn::PoolingLayer(); + return ret; +} + +int cv_dnn_PoolingLayer_propType_const(const cv::dnn::PoolingLayer* instance) { + int ret = instance->type; + return ret; +} + +void cv_dnn_PoolingLayer_propType_const_int(cv::dnn::PoolingLayer* instance, const int val) { + instance->type = val; +} + +std::vector* cv_dnn_PoolingLayer_propKernel_size_const(const cv::dnn::PoolingLayer* instance) { + std::vector ret = instance->kernel_size; + return new std::vector(ret); +} + +void cv_dnn_PoolingLayer_propKernel_size_const_vectorLsize_tG(cv::dnn::PoolingLayer* instance, const std::vector* val) { + instance->kernel_size = *val; +} + +std::vector* cv_dnn_PoolingLayer_propStrides_const(const cv::dnn::PoolingLayer* instance) { + std::vector ret = instance->strides; + return new std::vector(ret); +} + +void cv_dnn_PoolingLayer_propStrides_const_vectorLsize_tG(cv::dnn::PoolingLayer* instance, const std::vector* val) { + instance->strides = *val; +} + +std::vector* cv_dnn_PoolingLayer_propPads_begin_const(const cv::dnn::PoolingLayer* instance) { + std::vector ret = instance->pads_begin; + return new std::vector(ret); +} + +void cv_dnn_PoolingLayer_propPads_begin_const_vectorLsize_tG(cv::dnn::PoolingLayer* instance, const std::vector* val) { + instance->pads_begin = *val; +} + +std::vector* cv_dnn_PoolingLayer_propPads_end_const(const cv::dnn::PoolingLayer* instance) { + std::vector ret = instance->pads_end; + return new std::vector(ret); +} + +void cv_dnn_PoolingLayer_propPads_end_const_vectorLsize_tG(cv::dnn::PoolingLayer* instance, const std::vector* val) { + instance->pads_end = *val; +} + +bool cv_dnn_PoolingLayer_propGlobalPooling_const(const cv::dnn::PoolingLayer* instance) { + bool ret = instance->globalPooling; + return ret; +} + +void cv_dnn_PoolingLayer_propGlobalPooling_const_bool(cv::dnn::PoolingLayer* instance, const bool val) { + instance->globalPooling = val; +} + +std::vector* cv_dnn_PoolingLayer_propIsGlobalPooling_const(const cv::dnn::PoolingLayer* instance) { + std::vector ret = instance->isGlobalPooling; + return new std::vector(ret); +} + +void cv_dnn_PoolingLayer_propIsGlobalPooling_const_vectorLboolG(cv::dnn::PoolingLayer* instance, const std::vector* val) { + instance->isGlobalPooling = *val; +} + +bool cv_dnn_PoolingLayer_propComputeMaxIdx_const(const cv::dnn::PoolingLayer* instance) { + bool ret = instance->computeMaxIdx; + return ret; +} + +void cv_dnn_PoolingLayer_propComputeMaxIdx_const_bool(cv::dnn::PoolingLayer* instance, const bool val) { + instance->computeMaxIdx = val; +} + +void* cv_dnn_PoolingLayer_propPadMode_const(const cv::dnn::PoolingLayer* instance) { + cv::String ret = instance->padMode; + return ocvrs_create_string(ret.c_str()); +} + +void cv_dnn_PoolingLayer_propPadMode_const_String(cv::dnn::PoolingLayer* instance, const char* val) { + instance->padMode = std::string(val); +} + +bool cv_dnn_PoolingLayer_propCeilMode_const(const cv::dnn::PoolingLayer* instance) { + bool ret = instance->ceilMode; + return ret; +} + +void cv_dnn_PoolingLayer_propCeilMode_const_bool(cv::dnn::PoolingLayer* instance, const bool val) { + instance->ceilMode = val; +} + +bool cv_dnn_PoolingLayer_propAvePoolPaddedArea_const(const cv::dnn::PoolingLayer* instance) { + bool ret = instance->avePoolPaddedArea; + return ret; +} + +void cv_dnn_PoolingLayer_propAvePoolPaddedArea_const_bool(cv::dnn::PoolingLayer* instance, const bool val) { + instance->avePoolPaddedArea = val; +} + +void cv_dnn_PoolingLayer_propPooledSize_const(const cv::dnn::PoolingLayer* instance, cv::Size* ocvrs_return) { + cv::Size ret = instance->pooledSize; + *ocvrs_return = ret; +} + +void cv_dnn_PoolingLayer_propPooledSize_const_Size(cv::dnn::PoolingLayer* instance, const cv::Size* val) { + instance->pooledSize = *val; +} + +float cv_dnn_PoolingLayer_propSpatialScale_const(const cv::dnn::PoolingLayer* instance) { + float ret = instance->spatialScale; + return ret; +} + +void cv_dnn_PoolingLayer_propSpatialScale_const_float(cv::dnn::PoolingLayer* instance, const float val) { + instance->spatialScale = val; +} + +int cv_dnn_PoolingLayer_propPsRoiOutChannels_const(const cv::dnn::PoolingLayer* instance) { + int ret = instance->psRoiOutChannels; + return ret; +} + +void cv_dnn_PoolingLayer_propPsRoiOutChannels_const_int(cv::dnn::PoolingLayer* instance, const int val) { + instance->psRoiOutChannels = val; +} + +cv::Algorithm* cv_dnn_PoolingLayer_to_Algorithm(cv::dnn::PoolingLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_PoolingLayer_to_Layer(cv::dnn::PoolingLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_PoolingLayer_delete(cv::dnn::PoolingLayer* instance) { + delete instance; +} + +void cv_dnn_PoolingLayerInt8_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::PoolingLayerInt8::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::PoolingLayerInt8* cv_dnn_PoolingLayerInt8_defaultNew_const() { + cv::dnn::PoolingLayerInt8* ret = new cv::dnn::PoolingLayerInt8(); + return ret; +} + +int cv_dnn_PoolingLayerInt8_propInput_zp_const(const cv::dnn::PoolingLayerInt8* instance) { + int ret = instance->input_zp; + return ret; +} + +void cv_dnn_PoolingLayerInt8_propInput_zp_const_int(cv::dnn::PoolingLayerInt8* instance, const int val) { + instance->input_zp = val; +} + +int cv_dnn_PoolingLayerInt8_propOutput_zp_const(const cv::dnn::PoolingLayerInt8* instance) { + int ret = instance->output_zp; + return ret; +} + +void cv_dnn_PoolingLayerInt8_propOutput_zp_const_int(cv::dnn::PoolingLayerInt8* instance, const int val) { + instance->output_zp = val; +} + +float cv_dnn_PoolingLayerInt8_propInput_sc_const(const cv::dnn::PoolingLayerInt8* instance) { + float ret = instance->input_sc; + return ret; +} + +void cv_dnn_PoolingLayerInt8_propInput_sc_const_float(cv::dnn::PoolingLayerInt8* instance, const float val) { + instance->input_sc = val; +} + +float cv_dnn_PoolingLayerInt8_propOutput_sc_const(const cv::dnn::PoolingLayerInt8* instance) { + float ret = instance->output_sc; + return ret; +} + +void cv_dnn_PoolingLayerInt8_propOutput_sc_const_float(cv::dnn::PoolingLayerInt8* instance, const float val) { + instance->output_sc = val; +} + +cv::Algorithm* cv_dnn_PoolingLayerInt8_to_Algorithm(cv::dnn::PoolingLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_PoolingLayerInt8_to_Layer(cv::dnn::PoolingLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::PoolingLayer* cv_dnn_PoolingLayerInt8_to_PoolingLayer(cv::dnn::PoolingLayerInt8* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_PoolingLayerInt8_delete(cv::dnn::PoolingLayerInt8* instance) { + delete instance; +} + +void cv_dnn_PowerLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::PowerLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::PowerLayer* cv_dnn_PowerLayer_defaultNew_const() { + cv::dnn::PowerLayer* ret = new cv::dnn::PowerLayer(); + return ret; +} + +float cv_dnn_PowerLayer_propPower_const(const cv::dnn::PowerLayer* instance) { + float ret = instance->power; + return ret; +} + +void cv_dnn_PowerLayer_propPower_const_float(cv::dnn::PowerLayer* instance, const float val) { + instance->power = val; +} + +float cv_dnn_PowerLayer_propScale_const(const cv::dnn::PowerLayer* instance) { + float ret = instance->scale; + return ret; +} + +void cv_dnn_PowerLayer_propScale_const_float(cv::dnn::PowerLayer* instance, const float val) { + instance->scale = val; +} + +float cv_dnn_PowerLayer_propShift_const(const cv::dnn::PowerLayer* instance) { + float ret = instance->shift; + return ret; +} + +void cv_dnn_PowerLayer_propShift_const_float(cv::dnn::PowerLayer* instance, const float val) { + instance->shift = val; +} + +cv::dnn::ActivationLayer* cv_dnn_PowerLayer_to_ActivationLayer(cv::dnn::PowerLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_PowerLayer_to_Algorithm(cv::dnn::PowerLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_PowerLayer_to_Layer(cv::dnn::PowerLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_PowerLayer_delete(cv::dnn::PowerLayer* instance) { + delete instance; +} + +void cv_dnn_PriorBoxLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::PriorBoxLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::PriorBoxLayer* cv_dnn_PriorBoxLayer_defaultNew_const() { + cv::dnn::PriorBoxLayer* ret = new cv::dnn::PriorBoxLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_PriorBoxLayer_to_Algorithm(cv::dnn::PriorBoxLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_PriorBoxLayer_to_Layer(cv::dnn::PriorBoxLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_PriorBoxLayer_delete(cv::dnn::PriorBoxLayer* instance) { + delete instance; +} + +void cv_dnn_ProposalLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ProposalLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ProposalLayer* cv_dnn_ProposalLayer_defaultNew_const() { + cv::dnn::ProposalLayer* ret = new cv::dnn::ProposalLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_ProposalLayer_to_Algorithm(cv::dnn::ProposalLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ProposalLayer_to_Layer(cv::dnn::ProposalLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ProposalLayer_delete(cv::dnn::ProposalLayer* instance) { + delete instance; +} + +void cv_dnn_QuantizeLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::QuantizeLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::QuantizeLayer* cv_dnn_QuantizeLayer_defaultNew_const() { + cv::dnn::QuantizeLayer* ret = new cv::dnn::QuantizeLayer(); + return ret; +} + +std::vector* cv_dnn_QuantizeLayer_propScales_const(const cv::dnn::QuantizeLayer* instance) { + std::vector ret = instance->scales; + return new std::vector(ret); +} + +void cv_dnn_QuantizeLayer_propScales_const_vectorLfloatG(cv::dnn::QuantizeLayer* instance, const std::vector* val) { + instance->scales = *val; +} + +std::vector* cv_dnn_QuantizeLayer_propZeropoints_const(const cv::dnn::QuantizeLayer* instance) { + std::vector ret = instance->zeropoints; + return new std::vector(ret); +} + +void cv_dnn_QuantizeLayer_propZeropoints_const_vectorLintG(cv::dnn::QuantizeLayer* instance, const std::vector* val) { + instance->zeropoints = *val; +} + +cv::Algorithm* cv_dnn_QuantizeLayer_to_Algorithm(cv::dnn::QuantizeLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_QuantizeLayer_to_Layer(cv::dnn::QuantizeLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_QuantizeLayer_delete(cv::dnn::QuantizeLayer* instance) { + delete instance; +} + +void cv_dnn_RNNLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::RNNLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_RNNLayer_setWeights_const_MatR_const_MatR_const_MatR_const_MatR_const_MatR(cv::dnn::RNNLayer* instance, const cv::Mat* Wxh, const cv::Mat* bh, const cv::Mat* Whh, const cv::Mat* Who, const cv::Mat* bo, ResultVoid* ocvrs_return) { + try { + instance->setWeights(*Wxh, *bh, *Whh, *Who, *bo); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_RNNLayer_setProduceHiddenOutput_bool(cv::dnn::RNNLayer* instance, bool produce, ResultVoid* ocvrs_return) { + try { + instance->setProduceHiddenOutput(produce); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_RNNLayer_setProduceHiddenOutput(cv::dnn::RNNLayer* instance, ResultVoid* ocvrs_return) { + try { + instance->setProduceHiddenOutput(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::Algorithm* cv_dnn_RNNLayer_to_Algorithm(cv::dnn::RNNLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_RNNLayer_to_Layer(cv::dnn::RNNLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_RNNLayer_delete(cv::dnn::RNNLayer* instance) { + delete instance; +} + +void cv_dnn_ReLU6Layer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ReLU6Layer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ReLU6Layer* cv_dnn_ReLU6Layer_defaultNew_const() { + cv::dnn::ReLU6Layer* ret = new cv::dnn::ReLU6Layer(); + return ret; +} + +float cv_dnn_ReLU6Layer_propMinValue_const(const cv::dnn::ReLU6Layer* instance) { + float ret = instance->minValue; + return ret; +} + +void cv_dnn_ReLU6Layer_propMinValue_const_float(cv::dnn::ReLU6Layer* instance, const float val) { + instance->minValue = val; +} + +float cv_dnn_ReLU6Layer_propMaxValue_const(const cv::dnn::ReLU6Layer* instance) { + float ret = instance->maxValue; + return ret; +} + +void cv_dnn_ReLU6Layer_propMaxValue_const_float(cv::dnn::ReLU6Layer* instance, const float val) { + instance->maxValue = val; +} + +cv::dnn::ActivationLayer* cv_dnn_ReLU6Layer_to_ActivationLayer(cv::dnn::ReLU6Layer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_ReLU6Layer_to_Algorithm(cv::dnn::ReLU6Layer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ReLU6Layer_to_Layer(cv::dnn::ReLU6Layer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ReLU6Layer_delete(cv::dnn::ReLU6Layer* instance) { + delete instance; +} + +void cv_dnn_ReLULayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ReLULayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ReLULayer* cv_dnn_ReLULayer_defaultNew_const() { + cv::dnn::ReLULayer* ret = new cv::dnn::ReLULayer(); + return ret; +} + +float cv_dnn_ReLULayer_propNegativeSlope_const(const cv::dnn::ReLULayer* instance) { + float ret = instance->negativeSlope; + return ret; +} + +void cv_dnn_ReLULayer_propNegativeSlope_const_float(cv::dnn::ReLULayer* instance, const float val) { + instance->negativeSlope = val; +} + +cv::dnn::ActivationLayer* cv_dnn_ReLULayer_to_ActivationLayer(cv::dnn::ReLULayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_ReLULayer_to_Algorithm(cv::dnn::ReLULayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ReLULayer_to_Layer(cv::dnn::ReLULayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ReLULayer_delete(cv::dnn::ReLULayer* instance) { + delete instance; +} + +void cv_dnn_ReciprocalLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ReciprocalLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ReciprocalLayer* cv_dnn_ReciprocalLayer_defaultNew_const() { + cv::dnn::ReciprocalLayer* ret = new cv::dnn::ReciprocalLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_ReciprocalLayer_to_ActivationLayer(cv::dnn::ReciprocalLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_ReciprocalLayer_to_Algorithm(cv::dnn::ReciprocalLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ReciprocalLayer_to_Layer(cv::dnn::ReciprocalLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ReciprocalLayer_delete(cv::dnn::ReciprocalLayer* instance) { + delete instance; +} + +void cv_dnn_ReduceLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ReduceLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ReduceLayer* cv_dnn_ReduceLayer_defaultNew_const() { + cv::dnn::ReduceLayer* ret = new cv::dnn::ReduceLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_ReduceLayer_to_Algorithm(cv::dnn::ReduceLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ReduceLayer_to_Layer(cv::dnn::ReduceLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ReduceLayer_delete(cv::dnn::ReduceLayer* instance) { + delete instance; +} + +void cv_dnn_RegionLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::RegionLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::RegionLayer* cv_dnn_RegionLayer_defaultNew_const() { + cv::dnn::RegionLayer* ret = new cv::dnn::RegionLayer(); + return ret; +} + +float cv_dnn_RegionLayer_propNmsThreshold_const(const cv::dnn::RegionLayer* instance) { + float ret = instance->nmsThreshold; + return ret; +} + +void cv_dnn_RegionLayer_propNmsThreshold_const_float(cv::dnn::RegionLayer* instance, const float val) { + instance->nmsThreshold = val; +} + +cv::Algorithm* cv_dnn_RegionLayer_to_Algorithm(cv::dnn::RegionLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_RegionLayer_to_Layer(cv::dnn::RegionLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_RegionLayer_delete(cv::dnn::RegionLayer* instance) { + delete instance; +} + +void cv_dnn_ReorgLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ReorgLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ReorgLayer* cv_dnn_ReorgLayer_defaultNew_const() { + cv::dnn::ReorgLayer* ret = new cv::dnn::ReorgLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_ReorgLayer_to_Algorithm(cv::dnn::ReorgLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ReorgLayer_to_Layer(cv::dnn::ReorgLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ReorgLayer_delete(cv::dnn::ReorgLayer* instance) { + delete instance; +} + +void cv_dnn_RequantizeLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::RequantizeLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::RequantizeLayer* cv_dnn_RequantizeLayer_defaultNew_const() { + cv::dnn::RequantizeLayer* ret = new cv::dnn::RequantizeLayer(); + return ret; +} + +float cv_dnn_RequantizeLayer_propScale_const(const cv::dnn::RequantizeLayer* instance) { + float ret = instance->scale; + return ret; +} + +void cv_dnn_RequantizeLayer_propScale_const_float(cv::dnn::RequantizeLayer* instance, const float val) { + instance->scale = val; +} + +float cv_dnn_RequantizeLayer_propShift_const(const cv::dnn::RequantizeLayer* instance) { + float ret = instance->shift; + return ret; +} + +void cv_dnn_RequantizeLayer_propShift_const_float(cv::dnn::RequantizeLayer* instance, const float val) { + instance->shift = val; +} + +cv::Algorithm* cv_dnn_RequantizeLayer_to_Algorithm(cv::dnn::RequantizeLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_RequantizeLayer_to_Layer(cv::dnn::RequantizeLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_RequantizeLayer_delete(cv::dnn::RequantizeLayer* instance) { + delete instance; +} + +void cv_dnn_ReshapeLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ReshapeLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ReshapeLayer* cv_dnn_ReshapeLayer_defaultNew_const() { + cv::dnn::ReshapeLayer* ret = new cv::dnn::ReshapeLayer(); + return ret; +} + +cv::dnn::MatShape* cv_dnn_ReshapeLayer_propNewShapeDesc_const(const cv::dnn::ReshapeLayer* instance) { + cv::dnn::MatShape ret = instance->newShapeDesc; + return new cv::dnn::MatShape(ret); +} + +void cv_dnn_ReshapeLayer_propNewShapeDesc_const_MatShape(cv::dnn::ReshapeLayer* instance, const cv::dnn::MatShape* val) { + instance->newShapeDesc = *val; +} + +cv::Range* cv_dnn_ReshapeLayer_propNewShapeRange_const(const cv::dnn::ReshapeLayer* instance) { + cv::Range ret = instance->newShapeRange; + return new cv::Range(ret); +} + +void cv_dnn_ReshapeLayer_propNewShapeRange_const_Range(cv::dnn::ReshapeLayer* instance, const cv::Range* val) { + instance->newShapeRange = *val; +} + +cv::Algorithm* cv_dnn_ReshapeLayer_to_Algorithm(cv::dnn::ReshapeLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ReshapeLayer_to_Layer(cv::dnn::ReshapeLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ReshapeLayer_delete(cv::dnn::ReshapeLayer* instance) { + delete instance; +} + +void cv_dnn_ResizeLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ResizeLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ResizeLayer* cv_dnn_ResizeLayer_defaultNew_const() { + cv::dnn::ResizeLayer* ret = new cv::dnn::ResizeLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_ResizeLayer_to_Algorithm(cv::dnn::ResizeLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ResizeLayer_to_Layer(cv::dnn::ResizeLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ResizeLayer_delete(cv::dnn::ResizeLayer* instance) { + delete instance; +} + +void cv_dnn_RoundLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::RoundLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::RoundLayer* cv_dnn_RoundLayer_defaultNew_const() { + cv::dnn::RoundLayer* ret = new cv::dnn::RoundLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_RoundLayer_to_ActivationLayer(cv::dnn::RoundLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_RoundLayer_to_Algorithm(cv::dnn::RoundLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_RoundLayer_to_Layer(cv::dnn::RoundLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_RoundLayer_delete(cv::dnn::RoundLayer* instance) { + delete instance; +} + +void cv_dnn_ScaleLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ScaleLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ScaleLayer* cv_dnn_ScaleLayer_defaultNew_const() { + cv::dnn::ScaleLayer* ret = new cv::dnn::ScaleLayer(); + return ret; +} + +bool cv_dnn_ScaleLayer_propHasBias_const(const cv::dnn::ScaleLayer* instance) { + bool ret = instance->hasBias; + return ret; +} + +void cv_dnn_ScaleLayer_propHasBias_const_bool(cv::dnn::ScaleLayer* instance, const bool val) { + instance->hasBias = val; +} + +int cv_dnn_ScaleLayer_propAxis_const(const cv::dnn::ScaleLayer* instance) { + int ret = instance->axis; + return ret; +} + +void cv_dnn_ScaleLayer_propAxis_const_int(cv::dnn::ScaleLayer* instance, const int val) { + instance->axis = val; +} + +void* cv_dnn_ScaleLayer_propMode_const(const cv::dnn::ScaleLayer* instance) { + cv::String ret = instance->mode; + return ocvrs_create_string(ret.c_str()); +} + +void cv_dnn_ScaleLayer_propMode_const_String(cv::dnn::ScaleLayer* instance, const char* val) { + instance->mode = std::string(val); +} + +cv::Algorithm* cv_dnn_ScaleLayer_to_Algorithm(cv::dnn::ScaleLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ScaleLayer_to_Layer(cv::dnn::ScaleLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ScaleLayer_delete(cv::dnn::ScaleLayer* instance) { + delete instance; +} + +void cv_dnn_ScaleLayerInt8_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ScaleLayerInt8::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ScaleLayerInt8* cv_dnn_ScaleLayerInt8_defaultNew_const() { + cv::dnn::ScaleLayerInt8* ret = new cv::dnn::ScaleLayerInt8(); + return ret; +} + +float cv_dnn_ScaleLayerInt8_propOutput_sc_const(const cv::dnn::ScaleLayerInt8* instance) { + float ret = instance->output_sc; + return ret; +} + +void cv_dnn_ScaleLayerInt8_propOutput_sc_const_float(cv::dnn::ScaleLayerInt8* instance, const float val) { + instance->output_sc = val; +} + +int cv_dnn_ScaleLayerInt8_propOutput_zp_const(const cv::dnn::ScaleLayerInt8* instance) { + int ret = instance->output_zp; + return ret; +} + +void cv_dnn_ScaleLayerInt8_propOutput_zp_const_int(cv::dnn::ScaleLayerInt8* instance, const int val) { + instance->output_zp = val; +} + +cv::Algorithm* cv_dnn_ScaleLayerInt8_to_Algorithm(cv::dnn::ScaleLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ScaleLayerInt8_to_Layer(cv::dnn::ScaleLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::ScaleLayer* cv_dnn_ScaleLayerInt8_to_ScaleLayer(cv::dnn::ScaleLayerInt8* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ScaleLayerInt8_delete(cv::dnn::ScaleLayerInt8* instance) { + delete instance; +} + +void cv_dnn_ScatterLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ScatterLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ScatterLayer* cv_dnn_ScatterLayer_defaultNew_const() { + cv::dnn::ScatterLayer* ret = new cv::dnn::ScatterLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_ScatterLayer_to_Algorithm(cv::dnn::ScatterLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ScatterLayer_to_Layer(cv::dnn::ScatterLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ScatterLayer_delete(cv::dnn::ScatterLayer* instance) { + delete instance; +} + +void cv_dnn_ScatterNDLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ScatterNDLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ScatterNDLayer* cv_dnn_ScatterNDLayer_defaultNew_const() { + cv::dnn::ScatterNDLayer* ret = new cv::dnn::ScatterNDLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_ScatterNDLayer_to_Algorithm(cv::dnn::ScatterNDLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ScatterNDLayer_to_Layer(cv::dnn::ScatterNDLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ScatterNDLayer_delete(cv::dnn::ScatterNDLayer* instance) { + delete instance; +} + +void cv_dnn_SegmentationModel_SegmentationModel_const_StringR_const_StringR(const char* model, const char* config, Result* ocvrs_return) { + try { + cv::dnn::SegmentationModel* ret = new cv::dnn::SegmentationModel(std::string(model), std::string(config)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_SegmentationModel_SegmentationModel_const_StringR(const char* model, Result* ocvrs_return) { + try { + cv::dnn::SegmentationModel* ret = new cv::dnn::SegmentationModel(std::string(model)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_SegmentationModel_SegmentationModel_const_NetR(const cv::dnn::Net* network, Result* ocvrs_return) { + try { + cv::dnn::SegmentationModel* ret = new cv::dnn::SegmentationModel(*network); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_SegmentationModel_segment_const__InputArrayR_const__OutputArrayR(cv::dnn::SegmentationModel* instance, const cv::_InputArray* frame, const cv::_OutputArray* mask, ResultVoid* ocvrs_return) { + try { + instance->segment(*frame, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SegmentationModel* cv_dnn_SegmentationModel_implicitClone_const(const cv::dnn::SegmentationModel* instance) { + return new cv::dnn::SegmentationModel(*instance); +} + +cv::dnn::Model* cv_dnn_SegmentationModel_to_Model(cv::dnn::SegmentationModel* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SegmentationModel_delete(cv::dnn::SegmentationModel* instance) { + delete instance; +} + +void cv_dnn_SeluLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SeluLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SeluLayer* cv_dnn_SeluLayer_defaultNew_const() { + cv::dnn::SeluLayer* ret = new cv::dnn::SeluLayer(); + return ret; +} + +float cv_dnn_SeluLayer_propAlpha_const(const cv::dnn::SeluLayer* instance) { + float ret = instance->alpha; + return ret; +} + +void cv_dnn_SeluLayer_propAlpha_const_float(cv::dnn::SeluLayer* instance, const float val) { + instance->alpha = val; +} + +float cv_dnn_SeluLayer_propGamma_const(const cv::dnn::SeluLayer* instance) { + float ret = instance->gamma; + return ret; +} + +void cv_dnn_SeluLayer_propGamma_const_float(cv::dnn::SeluLayer* instance, const float val) { + instance->gamma = val; +} + +cv::dnn::ActivationLayer* cv_dnn_SeluLayer_to_ActivationLayer(cv::dnn::SeluLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_SeluLayer_to_Algorithm(cv::dnn::SeluLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SeluLayer_to_Layer(cv::dnn::SeluLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SeluLayer_delete(cv::dnn::SeluLayer* instance) { + delete instance; +} + +void cv_dnn_ShiftLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ShiftLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ShiftLayer* cv_dnn_ShiftLayer_defaultNew_const() { + cv::dnn::ShiftLayer* ret = new cv::dnn::ShiftLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_ShiftLayer_to_Algorithm(cv::dnn::ShiftLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ShiftLayer_to_Layer(cv::dnn::ShiftLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ShiftLayer_delete(cv::dnn::ShiftLayer* instance) { + delete instance; +} + +void cv_dnn_ShiftLayerInt8_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ShiftLayerInt8::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ShiftLayerInt8* cv_dnn_ShiftLayerInt8_defaultNew_const() { + cv::dnn::ShiftLayerInt8* ret = new cv::dnn::ShiftLayerInt8(); + return ret; +} + +cv::Algorithm* cv_dnn_ShiftLayerInt8_to_Algorithm(cv::dnn::ShiftLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ShiftLayerInt8_to_Layer(cv::dnn::ShiftLayerInt8* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ShiftLayerInt8_delete(cv::dnn::ShiftLayerInt8* instance) { + delete instance; +} + +void cv_dnn_ShrinkLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ShrinkLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ShrinkLayer* cv_dnn_ShrinkLayer_defaultNew_const() { + cv::dnn::ShrinkLayer* ret = new cv::dnn::ShrinkLayer(); + return ret; +} + +float cv_dnn_ShrinkLayer_propBias_const(const cv::dnn::ShrinkLayer* instance) { + float ret = instance->bias; + return ret; +} + +void cv_dnn_ShrinkLayer_propBias_const_float(cv::dnn::ShrinkLayer* instance, const float val) { + instance->bias = val; +} + +float cv_dnn_ShrinkLayer_propLambd_const(const cv::dnn::ShrinkLayer* instance) { + float ret = instance->lambd; + return ret; +} + +void cv_dnn_ShrinkLayer_propLambd_const_float(cv::dnn::ShrinkLayer* instance, const float val) { + instance->lambd = val; +} + +cv::dnn::ActivationLayer* cv_dnn_ShrinkLayer_to_ActivationLayer(cv::dnn::ShrinkLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_ShrinkLayer_to_Algorithm(cv::dnn::ShrinkLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ShrinkLayer_to_Layer(cv::dnn::ShrinkLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ShrinkLayer_delete(cv::dnn::ShrinkLayer* instance) { + delete instance; +} + +void cv_dnn_ShuffleChannelLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ShuffleChannelLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ShuffleChannelLayer* cv_dnn_ShuffleChannelLayer_defaultNew_const() { + cv::dnn::ShuffleChannelLayer* ret = new cv::dnn::ShuffleChannelLayer(); + return ret; +} + +int cv_dnn_ShuffleChannelLayer_propGroup_const(const cv::dnn::ShuffleChannelLayer* instance) { + int ret = instance->group; + return ret; +} + +void cv_dnn_ShuffleChannelLayer_propGroup_const_int(cv::dnn::ShuffleChannelLayer* instance, const int val) { + instance->group = val; +} + +cv::Algorithm* cv_dnn_ShuffleChannelLayer_to_Algorithm(cv::dnn::ShuffleChannelLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ShuffleChannelLayer_to_Layer(cv::dnn::ShuffleChannelLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ShuffleChannelLayer_delete(cv::dnn::ShuffleChannelLayer* instance) { + delete instance; +} + +void cv_dnn_SigmoidLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SigmoidLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SigmoidLayer* cv_dnn_SigmoidLayer_defaultNew_const() { + cv::dnn::SigmoidLayer* ret = new cv::dnn::SigmoidLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_SigmoidLayer_to_ActivationLayer(cv::dnn::SigmoidLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_SigmoidLayer_to_Algorithm(cv::dnn::SigmoidLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SigmoidLayer_to_Layer(cv::dnn::SigmoidLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SigmoidLayer_delete(cv::dnn::SigmoidLayer* instance) { + delete instance; +} + +void cv_dnn_SignLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SignLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SignLayer* cv_dnn_SignLayer_defaultNew_const() { + cv::dnn::SignLayer* ret = new cv::dnn::SignLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_SignLayer_to_ActivationLayer(cv::dnn::SignLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_SignLayer_to_Algorithm(cv::dnn::SignLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SignLayer_to_Layer(cv::dnn::SignLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SignLayer_delete(cv::dnn::SignLayer* instance) { + delete instance; +} + +void cv_dnn_SinLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SinLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SinLayer* cv_dnn_SinLayer_defaultNew_const() { + cv::dnn::SinLayer* ret = new cv::dnn::SinLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_SinLayer_to_ActivationLayer(cv::dnn::SinLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_SinLayer_to_Algorithm(cv::dnn::SinLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SinLayer_to_Layer(cv::dnn::SinLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SinLayer_delete(cv::dnn::SinLayer* instance) { + delete instance; +} + +void cv_dnn_SinhLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SinhLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SinhLayer* cv_dnn_SinhLayer_defaultNew_const() { + cv::dnn::SinhLayer* ret = new cv::dnn::SinhLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_SinhLayer_to_ActivationLayer(cv::dnn::SinhLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_SinhLayer_to_Algorithm(cv::dnn::SinhLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SinhLayer_to_Layer(cv::dnn::SinhLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SinhLayer_delete(cv::dnn::SinhLayer* instance) { + delete instance; +} + +void cv_dnn_SliceLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SliceLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SliceLayer* cv_dnn_SliceLayer_defaultNew_const() { + cv::dnn::SliceLayer* ret = new cv::dnn::SliceLayer(); + return ret; +} + +std::vector>* cv_dnn_SliceLayer_propSliceRanges_const(const cv::dnn::SliceLayer* instance) { + std::vector> ret = instance->sliceRanges; + return new std::vector>(ret); +} + +void cv_dnn_SliceLayer_propSliceRanges_const_vectorLvectorLRangeGG(cv::dnn::SliceLayer* instance, const std::vector>* val) { + instance->sliceRanges = *val; +} + +std::vector>* cv_dnn_SliceLayer_propSliceSteps_const(const cv::dnn::SliceLayer* instance) { + std::vector> ret = instance->sliceSteps; + return new std::vector>(ret); +} + +void cv_dnn_SliceLayer_propSliceSteps_const_vectorLvectorLintGG(cv::dnn::SliceLayer* instance, const std::vector>* val) { + instance->sliceSteps = *val; +} + +int cv_dnn_SliceLayer_propAxis_const(const cv::dnn::SliceLayer* instance) { + int ret = instance->axis; + return ret; +} + +void cv_dnn_SliceLayer_propAxis_const_int(cv::dnn::SliceLayer* instance, const int val) { + instance->axis = val; +} + +int cv_dnn_SliceLayer_propNum_split_const(const cv::dnn::SliceLayer* instance) { + int ret = instance->num_split; + return ret; +} + +void cv_dnn_SliceLayer_propNum_split_const_int(cv::dnn::SliceLayer* instance, const int val) { + instance->num_split = val; +} + +cv::Algorithm* cv_dnn_SliceLayer_to_Algorithm(cv::dnn::SliceLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SliceLayer_to_Layer(cv::dnn::SliceLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SliceLayer_delete(cv::dnn::SliceLayer* instance) { + delete instance; +} + +void cv_dnn_SoftmaxLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SoftmaxLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SoftmaxLayer* cv_dnn_SoftmaxLayer_defaultNew_const() { + cv::dnn::SoftmaxLayer* ret = new cv::dnn::SoftmaxLayer(); + return ret; +} + +bool cv_dnn_SoftmaxLayer_propLogSoftMax_const(const cv::dnn::SoftmaxLayer* instance) { + bool ret = instance->logSoftMax; + return ret; +} + +void cv_dnn_SoftmaxLayer_propLogSoftMax_const_bool(cv::dnn::SoftmaxLayer* instance, const bool val) { + instance->logSoftMax = val; +} + +cv::Algorithm* cv_dnn_SoftmaxLayer_to_Algorithm(cv::dnn::SoftmaxLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SoftmaxLayer_to_Layer(cv::dnn::SoftmaxLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SoftmaxLayer_delete(cv::dnn::SoftmaxLayer* instance) { + delete instance; +} + +void cv_dnn_SoftmaxLayerInt8_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SoftmaxLayerInt8::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SoftmaxLayerInt8* cv_dnn_SoftmaxLayerInt8_defaultNew_const() { + cv::dnn::SoftmaxLayerInt8* ret = new cv::dnn::SoftmaxLayerInt8(); + return ret; +} + +float cv_dnn_SoftmaxLayerInt8_propOutput_sc_const(const cv::dnn::SoftmaxLayerInt8* instance) { + float ret = instance->output_sc; + return ret; +} + +void cv_dnn_SoftmaxLayerInt8_propOutput_sc_const_float(cv::dnn::SoftmaxLayerInt8* instance, const float val) { + instance->output_sc = val; +} + +int cv_dnn_SoftmaxLayerInt8_propOutput_zp_const(const cv::dnn::SoftmaxLayerInt8* instance) { + int ret = instance->output_zp; + return ret; +} + +void cv_dnn_SoftmaxLayerInt8_propOutput_zp_const_int(cv::dnn::SoftmaxLayerInt8* instance, const int val) { + instance->output_zp = val; +} + +cv::Algorithm* cv_dnn_SoftmaxLayerInt8_to_Algorithm(cv::dnn::SoftmaxLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SoftmaxLayerInt8_to_Layer(cv::dnn::SoftmaxLayerInt8* instance) { + return dynamic_cast(instance); +} + +cv::dnn::SoftmaxLayer* cv_dnn_SoftmaxLayerInt8_to_SoftmaxLayer(cv::dnn::SoftmaxLayerInt8* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SoftmaxLayerInt8_delete(cv::dnn::SoftmaxLayerInt8* instance) { + delete instance; +} + +void cv_dnn_SoftplusLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SoftplusLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SoftplusLayer* cv_dnn_SoftplusLayer_defaultNew_const() { + cv::dnn::SoftplusLayer* ret = new cv::dnn::SoftplusLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_SoftplusLayer_to_ActivationLayer(cv::dnn::SoftplusLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_SoftplusLayer_to_Algorithm(cv::dnn::SoftplusLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SoftplusLayer_to_Layer(cv::dnn::SoftplusLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SoftplusLayer_delete(cv::dnn::SoftplusLayer* instance) { + delete instance; +} + +void cv_dnn_SoftsignLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SoftsignLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SoftsignLayer* cv_dnn_SoftsignLayer_defaultNew_const() { + cv::dnn::SoftsignLayer* ret = new cv::dnn::SoftsignLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_SoftsignLayer_to_ActivationLayer(cv::dnn::SoftsignLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_SoftsignLayer_to_Algorithm(cv::dnn::SoftsignLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SoftsignLayer_to_Layer(cv::dnn::SoftsignLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SoftsignLayer_delete(cv::dnn::SoftsignLayer* instance) { + delete instance; +} + +void cv_dnn_SpaceToDepthLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SpaceToDepthLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SpaceToDepthLayer* cv_dnn_SpaceToDepthLayer_defaultNew_const() { + cv::dnn::SpaceToDepthLayer* ret = new cv::dnn::SpaceToDepthLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_SpaceToDepthLayer_to_Algorithm(cv::dnn::SpaceToDepthLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SpaceToDepthLayer_to_Layer(cv::dnn::SpaceToDepthLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SpaceToDepthLayer_delete(cv::dnn::SpaceToDepthLayer* instance) { + delete instance; +} + +void cv_dnn_SplitLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SplitLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SplitLayer* cv_dnn_SplitLayer_defaultNew_const() { + cv::dnn::SplitLayer* ret = new cv::dnn::SplitLayer(); + return ret; +} + +int cv_dnn_SplitLayer_propOutputsCount_const(const cv::dnn::SplitLayer* instance) { + int ret = instance->outputsCount; + return ret; +} + +void cv_dnn_SplitLayer_propOutputsCount_const_int(cv::dnn::SplitLayer* instance, const int val) { + instance->outputsCount = val; +} + +cv::Algorithm* cv_dnn_SplitLayer_to_Algorithm(cv::dnn::SplitLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SplitLayer_to_Layer(cv::dnn::SplitLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SplitLayer_delete(cv::dnn::SplitLayer* instance) { + delete instance; +} + +void cv_dnn_SqrtLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SqrtLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SqrtLayer* cv_dnn_SqrtLayer_defaultNew_const() { + cv::dnn::SqrtLayer* ret = new cv::dnn::SqrtLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_SqrtLayer_to_ActivationLayer(cv::dnn::SqrtLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_SqrtLayer_to_Algorithm(cv::dnn::SqrtLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SqrtLayer_to_Layer(cv::dnn::SqrtLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SqrtLayer_delete(cv::dnn::SqrtLayer* instance) { + delete instance; +} + +void cv_dnn_SwishLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::SwishLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::SwishLayer* cv_dnn_SwishLayer_defaultNew_const() { + cv::dnn::SwishLayer* ret = new cv::dnn::SwishLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_SwishLayer_to_ActivationLayer(cv::dnn::SwishLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_SwishLayer_to_Algorithm(cv::dnn::SwishLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_SwishLayer_to_Layer(cv::dnn::SwishLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_SwishLayer_delete(cv::dnn::SwishLayer* instance) { + delete instance; +} + +void cv_dnn_TanHLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::TanHLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::TanHLayer* cv_dnn_TanHLayer_defaultNew_const() { + cv::dnn::TanHLayer* ret = new cv::dnn::TanHLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_TanHLayer_to_ActivationLayer(cv::dnn::TanHLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_TanHLayer_to_Algorithm(cv::dnn::TanHLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_TanHLayer_to_Layer(cv::dnn::TanHLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_TanHLayer_delete(cv::dnn::TanHLayer* instance) { + delete instance; +} + +void cv_dnn_TanLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::TanLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::TanLayer* cv_dnn_TanLayer_defaultNew_const() { + cv::dnn::TanLayer* ret = new cv::dnn::TanLayer(); + return ret; +} + +cv::dnn::ActivationLayer* cv_dnn_TanLayer_to_ActivationLayer(cv::dnn::TanLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_TanLayer_to_Algorithm(cv::dnn::TanLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_TanLayer_to_Layer(cv::dnn::TanLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_TanLayer_delete(cv::dnn::TanLayer* instance) { + delete instance; +} + +void cv_dnn_TextDetectionModel_detect_const_const__InputArrayR_vectorLvectorLPointGGR_vectorLfloatGR(const cv::dnn::TextDetectionModel* instance, const cv::_InputArray* frame, std::vector>* detections, std::vector* confidences, ResultVoid* ocvrs_return) { + try { + instance->detect(*frame, *detections, *confidences); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_detect_const_const__InputArrayR_vectorLvectorLPointGGR(const cv::dnn::TextDetectionModel* instance, const cv::_InputArray* frame, std::vector>* detections, ResultVoid* ocvrs_return) { + try { + instance->detect(*frame, *detections); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_detectTextRectangles_const_const__InputArrayR_vectorLRotatedRectGR_vectorLfloatGR(const cv::dnn::TextDetectionModel* instance, const cv::_InputArray* frame, std::vector* detections, std::vector* confidences, ResultVoid* ocvrs_return) { + try { + instance->detectTextRectangles(*frame, *detections, *confidences); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_detectTextRectangles_const_const__InputArrayR_vectorLRotatedRectGR(const cv::dnn::TextDetectionModel* instance, const cv::_InputArray* frame, std::vector* detections, ResultVoid* ocvrs_return) { + try { + instance->detectTextRectangles(*frame, *detections); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::Model* cv_dnn_TextDetectionModel_to_Model(cv::dnn::TextDetectionModel* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_TextDetectionModel_delete(cv::dnn::TextDetectionModel* instance) { + delete instance; +} + +void cv_dnn_TextDetectionModel_DB_TextDetectionModel_DB(Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_DB* ret = new cv::dnn::TextDetectionModel_DB(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_DB_TextDetectionModel_DB_const_NetR(const cv::dnn::Net* network, Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_DB* ret = new cv::dnn::TextDetectionModel_DB(*network); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_DB_TextDetectionModel_DB_const_stringR_const_stringR(const char* model, const char* config, Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_DB* ret = new cv::dnn::TextDetectionModel_DB(std::string(model), std::string(config)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_DB_TextDetectionModel_DB_const_stringR(const char* model, Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_DB* ret = new cv::dnn::TextDetectionModel_DB(std::string(model)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_DB_setBinaryThreshold_float(cv::dnn::TextDetectionModel_DB* instance, float binaryThreshold, Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_DB ret = instance->setBinaryThreshold(binaryThreshold); + Ok(new cv::dnn::TextDetectionModel_DB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_DB_getBinaryThreshold_const(const cv::dnn::TextDetectionModel_DB* instance, Result* ocvrs_return) { + try { + float ret = instance->getBinaryThreshold(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_DB_setPolygonThreshold_float(cv::dnn::TextDetectionModel_DB* instance, float polygonThreshold, Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_DB ret = instance->setPolygonThreshold(polygonThreshold); + Ok(new cv::dnn::TextDetectionModel_DB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_DB_getPolygonThreshold_const(const cv::dnn::TextDetectionModel_DB* instance, Result* ocvrs_return) { + try { + float ret = instance->getPolygonThreshold(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_DB_setUnclipRatio_double(cv::dnn::TextDetectionModel_DB* instance, double unclipRatio, Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_DB ret = instance->setUnclipRatio(unclipRatio); + Ok(new cv::dnn::TextDetectionModel_DB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_DB_getUnclipRatio_const(const cv::dnn::TextDetectionModel_DB* instance, Result* ocvrs_return) { + try { + double ret = instance->getUnclipRatio(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_DB_setMaxCandidates_int(cv::dnn::TextDetectionModel_DB* instance, int maxCandidates, Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_DB ret = instance->setMaxCandidates(maxCandidates); + Ok(new cv::dnn::TextDetectionModel_DB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_DB_getMaxCandidates_const(const cv::dnn::TextDetectionModel_DB* instance, Result* ocvrs_return) { + try { + int ret = instance->getMaxCandidates(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::TextDetectionModel_DB* cv_dnn_TextDetectionModel_DB_implicitClone_const(const cv::dnn::TextDetectionModel_DB* instance) { + return new cv::dnn::TextDetectionModel_DB(*instance); +} + +cv::dnn::Model* cv_dnn_TextDetectionModel_DB_to_Model(cv::dnn::TextDetectionModel_DB* instance) { + return dynamic_cast(instance); +} + +cv::dnn::TextDetectionModel* cv_dnn_TextDetectionModel_DB_to_TextDetectionModel(cv::dnn::TextDetectionModel_DB* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_TextDetectionModel_DB_delete(cv::dnn::TextDetectionModel_DB* instance) { + delete instance; +} + +void cv_dnn_TextDetectionModel_EAST_TextDetectionModel_EAST(Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_EAST* ret = new cv::dnn::TextDetectionModel_EAST(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_EAST_TextDetectionModel_EAST_const_NetR(const cv::dnn::Net* network, Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_EAST* ret = new cv::dnn::TextDetectionModel_EAST(*network); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_EAST_TextDetectionModel_EAST_const_stringR_const_stringR(const char* model, const char* config, Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_EAST* ret = new cv::dnn::TextDetectionModel_EAST(std::string(model), std::string(config)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_EAST_TextDetectionModel_EAST_const_stringR(const char* model, Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_EAST* ret = new cv::dnn::TextDetectionModel_EAST(std::string(model)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_EAST_setConfidenceThreshold_float(cv::dnn::TextDetectionModel_EAST* instance, float confThreshold, Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_EAST ret = instance->setConfidenceThreshold(confThreshold); + Ok(new cv::dnn::TextDetectionModel_EAST(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_EAST_getConfidenceThreshold_const(const cv::dnn::TextDetectionModel_EAST* instance, Result* ocvrs_return) { + try { + float ret = instance->getConfidenceThreshold(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_EAST_setNMSThreshold_float(cv::dnn::TextDetectionModel_EAST* instance, float nmsThreshold, Result* ocvrs_return) { + try { + cv::dnn::TextDetectionModel_EAST ret = instance->setNMSThreshold(nmsThreshold); + Ok(new cv::dnn::TextDetectionModel_EAST(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextDetectionModel_EAST_getNMSThreshold_const(const cv::dnn::TextDetectionModel_EAST* instance, Result* ocvrs_return) { + try { + float ret = instance->getNMSThreshold(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::TextDetectionModel_EAST* cv_dnn_TextDetectionModel_EAST_implicitClone_const(const cv::dnn::TextDetectionModel_EAST* instance) { + return new cv::dnn::TextDetectionModel_EAST(*instance); +} + +cv::dnn::Model* cv_dnn_TextDetectionModel_EAST_to_Model(cv::dnn::TextDetectionModel_EAST* instance) { + return dynamic_cast(instance); +} + +cv::dnn::TextDetectionModel* cv_dnn_TextDetectionModel_EAST_to_TextDetectionModel(cv::dnn::TextDetectionModel_EAST* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_TextDetectionModel_EAST_delete(cv::dnn::TextDetectionModel_EAST* instance) { + delete instance; +} + +void cv_dnn_TextRecognitionModel_TextRecognitionModel(Result* ocvrs_return) { + try { + cv::dnn::TextRecognitionModel* ret = new cv::dnn::TextRecognitionModel(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextRecognitionModel_TextRecognitionModel_const_NetR(const cv::dnn::Net* network, Result* ocvrs_return) { + try { + cv::dnn::TextRecognitionModel* ret = new cv::dnn::TextRecognitionModel(*network); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextRecognitionModel_TextRecognitionModel_const_stringR_const_stringR(const char* model, const char* config, Result* ocvrs_return) { + try { + cv::dnn::TextRecognitionModel* ret = new cv::dnn::TextRecognitionModel(std::string(model), std::string(config)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextRecognitionModel_TextRecognitionModel_const_stringR(const char* model, Result* ocvrs_return) { + try { + cv::dnn::TextRecognitionModel* ret = new cv::dnn::TextRecognitionModel(std::string(model)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextRecognitionModel_setDecodeType_const_stringR(cv::dnn::TextRecognitionModel* instance, const char* decodeType, Result* ocvrs_return) { + try { + cv::dnn::TextRecognitionModel ret = instance->setDecodeType(std::string(decodeType)); + Ok(new cv::dnn::TextRecognitionModel(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextRecognitionModel_getDecodeType_const(const cv::dnn::TextRecognitionModel* instance, Result* ocvrs_return) { + try { + const std::string ret = instance->getDecodeType(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextRecognitionModel_setDecodeOptsCTCPrefixBeamSearch_int_int(cv::dnn::TextRecognitionModel* instance, int beamSize, int vocPruneSize, Result* ocvrs_return) { + try { + cv::dnn::TextRecognitionModel ret = instance->setDecodeOptsCTCPrefixBeamSearch(beamSize, vocPruneSize); + Ok(new cv::dnn::TextRecognitionModel(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextRecognitionModel_setDecodeOptsCTCPrefixBeamSearch_int(cv::dnn::TextRecognitionModel* instance, int beamSize, Result* ocvrs_return) { + try { + cv::dnn::TextRecognitionModel ret = instance->setDecodeOptsCTCPrefixBeamSearch(beamSize); + Ok(new cv::dnn::TextRecognitionModel(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextRecognitionModel_setVocabulary_const_vectorLstringGR(cv::dnn::TextRecognitionModel* instance, const std::vector* vocabulary, Result* ocvrs_return) { + try { + cv::dnn::TextRecognitionModel ret = instance->setVocabulary(*vocabulary); + Ok(new cv::dnn::TextRecognitionModel(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextRecognitionModel_getVocabulary_const(const cv::dnn::TextRecognitionModel* instance, Result*>* ocvrs_return) { + try { + const std::vector ret = instance->getVocabulary(); + Ok(new const std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextRecognitionModel_recognize_const_const__InputArrayR(const cv::dnn::TextRecognitionModel* instance, const cv::_InputArray* frame, Result* ocvrs_return) { + try { + std::string ret = instance->recognize(*frame); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn_TextRecognitionModel_recognize_const_const__InputArrayR_const__InputArrayR_vectorLstringGR(const cv::dnn::TextRecognitionModel* instance, const cv::_InputArray* frame, const cv::_InputArray* roiRects, std::vector* results, ResultVoid* ocvrs_return) { + try { + instance->recognize(*frame, *roiRects, *results); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::TextRecognitionModel* cv_dnn_TextRecognitionModel_implicitClone_const(const cv::dnn::TextRecognitionModel* instance) { + return new cv::dnn::TextRecognitionModel(*instance); +} + +cv::dnn::Model* cv_dnn_TextRecognitionModel_to_Model(cv::dnn::TextRecognitionModel* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_TextRecognitionModel_delete(cv::dnn::TextRecognitionModel* instance) { + delete instance; +} + +void cv_dnn_ThresholdedReluLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::ThresholdedReluLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::ThresholdedReluLayer* cv_dnn_ThresholdedReluLayer_defaultNew_const() { + cv::dnn::ThresholdedReluLayer* ret = new cv::dnn::ThresholdedReluLayer(); + return ret; +} + +float cv_dnn_ThresholdedReluLayer_propAlpha_const(const cv::dnn::ThresholdedReluLayer* instance) { + float ret = instance->alpha; + return ret; +} + +void cv_dnn_ThresholdedReluLayer_propAlpha_const_float(cv::dnn::ThresholdedReluLayer* instance, const float val) { + instance->alpha = val; +} + +cv::dnn::ActivationLayer* cv_dnn_ThresholdedReluLayer_to_ActivationLayer(cv::dnn::ThresholdedReluLayer* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_dnn_ThresholdedReluLayer_to_Algorithm(cv::dnn::ThresholdedReluLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_ThresholdedReluLayer_to_Layer(cv::dnn::ThresholdedReluLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_ThresholdedReluLayer_delete(cv::dnn::ThresholdedReluLayer* instance) { + delete instance; +} + +void cv_dnn_TileLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::TileLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::TileLayer* cv_dnn_TileLayer_defaultNew_const() { + cv::dnn::TileLayer* ret = new cv::dnn::TileLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_TileLayer_to_Algorithm(cv::dnn::TileLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_TileLayer_to_Layer(cv::dnn::TileLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_TileLayer_delete(cv::dnn::TileLayer* instance) { + delete instance; +} + +void cv_dnn_TopKLayer_create_const_LayerParamsR(const cv::dnn::LayerParams* params, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::dnn::TopKLayer::create(*params); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::dnn::TopKLayer* cv_dnn_TopKLayer_defaultNew_const() { + cv::dnn::TopKLayer* ret = new cv::dnn::TopKLayer(); + return ret; +} + +cv::Algorithm* cv_dnn_TopKLayer_to_Algorithm(cv::dnn::TopKLayer* instance) { + return dynamic_cast(instance); +} + +cv::dnn::Layer* cv_dnn_TopKLayer_to_Layer(cv::dnn::TopKLayer* instance) { + return dynamic_cast(instance); +} + +void cv_dnn_TopKLayer_delete(cv::dnn::TopKLayer* instance) { + delete instance; +} + +void cv_dnn__Range__Range_const_RangeR(const cv::Range* r, Result* ocvrs_return) { + try { + cv::dnn::_Range* ret = new cv::dnn::_Range(*r); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn__Range__Range_int_int(int start_, int size_, Result* ocvrs_return) { + try { + cv::dnn::_Range* ret = new cv::dnn::_Range(start_, size_); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dnn__Range__Range_int(int start_, Result* ocvrs_return) { + try { + cv::dnn::_Range* ret = new cv::dnn::_Range(start_); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::Range* cv_dnn__Range_to_Range(cv::dnn::_Range* instance) { + return dynamic_cast(instance); +} + +void cv_dnn__Range_delete(cv::dnn::_Range* instance) { + delete instance; +} + +} diff --git a/target/debug/build/opencv-4e105b5546afc119/out/dnn_types.hpp b/target/debug/build/opencv-4e105b5546afc119/out/dnn_types.hpp new file mode 100644 index 0000000..ad3d1db --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/dnn_types.hpp @@ -0,0 +1,4321 @@ +extern "C" { + const cv::dnn::AbsLayer* cv_PtrLcv_dnn_AbsLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::AbsLayer* cv_PtrLcv_dnn_AbsLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_AbsLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_AbsLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_AbsLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AbsLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AbsLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AbsLayerG_new_const_AbsLayer(cv::dnn::AbsLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::AccumLayer* cv_PtrLcv_dnn_AccumLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::AccumLayer* cv_PtrLcv_dnn_AccumLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_AccumLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_AccumLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_AccumLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AccumLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AccumLayerG_new_const_AccumLayer(cv::dnn::AccumLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::AcosLayer* cv_PtrLcv_dnn_AcosLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::AcosLayer* cv_PtrLcv_dnn_AcosLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_AcosLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_AcosLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_AcosLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AcosLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AcosLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AcosLayerG_new_const_AcosLayer(cv::dnn::AcosLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::AcoshLayer* cv_PtrLcv_dnn_AcoshLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::AcoshLayer* cv_PtrLcv_dnn_AcoshLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_AcoshLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_AcoshLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_AcoshLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AcoshLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AcoshLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AcoshLayerG_new_const_AcoshLayer(cv::dnn::AcoshLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ActivationLayer* cv_PtrLcv_dnn_ActivationLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ActivationLayer* cv_PtrLcv_dnn_ActivationLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ActivationLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ActivationLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ActivationLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ActivationLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ActivationLayerG_new_const_ActivationLayer(cv::dnn::ActivationLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ActivationLayerInt8* cv_PtrLcv_dnn_ActivationLayerInt8G_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ActivationLayerInt8* cv_PtrLcv_dnn_ActivationLayerInt8G_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ActivationLayerInt8G_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ActivationLayerInt8G_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ActivationLayerInt8G_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ActivationLayerInt8G_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ActivationLayerInt8G_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ActivationLayerInt8G_new_const_ActivationLayerInt8(cv::dnn::ActivationLayerInt8* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ArgLayer* cv_PtrLcv_dnn_ArgLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ArgLayer* cv_PtrLcv_dnn_ArgLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ArgLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ArgLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ArgLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ArgLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ArgLayerG_new_const_ArgLayer(cv::dnn::ArgLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::AsinLayer* cv_PtrLcv_dnn_AsinLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::AsinLayer* cv_PtrLcv_dnn_AsinLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_AsinLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_AsinLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_AsinLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AsinLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AsinLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AsinLayerG_new_const_AsinLayer(cv::dnn::AsinLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::AsinhLayer* cv_PtrLcv_dnn_AsinhLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::AsinhLayer* cv_PtrLcv_dnn_AsinhLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_AsinhLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_AsinhLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_AsinhLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AsinhLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AsinhLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AsinhLayerG_new_const_AsinhLayer(cv::dnn::AsinhLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::AtanLayer* cv_PtrLcv_dnn_AtanLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::AtanLayer* cv_PtrLcv_dnn_AtanLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_AtanLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_AtanLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_AtanLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AtanLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AtanLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AtanLayerG_new_const_AtanLayer(cv::dnn::AtanLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::AtanhLayer* cv_PtrLcv_dnn_AtanhLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::AtanhLayer* cv_PtrLcv_dnn_AtanhLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_AtanhLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_AtanhLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_AtanhLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AtanhLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AtanhLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AtanhLayerG_new_const_AtanhLayer(cv::dnn::AtanhLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::AttentionLayer* cv_PtrLcv_dnn_AttentionLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::AttentionLayer* cv_PtrLcv_dnn_AttentionLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_AttentionLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_AttentionLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_AttentionLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AttentionLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_AttentionLayerG_new_const_AttentionLayer(cv::dnn::AttentionLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::BNLLLayer* cv_PtrLcv_dnn_BNLLLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::BNLLLayer* cv_PtrLcv_dnn_BNLLLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_BNLLLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_BNLLLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_BNLLLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BNLLLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BNLLLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BNLLLayerG_new_const_BNLLLayer(cv::dnn::BNLLLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::BackendNode* cv_PtrLcv_dnn_BackendNodeG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::BackendNode* cv_PtrLcv_dnn_BackendNodeG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_BackendNodeG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_BackendNodeG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_BackendNodeG_new_const_BackendNode(cv::dnn::BackendNode* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::BackendWrapper* cv_PtrLcv_dnn_BackendWrapperG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::BackendWrapper* cv_PtrLcv_dnn_BackendWrapperG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_BackendWrapperG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_BackendWrapperG_delete(cv::Ptr* instance) { + delete instance; + } + +} + +extern "C" { + const cv::dnn::BaseConvolutionLayer* cv_PtrLcv_dnn_BaseConvolutionLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::BaseConvolutionLayer* cv_PtrLcv_dnn_BaseConvolutionLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_BaseConvolutionLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_BaseConvolutionLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_BaseConvolutionLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BaseConvolutionLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BaseConvolutionLayerG_new_const_BaseConvolutionLayer(cv::dnn::BaseConvolutionLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::BatchNormLayer* cv_PtrLcv_dnn_BatchNormLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::BatchNormLayer* cv_PtrLcv_dnn_BatchNormLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_BatchNormLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_BatchNormLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_BatchNormLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BatchNormLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BatchNormLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BatchNormLayerG_new_const_BatchNormLayer(cv::dnn::BatchNormLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::BatchNormLayerInt8* cv_PtrLcv_dnn_BatchNormLayerInt8G_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::BatchNormLayerInt8* cv_PtrLcv_dnn_BatchNormLayerInt8G_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_BatchNormLayerInt8G_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_BatchNormLayerInt8G_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_BatchNormLayerInt8G_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BatchNormLayerInt8G_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BatchNormLayerInt8G_to_PtrOfBatchNormLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BatchNormLayerInt8G_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BatchNormLayerInt8G_new_const_BatchNormLayerInt8(cv::dnn::BatchNormLayerInt8* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::BlankLayer* cv_PtrLcv_dnn_BlankLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::BlankLayer* cv_PtrLcv_dnn_BlankLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_BlankLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_BlankLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_BlankLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BlankLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_BlankLayerG_new_const_BlankLayer(cv::dnn::BlankLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::CeilLayer* cv_PtrLcv_dnn_CeilLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::CeilLayer* cv_PtrLcv_dnn_CeilLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_CeilLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_CeilLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_CeilLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CeilLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CeilLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CeilLayerG_new_const_CeilLayer(cv::dnn::CeilLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::CeluLayer* cv_PtrLcv_dnn_CeluLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::CeluLayer* cv_PtrLcv_dnn_CeluLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_CeluLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_CeluLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_CeluLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CeluLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CeluLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CeluLayerG_new_const_CeluLayer(cv::dnn::CeluLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ChannelsPReLULayer* cv_PtrLcv_dnn_ChannelsPReLULayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ChannelsPReLULayer* cv_PtrLcv_dnn_ChannelsPReLULayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ChannelsPReLULayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ChannelsPReLULayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ChannelsPReLULayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ChannelsPReLULayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ChannelsPReLULayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ChannelsPReLULayerG_new_const_ChannelsPReLULayer(cv::dnn::ChannelsPReLULayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::CompareLayer* cv_PtrLcv_dnn_CompareLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::CompareLayer* cv_PtrLcv_dnn_CompareLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_CompareLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_CompareLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_CompareLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CompareLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CompareLayerG_new_const_CompareLayer(cv::dnn::CompareLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ConcatLayer* cv_PtrLcv_dnn_ConcatLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ConcatLayer* cv_PtrLcv_dnn_ConcatLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ConcatLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ConcatLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ConcatLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ConcatLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ConcatLayerG_new_const_ConcatLayer(cv::dnn::ConcatLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ConstLayer* cv_PtrLcv_dnn_ConstLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ConstLayer* cv_PtrLcv_dnn_ConstLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ConstLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ConstLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ConstLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ConstLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ConstLayerG_new_const_ConstLayer(cv::dnn::ConstLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ConvolutionLayer* cv_PtrLcv_dnn_ConvolutionLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ConvolutionLayer* cv_PtrLcv_dnn_ConvolutionLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ConvolutionLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ConvolutionLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ConvolutionLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ConvolutionLayerG_to_PtrOfBaseConvolutionLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ConvolutionLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ConvolutionLayerG_new_const_ConvolutionLayer(cv::dnn::ConvolutionLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ConvolutionLayerInt8* cv_PtrLcv_dnn_ConvolutionLayerInt8G_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ConvolutionLayerInt8* cv_PtrLcv_dnn_ConvolutionLayerInt8G_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ConvolutionLayerInt8G_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ConvolutionLayerInt8G_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ConvolutionLayerInt8G_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ConvolutionLayerInt8G_to_PtrOfBaseConvolutionLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ConvolutionLayerInt8G_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ConvolutionLayerInt8G_new_const_ConvolutionLayerInt8(cv::dnn::ConvolutionLayerInt8* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::CorrelationLayer* cv_PtrLcv_dnn_CorrelationLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::CorrelationLayer* cv_PtrLcv_dnn_CorrelationLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_CorrelationLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_CorrelationLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_CorrelationLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CorrelationLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CorrelationLayerG_new_const_CorrelationLayer(cv::dnn::CorrelationLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::CosLayer* cv_PtrLcv_dnn_CosLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::CosLayer* cv_PtrLcv_dnn_CosLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_CosLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_CosLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_CosLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CosLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CosLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CosLayerG_new_const_CosLayer(cv::dnn::CosLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::CoshLayer* cv_PtrLcv_dnn_CoshLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::CoshLayer* cv_PtrLcv_dnn_CoshLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_CoshLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_CoshLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_CoshLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CoshLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CoshLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CoshLayerG_new_const_CoshLayer(cv::dnn::CoshLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::CropAndResizeLayer* cv_PtrLcv_dnn_CropAndResizeLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::CropAndResizeLayer* cv_PtrLcv_dnn_CropAndResizeLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_CropAndResizeLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_CropAndResizeLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_CropAndResizeLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CropAndResizeLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CropAndResizeLayerG_new_const_CropAndResizeLayer(cv::dnn::CropAndResizeLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::CropLayer* cv_PtrLcv_dnn_CropLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::CropLayer* cv_PtrLcv_dnn_CropLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_CropLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_CropLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_CropLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CropLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CropLayerG_new_const_CropLayer(cv::dnn::CropLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::CumSumLayer* cv_PtrLcv_dnn_CumSumLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::CumSumLayer* cv_PtrLcv_dnn_CumSumLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_CumSumLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_CumSumLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_CumSumLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CumSumLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_CumSumLayerG_new_const_CumSumLayer(cv::dnn::CumSumLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::DataAugmentationLayer* cv_PtrLcv_dnn_DataAugmentationLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::DataAugmentationLayer* cv_PtrLcv_dnn_DataAugmentationLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_DataAugmentationLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_DataAugmentationLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_DataAugmentationLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_DataAugmentationLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_DataAugmentationLayerG_new_const_DataAugmentationLayer(cv::dnn::DataAugmentationLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::DeconvolutionLayer* cv_PtrLcv_dnn_DeconvolutionLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::DeconvolutionLayer* cv_PtrLcv_dnn_DeconvolutionLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_DeconvolutionLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_DeconvolutionLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_DeconvolutionLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_DeconvolutionLayerG_to_PtrOfBaseConvolutionLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_DeconvolutionLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_DeconvolutionLayerG_new_const_DeconvolutionLayer(cv::dnn::DeconvolutionLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::DepthToSpaceLayer* cv_PtrLcv_dnn_DepthToSpaceLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::DepthToSpaceLayer* cv_PtrLcv_dnn_DepthToSpaceLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_DepthToSpaceLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_DepthToSpaceLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_DepthToSpaceLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_DepthToSpaceLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_DepthToSpaceLayerG_new_const_DepthToSpaceLayer(cv::dnn::DepthToSpaceLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::DequantizeLayer* cv_PtrLcv_dnn_DequantizeLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::DequantizeLayer* cv_PtrLcv_dnn_DequantizeLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_DequantizeLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_DequantizeLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_DequantizeLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_DequantizeLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_DequantizeLayerG_new_const_DequantizeLayer(cv::dnn::DequantizeLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::DetectionOutputLayer* cv_PtrLcv_dnn_DetectionOutputLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::DetectionOutputLayer* cv_PtrLcv_dnn_DetectionOutputLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_DetectionOutputLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_DetectionOutputLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_DetectionOutputLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_DetectionOutputLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_DetectionOutputLayerG_new_const_DetectionOutputLayer(cv::dnn::DetectionOutputLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ELULayer* cv_PtrLcv_dnn_ELULayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ELULayer* cv_PtrLcv_dnn_ELULayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ELULayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ELULayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ELULayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ELULayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ELULayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ELULayerG_new_const_ELULayer(cv::dnn::ELULayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::EinsumLayer* cv_PtrLcv_dnn_EinsumLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::EinsumLayer* cv_PtrLcv_dnn_EinsumLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_EinsumLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_EinsumLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_EinsumLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_EinsumLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_EinsumLayerG_new_const_EinsumLayer(cv::dnn::EinsumLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::EltwiseLayer* cv_PtrLcv_dnn_EltwiseLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::EltwiseLayer* cv_PtrLcv_dnn_EltwiseLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_EltwiseLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_EltwiseLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_EltwiseLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_EltwiseLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_EltwiseLayerG_new_const_EltwiseLayer(cv::dnn::EltwiseLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::EltwiseLayerInt8* cv_PtrLcv_dnn_EltwiseLayerInt8G_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::EltwiseLayerInt8* cv_PtrLcv_dnn_EltwiseLayerInt8G_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_EltwiseLayerInt8G_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_EltwiseLayerInt8G_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_EltwiseLayerInt8G_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_EltwiseLayerInt8G_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_EltwiseLayerInt8G_new_const_EltwiseLayerInt8(cv::dnn::EltwiseLayerInt8* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ErfLayer* cv_PtrLcv_dnn_ErfLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ErfLayer* cv_PtrLcv_dnn_ErfLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ErfLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ErfLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ErfLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ErfLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ErfLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ErfLayerG_new_const_ErfLayer(cv::dnn::ErfLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ExpLayer* cv_PtrLcv_dnn_ExpLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ExpLayer* cv_PtrLcv_dnn_ExpLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ExpLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ExpLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ExpLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ExpLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ExpLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ExpLayerG_new_const_ExpLayer(cv::dnn::ExpLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ExpandLayer* cv_PtrLcv_dnn_ExpandLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ExpandLayer* cv_PtrLcv_dnn_ExpandLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ExpandLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ExpandLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ExpandLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ExpandLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ExpandLayerG_new_const_ExpandLayer(cv::dnn::ExpandLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::FlattenLayer* cv_PtrLcv_dnn_FlattenLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::FlattenLayer* cv_PtrLcv_dnn_FlattenLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_FlattenLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_FlattenLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_FlattenLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_FlattenLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_FlattenLayerG_new_const_FlattenLayer(cv::dnn::FlattenLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::FloorLayer* cv_PtrLcv_dnn_FloorLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::FloorLayer* cv_PtrLcv_dnn_FloorLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_FloorLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_FloorLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_FloorLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_FloorLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_FloorLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_FloorLayerG_new_const_FloorLayer(cv::dnn::FloorLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::FlowWarpLayer* cv_PtrLcv_dnn_FlowWarpLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::FlowWarpLayer* cv_PtrLcv_dnn_FlowWarpLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_FlowWarpLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_FlowWarpLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_FlowWarpLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_FlowWarpLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_FlowWarpLayerG_new_const_FlowWarpLayer(cv::dnn::FlowWarpLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::GRULayer* cv_PtrLcv_dnn_GRULayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::GRULayer* cv_PtrLcv_dnn_GRULayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_GRULayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_GRULayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_GRULayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GRULayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GRULayerG_new_const_GRULayer(cv::dnn::GRULayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::GatherElementsLayer* cv_PtrLcv_dnn_GatherElementsLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::GatherElementsLayer* cv_PtrLcv_dnn_GatherElementsLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_GatherElementsLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_GatherElementsLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_GatherElementsLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GatherElementsLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GatherElementsLayerG_new_const_GatherElementsLayer(cv::dnn::GatherElementsLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::GatherLayer* cv_PtrLcv_dnn_GatherLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::GatherLayer* cv_PtrLcv_dnn_GatherLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_GatherLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_GatherLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_GatherLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GatherLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GatherLayerG_new_const_GatherLayer(cv::dnn::GatherLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::GeluApproximationLayer* cv_PtrLcv_dnn_GeluApproximationLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::GeluApproximationLayer* cv_PtrLcv_dnn_GeluApproximationLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_GeluApproximationLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_GeluApproximationLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_GeluApproximationLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GeluApproximationLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GeluApproximationLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GeluApproximationLayerG_new_const_GeluApproximationLayer(cv::dnn::GeluApproximationLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::GeluLayer* cv_PtrLcv_dnn_GeluLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::GeluLayer* cv_PtrLcv_dnn_GeluLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_GeluLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_GeluLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_GeluLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GeluLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GeluLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GeluLayerG_new_const_GeluLayer(cv::dnn::GeluLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::GemmLayer* cv_PtrLcv_dnn_GemmLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::GemmLayer* cv_PtrLcv_dnn_GemmLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_GemmLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_GemmLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_GemmLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GemmLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GemmLayerG_new_const_GemmLayer(cv::dnn::GemmLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::GroupNormLayer* cv_PtrLcv_dnn_GroupNormLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::GroupNormLayer* cv_PtrLcv_dnn_GroupNormLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_GroupNormLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_GroupNormLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_GroupNormLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GroupNormLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_GroupNormLayerG_new_const_GroupNormLayer(cv::dnn::GroupNormLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::HardSigmoidLayer* cv_PtrLcv_dnn_HardSigmoidLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::HardSigmoidLayer* cv_PtrLcv_dnn_HardSigmoidLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_HardSigmoidLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_HardSigmoidLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_HardSigmoidLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_HardSigmoidLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_HardSigmoidLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_HardSigmoidLayerG_new_const_HardSigmoidLayer(cv::dnn::HardSigmoidLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::HardSwishLayer* cv_PtrLcv_dnn_HardSwishLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::HardSwishLayer* cv_PtrLcv_dnn_HardSwishLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_HardSwishLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_HardSwishLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_HardSwishLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_HardSwishLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_HardSwishLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_HardSwishLayerG_new_const_HardSwishLayer(cv::dnn::HardSwishLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::InnerProductLayer* cv_PtrLcv_dnn_InnerProductLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::InnerProductLayer* cv_PtrLcv_dnn_InnerProductLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_InnerProductLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_InnerProductLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_InnerProductLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_InnerProductLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_InnerProductLayerG_new_const_InnerProductLayer(cv::dnn::InnerProductLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::InnerProductLayerInt8* cv_PtrLcv_dnn_InnerProductLayerInt8G_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::InnerProductLayerInt8* cv_PtrLcv_dnn_InnerProductLayerInt8G_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_InnerProductLayerInt8G_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_InnerProductLayerInt8G_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_InnerProductLayerInt8G_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_InnerProductLayerInt8G_to_PtrOfInnerProductLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_InnerProductLayerInt8G_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_InnerProductLayerInt8G_new_const_InnerProductLayerInt8(cv::dnn::InnerProductLayerInt8* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::InstanceNormLayer* cv_PtrLcv_dnn_InstanceNormLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::InstanceNormLayer* cv_PtrLcv_dnn_InstanceNormLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_InstanceNormLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_InstanceNormLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_InstanceNormLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_InstanceNormLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_InstanceNormLayerG_new_const_InstanceNormLayer(cv::dnn::InstanceNormLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::InterpLayer* cv_PtrLcv_dnn_InterpLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::InterpLayer* cv_PtrLcv_dnn_InterpLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_InterpLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_InterpLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_InterpLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_InterpLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_InterpLayerG_new_const_InterpLayer(cv::dnn::InterpLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::LRNLayer* cv_PtrLcv_dnn_LRNLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::LRNLayer* cv_PtrLcv_dnn_LRNLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_LRNLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_LRNLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_LRNLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_LRNLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_LRNLayerG_new_const_LRNLayer(cv::dnn::LRNLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::LSTMLayer* cv_PtrLcv_dnn_LSTMLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::LSTMLayer* cv_PtrLcv_dnn_LSTMLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_LSTMLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_LSTMLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_LSTMLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_LSTMLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + +} + +extern "C" { + const cv::dnn::Layer* cv_PtrLcv_dnn_LayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::Layer* cv_PtrLcv_dnn_LayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_LayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_LayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_LayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_LayerG_new_const_Layer(cv::dnn::Layer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::LayerNormLayer* cv_PtrLcv_dnn_LayerNormLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::LayerNormLayer* cv_PtrLcv_dnn_LayerNormLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_LayerNormLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_LayerNormLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_LayerNormLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_LayerNormLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_LayerNormLayerG_new_const_LayerNormLayer(cv::dnn::LayerNormLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::LogLayer* cv_PtrLcv_dnn_LogLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::LogLayer* cv_PtrLcv_dnn_LogLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_LogLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_LogLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_LogLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_LogLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_LogLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_LogLayerG_new_const_LogLayer(cv::dnn::LogLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::MVNLayer* cv_PtrLcv_dnn_MVNLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::MVNLayer* cv_PtrLcv_dnn_MVNLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_MVNLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_MVNLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_MVNLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_MVNLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_MVNLayerG_new_const_MVNLayer(cv::dnn::MVNLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::MatMulLayer* cv_PtrLcv_dnn_MatMulLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::MatMulLayer* cv_PtrLcv_dnn_MatMulLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_MatMulLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_MatMulLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_MatMulLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_MatMulLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_MatMulLayerG_new_const_MatMulLayer(cv::dnn::MatMulLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::MaxUnpoolLayer* cv_PtrLcv_dnn_MaxUnpoolLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::MaxUnpoolLayer* cv_PtrLcv_dnn_MaxUnpoolLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_MaxUnpoolLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_MaxUnpoolLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_MaxUnpoolLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_MaxUnpoolLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_MaxUnpoolLayerG_new_const_MaxUnpoolLayer(cv::dnn::MaxUnpoolLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::MishLayer* cv_PtrLcv_dnn_MishLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::MishLayer* cv_PtrLcv_dnn_MishLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_MishLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_MishLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_MishLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_MishLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_MishLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_MishLayerG_new_const_MishLayer(cv::dnn::MishLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::NaryEltwiseLayer* cv_PtrLcv_dnn_NaryEltwiseLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::NaryEltwiseLayer* cv_PtrLcv_dnn_NaryEltwiseLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_NaryEltwiseLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_NaryEltwiseLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_NaryEltwiseLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_NaryEltwiseLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_NaryEltwiseLayerG_new_const_NaryEltwiseLayer(cv::dnn::NaryEltwiseLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::NormalizeBBoxLayer* cv_PtrLcv_dnn_NormalizeBBoxLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::NormalizeBBoxLayer* cv_PtrLcv_dnn_NormalizeBBoxLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_NormalizeBBoxLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_NormalizeBBoxLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_NormalizeBBoxLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_NormalizeBBoxLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_NormalizeBBoxLayerG_new_const_NormalizeBBoxLayer(cv::dnn::NormalizeBBoxLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::NotLayer* cv_PtrLcv_dnn_NotLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::NotLayer* cv_PtrLcv_dnn_NotLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_NotLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_NotLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_NotLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_NotLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_NotLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_NotLayerG_new_const_NotLayer(cv::dnn::NotLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::PaddingLayer* cv_PtrLcv_dnn_PaddingLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::PaddingLayer* cv_PtrLcv_dnn_PaddingLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_PaddingLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_PaddingLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_PaddingLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PaddingLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PaddingLayerG_new_const_PaddingLayer(cv::dnn::PaddingLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::PermuteLayer* cv_PtrLcv_dnn_PermuteLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::PermuteLayer* cv_PtrLcv_dnn_PermuteLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_PermuteLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_PermuteLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_PermuteLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PermuteLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PermuteLayerG_new_const_PermuteLayer(cv::dnn::PermuteLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::PoolingLayer* cv_PtrLcv_dnn_PoolingLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::PoolingLayer* cv_PtrLcv_dnn_PoolingLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_PoolingLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_PoolingLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_PoolingLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PoolingLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PoolingLayerG_new_const_PoolingLayer(cv::dnn::PoolingLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::PoolingLayerInt8* cv_PtrLcv_dnn_PoolingLayerInt8G_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::PoolingLayerInt8* cv_PtrLcv_dnn_PoolingLayerInt8G_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_PoolingLayerInt8G_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_PoolingLayerInt8G_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_PoolingLayerInt8G_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PoolingLayerInt8G_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PoolingLayerInt8G_to_PtrOfPoolingLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PoolingLayerInt8G_new_const_PoolingLayerInt8(cv::dnn::PoolingLayerInt8* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::PowerLayer* cv_PtrLcv_dnn_PowerLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::PowerLayer* cv_PtrLcv_dnn_PowerLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_PowerLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_PowerLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_PowerLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PowerLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PowerLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PowerLayerG_new_const_PowerLayer(cv::dnn::PowerLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::PriorBoxLayer* cv_PtrLcv_dnn_PriorBoxLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::PriorBoxLayer* cv_PtrLcv_dnn_PriorBoxLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_PriorBoxLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_PriorBoxLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_PriorBoxLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PriorBoxLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_PriorBoxLayerG_new_const_PriorBoxLayer(cv::dnn::PriorBoxLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ProposalLayer* cv_PtrLcv_dnn_ProposalLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ProposalLayer* cv_PtrLcv_dnn_ProposalLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ProposalLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ProposalLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ProposalLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ProposalLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ProposalLayerG_new_const_ProposalLayer(cv::dnn::ProposalLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::QuantizeLayer* cv_PtrLcv_dnn_QuantizeLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::QuantizeLayer* cv_PtrLcv_dnn_QuantizeLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_QuantizeLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_QuantizeLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_QuantizeLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_QuantizeLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_QuantizeLayerG_new_const_QuantizeLayer(cv::dnn::QuantizeLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::RNNLayer* cv_PtrLcv_dnn_RNNLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::RNNLayer* cv_PtrLcv_dnn_RNNLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_RNNLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_RNNLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_RNNLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_RNNLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + +} + +extern "C" { + const cv::dnn::ReLU6Layer* cv_PtrLcv_dnn_ReLU6LayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ReLU6Layer* cv_PtrLcv_dnn_ReLU6LayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ReLU6LayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ReLU6LayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ReLU6LayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReLU6LayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReLU6LayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReLU6LayerG_new_const_ReLU6Layer(cv::dnn::ReLU6Layer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ReLULayer* cv_PtrLcv_dnn_ReLULayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ReLULayer* cv_PtrLcv_dnn_ReLULayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ReLULayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ReLULayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ReLULayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReLULayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReLULayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReLULayerG_new_const_ReLULayer(cv::dnn::ReLULayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ReciprocalLayer* cv_PtrLcv_dnn_ReciprocalLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ReciprocalLayer* cv_PtrLcv_dnn_ReciprocalLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ReciprocalLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ReciprocalLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ReciprocalLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReciprocalLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReciprocalLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReciprocalLayerG_new_const_ReciprocalLayer(cv::dnn::ReciprocalLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ReduceLayer* cv_PtrLcv_dnn_ReduceLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ReduceLayer* cv_PtrLcv_dnn_ReduceLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ReduceLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ReduceLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ReduceLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReduceLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReduceLayerG_new_const_ReduceLayer(cv::dnn::ReduceLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::RegionLayer* cv_PtrLcv_dnn_RegionLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::RegionLayer* cv_PtrLcv_dnn_RegionLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_RegionLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_RegionLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_RegionLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_RegionLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_RegionLayerG_new_const_RegionLayer(cv::dnn::RegionLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ReorgLayer* cv_PtrLcv_dnn_ReorgLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ReorgLayer* cv_PtrLcv_dnn_ReorgLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ReorgLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ReorgLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ReorgLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReorgLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReorgLayerG_new_const_ReorgLayer(cv::dnn::ReorgLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::RequantizeLayer* cv_PtrLcv_dnn_RequantizeLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::RequantizeLayer* cv_PtrLcv_dnn_RequantizeLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_RequantizeLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_RequantizeLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_RequantizeLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_RequantizeLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_RequantizeLayerG_new_const_RequantizeLayer(cv::dnn::RequantizeLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ReshapeLayer* cv_PtrLcv_dnn_ReshapeLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ReshapeLayer* cv_PtrLcv_dnn_ReshapeLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ReshapeLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ReshapeLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ReshapeLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReshapeLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ReshapeLayerG_new_const_ReshapeLayer(cv::dnn::ReshapeLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ResizeLayer* cv_PtrLcv_dnn_ResizeLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ResizeLayer* cv_PtrLcv_dnn_ResizeLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ResizeLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ResizeLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ResizeLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ResizeLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ResizeLayerG_new_const_ResizeLayer(cv::dnn::ResizeLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::RoundLayer* cv_PtrLcv_dnn_RoundLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::RoundLayer* cv_PtrLcv_dnn_RoundLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_RoundLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_RoundLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_RoundLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_RoundLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_RoundLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_RoundLayerG_new_const_RoundLayer(cv::dnn::RoundLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ScaleLayer* cv_PtrLcv_dnn_ScaleLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ScaleLayer* cv_PtrLcv_dnn_ScaleLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ScaleLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ScaleLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ScaleLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ScaleLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ScaleLayerG_new_const_ScaleLayer(cv::dnn::ScaleLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ScaleLayerInt8* cv_PtrLcv_dnn_ScaleLayerInt8G_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ScaleLayerInt8* cv_PtrLcv_dnn_ScaleLayerInt8G_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ScaleLayerInt8G_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ScaleLayerInt8G_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ScaleLayerInt8G_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ScaleLayerInt8G_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ScaleLayerInt8G_to_PtrOfScaleLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ScaleLayerInt8G_new_const_ScaleLayerInt8(cv::dnn::ScaleLayerInt8* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ScatterLayer* cv_PtrLcv_dnn_ScatterLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ScatterLayer* cv_PtrLcv_dnn_ScatterLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ScatterLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ScatterLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ScatterLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ScatterLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ScatterLayerG_new_const_ScatterLayer(cv::dnn::ScatterLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ScatterNDLayer* cv_PtrLcv_dnn_ScatterNDLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ScatterNDLayer* cv_PtrLcv_dnn_ScatterNDLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ScatterNDLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ScatterNDLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ScatterNDLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ScatterNDLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ScatterNDLayerG_new_const_ScatterNDLayer(cv::dnn::ScatterNDLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SeluLayer* cv_PtrLcv_dnn_SeluLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SeluLayer* cv_PtrLcv_dnn_SeluLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SeluLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SeluLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SeluLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SeluLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SeluLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SeluLayerG_new_const_SeluLayer(cv::dnn::SeluLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ShiftLayer* cv_PtrLcv_dnn_ShiftLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ShiftLayer* cv_PtrLcv_dnn_ShiftLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ShiftLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ShiftLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ShiftLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ShiftLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ShiftLayerG_new_const_ShiftLayer(cv::dnn::ShiftLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ShiftLayerInt8* cv_PtrLcv_dnn_ShiftLayerInt8G_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ShiftLayerInt8* cv_PtrLcv_dnn_ShiftLayerInt8G_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ShiftLayerInt8G_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ShiftLayerInt8G_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ShiftLayerInt8G_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ShiftLayerInt8G_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ShiftLayerInt8G_new_const_ShiftLayerInt8(cv::dnn::ShiftLayerInt8* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ShrinkLayer* cv_PtrLcv_dnn_ShrinkLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ShrinkLayer* cv_PtrLcv_dnn_ShrinkLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ShrinkLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ShrinkLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ShrinkLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ShrinkLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ShrinkLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ShrinkLayerG_new_const_ShrinkLayer(cv::dnn::ShrinkLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ShuffleChannelLayer* cv_PtrLcv_dnn_ShuffleChannelLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ShuffleChannelLayer* cv_PtrLcv_dnn_ShuffleChannelLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ShuffleChannelLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ShuffleChannelLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ShuffleChannelLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ShuffleChannelLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ShuffleChannelLayerG_new_const_ShuffleChannelLayer(cv::dnn::ShuffleChannelLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SigmoidLayer* cv_PtrLcv_dnn_SigmoidLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SigmoidLayer* cv_PtrLcv_dnn_SigmoidLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SigmoidLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SigmoidLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SigmoidLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SigmoidLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SigmoidLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SigmoidLayerG_new_const_SigmoidLayer(cv::dnn::SigmoidLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SignLayer* cv_PtrLcv_dnn_SignLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SignLayer* cv_PtrLcv_dnn_SignLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SignLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SignLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SignLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SignLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SignLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SignLayerG_new_const_SignLayer(cv::dnn::SignLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SinLayer* cv_PtrLcv_dnn_SinLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SinLayer* cv_PtrLcv_dnn_SinLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SinLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SinLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SinLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SinLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SinLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SinLayerG_new_const_SinLayer(cv::dnn::SinLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SinhLayer* cv_PtrLcv_dnn_SinhLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SinhLayer* cv_PtrLcv_dnn_SinhLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SinhLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SinhLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SinhLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SinhLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SinhLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SinhLayerG_new_const_SinhLayer(cv::dnn::SinhLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SliceLayer* cv_PtrLcv_dnn_SliceLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SliceLayer* cv_PtrLcv_dnn_SliceLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SliceLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SliceLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SliceLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SliceLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SliceLayerG_new_const_SliceLayer(cv::dnn::SliceLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SoftmaxLayer* cv_PtrLcv_dnn_SoftmaxLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SoftmaxLayer* cv_PtrLcv_dnn_SoftmaxLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftmaxLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SoftmaxLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SoftmaxLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftmaxLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftmaxLayerG_new_const_SoftmaxLayer(cv::dnn::SoftmaxLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SoftmaxLayerInt8* cv_PtrLcv_dnn_SoftmaxLayerInt8G_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SoftmaxLayerInt8* cv_PtrLcv_dnn_SoftmaxLayerInt8G_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftmaxLayerInt8G_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SoftmaxLayerInt8G_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SoftmaxLayerInt8G_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftmaxLayerInt8G_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftmaxLayerInt8G_to_PtrOfSoftmaxLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftmaxLayerInt8G_new_const_SoftmaxLayerInt8(cv::dnn::SoftmaxLayerInt8* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SoftplusLayer* cv_PtrLcv_dnn_SoftplusLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SoftplusLayer* cv_PtrLcv_dnn_SoftplusLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftplusLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SoftplusLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SoftplusLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftplusLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftplusLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftplusLayerG_new_const_SoftplusLayer(cv::dnn::SoftplusLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SoftsignLayer* cv_PtrLcv_dnn_SoftsignLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SoftsignLayer* cv_PtrLcv_dnn_SoftsignLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftsignLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SoftsignLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SoftsignLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftsignLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftsignLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SoftsignLayerG_new_const_SoftsignLayer(cv::dnn::SoftsignLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SpaceToDepthLayer* cv_PtrLcv_dnn_SpaceToDepthLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SpaceToDepthLayer* cv_PtrLcv_dnn_SpaceToDepthLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SpaceToDepthLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SpaceToDepthLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SpaceToDepthLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SpaceToDepthLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SpaceToDepthLayerG_new_const_SpaceToDepthLayer(cv::dnn::SpaceToDepthLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SplitLayer* cv_PtrLcv_dnn_SplitLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SplitLayer* cv_PtrLcv_dnn_SplitLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SplitLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SplitLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SplitLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SplitLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SplitLayerG_new_const_SplitLayer(cv::dnn::SplitLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SqrtLayer* cv_PtrLcv_dnn_SqrtLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SqrtLayer* cv_PtrLcv_dnn_SqrtLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SqrtLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SqrtLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SqrtLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SqrtLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SqrtLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SqrtLayerG_new_const_SqrtLayer(cv::dnn::SqrtLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::SwishLayer* cv_PtrLcv_dnn_SwishLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::SwishLayer* cv_PtrLcv_dnn_SwishLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_SwishLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_SwishLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_SwishLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SwishLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SwishLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_SwishLayerG_new_const_SwishLayer(cv::dnn::SwishLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::TanHLayer* cv_PtrLcv_dnn_TanHLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::TanHLayer* cv_PtrLcv_dnn_TanHLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_TanHLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_TanHLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_TanHLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_TanHLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_TanHLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_TanHLayerG_new_const_TanHLayer(cv::dnn::TanHLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::TanLayer* cv_PtrLcv_dnn_TanLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::TanLayer* cv_PtrLcv_dnn_TanLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_TanLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_TanLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_TanLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_TanLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_TanLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_TanLayerG_new_const_TanLayer(cv::dnn::TanLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::ThresholdedReluLayer* cv_PtrLcv_dnn_ThresholdedReluLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::ThresholdedReluLayer* cv_PtrLcv_dnn_ThresholdedReluLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_ThresholdedReluLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_ThresholdedReluLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_ThresholdedReluLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ThresholdedReluLayerG_to_PtrOfActivationLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ThresholdedReluLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_ThresholdedReluLayerG_new_const_ThresholdedReluLayer(cv::dnn::ThresholdedReluLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::TileLayer* cv_PtrLcv_dnn_TileLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::TileLayer* cv_PtrLcv_dnn_TileLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_TileLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_TileLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_TileLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_TileLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_TileLayerG_new_const_TileLayer(cv::dnn::TileLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + const cv::dnn::TopKLayer* cv_PtrLcv_dnn_TopKLayerG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::dnn::TopKLayer* cv_PtrLcv_dnn_TopKLayerG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_dnn_TopKLayerG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_dnn_TopKLayerG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_dnn_TopKLayerG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_TopKLayerG_to_PtrOfLayer(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_dnn_TopKLayerG_new_const_TopKLayer(cv::dnn::TopKLayer* val) { + return new cv::Ptr(val); + } + +} + +extern "C" { + std::pair* std_pairLcv_dnn_Backend__cv_dnn_TargetG_new_const_Backend_Target(cv::dnn::Backend arg, cv::dnn::Target arg_1) { + std::pair* ret = new std::pair(arg, arg_1); + return ret; + } + + void std_pairLcv_dnn_Backend__cv_dnn_TargetG_get_0_const(const std::pair* instance, cv::dnn::Backend* ocvrs_return) { + cv::dnn::Backend ret = std::get<0>(*instance); + *ocvrs_return = ret; + } + + void std_pairLcv_dnn_Backend__cv_dnn_TargetG_get_1_const(const std::pair* instance, cv::dnn::Target* ocvrs_return) { + cv::dnn::Target ret = std::get<1>(*instance); + *ocvrs_return = ret; + } + + void std_pairLcv_dnn_Backend__cv_dnn_TargetG_delete(std::pair* instance) { + delete instance; + } + +} + +extern "C" { + std::vector* std_vectorLcv_dnn_MatShapeG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_dnn_MatShapeG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_dnn_MatShapeG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_dnn_MatShapeG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_dnn_MatShapeG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_dnn_MatShapeG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_dnn_MatShapeG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_dnn_MatShapeG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_dnn_MatShapeG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_dnn_MatShapeG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_dnn_MatShapeG_push_const_MatShape(std::vector* instance, const cv::dnn::MatShape* val) { + instance->push_back(*val); + } + + void std_vectorLcv_dnn_MatShapeG_insert_size_t_const_MatShape(std::vector* instance, size_t index, const cv::dnn::MatShape* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_dnn_MatShapeG_get_const_size_t(const std::vector* instance, size_t index, cv::dnn::MatShape** ocvrs_return) { + cv::dnn::MatShape ret = (*instance)[index]; + *ocvrs_return = new cv::dnn::MatShape(ret); + } + + void std_vectorLcv_dnn_MatShapeG_set_size_t_const_MatShape(std::vector* instance, size_t index, const cv::dnn::MatShape* val) { + (*instance)[index] = *val; + } + + void std_vectorLcv_dnn_MatShapeG_inputArray_const(const std::vector* instance, Result* ocvrs_return) { + try { + const cv::_InputArray ret = cv::_InputArray(*instance); + Ok(new const cv::_InputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_dnn_MatShapeG_outputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_OutputArray ret = cv::_OutputArray(*instance); + Ok(new cv::_OutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + + void std_vectorLcv_dnn_MatShapeG_inputOutputArray(std::vector* instance, Result* ocvrs_return) { + try { + cv::_InputOutputArray ret = cv::_InputOutputArray(*instance); + Ok(new cv::_InputOutputArray(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); + } + +} + + +extern "C" { + std::vector>* std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_new_const() { + std::vector>* ret = new std::vector>(); + return ret; + } + + void std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_delete(std::vector>* instance) { + delete instance; + } + + size_t std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_len_const(const std::vector>* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_isEmpty_const(const std::vector>* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_capacity_const(const std::vector>* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_shrinkToFit(std::vector>* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_reserve_size_t(std::vector>* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_remove_size_t(std::vector>* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_swap_size_t_size_t(std::vector>* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_clear(std::vector>* instance) { + instance->clear(); + } + + void std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_push_const_PtrLBackendWrapperG(std::vector>* instance, const cv::Ptr* val) { + instance->push_back(*val); + } + + void std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_insert_size_t_const_PtrLBackendWrapperG(std::vector>* instance, size_t index, const cv::Ptr* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_get_const_size_t(const std::vector>* instance, size_t index, cv::Ptr** ocvrs_return) { + cv::Ptr ret = (*instance)[index]; + *ocvrs_return = new cv::Ptr(ret); + } + + void std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_set_size_t_const_PtrLBackendWrapperG(std::vector>* instance, size_t index, const cv::Ptr* val) { + (*instance)[index] = *val; + } + +} + + +extern "C" { + std::vector>* std_vectorLcv_PtrLcv_dnn_LayerGG_new_const() { + std::vector>* ret = new std::vector>(); + return ret; + } + + void std_vectorLcv_PtrLcv_dnn_LayerGG_delete(std::vector>* instance) { + delete instance; + } + + size_t std_vectorLcv_PtrLcv_dnn_LayerGG_len_const(const std::vector>* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_PtrLcv_dnn_LayerGG_isEmpty_const(const std::vector>* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_PtrLcv_dnn_LayerGG_capacity_const(const std::vector>* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_PtrLcv_dnn_LayerGG_shrinkToFit(std::vector>* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_PtrLcv_dnn_LayerGG_reserve_size_t(std::vector>* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_PtrLcv_dnn_LayerGG_remove_size_t(std::vector>* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_PtrLcv_dnn_LayerGG_swap_size_t_size_t(std::vector>* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_PtrLcv_dnn_LayerGG_clear(std::vector>* instance) { + instance->clear(); + } + + void std_vectorLcv_PtrLcv_dnn_LayerGG_push_const_PtrLLayerG(std::vector>* instance, const cv::Ptr* val) { + instance->push_back(*val); + } + + void std_vectorLcv_PtrLcv_dnn_LayerGG_insert_size_t_const_PtrLLayerG(std::vector>* instance, size_t index, const cv::Ptr* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLcv_PtrLcv_dnn_LayerGG_get_const_size_t(const std::vector>* instance, size_t index, cv::Ptr** ocvrs_return) { + cv::Ptr ret = (*instance)[index]; + *ocvrs_return = new cv::Ptr(ret); + } + + void std_vectorLcv_PtrLcv_dnn_LayerGG_set_size_t_const_PtrLLayerG(std::vector>* instance, size_t index, const cv::Ptr* val) { + (*instance)[index] = *val; + } + +} + + +extern "C" { + std::vector* std_vectorLcv_dnn_TargetG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_dnn_TargetG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_dnn_TargetG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_dnn_TargetG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_dnn_TargetG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_dnn_TargetG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_dnn_TargetG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_dnn_TargetG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_dnn_TargetG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_dnn_TargetG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_dnn_TargetG_push_const_Target(std::vector* instance, const cv::dnn::Target val) { + instance->push_back(val); + } + + void std_vectorLcv_dnn_TargetG_insert_size_t_const_Target(std::vector* instance, size_t index, const cv::dnn::Target val) { + instance->insert(instance->begin() + index, val); + } + + void std_vectorLcv_dnn_TargetG_get_const_size_t(const std::vector* instance, size_t index, cv::dnn::Target* ocvrs_return) { + cv::dnn::Target ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_dnn_TargetG_set_size_t_const_Target(std::vector* instance, size_t index, const cv::dnn::Target val) { + (*instance)[index] = val; + } + + std::vector* std_vectorLcv_dnn_TargetG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::dnn::Target* std_vectorLcv_dnn_TargetG_data_const(const std::vector* instance) { + const cv::dnn::Target* ret = instance->data(); + return ret; + } + + cv::dnn::Target* std_vectorLcv_dnn_TargetG_dataMut(std::vector* instance) { + cv::dnn::Target* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_TargetX_size_t(const cv::dnn::Target* data, size_t len) { + return new std::vector(data, data + len); + } + +} + + +extern "C" { + std::vector>* std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_new_const() { + std::vector>* ret = new std::vector>(); + return ret; + } + + void std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_delete(std::vector>* instance) { + delete instance; + } + + size_t std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_len_const(const std::vector>* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_isEmpty_const(const std::vector>* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_capacity_const(const std::vector>* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_shrinkToFit(std::vector>* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_reserve_size_t(std::vector>* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_remove_size_t(std::vector>* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_swap_size_t_size_t(std::vector>* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_clear(std::vector>* instance) { + instance->clear(); + } + + void std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_push_const_pairLcv_dnn_Backend__cv_dnn_TargetG(std::vector>* instance, const std::pair* val) { + instance->push_back(*val); + } + + void std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_insert_size_t_const_pairLcv_dnn_Backend__cv_dnn_TargetG(std::vector>* instance, size_t index, const std::pair* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_get_const_size_t(const std::vector>* instance, size_t index, std::pair** ocvrs_return) { + std::pair ret = (*instance)[index]; + *ocvrs_return = new std::pair(ret); + } + + void std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_set_size_t_const_pairLcv_dnn_Backend__cv_dnn_TargetG(std::vector>* instance, size_t index, const std::pair* val) { + (*instance)[index] = *val; + } + +} + + +extern "C" { + std::vector>* std_vectorLstd_vectorLcv_dnn_MatShapeGG_new_const() { + std::vector>* ret = new std::vector>(); + return ret; + } + + void std_vectorLstd_vectorLcv_dnn_MatShapeGG_delete(std::vector>* instance) { + delete instance; + } + + size_t std_vectorLstd_vectorLcv_dnn_MatShapeGG_len_const(const std::vector>* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLstd_vectorLcv_dnn_MatShapeGG_isEmpty_const(const std::vector>* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLstd_vectorLcv_dnn_MatShapeGG_capacity_const(const std::vector>* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLstd_vectorLcv_dnn_MatShapeGG_shrinkToFit(std::vector>* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLstd_vectorLcv_dnn_MatShapeGG_reserve_size_t(std::vector>* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLstd_vectorLcv_dnn_MatShapeGG_remove_size_t(std::vector>* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLstd_vectorLcv_dnn_MatShapeGG_swap_size_t_size_t(std::vector>* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLstd_vectorLcv_dnn_MatShapeGG_clear(std::vector>* instance) { + instance->clear(); + } + + void std_vectorLstd_vectorLcv_dnn_MatShapeGG_push_const_vectorLMatShapeG(std::vector>* instance, const std::vector* val) { + instance->push_back(*val); + } + + void std_vectorLstd_vectorLcv_dnn_MatShapeGG_insert_size_t_const_vectorLMatShapeG(std::vector>* instance, size_t index, const std::vector* val) { + instance->insert(instance->begin() + index, *val); + } + + void std_vectorLstd_vectorLcv_dnn_MatShapeGG_get_const_size_t(const std::vector>* instance, size_t index, std::vector** ocvrs_return) { + std::vector ret = (*instance)[index]; + *ocvrs_return = new std::vector(ret); + } + + void std_vectorLstd_vectorLcv_dnn_MatShapeGG_set_size_t_const_vectorLMatShapeG(std::vector>* instance, size_t index, const std::vector* val) { + (*instance)[index] = *val; + } + +} + + diff --git a/target/debug/build/opencv-4e105b5546afc119/out/flag_check b/target/debug/build/opencv-4e105b5546afc119/out/flag_check new file mode 100644 index 0000000..a4f4139 Binary files /dev/null and b/target/debug/build/opencv-4e105b5546afc119/out/flag_check differ diff --git a/target/debug/build/opencv-4e105b5546afc119/out/flag_check.cpp b/target/debug/build/opencv-4e105b5546afc119/out/flag_check.cpp new file mode 100644 index 0000000..f1d95ed --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/flag_check.cpp @@ -0,0 +1 @@ +int main(void) { return 0; } \ No newline at end of file diff --git a/target/debug/build/opencv-4e105b5546afc119/out/highgui.cpp b/target/debug/build/opencv-4e105b5546afc119/out/highgui.cpp new file mode 100644 index 0000000..c76e671 --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/highgui.cpp @@ -0,0 +1,457 @@ +#include "ocvrs_common.hpp" +#include +#include "highgui_types.hpp" + +extern "C" { +void cv_addText_const_MatR_const_StringR_Point_const_QtFontR(const cv::Mat* img, const char* text, cv::Point* org, const cv::QtFont* font, ResultVoid* ocvrs_return) { + try { + cv::addText(*img, std::string(text), *org, *font); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_addText_const_MatR_const_StringR_Point_const_StringR(const cv::Mat* img, const char* text, cv::Point* org, const char* nameFont, ResultVoid* ocvrs_return) { + try { + cv::addText(*img, std::string(text), *org, std::string(nameFont)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_addText_const_MatR_const_StringR_Point_const_StringR_int_Scalar_int_int_int(const cv::Mat* img, const char* text, cv::Point* org, const char* nameFont, int pointSize, cv::Scalar* color, int weight, int style, int spacing, ResultVoid* ocvrs_return) { + try { + cv::addText(*img, std::string(text), *org, std::string(nameFont), pointSize, *color, weight, style, spacing); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_createButton_const_StringR_ButtonCallback_voidX(const char* bar_name, cv::ButtonCallback on_change, void* userdata, Result* ocvrs_return) { + try { + int ret = cv::createButton(std::string(bar_name), on_change, userdata); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_createButton_const_StringR_ButtonCallback_voidX_int_bool(const char* bar_name, cv::ButtonCallback on_change, void* userdata, int type, bool initial_button_state, Result* ocvrs_return) { + try { + int ret = cv::createButton(std::string(bar_name), on_change, userdata, type, initial_button_state); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_createTrackbar_const_StringR_const_StringR_intX_int_TrackbarCallback_voidX(const char* trackbarname, const char* winname, int* value, int count, cv::TrackbarCallback onChange, void* userdata, Result* ocvrs_return) { + try { + int ret = cv::createTrackbar(std::string(trackbarname), std::string(winname), value, count, onChange, userdata); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_currentUIFramework(Result* ocvrs_return) { + try { + const std::string ret = cv::currentUIFramework(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_destroyAllWindows(ResultVoid* ocvrs_return) { + try { + cv::destroyAllWindows(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_destroyWindow_const_StringR(const char* winname, ResultVoid* ocvrs_return) { + try { + cv::destroyWindow(std::string(winname)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_displayOverlay_const_StringR_const_StringR(const char* winname, const char* text, ResultVoid* ocvrs_return) { + try { + cv::displayOverlay(std::string(winname), std::string(text)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_displayOverlay_const_StringR_const_StringR_int(const char* winname, const char* text, int delayms, ResultVoid* ocvrs_return) { + try { + cv::displayOverlay(std::string(winname), std::string(text), delayms); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_displayStatusBar_const_StringR_const_StringR(const char* winname, const char* text, ResultVoid* ocvrs_return) { + try { + cv::displayStatusBar(std::string(winname), std::string(text)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_displayStatusBar_const_StringR_const_StringR_int(const char* winname, const char* text, int delayms, ResultVoid* ocvrs_return) { + try { + cv::displayStatusBar(std::string(winname), std::string(text), delayms); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_fontQt_const_StringR(const char* nameFont, Result* ocvrs_return) { + try { + cv::QtFont ret = cv::fontQt(std::string(nameFont)); + Ok(new cv::QtFont(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_fontQt_const_StringR_int_Scalar_int_int_int(const char* nameFont, int pointSize, cv::Scalar* color, int weight, int style, int spacing, Result* ocvrs_return) { + try { + cv::QtFont ret = cv::fontQt(std::string(nameFont), pointSize, *color, weight, style, spacing); + Ok(new cv::QtFont(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getMouseWheelDelta_int(int flags, Result* ocvrs_return) { + try { + int ret = cv::getMouseWheelDelta(flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getTrackbarPos_const_StringR_const_StringR(const char* trackbarname, const char* winname, Result* ocvrs_return) { + try { + int ret = cv::getTrackbarPos(std::string(trackbarname), std::string(winname)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getWindowImageRect_const_StringR(const char* winname, Result* ocvrs_return) { + try { + cv::Rect ret = cv::getWindowImageRect(std::string(winname)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getWindowProperty_const_StringR_int(const char* winname, int prop_id, Result* ocvrs_return) { + try { + double ret = cv::getWindowProperty(std::string(winname), prop_id); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_imshow_const_StringR_const__InputArrayR(const char* winname, const cv::_InputArray* mat, ResultVoid* ocvrs_return) { + try { + cv::imshow(std::string(winname), *mat); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_loadWindowParameters_const_StringR(const char* windowName, ResultVoid* ocvrs_return) { + try { + cv::loadWindowParameters(std::string(windowName)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_moveWindow_const_StringR_int_int(const char* winname, int x, int y, ResultVoid* ocvrs_return) { + try { + cv::moveWindow(std::string(winname), x, y); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_namedWindow_const_StringR(const char* winname, ResultVoid* ocvrs_return) { + try { + cv::namedWindow(std::string(winname)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_namedWindow_const_StringR_int(const char* winname, int flags, ResultVoid* ocvrs_return) { + try { + cv::namedWindow(std::string(winname), flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_pollKey(Result* ocvrs_return) { + try { + int ret = cv::pollKey(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_resizeWindow_const_StringR_const_SizeR(const char* winname, const cv::Size* size, ResultVoid* ocvrs_return) { + try { + cv::resizeWindow(std::string(winname), *size); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_resizeWindow_const_StringR_int_int(const char* winname, int width, int height, ResultVoid* ocvrs_return) { + try { + cv::resizeWindow(std::string(winname), width, height); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_saveWindowParameters_const_StringR(const char* windowName, ResultVoid* ocvrs_return) { + try { + cv::saveWindowParameters(std::string(windowName)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_selectROI_const_StringR_const__InputArrayR(const char* windowName, const cv::_InputArray* img, Result* ocvrs_return) { + try { + cv::Rect ret = cv::selectROI(std::string(windowName), *img); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_selectROI_const_StringR_const__InputArrayR_bool_bool_bool(const char* windowName, const cv::_InputArray* img, bool showCrosshair, bool fromCenter, bool printNotice, Result* ocvrs_return) { + try { + cv::Rect ret = cv::selectROI(std::string(windowName), *img, showCrosshair, fromCenter, printNotice); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_selectROI_const__InputArrayR(const cv::_InputArray* img, Result* ocvrs_return) { + try { + cv::Rect ret = cv::selectROI(*img); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_selectROI_const__InputArrayR_bool_bool_bool(const cv::_InputArray* img, bool showCrosshair, bool fromCenter, bool printNotice, Result* ocvrs_return) { + try { + cv::Rect ret = cv::selectROI(*img, showCrosshair, fromCenter, printNotice); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_selectROIs_const_StringR_const__InputArrayR_vectorLRectGR(const char* windowName, const cv::_InputArray* img, std::vector* boundingBoxes, ResultVoid* ocvrs_return) { + try { + cv::selectROIs(std::string(windowName), *img, *boundingBoxes); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_selectROIs_const_StringR_const__InputArrayR_vectorLRectGR_bool_bool_bool(const char* windowName, const cv::_InputArray* img, std::vector* boundingBoxes, bool showCrosshair, bool fromCenter, bool printNotice, ResultVoid* ocvrs_return) { + try { + cv::selectROIs(std::string(windowName), *img, *boundingBoxes, showCrosshair, fromCenter, printNotice); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setMouseCallback_const_StringR_MouseCallback_voidX(const char* winname, cv::MouseCallback onMouse, void* userdata, ResultVoid* ocvrs_return) { + try { + cv::setMouseCallback(std::string(winname), onMouse, userdata); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setOpenGlContext_const_StringR(const char* winname, ResultVoid* ocvrs_return) { + try { + cv::setOpenGlContext(std::string(winname)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setOpenGlDrawCallback_const_StringR_OpenGlDrawCallback_voidX(const char* winname, cv::OpenGlDrawCallback onOpenGlDraw, void* userdata, ResultVoid* ocvrs_return) { + try { + cv::setOpenGlDrawCallback(std::string(winname), onOpenGlDraw, userdata); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setTrackbarMax_const_StringR_const_StringR_int(const char* trackbarname, const char* winname, int maxval, ResultVoid* ocvrs_return) { + try { + cv::setTrackbarMax(std::string(trackbarname), std::string(winname), maxval); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setTrackbarMin_const_StringR_const_StringR_int(const char* trackbarname, const char* winname, int minval, ResultVoid* ocvrs_return) { + try { + cv::setTrackbarMin(std::string(trackbarname), std::string(winname), minval); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setTrackbarPos_const_StringR_const_StringR_int(const char* trackbarname, const char* winname, int pos, ResultVoid* ocvrs_return) { + try { + cv::setTrackbarPos(std::string(trackbarname), std::string(winname), pos); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setWindowProperty_const_StringR_int_double(const char* winname, int prop_id, double prop_value, ResultVoid* ocvrs_return) { + try { + cv::setWindowProperty(std::string(winname), prop_id, prop_value); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_setWindowTitle_const_StringR_const_StringR(const char* winname, const char* title, ResultVoid* ocvrs_return) { + try { + cv::setWindowTitle(std::string(winname), std::string(title)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_startLoop_int__X__int__charXX__int_charXX(int (*pt2Func)(int, char**), int argc, char** argv, Result* ocvrs_return) { + try { + int ret = cv::startLoop(pt2Func, argc, argv); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_startWindowThread(Result* ocvrs_return) { + try { + int ret = cv::startWindowThread(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_stopLoop(ResultVoid* ocvrs_return) { + try { + cv::stopLoop(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_updateWindow_const_StringR(const char* winname, ResultVoid* ocvrs_return) { + try { + cv::updateWindow(std::string(winname)); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_waitKey(Result* ocvrs_return) { + try { + int ret = cv::waitKey(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_waitKeyEx(Result* ocvrs_return) { + try { + int ret = cv::waitKeyEx(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_waitKeyEx_int(int delay, Result* ocvrs_return) { + try { + int ret = cv::waitKeyEx(delay); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_waitKey_int(int delay, Result* ocvrs_return) { + try { + int ret = cv::waitKey(delay); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::QtFont* cv_QtFont_defaultNew_const() { + cv::QtFont* ret = new cv::QtFont(); + return ret; +} + +void* cv_QtFont_propNameFont_const(const cv::QtFont* instance) { + const char* ret = instance->nameFont; + return ocvrs_create_string(ret); +} + +void cv_QtFont_propColor_const(const cv::QtFont* instance, cv::Scalar* ocvrs_return) { + cv::Scalar ret = instance->color; + *ocvrs_return = ret; +} + +void cv_QtFont_propColor_const_Scalar(cv::QtFont* instance, const cv::Scalar* val) { + instance->color = *val; +} + +int cv_QtFont_propFont_face_const(const cv::QtFont* instance) { + int ret = instance->font_face; + return ret; +} + +void cv_QtFont_propFont_face_const_int(cv::QtFont* instance, const int val) { + instance->font_face = val; +} + +const int* cv_QtFont_propAscii_const(const cv::QtFont* instance) { + const int* ret = instance->ascii; + return ret; +} + +const int* cv_QtFont_propGreek_const(const cv::QtFont* instance) { + const int* ret = instance->greek; + return ret; +} + +const int* cv_QtFont_propCyrillic_const(const cv::QtFont* instance) { + const int* ret = instance->cyrillic; + return ret; +} + +float cv_QtFont_propHscale_const(const cv::QtFont* instance) { + float ret = instance->hscale; + return ret; +} + +void cv_QtFont_propHscale_const_float(cv::QtFont* instance, const float val) { + instance->hscale = val; +} + +float cv_QtFont_propVscale_const(const cv::QtFont* instance) { + float ret = instance->vscale; + return ret; +} + +void cv_QtFont_propVscale_const_float(cv::QtFont* instance, const float val) { + instance->vscale = val; +} + +float cv_QtFont_propShear_const(const cv::QtFont* instance) { + float ret = instance->shear; + return ret; +} + +void cv_QtFont_propShear_const_float(cv::QtFont* instance, const float val) { + instance->shear = val; +} + +int cv_QtFont_propThickness_const(const cv::QtFont* instance) { + int ret = instance->thickness; + return ret; +} + +void cv_QtFont_propThickness_const_int(cv::QtFont* instance, const int val) { + instance->thickness = val; +} + +float cv_QtFont_propDx_const(const cv::QtFont* instance) { + float ret = instance->dx; + return ret; +} + +void cv_QtFont_propDx_const_float(cv::QtFont* instance, const float val) { + instance->dx = val; +} + +int cv_QtFont_propLine_type_const(const cv::QtFont* instance) { + int ret = instance->line_type; + return ret; +} + +void cv_QtFont_propLine_type_const_int(cv::QtFont* instance, const int val) { + instance->line_type = val; +} + +void cv_QtFont_delete(cv::QtFont* instance) { + delete instance; +} + +} diff --git a/target/debug/build/opencv-4e105b5546afc119/out/highgui_types.hpp b/target/debug/build/opencv-4e105b5546afc119/out/highgui_types.hpp new file mode 100644 index 0000000..e69de29 diff --git a/target/debug/build/opencv-4e105b5546afc119/out/imgproc.cpp b/target/debug/build/opencv-4e105b5546afc119/out/imgproc.cpp new file mode 100644 index 0000000..b5752a7 --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/imgproc.cpp @@ -0,0 +1,2615 @@ +#include "ocvrs_common.hpp" +#include +#include "imgproc_types.hpp" + +extern "C" { +void cv_Canny_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double_double(const cv::_InputArray* dx, const cv::_InputArray* dy, const cv::_OutputArray* edges, double threshold1, double threshold2, ResultVoid* ocvrs_return) { + try { + cv::Canny(*dx, *dy, *edges, threshold1, threshold2); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Canny_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double_double_bool(const cv::_InputArray* dx, const cv::_InputArray* dy, const cv::_OutputArray* edges, double threshold1, double threshold2, bool L2gradient, ResultVoid* ocvrs_return) { + try { + cv::Canny(*dx, *dy, *edges, threshold1, threshold2, L2gradient); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Canny_const__InputArrayR_const__OutputArrayR_double_double(const cv::_InputArray* image, const cv::_OutputArray* edges, double threshold1, double threshold2, ResultVoid* ocvrs_return) { + try { + cv::Canny(*image, *edges, threshold1, threshold2); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Canny_const__InputArrayR_const__OutputArrayR_double_double_int_bool(const cv::_InputArray* image, const cv::_OutputArray* edges, double threshold1, double threshold2, int apertureSize, bool L2gradient, ResultVoid* ocvrs_return) { + try { + cv::Canny(*image, *edges, threshold1, threshold2, apertureSize, L2gradient); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_EMD_const__InputArrayR_const__InputArrayR_int(const cv::_InputArray* signature1, const cv::_InputArray* signature2, int distType, Result* ocvrs_return) { + try { + float ret = cv::EMD(*signature1, *signature2, distType); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_EMD_const__InputArrayR_const__InputArrayR_int_const__InputArrayR_floatX_const__OutputArrayR(const cv::_InputArray* signature1, const cv::_InputArray* signature2, int distType, const cv::_InputArray* cost, float* lowerBound, const cv::_OutputArray* flow, Result* ocvrs_return) { + try { + float ret = cv::EMD(*signature1, *signature2, distType, *cost, lowerBound, *flow); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GaussianBlur_const__InputArrayR_const__OutputArrayR_Size_double(const cv::_InputArray* src, const cv::_OutputArray* dst, cv::Size* ksize, double sigmaX, ResultVoid* ocvrs_return) { + try { + cv::GaussianBlur(*src, *dst, *ksize, sigmaX); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GaussianBlur_const__InputArrayR_const__OutputArrayR_Size_double_double_int_AlgorithmHint(const cv::_InputArray* src, const cv::_OutputArray* dst, cv::Size* ksize, double sigmaX, double sigmaY, int borderType, cv::AlgorithmHint hint, ResultVoid* ocvrs_return) { + try { + cv::GaussianBlur(*src, *dst, *ksize, sigmaX, sigmaY, borderType, hint); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_HoughCircles_const__InputArrayR_const__OutputArrayR_int_double_double(const cv::_InputArray* image, const cv::_OutputArray* circles, int method, double dp, double minDist, ResultVoid* ocvrs_return) { + try { + cv::HoughCircles(*image, *circles, method, dp, minDist); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_HoughCircles_const__InputArrayR_const__OutputArrayR_int_double_double_double_double_int_int(const cv::_InputArray* image, const cv::_OutputArray* circles, int method, double dp, double minDist, double param1, double param2, int minRadius, int maxRadius, ResultVoid* ocvrs_return) { + try { + cv::HoughCircles(*image, *circles, method, dp, minDist, param1, param2, minRadius, maxRadius); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_HoughLinesP_const__InputArrayR_const__OutputArrayR_double_double_int(const cv::_InputArray* image, const cv::_OutputArray* lines, double rho, double theta, int threshold, ResultVoid* ocvrs_return) { + try { + cv::HoughLinesP(*image, *lines, rho, theta, threshold); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_HoughLinesP_const__InputArrayR_const__OutputArrayR_double_double_int_double_double(const cv::_InputArray* image, const cv::_OutputArray* lines, double rho, double theta, int threshold, double minLineLength, double maxLineGap, ResultVoid* ocvrs_return) { + try { + cv::HoughLinesP(*image, *lines, rho, theta, threshold, minLineLength, maxLineGap); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_HoughLinesPointSet_const__InputArrayR_const__OutputArrayR_int_int_double_double_double_double_double_double(const cv::_InputArray* point, const cv::_OutputArray* lines, int lines_max, int threshold, double min_rho, double max_rho, double rho_step, double min_theta, double max_theta, double theta_step, ResultVoid* ocvrs_return) { + try { + cv::HoughLinesPointSet(*point, *lines, lines_max, threshold, min_rho, max_rho, rho_step, min_theta, max_theta, theta_step); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_HoughLines_const__InputArrayR_const__OutputArrayR_double_double_int(const cv::_InputArray* image, const cv::_OutputArray* lines, double rho, double theta, int threshold, ResultVoid* ocvrs_return) { + try { + cv::HoughLines(*image, *lines, rho, theta, threshold); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_HoughLines_const__InputArrayR_const__OutputArrayR_double_double_int_double_double_double_double_bool(const cv::_InputArray* image, const cv::_OutputArray* lines, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta, bool use_edgeval, ResultVoid* ocvrs_return) { + try { + cv::HoughLines(*image, *lines, rho, theta, threshold, srn, stn, min_theta, max_theta, use_edgeval); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_HuMoments_const_MomentsR_const__OutputArrayR(const cv::Moments* m, const cv::_OutputArray* hu, ResultVoid* ocvrs_return) { + try { + cv::HuMoments(*m, *hu); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_HuMoments_const_MomentsR_doubleXX(const cv::Moments* moments, double(*hu)[7], ResultVoid* ocvrs_return) { + try { + cv::HuMoments(*moments, *hu); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Laplacian_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, ResultVoid* ocvrs_return) { + try { + cv::Laplacian(*src, *dst, ddepth); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Laplacian_const__InputArrayR_const__OutputArrayR_int_int_double_double_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, int ksize, double scale, double delta, int borderType, ResultVoid* ocvrs_return) { + try { + cv::Laplacian(*src, *dst, ddepth, ksize, scale, delta, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Scharr_const__InputArrayR_const__OutputArrayR_int_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, int dx, int dy, ResultVoid* ocvrs_return) { + try { + cv::Scharr(*src, *dst, ddepth, dx, dy); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Scharr_const__InputArrayR_const__OutputArrayR_int_int_int_double_double_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, int dx, int dy, double scale, double delta, int borderType, ResultVoid* ocvrs_return) { + try { + cv::Scharr(*src, *dst, ddepth, dx, dy, scale, delta, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Sobel_const__InputArrayR_const__OutputArrayR_int_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, int dx, int dy, ResultVoid* ocvrs_return) { + try { + cv::Sobel(*src, *dst, ddepth, dx, dy); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Sobel_const__InputArrayR_const__OutputArrayR_int_int_int_int_double_double_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, int dx, int dy, int ksize, double scale, double delta, int borderType, ResultVoid* ocvrs_return) { + try { + cv::Sobel(*src, *dst, ddepth, dx, dy, ksize, scale, delta, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_accumulateProduct_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_InputOutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::accumulateProduct(*src1, *src2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_accumulateProduct_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_InputOutputArray* dst, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::accumulateProduct(*src1, *src2, *dst, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_accumulateSquare_const__InputArrayR_const__InputOutputArrayR(const cv::_InputArray* src, const cv::_InputOutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::accumulateSquare(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_accumulateSquare_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_InputOutputArray* dst, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::accumulateSquare(*src, *dst, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_accumulateWeighted_const__InputArrayR_const__InputOutputArrayR_double(const cv::_InputArray* src, const cv::_InputOutputArray* dst, double alpha, ResultVoid* ocvrs_return) { + try { + cv::accumulateWeighted(*src, *dst, alpha); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_accumulateWeighted_const__InputArrayR_const__InputOutputArrayR_double_const__InputArrayR(const cv::_InputArray* src, const cv::_InputOutputArray* dst, double alpha, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::accumulateWeighted(*src, *dst, alpha, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_accumulate_const__InputArrayR_const__InputOutputArrayR(const cv::_InputArray* src, const cv::_InputOutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::accumulate(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_accumulate_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_InputOutputArray* dst, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::accumulate(*src, *dst, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_adaptiveThreshold_const__InputArrayR_const__OutputArrayR_double_int_int_int_double(const cv::_InputArray* src, const cv::_OutputArray* dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C, ResultVoid* ocvrs_return) { + try { + cv::adaptiveThreshold(*src, *dst, maxValue, adaptiveMethod, thresholdType, blockSize, C); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_applyColorMap_const__InputArrayR_const__OutputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* userColor, ResultVoid* ocvrs_return) { + try { + cv::applyColorMap(*src, *dst, *userColor); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_applyColorMap_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int colormap, ResultVoid* ocvrs_return) { + try { + cv::applyColorMap(*src, *dst, colormap); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_approxPolyDP_const__InputArrayR_const__OutputArrayR_double_bool(const cv::_InputArray* curve, const cv::_OutputArray* approxCurve, double epsilon, bool closed, ResultVoid* ocvrs_return) { + try { + cv::approxPolyDP(*curve, *approxCurve, epsilon, closed); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_approxPolyN_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* curve, const cv::_OutputArray* approxCurve, int nsides, ResultVoid* ocvrs_return) { + try { + cv::approxPolyN(*curve, *approxCurve, nsides); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_approxPolyN_const__InputArrayR_const__OutputArrayR_int_float_bool(const cv::_InputArray* curve, const cv::_OutputArray* approxCurve, int nsides, float epsilon_percentage, bool ensure_convex, ResultVoid* ocvrs_return) { + try { + cv::approxPolyN(*curve, *approxCurve, nsides, epsilon_percentage, ensure_convex); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_arcLength_const__InputArrayR_bool(const cv::_InputArray* curve, bool closed, Result* ocvrs_return) { + try { + double ret = cv::arcLength(*curve, closed); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_arrowedLine_const__InputOutputArrayR_Point_Point_const_ScalarR(const cv::_InputOutputArray* img, cv::Point* pt1, cv::Point* pt2, const cv::Scalar* color, ResultVoid* ocvrs_return) { + try { + cv::arrowedLine(*img, *pt1, *pt2, *color); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_arrowedLine_const__InputOutputArrayR_Point_Point_const_ScalarR_int_int_int_double(const cv::_InputOutputArray* img, cv::Point* pt1, cv::Point* pt2, const cv::Scalar* color, int thickness, int line_type, int shift, double tipLength, ResultVoid* ocvrs_return) { + try { + cv::arrowedLine(*img, *pt1, *pt2, *color, thickness, line_type, shift, tipLength); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_bilateralFilter_const__InputArrayR_const__OutputArrayR_int_double_double(const cv::_InputArray* src, const cv::_OutputArray* dst, int d, double sigmaColor, double sigmaSpace, ResultVoid* ocvrs_return) { + try { + cv::bilateralFilter(*src, *dst, d, sigmaColor, sigmaSpace); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_bilateralFilter_const__InputArrayR_const__OutputArrayR_int_double_double_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int d, double sigmaColor, double sigmaSpace, int borderType, ResultVoid* ocvrs_return) { + try { + cv::bilateralFilter(*src, *dst, d, sigmaColor, sigmaSpace, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_blendLinear_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_InputArray* weights1, const cv::_InputArray* weights2, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::blendLinear(*src1, *src2, *weights1, *weights2, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_blur_const__InputArrayR_const__OutputArrayR_Size(const cv::_InputArray* src, const cv::_OutputArray* dst, cv::Size* ksize, ResultVoid* ocvrs_return) { + try { + cv::blur(*src, *dst, *ksize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_blur_const__InputArrayR_const__OutputArrayR_Size_Point_int(const cv::_InputArray* src, const cv::_OutputArray* dst, cv::Size* ksize, cv::Point* anchor, int borderType, ResultVoid* ocvrs_return) { + try { + cv::blur(*src, *dst, *ksize, *anchor, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_boundingRect_const__InputArrayR(const cv::_InputArray* array, Result* ocvrs_return) { + try { + cv::Rect ret = cv::boundingRect(*array); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_boxFilter_const__InputArrayR_const__OutputArrayR_int_Size(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, cv::Size* ksize, ResultVoid* ocvrs_return) { + try { + cv::boxFilter(*src, *dst, ddepth, *ksize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_boxFilter_const__InputArrayR_const__OutputArrayR_int_Size_Point_bool_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, cv::Size* ksize, cv::Point* anchor, bool normalize, int borderType, ResultVoid* ocvrs_return) { + try { + cv::boxFilter(*src, *dst, ddepth, *ksize, *anchor, normalize, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_boxPoints_RotatedRect_const__OutputArrayR(cv::RotatedRect* box, const cv::_OutputArray* points, ResultVoid* ocvrs_return) { + try { + cv::boxPoints(*box, *points); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_buildPyramid_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int maxlevel, ResultVoid* ocvrs_return) { + try { + cv::buildPyramid(*src, *dst, maxlevel); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_buildPyramid_const__InputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int maxlevel, int borderType, ResultVoid* ocvrs_return) { + try { + cv::buildPyramid(*src, *dst, maxlevel, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_calcBackProject_const__InputArrayR_const_vectorLintGR_const__InputArrayR_const__OutputArrayR_const_vectorLfloatGR_double(const cv::_InputArray* images, const std::vector* channels, const cv::_InputArray* hist, const cv::_OutputArray* dst, const std::vector* ranges, double scale, ResultVoid* ocvrs_return) { + try { + cv::calcBackProject(*images, *channels, *hist, *dst, *ranges, scale); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_calcHist_const__InputArrayR_const_vectorLintGR_const__InputArrayR_const__OutputArrayR_const_vectorLintGR_const_vectorLfloatGR(const cv::_InputArray* images, const std::vector* channels, const cv::_InputArray* mask, const cv::_OutputArray* hist, const std::vector* histSize, const std::vector* ranges, ResultVoid* ocvrs_return) { + try { + cv::calcHist(*images, *channels, *mask, *hist, *histSize, *ranges); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_calcHist_const__InputArrayR_const_vectorLintGR_const__InputArrayR_const__OutputArrayR_const_vectorLintGR_const_vectorLfloatGR_bool(const cv::_InputArray* images, const std::vector* channels, const cv::_InputArray* mask, const cv::_OutputArray* hist, const std::vector* histSize, const std::vector* ranges, bool accumulate, ResultVoid* ocvrs_return) { + try { + cv::calcHist(*images, *channels, *mask, *hist, *histSize, *ranges, accumulate); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_circle_const__InputOutputArrayR_Point_int_const_ScalarR(const cv::_InputOutputArray* img, cv::Point* center, int radius, const cv::Scalar* color, ResultVoid* ocvrs_return) { + try { + cv::circle(*img, *center, radius, *color); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_circle_const__InputOutputArrayR_Point_int_const_ScalarR_int_int_int(const cv::_InputOutputArray* img, cv::Point* center, int radius, const cv::Scalar* color, int thickness, int lineType, int shift, ResultVoid* ocvrs_return) { + try { + cv::circle(*img, *center, radius, *color, thickness, lineType, shift); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_clipLine_Rect_PointR_PointR(cv::Rect* imgRect, cv::Point* pt1, cv::Point* pt2, Result* ocvrs_return) { + try { + bool ret = cv::clipLine(*imgRect, *pt1, *pt2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_clipLine_Size2l_Point2lR_Point2lR(cv::Size2l* imgSize, cv::Point2l* pt1, cv::Point2l* pt2, Result* ocvrs_return) { + try { + bool ret = cv::clipLine(*imgSize, *pt1, *pt2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_clipLine_Size_PointR_PointR(cv::Size* imgSize, cv::Point* pt1, cv::Point* pt2, Result* ocvrs_return) { + try { + bool ret = cv::clipLine(*imgSize, *pt1, *pt2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_compareHist_const_SparseMatR_const_SparseMatR_int(const cv::SparseMat* H1, const cv::SparseMat* H2, int method, Result* ocvrs_return) { + try { + double ret = cv::compareHist(*H1, *H2, method); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_compareHist_const__InputArrayR_const__InputArrayR_int(const cv::_InputArray* H1, const cv::_InputArray* H2, int method, Result* ocvrs_return) { + try { + double ret = cv::compareHist(*H1, *H2, method); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_connectedComponentsWithStats_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::_InputArray* image, const cv::_OutputArray* labels, const cv::_OutputArray* stats, const cv::_OutputArray* centroids, Result* ocvrs_return) { + try { + int ret = cv::connectedComponentsWithStats(*image, *labels, *stats, *centroids); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_connectedComponentsWithStats_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* image, const cv::_OutputArray* labels, const cv::_OutputArray* stats, const cv::_OutputArray* centroids, int connectivity, int ltype, Result* ocvrs_return) { + try { + int ret = cv::connectedComponentsWithStats(*image, *labels, *stats, *centroids, connectivity, ltype); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_connectedComponentsWithStats_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_int_int(const cv::_InputArray* image, const cv::_OutputArray* labels, const cv::_OutputArray* stats, const cv::_OutputArray* centroids, int connectivity, int ltype, int ccltype, Result* ocvrs_return) { + try { + int ret = cv::connectedComponentsWithStats(*image, *labels, *stats, *centroids, connectivity, ltype, ccltype); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_connectedComponents_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* image, const cv::_OutputArray* labels, Result* ocvrs_return) { + try { + int ret = cv::connectedComponents(*image, *labels); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_connectedComponents_const__InputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* image, const cv::_OutputArray* labels, int connectivity, int ltype, Result* ocvrs_return) { + try { + int ret = cv::connectedComponents(*image, *labels, connectivity, ltype); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_connectedComponents_const__InputArrayR_const__OutputArrayR_int_int_int(const cv::_InputArray* image, const cv::_OutputArray* labels, int connectivity, int ltype, int ccltype, Result* ocvrs_return) { + try { + int ret = cv::connectedComponents(*image, *labels, connectivity, ltype, ccltype); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_contourArea_const__InputArrayR(const cv::_InputArray* contour, Result* ocvrs_return) { + try { + double ret = cv::contourArea(*contour); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_contourArea_const__InputArrayR_bool(const cv::_InputArray* contour, bool oriented, Result* ocvrs_return) { + try { + double ret = cv::contourArea(*contour, oriented); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_convertMaps_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int(const cv::_InputArray* map1, const cv::_InputArray* map2, const cv::_OutputArray* dstmap1, const cv::_OutputArray* dstmap2, int dstmap1type, ResultVoid* ocvrs_return) { + try { + cv::convertMaps(*map1, *map2, *dstmap1, *dstmap2, dstmap1type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_convertMaps_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_bool(const cv::_InputArray* map1, const cv::_InputArray* map2, const cv::_OutputArray* dstmap1, const cv::_OutputArray* dstmap2, int dstmap1type, bool nninterpolation, ResultVoid* ocvrs_return) { + try { + cv::convertMaps(*map1, *map2, *dstmap1, *dstmap2, dstmap1type, nninterpolation); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_convexHull_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* points, const cv::_OutputArray* hull, ResultVoid* ocvrs_return) { + try { + cv::convexHull(*points, *hull); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_convexHull_const__InputArrayR_const__OutputArrayR_bool_bool(const cv::_InputArray* points, const cv::_OutputArray* hull, bool clockwise, bool returnPoints, ResultVoid* ocvrs_return) { + try { + cv::convexHull(*points, *hull, clockwise, returnPoints); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_convexityDefects_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* contour, const cv::_InputArray* convexhull, const cv::_OutputArray* convexityDefects, ResultVoid* ocvrs_return) { + try { + cv::convexityDefects(*contour, *convexhull, *convexityDefects); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cornerEigenValsAndVecs_const__InputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int blockSize, int ksize, ResultVoid* ocvrs_return) { + try { + cv::cornerEigenValsAndVecs(*src, *dst, blockSize, ksize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cornerEigenValsAndVecs_const__InputArrayR_const__OutputArrayR_int_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int blockSize, int ksize, int borderType, ResultVoid* ocvrs_return) { + try { + cv::cornerEigenValsAndVecs(*src, *dst, blockSize, ksize, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cornerHarris_const__InputArrayR_const__OutputArrayR_int_int_double(const cv::_InputArray* src, const cv::_OutputArray* dst, int blockSize, int ksize, double k, ResultVoid* ocvrs_return) { + try { + cv::cornerHarris(*src, *dst, blockSize, ksize, k); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cornerHarris_const__InputArrayR_const__OutputArrayR_int_int_double_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int blockSize, int ksize, double k, int borderType, ResultVoid* ocvrs_return) { + try { + cv::cornerHarris(*src, *dst, blockSize, ksize, k, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cornerMinEigenVal_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int blockSize, ResultVoid* ocvrs_return) { + try { + cv::cornerMinEigenVal(*src, *dst, blockSize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cornerMinEigenVal_const__InputArrayR_const__OutputArrayR_int_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int blockSize, int ksize, int borderType, ResultVoid* ocvrs_return) { + try { + cv::cornerMinEigenVal(*src, *dst, blockSize, ksize, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cornerSubPix_const__InputArrayR_const__InputOutputArrayR_Size_Size_TermCriteria(const cv::_InputArray* image, const cv::_InputOutputArray* corners, cv::Size* winSize, cv::Size* zeroZone, cv::TermCriteria* criteria, ResultVoid* ocvrs_return) { + try { + cv::cornerSubPix(*image, *corners, *winSize, *zeroZone, *criteria); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_createCLAHE(Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::createCLAHE(); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_createCLAHE_double_Size(double clipLimit, cv::Size* tileGridSize, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::createCLAHE(clipLimit, *tileGridSize); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_createGeneralizedHoughBallard(Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::createGeneralizedHoughBallard(); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_createGeneralizedHoughGuil(Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::createGeneralizedHoughGuil(); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_createHanningWindow_const__OutputArrayR_Size_int(const cv::_OutputArray* dst, cv::Size* winSize, int type, ResultVoid* ocvrs_return) { + try { + cv::createHanningWindow(*dst, *winSize, type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_createLineSegmentDetector(Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::createLineSegmentDetector(); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_createLineSegmentDetector_int_double_double_double_double_double_double_int(int refine, double scale, double sigma_scale, double quant, double ang_th, double log_eps, double density_th, int n_bins, Result*>* ocvrs_return) { + try { + cv::Ptr ret = cv::createLineSegmentDetector(refine, scale, sigma_scale, quant, ang_th, log_eps, density_th, n_bins); + Ok(new cv::Ptr(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cvtColorTwoPlane_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, int code, ResultVoid* ocvrs_return) { + try { + cv::cvtColorTwoPlane(*src1, *src2, *dst, code); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cvtColorTwoPlane_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_AlgorithmHint(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_OutputArray* dst, int code, cv::AlgorithmHint hint, ResultVoid* ocvrs_return) { + try { + cv::cvtColorTwoPlane(*src1, *src2, *dst, code, hint); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cvtColor_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int code, ResultVoid* ocvrs_return) { + try { + cv::cvtColor(*src, *dst, code); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_cvtColor_const__InputArrayR_const__OutputArrayR_int_int_AlgorithmHint(const cv::_InputArray* src, const cv::_OutputArray* dst, int code, int dstCn, cv::AlgorithmHint hint, ResultVoid* ocvrs_return) { + try { + cv::cvtColor(*src, *dst, code, dstCn, hint); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_demosaicing_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int code, ResultVoid* ocvrs_return) { + try { + cv::demosaicing(*src, *dst, code); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_demosaicing_const__InputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int code, int dstCn, ResultVoid* ocvrs_return) { + try { + cv::demosaicing(*src, *dst, code, dstCn); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dilate_const__InputArrayR_const__OutputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* kernel, ResultVoid* ocvrs_return) { + try { + cv::dilate(*src, *dst, *kernel); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_dilate_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Point_int_int_const_ScalarR(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* kernel, cv::Point* anchor, int iterations, int borderType, const cv::Scalar* borderValue, ResultVoid* ocvrs_return) { + try { + cv::dilate(*src, *dst, *kernel, *anchor, iterations, borderType, *borderValue); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_distanceTransform_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_OutputArray* labels, int distanceType, int maskSize, ResultVoid* ocvrs_return) { + try { + cv::distanceTransform(*src, *dst, *labels, distanceType, maskSize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_distanceTransform_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_OutputArray* labels, int distanceType, int maskSize, int labelType, ResultVoid* ocvrs_return) { + try { + cv::distanceTransform(*src, *dst, *labels, distanceType, maskSize, labelType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_distanceTransform_const__InputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int distanceType, int maskSize, ResultVoid* ocvrs_return) { + try { + cv::distanceTransform(*src, *dst, distanceType, maskSize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_distanceTransform_const__InputArrayR_const__OutputArrayR_int_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int distanceType, int maskSize, int dstType, ResultVoid* ocvrs_return) { + try { + cv::distanceTransform(*src, *dst, distanceType, maskSize, dstType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_divSpectrums_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* a, const cv::_InputArray* b, const cv::_OutputArray* c, int flags, ResultVoid* ocvrs_return) { + try { + cv::divSpectrums(*a, *b, *c, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_divSpectrums_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_bool(const cv::_InputArray* a, const cv::_InputArray* b, const cv::_OutputArray* c, int flags, bool conjB, ResultVoid* ocvrs_return) { + try { + cv::divSpectrums(*a, *b, *c, flags, conjB); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_drawContours_const__InputOutputArrayR_const__InputArrayR_int_const_ScalarR(const cv::_InputOutputArray* image, const cv::_InputArray* contours, int contourIdx, const cv::Scalar* color, ResultVoid* ocvrs_return) { + try { + cv::drawContours(*image, *contours, contourIdx, *color); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_drawContours_const__InputOutputArrayR_const__InputArrayR_int_const_ScalarR_int_int_const__InputArrayR_int_Point(const cv::_InputOutputArray* image, const cv::_InputArray* contours, int contourIdx, const cv::Scalar* color, int thickness, int lineType, const cv::_InputArray* hierarchy, int maxLevel, cv::Point* offset, ResultVoid* ocvrs_return) { + try { + cv::drawContours(*image, *contours, contourIdx, *color, thickness, lineType, *hierarchy, maxLevel, *offset); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_drawMarker_const__InputOutputArrayR_Point_const_ScalarR(const cv::_InputOutputArray* img, cv::Point* position, const cv::Scalar* color, ResultVoid* ocvrs_return) { + try { + cv::drawMarker(*img, *position, *color); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_drawMarker_const__InputOutputArrayR_Point_const_ScalarR_int_int_int_int(const cv::_InputOutputArray* img, cv::Point* position, const cv::Scalar* color, int markerType, int markerSize, int thickness, int line_type, ResultVoid* ocvrs_return) { + try { + cv::drawMarker(*img, *position, *color, markerType, markerSize, thickness, line_type); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ellipse2Poly_Point2d_Size2d_int_int_int_int_vectorLPoint2dGR(cv::Point2d* center, cv::Size2d* axes, int angle, int arcStart, int arcEnd, int delta, std::vector* pts, ResultVoid* ocvrs_return) { + try { + cv::ellipse2Poly(*center, *axes, angle, arcStart, arcEnd, delta, *pts); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ellipse2Poly_Point_Size_int_int_int_int_vectorLPointGR(cv::Point* center, cv::Size* axes, int angle, int arcStart, int arcEnd, int delta, std::vector* pts, ResultVoid* ocvrs_return) { + try { + cv::ellipse2Poly(*center, *axes, angle, arcStart, arcEnd, delta, *pts); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ellipse_const__InputOutputArrayR_Point_Size_double_double_double_const_ScalarR(const cv::_InputOutputArray* img, cv::Point* center, cv::Size* axes, double angle, double startAngle, double endAngle, const cv::Scalar* color, ResultVoid* ocvrs_return) { + try { + cv::ellipse(*img, *center, *axes, angle, startAngle, endAngle, *color); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ellipse_const__InputOutputArrayR_Point_Size_double_double_double_const_ScalarR_int_int_int(const cv::_InputOutputArray* img, cv::Point* center, cv::Size* axes, double angle, double startAngle, double endAngle, const cv::Scalar* color, int thickness, int lineType, int shift, ResultVoid* ocvrs_return) { + try { + cv::ellipse(*img, *center, *axes, angle, startAngle, endAngle, *color, thickness, lineType, shift); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ellipse_const__InputOutputArrayR_const_RotatedRectR_const_ScalarR(const cv::_InputOutputArray* img, const cv::RotatedRect* box, const cv::Scalar* color, ResultVoid* ocvrs_return) { + try { + cv::ellipse(*img, *box, *color); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_ellipse_const__InputOutputArrayR_const_RotatedRectR_const_ScalarR_int_int(const cv::_InputOutputArray* img, const cv::RotatedRect* box, const cv::Scalar* color, int thickness, int lineType, ResultVoid* ocvrs_return) { + try { + cv::ellipse(*img, *box, *color, thickness, lineType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_equalizeHist_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::equalizeHist(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_erode_const__InputArrayR_const__OutputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* kernel, ResultVoid* ocvrs_return) { + try { + cv::erode(*src, *dst, *kernel); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_erode_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Point_int_int_const_ScalarR(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* kernel, cv::Point* anchor, int iterations, int borderType, const cv::Scalar* borderValue, ResultVoid* ocvrs_return) { + try { + cv::erode(*src, *dst, *kernel, *anchor, iterations, borderType, *borderValue); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_fillConvexPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR(const cv::_InputOutputArray* img, const cv::_InputArray* points, const cv::Scalar* color, ResultVoid* ocvrs_return) { + try { + cv::fillConvexPoly(*img, *points, *color); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_fillConvexPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR_int_int(const cv::_InputOutputArray* img, const cv::_InputArray* points, const cv::Scalar* color, int lineType, int shift, ResultVoid* ocvrs_return) { + try { + cv::fillConvexPoly(*img, *points, *color, lineType, shift); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_fillPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR(const cv::_InputOutputArray* img, const cv::_InputArray* pts, const cv::Scalar* color, ResultVoid* ocvrs_return) { + try { + cv::fillPoly(*img, *pts, *color); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_fillPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR_int_int_Point(const cv::_InputOutputArray* img, const cv::_InputArray* pts, const cv::Scalar* color, int lineType, int shift, cv::Point* offset, ResultVoid* ocvrs_return) { + try { + cv::fillPoly(*img, *pts, *color, lineType, shift, *offset); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_filter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, const cv::_InputArray* kernel, ResultVoid* ocvrs_return) { + try { + cv::filter2D(*src, *dst, ddepth, *kernel); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_filter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_Point_double_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, const cv::_InputArray* kernel, cv::Point* anchor, double delta, int borderType, ResultVoid* ocvrs_return) { + try { + cv::filter2D(*src, *dst, ddepth, *kernel, *anchor, delta, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_findContoursLinkRuns_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* image, const cv::_OutputArray* contours, ResultVoid* ocvrs_return) { + try { + cv::findContoursLinkRuns(*image, *contours); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_findContoursLinkRuns_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::_InputArray* image, const cv::_OutputArray* contours, const cv::_OutputArray* hierarchy, ResultVoid* ocvrs_return) { + try { + cv::findContoursLinkRuns(*image, *contours, *hierarchy); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_findContours_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* image, const cv::_OutputArray* contours, const cv::_OutputArray* hierarchy, int mode, int method, ResultVoid* ocvrs_return) { + try { + cv::findContours(*image, *contours, *hierarchy, mode, method); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_findContours_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int_Point(const cv::_InputArray* image, const cv::_OutputArray* contours, const cv::_OutputArray* hierarchy, int mode, int method, cv::Point* offset, ResultVoid* ocvrs_return) { + try { + cv::findContours(*image, *contours, *hierarchy, mode, method, *offset); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_findContours_const__InputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* image, const cv::_OutputArray* contours, int mode, int method, ResultVoid* ocvrs_return) { + try { + cv::findContours(*image, *contours, mode, method); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_findContours_const__InputArrayR_const__OutputArrayR_int_int_Point(const cv::_InputArray* image, const cv::_OutputArray* contours, int mode, int method, cv::Point* offset, ResultVoid* ocvrs_return) { + try { + cv::findContours(*image, *contours, mode, method, *offset); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_fitEllipseAMS_const__InputArrayR(const cv::_InputArray* points, Result* ocvrs_return) { + try { + cv::RotatedRect ret = cv::fitEllipseAMS(*points); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_fitEllipseDirect_const__InputArrayR(const cv::_InputArray* points, Result* ocvrs_return) { + try { + cv::RotatedRect ret = cv::fitEllipseDirect(*points); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_fitEllipse_const__InputArrayR(const cv::_InputArray* points, Result* ocvrs_return) { + try { + cv::RotatedRect ret = cv::fitEllipse(*points); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_fitLine_const__InputArrayR_const__OutputArrayR_int_double_double_double(const cv::_InputArray* points, const cv::_OutputArray* line, int distType, double param, double reps, double aeps, ResultVoid* ocvrs_return) { + try { + cv::fitLine(*points, *line, distType, param, reps, aeps); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_floodFill_const__InputOutputArrayR_Point_Scalar(const cv::_InputOutputArray* image, cv::Point* seedPoint, cv::Scalar* newVal, Result* ocvrs_return) { + try { + int ret = cv::floodFill(*image, *seedPoint, *newVal); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_floodFill_const__InputOutputArrayR_Point_Scalar_RectX_Scalar_Scalar_int(const cv::_InputOutputArray* image, cv::Point* seedPoint, cv::Scalar* newVal, cv::Rect* rect, cv::Scalar* loDiff, cv::Scalar* upDiff, int flags, Result* ocvrs_return) { + try { + int ret = cv::floodFill(*image, *seedPoint, *newVal, rect, *loDiff, *upDiff, flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_floodFill_const__InputOutputArrayR_const__InputOutputArrayR_Point_Scalar(const cv::_InputOutputArray* image, const cv::_InputOutputArray* mask, cv::Point* seedPoint, cv::Scalar* newVal, Result* ocvrs_return) { + try { + int ret = cv::floodFill(*image, *mask, *seedPoint, *newVal); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_floodFill_const__InputOutputArrayR_const__InputOutputArrayR_Point_Scalar_RectX_Scalar_Scalar_int(const cv::_InputOutputArray* image, const cv::_InputOutputArray* mask, cv::Point* seedPoint, cv::Scalar* newVal, cv::Rect* rect, cv::Scalar* loDiff, cv::Scalar* upDiff, int flags, Result* ocvrs_return) { + try { + int ret = cv::floodFill(*image, *mask, *seedPoint, *newVal, rect, *loDiff, *upDiff, flags); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getAffineTransform_const_Point2fXX_const_Point2fXX(const cv::Point2f(*src)[3], const cv::Point2f(*dst)[3], Result* ocvrs_return) { + try { + cv::Mat ret = cv::getAffineTransform(*src, *dst); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getAffineTransform_const__InputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_InputArray* dst, Result* ocvrs_return) { + try { + cv::Mat ret = cv::getAffineTransform(*src, *dst); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getClosestEllipsePoints_const_RotatedRectR_const__InputArrayR_const__OutputArrayR(const cv::RotatedRect* ellipse_params, const cv::_InputArray* points, const cv::_OutputArray* closest_pts, ResultVoid* ocvrs_return) { + try { + cv::getClosestEllipsePoints(*ellipse_params, *points, *closest_pts); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getDerivKernels_const__OutputArrayR_const__OutputArrayR_int_int_int(const cv::_OutputArray* kx, const cv::_OutputArray* ky, int dx, int dy, int ksize, ResultVoid* ocvrs_return) { + try { + cv::getDerivKernels(*kx, *ky, dx, dy, ksize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getDerivKernels_const__OutputArrayR_const__OutputArrayR_int_int_int_bool_int(const cv::_OutputArray* kx, const cv::_OutputArray* ky, int dx, int dy, int ksize, bool normalize, int ktype, ResultVoid* ocvrs_return) { + try { + cv::getDerivKernels(*kx, *ky, dx, dy, ksize, normalize, ktype); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getFontScaleFromHeight_const_int_const_int(const int fontFace, const int pixelHeight, Result* ocvrs_return) { + try { + double ret = cv::getFontScaleFromHeight(fontFace, pixelHeight); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getFontScaleFromHeight_const_int_const_int_const_int(const int fontFace, const int pixelHeight, const int thickness, Result* ocvrs_return) { + try { + double ret = cv::getFontScaleFromHeight(fontFace, pixelHeight, thickness); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getGaborKernel_Size_double_double_double_double(cv::Size* ksize, double sigma, double theta, double lambd, double gamma, Result* ocvrs_return) { + try { + cv::Mat ret = cv::getGaborKernel(*ksize, sigma, theta, lambd, gamma); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getGaborKernel_Size_double_double_double_double_double_int(cv::Size* ksize, double sigma, double theta, double lambd, double gamma, double psi, int ktype, Result* ocvrs_return) { + try { + cv::Mat ret = cv::getGaborKernel(*ksize, sigma, theta, lambd, gamma, psi, ktype); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getGaussianKernel_int_double(int ksize, double sigma, Result* ocvrs_return) { + try { + cv::Mat ret = cv::getGaussianKernel(ksize, sigma); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getGaussianKernel_int_double_int(int ksize, double sigma, int ktype, Result* ocvrs_return) { + try { + cv::Mat ret = cv::getGaussianKernel(ksize, sigma, ktype); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getPerspectiveTransform_const_Point2fXX_const_Point2fXX(const cv::Point2f(*src)[4], const cv::Point2f(*dst)[4], Result* ocvrs_return) { + try { + cv::Mat ret = cv::getPerspectiveTransform(*src, *dst); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getPerspectiveTransform_const_Point2fXX_const_Point2fXX_int(const cv::Point2f(*src)[4], const cv::Point2f(*dst)[4], int solveMethod, Result* ocvrs_return) { + try { + cv::Mat ret = cv::getPerspectiveTransform(*src, *dst, solveMethod); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getPerspectiveTransform_const__InputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_InputArray* dst, Result* ocvrs_return) { + try { + cv::Mat ret = cv::getPerspectiveTransform(*src, *dst); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getPerspectiveTransform_const__InputArrayR_const__InputArrayR_int(const cv::_InputArray* src, const cv::_InputArray* dst, int solveMethod, Result* ocvrs_return) { + try { + cv::Mat ret = cv::getPerspectiveTransform(*src, *dst, solveMethod); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getRectSubPix_const__InputArrayR_Size_Point2f_const__OutputArrayR(const cv::_InputArray* image, cv::Size* patchSize, cv::Point2f* center, const cv::_OutputArray* patch, ResultVoid* ocvrs_return) { + try { + cv::getRectSubPix(*image, *patchSize, *center, *patch); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getRectSubPix_const__InputArrayR_Size_Point2f_const__OutputArrayR_int(const cv::_InputArray* image, cv::Size* patchSize, cv::Point2f* center, const cv::_OutputArray* patch, int patchType, ResultVoid* ocvrs_return) { + try { + cv::getRectSubPix(*image, *patchSize, *center, *patch, patchType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getRotationMatrix2D_Point2f_double_double(cv::Point2f* center, double angle, double scale, Result* ocvrs_return) { + try { + cv::Mat ret = cv::getRotationMatrix2D(*center, angle, scale); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +#if (!OCVRS_TARGET_OS_WINDOWS) +void cv_getRotationMatrix2D__Point2f_double_double(cv::Point2f* center, double angle, double scale, Result* ocvrs_return) { + try { + cv::Matx23d ret = cv::getRotationMatrix2D_(*center, angle, scale); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} +#endif + +void cv_getStructuringElement_int_Size(int shape, cv::Size* ksize, Result* ocvrs_return) { + try { + cv::Mat ret = cv::getStructuringElement(shape, *ksize); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getStructuringElement_int_Size_Point(int shape, cv::Size* ksize, cv::Point* anchor, Result* ocvrs_return) { + try { + cv::Mat ret = cv::getStructuringElement(shape, *ksize, *anchor); + Ok(new cv::Mat(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_getTextSize_const_StringR_int_double_int_intX(const char* text, int fontFace, double fontScale, int thickness, int* baseLine, Result* ocvrs_return) { + try { + cv::Size ret = cv::getTextSize(std::string(text), fontFace, fontScale, thickness, baseLine); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double(const cv::_InputArray* image, const cv::_OutputArray* corners, int maxCorners, double qualityLevel, double minDistance, ResultVoid* ocvrs_return) { + try { + cv::goodFeaturesToTrack(*image, *corners, maxCorners, qualityLevel, minDistance); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* image, const cv::_OutputArray* corners, int maxCorners, double qualityLevel, double minDistance, const cv::_InputArray* mask, const cv::_OutputArray* cornersQuality, ResultVoid* ocvrs_return) { + try { + cv::goodFeaturesToTrack(*image, *corners, maxCorners, qualityLevel, minDistance, *mask, *cornersQuality); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_const__OutputArrayR_int_int_bool_double(const cv::_InputArray* image, const cv::_OutputArray* corners, int maxCorners, double qualityLevel, double minDistance, const cv::_InputArray* mask, const cv::_OutputArray* cornersQuality, int blockSize, int gradientSize, bool useHarrisDetector, double k, ResultVoid* ocvrs_return) { + try { + cv::goodFeaturesToTrack(*image, *corners, maxCorners, qualityLevel, minDistance, *mask, *cornersQuality, blockSize, gradientSize, useHarrisDetector, k); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_int_bool_double(const cv::_InputArray* image, const cv::_OutputArray* corners, int maxCorners, double qualityLevel, double minDistance, const cv::_InputArray* mask, int blockSize, bool useHarrisDetector, double k, ResultVoid* ocvrs_return) { + try { + cv::goodFeaturesToTrack(*image, *corners, maxCorners, qualityLevel, minDistance, *mask, blockSize, useHarrisDetector, k); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_int_int(const cv::_InputArray* image, const cv::_OutputArray* corners, int maxCorners, double qualityLevel, double minDistance, const cv::_InputArray* mask, int blockSize, int gradientSize, ResultVoid* ocvrs_return) { + try { + cv::goodFeaturesToTrack(*image, *corners, maxCorners, qualityLevel, minDistance, *mask, blockSize, gradientSize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_int_int_bool_double(const cv::_InputArray* image, const cv::_OutputArray* corners, int maxCorners, double qualityLevel, double minDistance, const cv::_InputArray* mask, int blockSize, int gradientSize, bool useHarrisDetector, double k, ResultVoid* ocvrs_return) { + try { + cv::goodFeaturesToTrack(*image, *corners, maxCorners, qualityLevel, minDistance, *mask, blockSize, gradientSize, useHarrisDetector, k); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_grabCut_const__InputArrayR_const__InputOutputArrayR_Rect_const__InputOutputArrayR_const__InputOutputArrayR_int(const cv::_InputArray* img, const cv::_InputOutputArray* mask, cv::Rect* rect, const cv::_InputOutputArray* bgdModel, const cv::_InputOutputArray* fgdModel, int iterCount, ResultVoid* ocvrs_return) { + try { + cv::grabCut(*img, *mask, *rect, *bgdModel, *fgdModel, iterCount); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_grabCut_const__InputArrayR_const__InputOutputArrayR_Rect_const__InputOutputArrayR_const__InputOutputArrayR_int_int(const cv::_InputArray* img, const cv::_InputOutputArray* mask, cv::Rect* rect, const cv::_InputOutputArray* bgdModel, const cv::_InputOutputArray* fgdModel, int iterCount, int mode, ResultVoid* ocvrs_return) { + try { + cv::grabCut(*img, *mask, *rect, *bgdModel, *fgdModel, iterCount, mode); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_integral_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* sum, ResultVoid* ocvrs_return) { + try { + cv::integral(*src, *sum); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* sum, const cv::_OutputArray* sqsum, ResultVoid* ocvrs_return) { + try { + cv::integral(*src, *sum, *sqsum); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* sum, const cv::_OutputArray* sqsum, const cv::_OutputArray* tilted, ResultVoid* ocvrs_return) { + try { + cv::integral(*src, *sum, *sqsum, *tilted); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* src, const cv::_OutputArray* sum, const cv::_OutputArray* sqsum, const cv::_OutputArray* tilted, int sdepth, int sqdepth, ResultVoid* ocvrs_return) { + try { + cv::integral(*src, *sum, *sqsum, *tilted, sdepth, sqdepth); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* src, const cv::_OutputArray* sum, const cv::_OutputArray* sqsum, int sdepth, int sqdepth, ResultVoid* ocvrs_return) { + try { + cv::integral(*src, *sum, *sqsum, sdepth, sqdepth); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_integral_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* sum, int sdepth, ResultVoid* ocvrs_return) { + try { + cv::integral(*src, *sum, sdepth); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_intersectConvexConvex_const__InputArrayR_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* p1, const cv::_InputArray* p2, const cv::_OutputArray* p12, Result* ocvrs_return) { + try { + float ret = cv::intersectConvexConvex(*p1, *p2, *p12); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_intersectConvexConvex_const__InputArrayR_const__InputArrayR_const__OutputArrayR_bool(const cv::_InputArray* p1, const cv::_InputArray* p2, const cv::_OutputArray* p12, bool handleNested, Result* ocvrs_return) { + try { + float ret = cv::intersectConvexConvex(*p1, *p2, *p12, handleNested); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_invertAffineTransform_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* M, const cv::_OutputArray* iM, ResultVoid* ocvrs_return) { + try { + cv::invertAffineTransform(*M, *iM); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_isContourConvex_const__InputArrayR(const cv::_InputArray* contour, Result* ocvrs_return) { + try { + bool ret = cv::isContourConvex(*contour); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_line_const__InputOutputArrayR_Point_Point_const_ScalarR(const cv::_InputOutputArray* img, cv::Point* pt1, cv::Point* pt2, const cv::Scalar* color, ResultVoid* ocvrs_return) { + try { + cv::line(*img, *pt1, *pt2, *color); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_line_const__InputOutputArrayR_Point_Point_const_ScalarR_int_int_int(const cv::_InputOutputArray* img, cv::Point* pt1, cv::Point* pt2, const cv::Scalar* color, int thickness, int lineType, int shift, ResultVoid* ocvrs_return) { + try { + cv::line(*img, *pt1, *pt2, *color, thickness, lineType, shift); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_linearPolar_const__InputArrayR_const__OutputArrayR_Point2f_double_int(const cv::_InputArray* src, const cv::_OutputArray* dst, cv::Point2f* center, double maxRadius, int flags, ResultVoid* ocvrs_return) { + try { + cv::linearPolar(*src, *dst, *center, maxRadius, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_logPolar_const__InputArrayR_const__OutputArrayR_Point2f_double_int(const cv::_InputArray* src, const cv::_OutputArray* dst, cv::Point2f* center, double M, int flags, ResultVoid* ocvrs_return) { + try { + cv::logPolar(*src, *dst, *center, M, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_matchShapes_const__InputArrayR_const__InputArrayR_int_double(const cv::_InputArray* contour1, const cv::_InputArray* contour2, int method, double parameter, Result* ocvrs_return) { + try { + double ret = cv::matchShapes(*contour1, *contour2, method, parameter); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_matchTemplate_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* image, const cv::_InputArray* templ, const cv::_OutputArray* result, int method, ResultVoid* ocvrs_return) { + try { + cv::matchTemplate(*image, *templ, *result, method); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_matchTemplate_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR(const cv::_InputArray* image, const cv::_InputArray* templ, const cv::_OutputArray* result, int method, const cv::_InputArray* mask, ResultVoid* ocvrs_return) { + try { + cv::matchTemplate(*image, *templ, *result, method, *mask); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_medianBlur_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int ksize, ResultVoid* ocvrs_return) { + try { + cv::medianBlur(*src, *dst, ksize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_minAreaRect_const__InputArrayR(const cv::_InputArray* points, Result* ocvrs_return) { + try { + cv::RotatedRect ret = cv::minAreaRect(*points); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_minEnclosingCircle_const__InputArrayR_Point2fR_floatR(const cv::_InputArray* points, cv::Point2f* center, float* radius, ResultVoid* ocvrs_return) { + try { + cv::minEnclosingCircle(*points, *center, *radius); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_minEnclosingConvexPolygon_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* points, const cv::_OutputArray* polygon, int k, Result* ocvrs_return) { + try { + double ret = cv::minEnclosingConvexPolygon(*points, *polygon, k); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_minEnclosingTriangle_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* points, const cv::_OutputArray* triangle, Result* ocvrs_return) { + try { + double ret = cv::minEnclosingTriangle(*points, *triangle); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_moments_const__InputArrayR(const cv::_InputArray* array, Result* ocvrs_return) { + try { + cv::Moments ret = cv::moments(*array); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_moments_const__InputArrayR_bool(const cv::_InputArray* array, bool binaryImage, Result* ocvrs_return) { + try { + cv::Moments ret = cv::moments(*array, binaryImage); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_morphologyDefaultBorderValue(Result* ocvrs_return) { + try { + cv::Scalar ret = cv::morphologyDefaultBorderValue(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_morphologyEx_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, int op, const cv::_InputArray* kernel, ResultVoid* ocvrs_return) { + try { + cv::morphologyEx(*src, *dst, op, *kernel); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_morphologyEx_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_Point_int_int_const_ScalarR(const cv::_InputArray* src, const cv::_OutputArray* dst, int op, const cv::_InputArray* kernel, cv::Point* anchor, int iterations, int borderType, const cv::Scalar* borderValue, ResultVoid* ocvrs_return) { + try { + cv::morphologyEx(*src, *dst, op, *kernel, *anchor, iterations, borderType, *borderValue); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_phaseCorrelateIterative_const__InputArrayR_const__InputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, Result* ocvrs_return) { + try { + cv::Point2d ret = cv::phaseCorrelateIterative(*src1, *src2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_phaseCorrelateIterative_const__InputArrayR_const__InputArrayR_int_int(const cv::_InputArray* src1, const cv::_InputArray* src2, int L2size, int maxIters, Result* ocvrs_return) { + try { + cv::Point2d ret = cv::phaseCorrelateIterative(*src1, *src2, L2size, maxIters); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_phaseCorrelate_const__InputArrayR_const__InputArrayR(const cv::_InputArray* src1, const cv::_InputArray* src2, Result* ocvrs_return) { + try { + cv::Point2d ret = cv::phaseCorrelate(*src1, *src2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_phaseCorrelate_const__InputArrayR_const__InputArrayR_const__InputArrayR_doubleX(const cv::_InputArray* src1, const cv::_InputArray* src2, const cv::_InputArray* window, double* response, Result* ocvrs_return) { + try { + cv::Point2d ret = cv::phaseCorrelate(*src1, *src2, *window, response); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_pointPolygonTest_const__InputArrayR_Point2f_bool(const cv::_InputArray* contour, cv::Point2f* pt, bool measureDist, Result* ocvrs_return) { + try { + double ret = cv::pointPolygonTest(*contour, *pt, measureDist); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_polylines_const__InputOutputArrayR_const__InputArrayR_bool_const_ScalarR(const cv::_InputOutputArray* img, const cv::_InputArray* pts, bool isClosed, const cv::Scalar* color, ResultVoid* ocvrs_return) { + try { + cv::polylines(*img, *pts, isClosed, *color); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_polylines_const__InputOutputArrayR_const__InputArrayR_bool_const_ScalarR_int_int_int(const cv::_InputOutputArray* img, const cv::_InputArray* pts, bool isClosed, const cv::Scalar* color, int thickness, int lineType, int shift, ResultVoid* ocvrs_return) { + try { + cv::polylines(*img, *pts, isClosed, *color, thickness, lineType, shift); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_preCornerDetect_const__InputArrayR_const__OutputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int ksize, ResultVoid* ocvrs_return) { + try { + cv::preCornerDetect(*src, *dst, ksize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_preCornerDetect_const__InputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int ksize, int borderType, ResultVoid* ocvrs_return) { + try { + cv::preCornerDetect(*src, *dst, ksize, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_putText_const__InputOutputArrayR_const_StringR_Point_int_double_Scalar(const cv::_InputOutputArray* img, const char* text, cv::Point* org, int fontFace, double fontScale, cv::Scalar* color, ResultVoid* ocvrs_return) { + try { + cv::putText(*img, std::string(text), *org, fontFace, fontScale, *color); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_putText_const__InputOutputArrayR_const_StringR_Point_int_double_Scalar_int_int_bool(const cv::_InputOutputArray* img, const char* text, cv::Point* org, int fontFace, double fontScale, cv::Scalar* color, int thickness, int lineType, bool bottomLeftOrigin, ResultVoid* ocvrs_return) { + try { + cv::putText(*img, std::string(text), *org, fontFace, fontScale, *color, thickness, lineType, bottomLeftOrigin); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_pyrDown_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::pyrDown(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_pyrDown_const__InputArrayR_const__OutputArrayR_const_SizeR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::Size* dstsize, int borderType, ResultVoid* ocvrs_return) { + try { + cv::pyrDown(*src, *dst, *dstsize, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_pyrMeanShiftFiltering_const__InputArrayR_const__OutputArrayR_double_double(const cv::_InputArray* src, const cv::_OutputArray* dst, double sp, double sr, ResultVoid* ocvrs_return) { + try { + cv::pyrMeanShiftFiltering(*src, *dst, sp, sr); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_pyrMeanShiftFiltering_const__InputArrayR_const__OutputArrayR_double_double_int_TermCriteria(const cv::_InputArray* src, const cv::_OutputArray* dst, double sp, double sr, int maxLevel, cv::TermCriteria* termcrit, ResultVoid* ocvrs_return) { + try { + cv::pyrMeanShiftFiltering(*src, *dst, sp, sr, maxLevel, *termcrit); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_pyrUp_const__InputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + cv::pyrUp(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_pyrUp_const__InputArrayR_const__OutputArrayR_const_SizeR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::Size* dstsize, int borderType, ResultVoid* ocvrs_return) { + try { + cv::pyrUp(*src, *dst, *dstsize, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_rectangle_const__InputOutputArrayR_Point_Point_const_ScalarR(const cv::_InputOutputArray* img, cv::Point* pt1, cv::Point* pt2, const cv::Scalar* color, ResultVoid* ocvrs_return) { + try { + cv::rectangle(*img, *pt1, *pt2, *color); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_rectangle_const__InputOutputArrayR_Point_Point_const_ScalarR_int_int_int(const cv::_InputOutputArray* img, cv::Point* pt1, cv::Point* pt2, const cv::Scalar* color, int thickness, int lineType, int shift, ResultVoid* ocvrs_return) { + try { + cv::rectangle(*img, *pt1, *pt2, *color, thickness, lineType, shift); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_rectangle_const__InputOutputArrayR_Rect_const_ScalarR(const cv::_InputOutputArray* img, cv::Rect* rec, const cv::Scalar* color, ResultVoid* ocvrs_return) { + try { + cv::rectangle(*img, *rec, *color); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_rectangle_const__InputOutputArrayR_Rect_const_ScalarR_int_int_int(const cv::_InputOutputArray* img, cv::Rect* rec, const cv::Scalar* color, int thickness, int lineType, int shift, ResultVoid* ocvrs_return) { + try { + cv::rectangle(*img, *rec, *color, thickness, lineType, shift); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_remap_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_int(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* map1, const cv::_InputArray* map2, int interpolation, ResultVoid* ocvrs_return) { + try { + cv::remap(*src, *dst, *map1, *map2, interpolation); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_remap_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_int_int_const_ScalarR(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* map1, const cv::_InputArray* map2, int interpolation, int borderMode, const cv::Scalar* borderValue, ResultVoid* ocvrs_return) { + try { + cv::remap(*src, *dst, *map1, *map2, interpolation, borderMode, *borderValue); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_resize_const__InputArrayR_const__OutputArrayR_Size(const cv::_InputArray* src, const cv::_OutputArray* dst, cv::Size* dsize, ResultVoid* ocvrs_return) { + try { + cv::resize(*src, *dst, *dsize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_resize_const__InputArrayR_const__OutputArrayR_Size_double_double_int(const cv::_InputArray* src, const cv::_OutputArray* dst, cv::Size* dsize, double fx, double fy, int interpolation, ResultVoid* ocvrs_return) { + try { + cv::resize(*src, *dst, *dsize, fx, fy, interpolation); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_rotatedRectangleIntersection_const_RotatedRectR_const_RotatedRectR_const__OutputArrayR(const cv::RotatedRect* rect1, const cv::RotatedRect* rect2, const cv::_OutputArray* intersectingRegion, Result* ocvrs_return) { + try { + int ret = cv::rotatedRectangleIntersection(*rect1, *rect2, *intersectingRegion); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_sepFilter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_const__InputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, const cv::_InputArray* kernelX, const cv::_InputArray* kernelY, ResultVoid* ocvrs_return) { + try { + cv::sepFilter2D(*src, *dst, ddepth, *kernelX, *kernelY); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_sepFilter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_const__InputArrayR_Point_double_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, const cv::_InputArray* kernelX, const cv::_InputArray* kernelY, cv::Point* anchor, double delta, int borderType, ResultVoid* ocvrs_return) { + try { + cv::sepFilter2D(*src, *dst, ddepth, *kernelX, *kernelY, *anchor, delta, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_spatialGradient_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(const cv::_InputArray* src, const cv::_OutputArray* dx, const cv::_OutputArray* dy, ResultVoid* ocvrs_return) { + try { + cv::spatialGradient(*src, *dx, *dy); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_spatialGradient_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(const cv::_InputArray* src, const cv::_OutputArray* dx, const cv::_OutputArray* dy, int ksize, int borderType, ResultVoid* ocvrs_return) { + try { + cv::spatialGradient(*src, *dx, *dy, ksize, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_sqrBoxFilter_const__InputArrayR_const__OutputArrayR_int_Size(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, cv::Size* ksize, ResultVoid* ocvrs_return) { + try { + cv::sqrBoxFilter(*src, *dst, ddepth, *ksize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_sqrBoxFilter_const__InputArrayR_const__OutputArrayR_int_Size_Point_bool_int(const cv::_InputArray* src, const cv::_OutputArray* dst, int ddepth, cv::Size* ksize, cv::Point* anchor, bool normalize, int borderType, ResultVoid* ocvrs_return) { + try { + cv::sqrBoxFilter(*src, *dst, ddepth, *ksize, *anchor, normalize, borderType); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_stackBlur_const__InputArrayR_const__OutputArrayR_Size(const cv::_InputArray* src, const cv::_OutputArray* dst, cv::Size* ksize, ResultVoid* ocvrs_return) { + try { + cv::stackBlur(*src, *dst, *ksize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_thresholdWithMask_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR_double_double_int(const cv::_InputArray* src, const cv::_InputOutputArray* dst, const cv::_InputArray* mask, double thresh, double maxval, int type, Result* ocvrs_return) { + try { + double ret = cv::thresholdWithMask(*src, *dst, *mask, thresh, maxval, type); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_threshold_const__InputArrayR_const__OutputArrayR_double_double_int(const cv::_InputArray* src, const cv::_OutputArray* dst, double thresh, double maxval, int type, Result* ocvrs_return) { + try { + double ret = cv::threshold(*src, *dst, thresh, maxval, type); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_warpAffine_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* M, cv::Size* dsize, ResultVoid* ocvrs_return) { + try { + cv::warpAffine(*src, *dst, *M, *dsize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_warpAffine_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size_int_int_const_ScalarR(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* M, cv::Size* dsize, int flags, int borderMode, const cv::Scalar* borderValue, ResultVoid* ocvrs_return) { + try { + cv::warpAffine(*src, *dst, *M, *dsize, flags, borderMode, *borderValue); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_warpPerspective_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* M, cv::Size* dsize, ResultVoid* ocvrs_return) { + try { + cv::warpPerspective(*src, *dst, *M, *dsize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_warpPerspective_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size_int_int_const_ScalarR(const cv::_InputArray* src, const cv::_OutputArray* dst, const cv::_InputArray* M, cv::Size* dsize, int flags, int borderMode, const cv::Scalar* borderValue, ResultVoid* ocvrs_return) { + try { + cv::warpPerspective(*src, *dst, *M, *dsize, flags, borderMode, *borderValue); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_warpPolar_const__InputArrayR_const__OutputArrayR_Size_Point2f_double_int(const cv::_InputArray* src, const cv::_OutputArray* dst, cv::Size* dsize, cv::Point2f* center, double maxRadius, int flags, ResultVoid* ocvrs_return) { + try { + cv::warpPolar(*src, *dst, *dsize, *center, maxRadius, flags); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_watershed_const__InputArrayR_const__InputOutputArrayR(const cv::_InputArray* image, const cv::_InputOutputArray* markers, ResultVoid* ocvrs_return) { + try { + cv::watershed(*image, *markers); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_wrapperEMD_const__InputArrayR_const__InputArrayR_int(const cv::_InputArray* signature1, const cv::_InputArray* signature2, int distType, Result* ocvrs_return) { + try { + float ret = cv::wrapperEMD(*signature1, *signature2, distType); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_wrapperEMD_const__InputArrayR_const__InputArrayR_int_const__InputArrayR_PtrLfloatG_const__OutputArrayR(const cv::_InputArray* signature1, const cv::_InputArray* signature2, int distType, const cv::_InputArray* cost, cv::Ptr* lowerBound, const cv::_OutputArray* flow, Result* ocvrs_return) { + try { + float ret = cv::wrapperEMD(*signature1, *signature2, distType, *cost, *lowerBound, *flow); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CLAHE_apply_const__InputArrayR_const__OutputArrayR(cv::CLAHE* instance, const cv::_InputArray* src, const cv::_OutputArray* dst, ResultVoid* ocvrs_return) { + try { + instance->apply(*src, *dst); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CLAHE_setClipLimit_double(cv::CLAHE* instance, double clipLimit, ResultVoid* ocvrs_return) { + try { + instance->setClipLimit(clipLimit); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CLAHE_getClipLimit_const(const cv::CLAHE* instance, Result* ocvrs_return) { + try { + double ret = instance->getClipLimit(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CLAHE_setTilesGridSize_Size(cv::CLAHE* instance, cv::Size* tileGridSize, ResultVoid* ocvrs_return) { + try { + instance->setTilesGridSize(*tileGridSize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CLAHE_getTilesGridSize_const(const cv::CLAHE* instance, Result* ocvrs_return) { + try { + cv::Size ret = instance->getTilesGridSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CLAHE_setBitShift_int(cv::CLAHE* instance, int bitShift, ResultVoid* ocvrs_return) { + try { + instance->setBitShift(bitShift); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CLAHE_getBitShift_const(const cv::CLAHE* instance, Result* ocvrs_return) { + try { + int ret = instance->getBitShift(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_CLAHE_collectGarbage(cv::CLAHE* instance, ResultVoid* ocvrs_return) { + try { + instance->collectGarbage(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::Algorithm* cv_CLAHE_to_Algorithm(cv::CLAHE* instance) { + return dynamic_cast(instance); +} + +void cv_CLAHE_delete(cv::CLAHE* instance) { + delete instance; +} + +void cv_GeneralizedHough_setTemplate_const__InputArrayR_Point(cv::GeneralizedHough* instance, const cv::_InputArray* templ, cv::Point* templCenter, ResultVoid* ocvrs_return) { + try { + instance->setTemplate(*templ, *templCenter); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_setTemplate_const__InputArrayR(cv::GeneralizedHough* instance, const cv::_InputArray* templ, ResultVoid* ocvrs_return) { + try { + instance->setTemplate(*templ); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_setTemplate_const__InputArrayR_const__InputArrayR_const__InputArrayR_Point(cv::GeneralizedHough* instance, const cv::_InputArray* edges, const cv::_InputArray* dx, const cv::_InputArray* dy, cv::Point* templCenter, ResultVoid* ocvrs_return) { + try { + instance->setTemplate(*edges, *dx, *dy, *templCenter); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_setTemplate_const__InputArrayR_const__InputArrayR_const__InputArrayR(cv::GeneralizedHough* instance, const cv::_InputArray* edges, const cv::_InputArray* dx, const cv::_InputArray* dy, ResultVoid* ocvrs_return) { + try { + instance->setTemplate(*edges, *dx, *dy); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_detect_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(cv::GeneralizedHough* instance, const cv::_InputArray* image, const cv::_OutputArray* positions, const cv::_OutputArray* votes, ResultVoid* ocvrs_return) { + try { + instance->detect(*image, *positions, *votes); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_detect_const__InputArrayR_const__OutputArrayR(cv::GeneralizedHough* instance, const cv::_InputArray* image, const cv::_OutputArray* positions, ResultVoid* ocvrs_return) { + try { + instance->detect(*image, *positions); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_detect_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(cv::GeneralizedHough* instance, const cv::_InputArray* edges, const cv::_InputArray* dx, const cv::_InputArray* dy, const cv::_OutputArray* positions, const cv::_OutputArray* votes, ResultVoid* ocvrs_return) { + try { + instance->detect(*edges, *dx, *dy, *positions, *votes); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_detect_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(cv::GeneralizedHough* instance, const cv::_InputArray* edges, const cv::_InputArray* dx, const cv::_InputArray* dy, const cv::_OutputArray* positions, ResultVoid* ocvrs_return) { + try { + instance->detect(*edges, *dx, *dy, *positions); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_setCannyLowThresh_int(cv::GeneralizedHough* instance, int cannyLowThresh, ResultVoid* ocvrs_return) { + try { + instance->setCannyLowThresh(cannyLowThresh); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_getCannyLowThresh_const(const cv::GeneralizedHough* instance, Result* ocvrs_return) { + try { + int ret = instance->getCannyLowThresh(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_setCannyHighThresh_int(cv::GeneralizedHough* instance, int cannyHighThresh, ResultVoid* ocvrs_return) { + try { + instance->setCannyHighThresh(cannyHighThresh); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_getCannyHighThresh_const(const cv::GeneralizedHough* instance, Result* ocvrs_return) { + try { + int ret = instance->getCannyHighThresh(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_setMinDist_double(cv::GeneralizedHough* instance, double minDist, ResultVoid* ocvrs_return) { + try { + instance->setMinDist(minDist); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_getMinDist_const(const cv::GeneralizedHough* instance, Result* ocvrs_return) { + try { + double ret = instance->getMinDist(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_setDp_double(cv::GeneralizedHough* instance, double dp, ResultVoid* ocvrs_return) { + try { + instance->setDp(dp); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_getDp_const(const cv::GeneralizedHough* instance, Result* ocvrs_return) { + try { + double ret = instance->getDp(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_setMaxBufferSize_int(cv::GeneralizedHough* instance, int maxBufferSize, ResultVoid* ocvrs_return) { + try { + instance->setMaxBufferSize(maxBufferSize); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHough_getMaxBufferSize_const(const cv::GeneralizedHough* instance, Result* ocvrs_return) { + try { + int ret = instance->getMaxBufferSize(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::GeneralizedHoughBallard* cv_GeneralizedHough_to_GeneralizedHoughBallard(cv::GeneralizedHough* instance) { + return dynamic_cast(instance); +} + +cv::GeneralizedHoughGuil* cv_GeneralizedHough_to_GeneralizedHoughGuil(cv::GeneralizedHough* instance) { + return dynamic_cast(instance); +} + +cv::Algorithm* cv_GeneralizedHough_to_Algorithm(cv::GeneralizedHough* instance) { + return dynamic_cast(instance); +} + +void cv_GeneralizedHough_delete(cv::GeneralizedHough* instance) { + delete instance; +} + +void cv_GeneralizedHoughBallard_setLevels_int(cv::GeneralizedHoughBallard* instance, int levels, ResultVoid* ocvrs_return) { + try { + instance->setLevels(levels); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughBallard_getLevels_const(const cv::GeneralizedHoughBallard* instance, Result* ocvrs_return) { + try { + int ret = instance->getLevels(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughBallard_setVotesThreshold_int(cv::GeneralizedHoughBallard* instance, int votesThreshold, ResultVoid* ocvrs_return) { + try { + instance->setVotesThreshold(votesThreshold); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughBallard_getVotesThreshold_const(const cv::GeneralizedHoughBallard* instance, Result* ocvrs_return) { + try { + int ret = instance->getVotesThreshold(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::Algorithm* cv_GeneralizedHoughBallard_to_Algorithm(cv::GeneralizedHoughBallard* instance) { + return dynamic_cast(instance); +} + +cv::GeneralizedHough* cv_GeneralizedHoughBallard_to_GeneralizedHough(cv::GeneralizedHoughBallard* instance) { + return dynamic_cast(instance); +} + +void cv_GeneralizedHoughBallard_delete(cv::GeneralizedHoughBallard* instance) { + delete instance; +} + +void cv_GeneralizedHoughGuil_setXi_double(cv::GeneralizedHoughGuil* instance, double xi, ResultVoid* ocvrs_return) { + try { + instance->setXi(xi); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_getXi_const(const cv::GeneralizedHoughGuil* instance, Result* ocvrs_return) { + try { + double ret = instance->getXi(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_setLevels_int(cv::GeneralizedHoughGuil* instance, int levels, ResultVoid* ocvrs_return) { + try { + instance->setLevels(levels); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_getLevels_const(const cv::GeneralizedHoughGuil* instance, Result* ocvrs_return) { + try { + int ret = instance->getLevels(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_setAngleEpsilon_double(cv::GeneralizedHoughGuil* instance, double angleEpsilon, ResultVoid* ocvrs_return) { + try { + instance->setAngleEpsilon(angleEpsilon); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_getAngleEpsilon_const(const cv::GeneralizedHoughGuil* instance, Result* ocvrs_return) { + try { + double ret = instance->getAngleEpsilon(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_setMinAngle_double(cv::GeneralizedHoughGuil* instance, double minAngle, ResultVoid* ocvrs_return) { + try { + instance->setMinAngle(minAngle); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_getMinAngle_const(const cv::GeneralizedHoughGuil* instance, Result* ocvrs_return) { + try { + double ret = instance->getMinAngle(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_setMaxAngle_double(cv::GeneralizedHoughGuil* instance, double maxAngle, ResultVoid* ocvrs_return) { + try { + instance->setMaxAngle(maxAngle); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_getMaxAngle_const(const cv::GeneralizedHoughGuil* instance, Result* ocvrs_return) { + try { + double ret = instance->getMaxAngle(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_setAngleStep_double(cv::GeneralizedHoughGuil* instance, double angleStep, ResultVoid* ocvrs_return) { + try { + instance->setAngleStep(angleStep); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_getAngleStep_const(const cv::GeneralizedHoughGuil* instance, Result* ocvrs_return) { + try { + double ret = instance->getAngleStep(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_setAngleThresh_int(cv::GeneralizedHoughGuil* instance, int angleThresh, ResultVoid* ocvrs_return) { + try { + instance->setAngleThresh(angleThresh); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_getAngleThresh_const(const cv::GeneralizedHoughGuil* instance, Result* ocvrs_return) { + try { + int ret = instance->getAngleThresh(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_setMinScale_double(cv::GeneralizedHoughGuil* instance, double minScale, ResultVoid* ocvrs_return) { + try { + instance->setMinScale(minScale); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_getMinScale_const(const cv::GeneralizedHoughGuil* instance, Result* ocvrs_return) { + try { + double ret = instance->getMinScale(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_setMaxScale_double(cv::GeneralizedHoughGuil* instance, double maxScale, ResultVoid* ocvrs_return) { + try { + instance->setMaxScale(maxScale); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_getMaxScale_const(const cv::GeneralizedHoughGuil* instance, Result* ocvrs_return) { + try { + double ret = instance->getMaxScale(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_setScaleStep_double(cv::GeneralizedHoughGuil* instance, double scaleStep, ResultVoid* ocvrs_return) { + try { + instance->setScaleStep(scaleStep); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_getScaleStep_const(const cv::GeneralizedHoughGuil* instance, Result* ocvrs_return) { + try { + double ret = instance->getScaleStep(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_setScaleThresh_int(cv::GeneralizedHoughGuil* instance, int scaleThresh, ResultVoid* ocvrs_return) { + try { + instance->setScaleThresh(scaleThresh); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_getScaleThresh_const(const cv::GeneralizedHoughGuil* instance, Result* ocvrs_return) { + try { + int ret = instance->getScaleThresh(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_setPosThresh_int(cv::GeneralizedHoughGuil* instance, int posThresh, ResultVoid* ocvrs_return) { + try { + instance->setPosThresh(posThresh); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_GeneralizedHoughGuil_getPosThresh_const(const cv::GeneralizedHoughGuil* instance, Result* ocvrs_return) { + try { + int ret = instance->getPosThresh(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::Algorithm* cv_GeneralizedHoughGuil_to_Algorithm(cv::GeneralizedHoughGuil* instance) { + return dynamic_cast(instance); +} + +cv::GeneralizedHough* cv_GeneralizedHoughGuil_to_GeneralizedHough(cv::GeneralizedHoughGuil* instance) { + return dynamic_cast(instance); +} + +void cv_GeneralizedHoughGuil_delete(cv::GeneralizedHoughGuil* instance) { + delete instance; +} + +void cv_LineIterator_LineIterator_const_MatR_Point_Point_int_bool(const cv::Mat* img, cv::Point* pt1, cv::Point* pt2, int connectivity, bool leftToRight, Result* ocvrs_return) { + try { + cv::LineIterator* ret = new cv::LineIterator(*img, *pt1, *pt2, connectivity, leftToRight); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineIterator_LineIterator_const_MatR_Point_Point(const cv::Mat* img, cv::Point* pt1, cv::Point* pt2, Result* ocvrs_return) { + try { + cv::LineIterator* ret = new cv::LineIterator(*img, *pt1, *pt2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineIterator_LineIterator_Point_Point_int_bool(cv::Point* pt1, cv::Point* pt2, int connectivity, bool leftToRight, Result* ocvrs_return) { + try { + cv::LineIterator* ret = new cv::LineIterator(*pt1, *pt2, connectivity, leftToRight); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineIterator_LineIterator_Point_Point(cv::Point* pt1, cv::Point* pt2, Result* ocvrs_return) { + try { + cv::LineIterator* ret = new cv::LineIterator(*pt1, *pt2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineIterator_LineIterator_Size_Point_Point_int_bool(cv::Size* boundingAreaSize, cv::Point* pt1, cv::Point* pt2, int connectivity, bool leftToRight, Result* ocvrs_return) { + try { + cv::LineIterator* ret = new cv::LineIterator(*boundingAreaSize, *pt1, *pt2, connectivity, leftToRight); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineIterator_LineIterator_Size_Point_Point(cv::Size* boundingAreaSize, cv::Point* pt1, cv::Point* pt2, Result* ocvrs_return) { + try { + cv::LineIterator* ret = new cv::LineIterator(*boundingAreaSize, *pt1, *pt2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineIterator_LineIterator_Rect_Point_Point_int_bool(cv::Rect* boundingAreaRect, cv::Point* pt1, cv::Point* pt2, int connectivity, bool leftToRight, Result* ocvrs_return) { + try { + cv::LineIterator* ret = new cv::LineIterator(*boundingAreaRect, *pt1, *pt2, connectivity, leftToRight); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineIterator_LineIterator_Rect_Point_Point(cv::Rect* boundingAreaRect, cv::Point* pt1, cv::Point* pt2, Result* ocvrs_return) { + try { + cv::LineIterator* ret = new cv::LineIterator(*boundingAreaRect, *pt1, *pt2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineIterator_init_const_MatX_Rect_Point_Point_int_bool(cv::LineIterator* instance, const cv::Mat* img, cv::Rect* boundingAreaRect, cv::Point* pt1, cv::Point* pt2, int connectivity, bool leftToRight, ResultVoid* ocvrs_return) { + try { + instance->init(img, *boundingAreaRect, *pt1, *pt2, connectivity, leftToRight); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineIterator_operatorX(cv::LineIterator* instance, Result* ocvrs_return) { + try { + unsigned char* ret = instance->operator*(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineIterator_operatorAA(cv::LineIterator* instance, Result* ocvrs_return) { + try { + cv::LineIterator ret = instance->operator++(); + Ok(new cv::LineIterator(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineIterator_pos_const(const cv::LineIterator* instance, Result* ocvrs_return) { + try { + cv::Point ret = instance->pos(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +unsigned char* cv_LineIterator_propPtr_const(const cv::LineIterator* instance) { + unsigned char* const ret = instance->ptr; + return ret; +} + +unsigned char* cv_LineIterator_propPtr(cv::LineIterator* instance) { + unsigned char* ret = instance->ptr; + return ret; +} + +void cv_LineIterator_propPtr_unsigned_charX(cv::LineIterator* instance, unsigned char* const val) { + instance->ptr = val; +} + +const unsigned char* cv_LineIterator_propPtr0_const(const cv::LineIterator* instance) { + const unsigned char* ret = instance->ptr0; + return ret; +} + +int cv_LineIterator_propStep_const(const cv::LineIterator* instance) { + int ret = instance->step; + return ret; +} + +void cv_LineIterator_propStep_const_int(cv::LineIterator* instance, const int val) { + instance->step = val; +} + +int cv_LineIterator_propElemSize_const(const cv::LineIterator* instance) { + int ret = instance->elemSize; + return ret; +} + +void cv_LineIterator_propElemSize_const_int(cv::LineIterator* instance, const int val) { + instance->elemSize = val; +} + +int cv_LineIterator_propErr_const(const cv::LineIterator* instance) { + int ret = instance->err; + return ret; +} + +void cv_LineIterator_propErr_const_int(cv::LineIterator* instance, const int val) { + instance->err = val; +} + +int cv_LineIterator_propCount_const(const cv::LineIterator* instance) { + int ret = instance->count; + return ret; +} + +void cv_LineIterator_propCount_const_int(cv::LineIterator* instance, const int val) { + instance->count = val; +} + +int cv_LineIterator_propMinusDelta_const(const cv::LineIterator* instance) { + int ret = instance->minusDelta; + return ret; +} + +void cv_LineIterator_propMinusDelta_const_int(cv::LineIterator* instance, const int val) { + instance->minusDelta = val; +} + +int cv_LineIterator_propPlusDelta_const(const cv::LineIterator* instance) { + int ret = instance->plusDelta; + return ret; +} + +void cv_LineIterator_propPlusDelta_const_int(cv::LineIterator* instance, const int val) { + instance->plusDelta = val; +} + +int cv_LineIterator_propMinusStep_const(const cv::LineIterator* instance) { + int ret = instance->minusStep; + return ret; +} + +void cv_LineIterator_propMinusStep_const_int(cv::LineIterator* instance, const int val) { + instance->minusStep = val; +} + +int cv_LineIterator_propPlusStep_const(const cv::LineIterator* instance) { + int ret = instance->plusStep; + return ret; +} + +void cv_LineIterator_propPlusStep_const_int(cv::LineIterator* instance, const int val) { + instance->plusStep = val; +} + +int cv_LineIterator_propMinusShift_const(const cv::LineIterator* instance) { + int ret = instance->minusShift; + return ret; +} + +void cv_LineIterator_propMinusShift_const_int(cv::LineIterator* instance, const int val) { + instance->minusShift = val; +} + +int cv_LineIterator_propPlusShift_const(const cv::LineIterator* instance) { + int ret = instance->plusShift; + return ret; +} + +void cv_LineIterator_propPlusShift_const_int(cv::LineIterator* instance, const int val) { + instance->plusShift = val; +} + +void cv_LineIterator_propP_const(const cv::LineIterator* instance, cv::Point* ocvrs_return) { + cv::Point ret = instance->p; + *ocvrs_return = ret; +} + +void cv_LineIterator_propP_const_Point(cv::LineIterator* instance, const cv::Point* val) { + instance->p = *val; +} + +bool cv_LineIterator_propPtmode_const(const cv::LineIterator* instance) { + bool ret = instance->ptmode; + return ret; +} + +void cv_LineIterator_propPtmode_const_bool(cv::LineIterator* instance, const bool val) { + instance->ptmode = val; +} + +void cv_LineIterator_delete(cv::LineIterator* instance) { + delete instance; +} + +void cv_LineSegmentDetector_detect_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(cv::LineSegmentDetector* instance, const cv::_InputArray* image, const cv::_OutputArray* lines, const cv::_OutputArray* width, const cv::_OutputArray* prec, const cv::_OutputArray* nfa, ResultVoid* ocvrs_return) { + try { + instance->detect(*image, *lines, *width, *prec, *nfa); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineSegmentDetector_detect_const__InputArrayR_const__OutputArrayR(cv::LineSegmentDetector* instance, const cv::_InputArray* image, const cv::_OutputArray* lines, ResultVoid* ocvrs_return) { + try { + instance->detect(*image, *lines); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineSegmentDetector_drawSegments_const__InputOutputArrayR_const__InputArrayR(cv::LineSegmentDetector* instance, const cv::_InputOutputArray* image, const cv::_InputArray* lines, ResultVoid* ocvrs_return) { + try { + instance->drawSegments(*image, *lines); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineSegmentDetector_compareSegments_const_SizeR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR(cv::LineSegmentDetector* instance, const cv::Size* size, const cv::_InputArray* lines1, const cv::_InputArray* lines2, const cv::_InputOutputArray* image, Result* ocvrs_return) { + try { + int ret = instance->compareSegments(*size, *lines1, *lines2, *image); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_LineSegmentDetector_compareSegments_const_SizeR_const__InputArrayR_const__InputArrayR(cv::LineSegmentDetector* instance, const cv::Size* size, const cv::_InputArray* lines1, const cv::_InputArray* lines2, Result* ocvrs_return) { + try { + int ret = instance->compareSegments(*size, *lines1, *lines2); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::Algorithm* cv_LineSegmentDetector_to_Algorithm(cv::LineSegmentDetector* instance) { + return dynamic_cast(instance); +} + +void cv_LineSegmentDetector_delete(cv::LineSegmentDetector* instance) { + delete instance; +} + +void cv_Subdiv2D_Subdiv2D(Result* ocvrs_return) { + try { + cv::Subdiv2D* ret = new cv::Subdiv2D(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_Subdiv2D_Rect(cv::Rect* rect, Result* ocvrs_return) { + try { + cv::Subdiv2D* ret = new cv::Subdiv2D(*rect); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_Subdiv2D_Rect2f(cv::Rect2f* rect2f, Result* ocvrs_return) { + try { + cv::Subdiv2D* ret = new cv::Subdiv2D(*rect2f); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_initDelaunay_Rect(cv::Subdiv2D* instance, cv::Rect* rect, ResultVoid* ocvrs_return) { + try { + instance->initDelaunay(*rect); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_initDelaunay_Rect2f(cv::Subdiv2D* instance, cv::Rect2f* rect, ResultVoid* ocvrs_return) { + try { + instance->initDelaunay(*rect); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_insert_Point2f(cv::Subdiv2D* instance, cv::Point2f* pt, Result* ocvrs_return) { + try { + int ret = instance->insert(*pt); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_insert_const_vectorLPoint2fGR(cv::Subdiv2D* instance, const std::vector* ptvec, ResultVoid* ocvrs_return) { + try { + instance->insert(*ptvec); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_locate_Point2f_intR_intR(cv::Subdiv2D* instance, cv::Point2f* pt, int* edge, int* vertex, Result* ocvrs_return) { + try { + int ret = instance->locate(*pt, *edge, *vertex); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_findNearest_Point2f_Point2fX(cv::Subdiv2D* instance, cv::Point2f* pt, cv::Point2f* nearestPt, Result* ocvrs_return) { + try { + int ret = instance->findNearest(*pt, nearestPt); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_findNearest_Point2f(cv::Subdiv2D* instance, cv::Point2f* pt, Result* ocvrs_return) { + try { + int ret = instance->findNearest(*pt); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_getEdgeList_const_vectorLVec4fGR(const cv::Subdiv2D* instance, std::vector* edgeList, ResultVoid* ocvrs_return) { + try { + instance->getEdgeList(*edgeList); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_getLeadingEdgeList_const_vectorLintGR(const cv::Subdiv2D* instance, std::vector* leadingEdgeList, ResultVoid* ocvrs_return) { + try { + instance->getLeadingEdgeList(*leadingEdgeList); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_getTriangleList_const_vectorLVec6fGR(const cv::Subdiv2D* instance, std::vector* triangleList, ResultVoid* ocvrs_return) { + try { + instance->getTriangleList(*triangleList); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_getVoronoiFacetList_const_vectorLintGR_vectorLvectorLPoint2fGGR_vectorLPoint2fGR(cv::Subdiv2D* instance, const std::vector* idx, std::vector>* facetList, std::vector* facetCenters, ResultVoid* ocvrs_return) { + try { + instance->getVoronoiFacetList(*idx, *facetList, *facetCenters); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_getVertex_const_int_intX(const cv::Subdiv2D* instance, int vertex, int* firstEdge, Result* ocvrs_return) { + try { + cv::Point2f ret = instance->getVertex(vertex, firstEdge); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_getVertex_const_int(const cv::Subdiv2D* instance, int vertex, Result* ocvrs_return) { + try { + cv::Point2f ret = instance->getVertex(vertex); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_getEdge_const_int_int(const cv::Subdiv2D* instance, int edge, int nextEdgeType, Result* ocvrs_return) { + try { + int ret = instance->getEdge(edge, nextEdgeType); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_nextEdge_const_int(const cv::Subdiv2D* instance, int edge, Result* ocvrs_return) { + try { + int ret = instance->nextEdge(edge); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_rotateEdge_const_int_int(const cv::Subdiv2D* instance, int edge, int rotate, Result* ocvrs_return) { + try { + int ret = instance->rotateEdge(edge, rotate); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_symEdge_const_int(const cv::Subdiv2D* instance, int edge, Result* ocvrs_return) { + try { + int ret = instance->symEdge(edge); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_edgeOrg_const_int_Point2fX(const cv::Subdiv2D* instance, int edge, cv::Point2f* orgpt, Result* ocvrs_return) { + try { + int ret = instance->edgeOrg(edge, orgpt); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_edgeOrg_const_int(const cv::Subdiv2D* instance, int edge, Result* ocvrs_return) { + try { + int ret = instance->edgeOrg(edge); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_edgeDst_const_int_Point2fX(const cv::Subdiv2D* instance, int edge, cv::Point2f* dstpt, Result* ocvrs_return) { + try { + int ret = instance->edgeDst(edge, dstpt); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_edgeDst_const_int(const cv::Subdiv2D* instance, int edge, Result* ocvrs_return) { + try { + int ret = instance->edgeDst(edge); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_Subdiv2D_delete(cv::Subdiv2D* instance) { + delete instance; +} + +void cv_segmentation_IntelligentScissorsMB_IntelligentScissorsMB(Result* ocvrs_return) { + try { + cv::segmentation::IntelligentScissorsMB* ret = new cv::segmentation::IntelligentScissorsMB(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_segmentation_IntelligentScissorsMB_setWeights_float_float_float(cv::segmentation::IntelligentScissorsMB* instance, float weight_non_edge, float weight_gradient_direction, float weight_gradient_magnitude, Result* ocvrs_return) { + try { + cv::segmentation::IntelligentScissorsMB ret = instance->setWeights(weight_non_edge, weight_gradient_direction, weight_gradient_magnitude); + Ok(new cv::segmentation::IntelligentScissorsMB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_segmentation_IntelligentScissorsMB_setGradientMagnitudeMaxLimit_float(cv::segmentation::IntelligentScissorsMB* instance, float gradient_magnitude_threshold_max, Result* ocvrs_return) { + try { + cv::segmentation::IntelligentScissorsMB ret = instance->setGradientMagnitudeMaxLimit(gradient_magnitude_threshold_max); + Ok(new cv::segmentation::IntelligentScissorsMB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_segmentation_IntelligentScissorsMB_setGradientMagnitudeMaxLimit(cv::segmentation::IntelligentScissorsMB* instance, Result* ocvrs_return) { + try { + cv::segmentation::IntelligentScissorsMB ret = instance->setGradientMagnitudeMaxLimit(); + Ok(new cv::segmentation::IntelligentScissorsMB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_segmentation_IntelligentScissorsMB_setEdgeFeatureZeroCrossingParameters_float(cv::segmentation::IntelligentScissorsMB* instance, float gradient_magnitude_min_value, Result* ocvrs_return) { + try { + cv::segmentation::IntelligentScissorsMB ret = instance->setEdgeFeatureZeroCrossingParameters(gradient_magnitude_min_value); + Ok(new cv::segmentation::IntelligentScissorsMB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_segmentation_IntelligentScissorsMB_setEdgeFeatureZeroCrossingParameters(cv::segmentation::IntelligentScissorsMB* instance, Result* ocvrs_return) { + try { + cv::segmentation::IntelligentScissorsMB ret = instance->setEdgeFeatureZeroCrossingParameters(); + Ok(new cv::segmentation::IntelligentScissorsMB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_segmentation_IntelligentScissorsMB_setEdgeFeatureCannyParameters_double_double_int_bool(cv::segmentation::IntelligentScissorsMB* instance, double threshold1, double threshold2, int apertureSize, bool L2gradient, Result* ocvrs_return) { + try { + cv::segmentation::IntelligentScissorsMB ret = instance->setEdgeFeatureCannyParameters(threshold1, threshold2, apertureSize, L2gradient); + Ok(new cv::segmentation::IntelligentScissorsMB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_segmentation_IntelligentScissorsMB_setEdgeFeatureCannyParameters_double_double(cv::segmentation::IntelligentScissorsMB* instance, double threshold1, double threshold2, Result* ocvrs_return) { + try { + cv::segmentation::IntelligentScissorsMB ret = instance->setEdgeFeatureCannyParameters(threshold1, threshold2); + Ok(new cv::segmentation::IntelligentScissorsMB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_segmentation_IntelligentScissorsMB_applyImage_const__InputArrayR(cv::segmentation::IntelligentScissorsMB* instance, const cv::_InputArray* image, Result* ocvrs_return) { + try { + cv::segmentation::IntelligentScissorsMB ret = instance->applyImage(*image); + Ok(new cv::segmentation::IntelligentScissorsMB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_segmentation_IntelligentScissorsMB_applyImageFeatures_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR(cv::segmentation::IntelligentScissorsMB* instance, const cv::_InputArray* non_edge, const cv::_InputArray* gradient_direction, const cv::_InputArray* gradient_magnitude, const cv::_InputArray* image, Result* ocvrs_return) { + try { + cv::segmentation::IntelligentScissorsMB ret = instance->applyImageFeatures(*non_edge, *gradient_direction, *gradient_magnitude, *image); + Ok(new cv::segmentation::IntelligentScissorsMB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_segmentation_IntelligentScissorsMB_applyImageFeatures_const__InputArrayR_const__InputArrayR_const__InputArrayR(cv::segmentation::IntelligentScissorsMB* instance, const cv::_InputArray* non_edge, const cv::_InputArray* gradient_direction, const cv::_InputArray* gradient_magnitude, Result* ocvrs_return) { + try { + cv::segmentation::IntelligentScissorsMB ret = instance->applyImageFeatures(*non_edge, *gradient_direction, *gradient_magnitude); + Ok(new cv::segmentation::IntelligentScissorsMB(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_segmentation_IntelligentScissorsMB_buildMap_const_PointR(cv::segmentation::IntelligentScissorsMB* instance, const cv::Point* sourcePt, ResultVoid* ocvrs_return) { + try { + instance->buildMap(*sourcePt); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_segmentation_IntelligentScissorsMB_getContour_const_const_PointR_const__OutputArrayR_bool(const cv::segmentation::IntelligentScissorsMB* instance, const cv::Point* targetPt, const cv::_OutputArray* contour, bool backward, ResultVoid* ocvrs_return) { + try { + instance->getContour(*targetPt, *contour, backward); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_segmentation_IntelligentScissorsMB_getContour_const_const_PointR_const__OutputArrayR(const cv::segmentation::IntelligentScissorsMB* instance, const cv::Point* targetPt, const cv::_OutputArray* contour, ResultVoid* ocvrs_return) { + try { + instance->getContour(*targetPt, *contour); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +cv::segmentation::IntelligentScissorsMB* cv_segmentation_IntelligentScissorsMB_implicitClone_const(const cv::segmentation::IntelligentScissorsMB* instance) { + return new cv::segmentation::IntelligentScissorsMB(*instance); +} + +void cv_segmentation_IntelligentScissorsMB_delete(cv::segmentation::IntelligentScissorsMB* instance) { + delete instance; +} + +} diff --git a/target/debug/build/opencv-4e105b5546afc119/out/imgproc_types.hpp b/target/debug/build/opencv-4e105b5546afc119/out/imgproc_types.hpp new file mode 100644 index 0000000..969b365 --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/imgproc_types.hpp @@ -0,0 +1,123 @@ +extern "C" { + const cv::CLAHE* cv_PtrLcv_CLAHEG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::CLAHE* cv_PtrLcv_CLAHEG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_CLAHEG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_CLAHEG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_CLAHEG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + +} + +extern "C" { + const cv::GeneralizedHough* cv_PtrLcv_GeneralizedHoughG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::GeneralizedHough* cv_PtrLcv_GeneralizedHoughG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_GeneralizedHoughG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_GeneralizedHoughG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_GeneralizedHoughG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + +} + +extern "C" { + const cv::GeneralizedHoughBallard* cv_PtrLcv_GeneralizedHoughBallardG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::GeneralizedHoughBallard* cv_PtrLcv_GeneralizedHoughBallardG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_GeneralizedHoughBallardG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_GeneralizedHoughBallardG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_GeneralizedHoughBallardG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_GeneralizedHoughBallardG_to_PtrOfGeneralizedHough(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + +} + +extern "C" { + const cv::GeneralizedHoughGuil* cv_PtrLcv_GeneralizedHoughGuilG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::GeneralizedHoughGuil* cv_PtrLcv_GeneralizedHoughGuilG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_GeneralizedHoughGuilG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_GeneralizedHoughGuilG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_GeneralizedHoughGuilG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + + cv::Ptr* cv_PtrLcv_GeneralizedHoughGuilG_to_PtrOfGeneralizedHough(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + +} + +extern "C" { + const cv::LineSegmentDetector* cv_PtrLcv_LineSegmentDetectorG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::LineSegmentDetector* cv_PtrLcv_LineSegmentDetectorG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_LineSegmentDetectorG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_LineSegmentDetectorG_delete(cv::Ptr* instance) { + delete instance; + } + + cv::Ptr* cv_PtrLcv_LineSegmentDetectorG_to_PtrOfAlgorithm(cv::Ptr* instance) { + return new cv::Ptr(instance->dynamicCast()); + } + +} + diff --git a/target/debug/build/opencv-4e105b5546afc119/out/libocvrs.a b/target/debug/build/opencv-4e105b5546afc119/out/libocvrs.a new file mode 100644 index 0000000..6e45c90 Binary files /dev/null and b/target/debug/build/opencv-4e105b5546afc119/out/libocvrs.a differ diff --git a/target/debug/build/opencv-4e105b5546afc119/out/opencv/cond_macros.rs b/target/debug/build/opencv-4e105b5546afc119/out/opencv/cond_macros.rs new file mode 100644 index 0000000..0663b10 --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/opencv/cond_macros.rs @@ -0,0 +1,6412 @@ +/// Conditional compilation macro based on OpenCV branch version for usage in external crates. +/// +/// # Examples +/// +/// Alternative import: +/// ``` +/// opencv::not_opencv_branch_34! { +/// use opencv::imgproc::LINE_8; +/// } +/// opencv::opencv_branch_34! { +/// use opencv::core::LINE_8; +/// } +/// ``` +/// +/// Alternative function call: +/// ``` +/// let mut cam = opencv::opencv_branch_34! { +/// { +/// opencv::videoio::VideoCapture::new_default(0)? +/// } else { +/// opencv::videoio::VideoCapture::new(0, videoio::CAP_ANY)? +/// } +/// }; +/// ``` +#[cfg(ocvrs_opencv_branch_34)] +#[macro_export] +macro_rules! opencv_branch_34 { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* } +} + +/// Conditional compilation macro based on OpenCV branch version for usage in external crates. +/// +/// # Examples +/// +/// Alternative import: +/// ``` +/// opencv::not_opencv_branch_34! { +/// use opencv::imgproc::LINE_8; +/// } +/// opencv::opencv_branch_34! { +/// use opencv::core::LINE_8; +/// } +/// ``` +#[cfg(ocvrs_opencv_branch_34)] +#[macro_export] +macro_rules! not_opencv_branch_34 { + ($($tt:tt)*) => { } +} + +/// Conditional compilation macro based on OpenCV branch version for usage in external crates. +/// +/// # Examples +/// +/// Alternative import: +/// ``` +/// opencv::not_opencv_branch_34! { +/// use opencv::imgproc::LINE_8; +/// } +/// opencv::opencv_branch_34! { +/// use opencv::core::LINE_8; +/// } +/// ``` +/// +/// Alternative function call: +/// ``` +/// let mut cam = opencv::opencv_branch_34! { +/// { +/// opencv::videoio::VideoCapture::new_default(0)? +/// } else { +/// opencv::videoio::VideoCapture::new(0, videoio::CAP_ANY)? +/// } +/// }; +/// ``` +#[cfg(not(ocvrs_opencv_branch_34))] +#[macro_export] +macro_rules! opencv_branch_34 { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { } +} + +/// Conditional compilation macro based on OpenCV branch version for usage in external crates. +/// +/// # Examples +/// +/// Alternative import: +/// ``` +/// opencv::not_opencv_branch_34! { +/// use opencv::imgproc::LINE_8; +/// } +/// opencv::opencv_branch_34! { +/// use opencv::core::LINE_8; +/// } +/// ``` +#[cfg(not(ocvrs_opencv_branch_34))] +#[macro_export] +macro_rules! not_opencv_branch_34 { + ($($tt:tt)*) => { $($tt)* } +} + +/// Conditional compilation macro based on OpenCV branch version for usage in external crates. +/// +/// # Examples +/// +/// Alternative import: +/// ``` +/// opencv::not_opencv_branch_34! { +/// use opencv::imgproc::LINE_8; +/// } +/// opencv::opencv_branch_34! { +/// use opencv::core::LINE_8; +/// } +/// ``` +/// +/// Alternative function call: +/// ``` +/// let mut cam = opencv::opencv_branch_34! { +/// { +/// opencv::videoio::VideoCapture::new_default(0)? +/// } else { +/// opencv::videoio::VideoCapture::new(0, videoio::CAP_ANY)? +/// } +/// }; +/// ``` +#[cfg(ocvrs_opencv_branch_4)] +#[macro_export] +macro_rules! opencv_branch_4 { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* } +} + +/// Conditional compilation macro based on OpenCV branch version for usage in external crates. +/// +/// # Examples +/// +/// Alternative import: +/// ``` +/// opencv::not_opencv_branch_34! { +/// use opencv::imgproc::LINE_8; +/// } +/// opencv::opencv_branch_34! { +/// use opencv::core::LINE_8; +/// } +/// ``` +#[cfg(ocvrs_opencv_branch_4)] +#[macro_export] +macro_rules! not_opencv_branch_4 { + ($($tt:tt)*) => { } +} + +/// Conditional compilation macro based on OpenCV branch version for usage in external crates. +/// +/// # Examples +/// +/// Alternative import: +/// ``` +/// opencv::not_opencv_branch_34! { +/// use opencv::imgproc::LINE_8; +/// } +/// opencv::opencv_branch_34! { +/// use opencv::core::LINE_8; +/// } +/// ``` +/// +/// Alternative function call: +/// ``` +/// let mut cam = opencv::opencv_branch_34! { +/// { +/// opencv::videoio::VideoCapture::new_default(0)? +/// } else { +/// opencv::videoio::VideoCapture::new(0, videoio::CAP_ANY)? +/// } +/// }; +/// ``` +#[cfg(not(ocvrs_opencv_branch_4))] +#[macro_export] +macro_rules! opencv_branch_4 { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { } +} + +/// Conditional compilation macro based on OpenCV branch version for usage in external crates. +/// +/// # Examples +/// +/// Alternative import: +/// ``` +/// opencv::not_opencv_branch_34! { +/// use opencv::imgproc::LINE_8; +/// } +/// opencv::opencv_branch_34! { +/// use opencv::core::LINE_8; +/// } +/// ``` +#[cfg(not(ocvrs_opencv_branch_4))] +#[macro_export] +macro_rules! not_opencv_branch_4 { + ($($tt:tt)*) => { $($tt)* } +} + +/// Conditional compilation macro based on OpenCV branch version for usage in external crates. +/// +/// # Examples +/// +/// Alternative import: +/// ``` +/// opencv::not_opencv_branch_34! { +/// use opencv::imgproc::LINE_8; +/// } +/// opencv::opencv_branch_34! { +/// use opencv::core::LINE_8; +/// } +/// ``` +/// +/// Alternative function call: +/// ``` +/// let mut cam = opencv::opencv_branch_34! { +/// { +/// opencv::videoio::VideoCapture::new_default(0)? +/// } else { +/// opencv::videoio::VideoCapture::new(0, videoio::CAP_ANY)? +/// } +/// }; +/// ``` +#[cfg(ocvrs_opencv_branch_5)] +#[macro_export] +macro_rules! opencv_branch_5 { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* } +} + +/// Conditional compilation macro based on OpenCV branch version for usage in external crates. +/// +/// # Examples +/// +/// Alternative import: +/// ``` +/// opencv::not_opencv_branch_34! { +/// use opencv::imgproc::LINE_8; +/// } +/// opencv::opencv_branch_34! { +/// use opencv::core::LINE_8; +/// } +/// ``` +#[cfg(ocvrs_opencv_branch_5)] +#[macro_export] +macro_rules! not_opencv_branch_5 { + ($($tt:tt)*) => { } +} + +/// Conditional compilation macro based on OpenCV branch version for usage in external crates. +/// +/// # Examples +/// +/// Alternative import: +/// ``` +/// opencv::not_opencv_branch_34! { +/// use opencv::imgproc::LINE_8; +/// } +/// opencv::opencv_branch_34! { +/// use opencv::core::LINE_8; +/// } +/// ``` +/// +/// Alternative function call: +/// ``` +/// let mut cam = opencv::opencv_branch_34! { +/// { +/// opencv::videoio::VideoCapture::new_default(0)? +/// } else { +/// opencv::videoio::VideoCapture::new(0, videoio::CAP_ANY)? +/// } +/// }; +/// ``` +#[cfg(not(ocvrs_opencv_branch_5))] +#[macro_export] +macro_rules! opencv_branch_5 { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { } +} + +/// Conditional compilation macro based on OpenCV branch version for usage in external crates. +/// +/// # Examples +/// +/// Alternative import: +/// ``` +/// opencv::not_opencv_branch_34! { +/// use opencv::imgproc::LINE_8; +/// } +/// opencv::opencv_branch_34! { +/// use opencv::core::LINE_8; +/// } +/// ``` +#[cfg(not(ocvrs_opencv_branch_5))] +#[macro_export] +macro_rules! not_opencv_branch_5 { + ($($tt:tt)*) => { $($tt)* } +} + +/// Conditional compilation macro based on whether the 3d OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_3d)] +#[macro_export] +macro_rules! opencv_has_module_3d { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the 3d OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_3d))] +#[macro_export] +macro_rules! opencv_has_module_3d { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the alphamat OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_alphamat)] +#[macro_export] +macro_rules! opencv_has_module_alphamat { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the alphamat OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_alphamat))] +#[macro_export] +macro_rules! opencv_has_module_alphamat { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the aruco OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_aruco)] +#[macro_export] +macro_rules! opencv_has_module_aruco { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the aruco OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_aruco))] +#[macro_export] +macro_rules! opencv_has_module_aruco { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the aruco_detector OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_aruco_detector)] +#[macro_export] +macro_rules! opencv_has_module_aruco_detector { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the aruco_detector OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_aruco_detector))] +#[macro_export] +macro_rules! opencv_has_module_aruco_detector { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the barcode OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_barcode)] +#[macro_export] +macro_rules! opencv_has_module_barcode { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the barcode OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_barcode))] +#[macro_export] +macro_rules! opencv_has_module_barcode { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the bgsegm OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_bgsegm)] +#[macro_export] +macro_rules! opencv_has_module_bgsegm { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the bgsegm OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_bgsegm))] +#[macro_export] +macro_rules! opencv_has_module_bgsegm { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the bioinspired OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_bioinspired)] +#[macro_export] +macro_rules! opencv_has_module_bioinspired { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the bioinspired OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_bioinspired))] +#[macro_export] +macro_rules! opencv_has_module_bioinspired { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the calib OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_calib)] +#[macro_export] +macro_rules! opencv_has_module_calib { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the calib OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_calib))] +#[macro_export] +macro_rules! opencv_has_module_calib { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the calib3d OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_calib3d)] +#[macro_export] +macro_rules! opencv_has_module_calib3d { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the calib3d OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_calib3d))] +#[macro_export] +macro_rules! opencv_has_module_calib3d { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the ccalib OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_ccalib)] +#[macro_export] +macro_rules! opencv_has_module_ccalib { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the ccalib OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_ccalib))] +#[macro_export] +macro_rules! opencv_has_module_ccalib { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the core OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_core)] +#[macro_export] +macro_rules! opencv_has_module_core { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the core OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_core))] +#[macro_export] +macro_rules! opencv_has_module_core { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the cudaarithm OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_cudaarithm)] +#[macro_export] +macro_rules! opencv_has_module_cudaarithm { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the cudaarithm OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_cudaarithm))] +#[macro_export] +macro_rules! opencv_has_module_cudaarithm { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the cudabgsegm OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_cudabgsegm)] +#[macro_export] +macro_rules! opencv_has_module_cudabgsegm { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the cudabgsegm OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_cudabgsegm))] +#[macro_export] +macro_rules! opencv_has_module_cudabgsegm { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the cudacodec OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_cudacodec)] +#[macro_export] +macro_rules! opencv_has_module_cudacodec { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the cudacodec OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_cudacodec))] +#[macro_export] +macro_rules! opencv_has_module_cudacodec { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the cudafeatures2d OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_cudafeatures2d)] +#[macro_export] +macro_rules! opencv_has_module_cudafeatures2d { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the cudafeatures2d OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_cudafeatures2d))] +#[macro_export] +macro_rules! opencv_has_module_cudafeatures2d { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the cudafilters OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_cudafilters)] +#[macro_export] +macro_rules! opencv_has_module_cudafilters { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the cudafilters OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_cudafilters))] +#[macro_export] +macro_rules! opencv_has_module_cudafilters { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the cudaimgproc OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_cudaimgproc)] +#[macro_export] +macro_rules! opencv_has_module_cudaimgproc { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the cudaimgproc OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_cudaimgproc))] +#[macro_export] +macro_rules! opencv_has_module_cudaimgproc { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the cudalegacy OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_cudalegacy)] +#[macro_export] +macro_rules! opencv_has_module_cudalegacy { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the cudalegacy OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_cudalegacy))] +#[macro_export] +macro_rules! opencv_has_module_cudalegacy { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the cudaobjdetect OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_cudaobjdetect)] +#[macro_export] +macro_rules! opencv_has_module_cudaobjdetect { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the cudaobjdetect OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_cudaobjdetect))] +#[macro_export] +macro_rules! opencv_has_module_cudaobjdetect { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the cudaoptflow OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_cudaoptflow)] +#[macro_export] +macro_rules! opencv_has_module_cudaoptflow { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the cudaoptflow OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_cudaoptflow))] +#[macro_export] +macro_rules! opencv_has_module_cudaoptflow { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the cudastereo OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_cudastereo)] +#[macro_export] +macro_rules! opencv_has_module_cudastereo { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the cudastereo OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_cudastereo))] +#[macro_export] +macro_rules! opencv_has_module_cudastereo { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the cudawarping OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_cudawarping)] +#[macro_export] +macro_rules! opencv_has_module_cudawarping { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the cudawarping OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_cudawarping))] +#[macro_export] +macro_rules! opencv_has_module_cudawarping { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the cvv OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_cvv)] +#[macro_export] +macro_rules! opencv_has_module_cvv { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the cvv OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_cvv))] +#[macro_export] +macro_rules! opencv_has_module_cvv { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the dnn OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_dnn)] +#[macro_export] +macro_rules! opencv_has_module_dnn { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the dnn OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_dnn))] +#[macro_export] +macro_rules! opencv_has_module_dnn { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the dnn_superres OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_dnn_superres)] +#[macro_export] +macro_rules! opencv_has_module_dnn_superres { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the dnn_superres OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_dnn_superres))] +#[macro_export] +macro_rules! opencv_has_module_dnn_superres { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the dpm OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_dpm)] +#[macro_export] +macro_rules! opencv_has_module_dpm { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the dpm OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_dpm))] +#[macro_export] +macro_rules! opencv_has_module_dpm { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the face OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_face)] +#[macro_export] +macro_rules! opencv_has_module_face { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the face OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_face))] +#[macro_export] +macro_rules! opencv_has_module_face { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the features OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_features)] +#[macro_export] +macro_rules! opencv_has_module_features { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the features OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_features))] +#[macro_export] +macro_rules! opencv_has_module_features { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the features2d OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_features2d)] +#[macro_export] +macro_rules! opencv_has_module_features2d { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the features2d OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_features2d))] +#[macro_export] +macro_rules! opencv_has_module_features2d { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the flann OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_flann)] +#[macro_export] +macro_rules! opencv_has_module_flann { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the flann OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_flann))] +#[macro_export] +macro_rules! opencv_has_module_flann { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the freetype OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_freetype)] +#[macro_export] +macro_rules! opencv_has_module_freetype { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the freetype OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_freetype))] +#[macro_export] +macro_rules! opencv_has_module_freetype { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the fuzzy OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_fuzzy)] +#[macro_export] +macro_rules! opencv_has_module_fuzzy { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the fuzzy OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_fuzzy))] +#[macro_export] +macro_rules! opencv_has_module_fuzzy { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the gapi OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_gapi)] +#[macro_export] +macro_rules! opencv_has_module_gapi { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the gapi OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_gapi))] +#[macro_export] +macro_rules! opencv_has_module_gapi { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the hdf OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_hdf)] +#[macro_export] +macro_rules! opencv_has_module_hdf { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the hdf OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_hdf))] +#[macro_export] +macro_rules! opencv_has_module_hdf { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the hfs OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_hfs)] +#[macro_export] +macro_rules! opencv_has_module_hfs { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the hfs OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_hfs))] +#[macro_export] +macro_rules! opencv_has_module_hfs { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the highgui OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_highgui)] +#[macro_export] +macro_rules! opencv_has_module_highgui { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the highgui OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_highgui))] +#[macro_export] +macro_rules! opencv_has_module_highgui { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the img_hash OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_img_hash)] +#[macro_export] +macro_rules! opencv_has_module_img_hash { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the img_hash OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_img_hash))] +#[macro_export] +macro_rules! opencv_has_module_img_hash { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the imgcodecs OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_imgcodecs)] +#[macro_export] +macro_rules! opencv_has_module_imgcodecs { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the imgcodecs OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_imgcodecs))] +#[macro_export] +macro_rules! opencv_has_module_imgcodecs { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the imgproc OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_imgproc)] +#[macro_export] +macro_rules! opencv_has_module_imgproc { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the imgproc OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_imgproc))] +#[macro_export] +macro_rules! opencv_has_module_imgproc { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the intensity_transform OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_intensity_transform)] +#[macro_export] +macro_rules! opencv_has_module_intensity_transform { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the intensity_transform OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_intensity_transform))] +#[macro_export] +macro_rules! opencv_has_module_intensity_transform { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the line_descriptor OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_line_descriptor)] +#[macro_export] +macro_rules! opencv_has_module_line_descriptor { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the line_descriptor OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_line_descriptor))] +#[macro_export] +macro_rules! opencv_has_module_line_descriptor { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the mcc OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_mcc)] +#[macro_export] +macro_rules! opencv_has_module_mcc { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the mcc OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_mcc))] +#[macro_export] +macro_rules! opencv_has_module_mcc { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the ml OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_ml)] +#[macro_export] +macro_rules! opencv_has_module_ml { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the ml OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_ml))] +#[macro_export] +macro_rules! opencv_has_module_ml { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the objdetect OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_objdetect)] +#[macro_export] +macro_rules! opencv_has_module_objdetect { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the objdetect OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_objdetect))] +#[macro_export] +macro_rules! opencv_has_module_objdetect { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the optflow OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_optflow)] +#[macro_export] +macro_rules! opencv_has_module_optflow { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the optflow OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_optflow))] +#[macro_export] +macro_rules! opencv_has_module_optflow { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the ovis OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_ovis)] +#[macro_export] +macro_rules! opencv_has_module_ovis { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the ovis OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_ovis))] +#[macro_export] +macro_rules! opencv_has_module_ovis { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the phase_unwrapping OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_phase_unwrapping)] +#[macro_export] +macro_rules! opencv_has_module_phase_unwrapping { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the phase_unwrapping OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_phase_unwrapping))] +#[macro_export] +macro_rules! opencv_has_module_phase_unwrapping { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the photo OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_photo)] +#[macro_export] +macro_rules! opencv_has_module_photo { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the photo OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_photo))] +#[macro_export] +macro_rules! opencv_has_module_photo { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the plot OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_plot)] +#[macro_export] +macro_rules! opencv_has_module_plot { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the plot OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_plot))] +#[macro_export] +macro_rules! opencv_has_module_plot { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the quality OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_quality)] +#[macro_export] +macro_rules! opencv_has_module_quality { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the quality OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_quality))] +#[macro_export] +macro_rules! opencv_has_module_quality { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the rapid OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_rapid)] +#[macro_export] +macro_rules! opencv_has_module_rapid { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the rapid OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_rapid))] +#[macro_export] +macro_rules! opencv_has_module_rapid { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the rgbd OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_rgbd)] +#[macro_export] +macro_rules! opencv_has_module_rgbd { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the rgbd OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_rgbd))] +#[macro_export] +macro_rules! opencv_has_module_rgbd { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the saliency OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_saliency)] +#[macro_export] +macro_rules! opencv_has_module_saliency { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the saliency OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_saliency))] +#[macro_export] +macro_rules! opencv_has_module_saliency { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the sfm OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_sfm)] +#[macro_export] +macro_rules! opencv_has_module_sfm { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the sfm OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_sfm))] +#[macro_export] +macro_rules! opencv_has_module_sfm { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the shape OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_shape)] +#[macro_export] +macro_rules! opencv_has_module_shape { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the shape OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_shape))] +#[macro_export] +macro_rules! opencv_has_module_shape { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the signal OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_signal)] +#[macro_export] +macro_rules! opencv_has_module_signal { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the signal OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_signal))] +#[macro_export] +macro_rules! opencv_has_module_signal { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the stereo OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_stereo)] +#[macro_export] +macro_rules! opencv_has_module_stereo { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the stereo OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_stereo))] +#[macro_export] +macro_rules! opencv_has_module_stereo { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the stitching OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_stitching)] +#[macro_export] +macro_rules! opencv_has_module_stitching { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the stitching OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_stitching))] +#[macro_export] +macro_rules! opencv_has_module_stitching { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the structured_light OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_structured_light)] +#[macro_export] +macro_rules! opencv_has_module_structured_light { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the structured_light OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_structured_light))] +#[macro_export] +macro_rules! opencv_has_module_structured_light { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the superres OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_superres)] +#[macro_export] +macro_rules! opencv_has_module_superres { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the superres OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_superres))] +#[macro_export] +macro_rules! opencv_has_module_superres { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the surface_matching OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_surface_matching)] +#[macro_export] +macro_rules! opencv_has_module_surface_matching { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the surface_matching OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_surface_matching))] +#[macro_export] +macro_rules! opencv_has_module_surface_matching { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the text OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_text)] +#[macro_export] +macro_rules! opencv_has_module_text { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the text OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_text))] +#[macro_export] +macro_rules! opencv_has_module_text { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the tracking OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_tracking)] +#[macro_export] +macro_rules! opencv_has_module_tracking { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the tracking OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_tracking))] +#[macro_export] +macro_rules! opencv_has_module_tracking { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the video OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_video)] +#[macro_export] +macro_rules! opencv_has_module_video { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the video OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_video))] +#[macro_export] +macro_rules! opencv_has_module_video { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the videoio OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_videoio)] +#[macro_export] +macro_rules! opencv_has_module_videoio { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the videoio OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_videoio))] +#[macro_export] +macro_rules! opencv_has_module_videoio { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the videostab OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_videostab)] +#[macro_export] +macro_rules! opencv_has_module_videostab { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the videostab OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_videostab))] +#[macro_export] +macro_rules! opencv_has_module_videostab { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the viz OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_viz)] +#[macro_export] +macro_rules! opencv_has_module_viz { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the viz OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_viz))] +#[macro_export] +macro_rules! opencv_has_module_viz { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the wechat_qrcode OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_wechat_qrcode)] +#[macro_export] +macro_rules! opencv_has_module_wechat_qrcode { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the wechat_qrcode OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_wechat_qrcode))] +#[macro_export] +macro_rules! opencv_has_module_wechat_qrcode { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the xfeatures2d OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_xfeatures2d)] +#[macro_export] +macro_rules! opencv_has_module_xfeatures2d { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the xfeatures2d OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_xfeatures2d))] +#[macro_export] +macro_rules! opencv_has_module_xfeatures2d { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the ximgproc OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_ximgproc)] +#[macro_export] +macro_rules! opencv_has_module_ximgproc { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the ximgproc OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_ximgproc))] +#[macro_export] +macro_rules! opencv_has_module_ximgproc { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the xobjdetect OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_xobjdetect)] +#[macro_export] +macro_rules! opencv_has_module_xobjdetect { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the xobjdetect OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_xobjdetect))] +#[macro_export] +macro_rules! opencv_has_module_xobjdetect { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the xphoto OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_xphoto)] +#[macro_export] +macro_rules! opencv_has_module_xphoto { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the xphoto OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_xphoto))] +#[macro_export] +macro_rules! opencv_has_module_xphoto { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether the xstereo OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(ocvrs_has_module_xstereo)] +#[macro_export] +macro_rules! opencv_has_module_xstereo { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether the xstereo OpenCV module is enabled +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let mut imgproc_enabled = opencv::opencv_has_module_imgproc! { +/// { +/// let mut input = Mat::default(); +/// let rect = opencv::core::Rect::default(); +/// opencv::imgproc::rectangle_def(&mut input, rect, (255., 0., 0.).into()); +/// true +/// } else { +/// false +/// } +/// }; +/// if !imgproc_enabled { +/// panic!("imgproc module is required"); +/// } +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_module_imgproc! { +/// use opencv::imgproc::rectangle_def; +/// } +/// ``` +#[cfg(not(ocvrs_has_module_xstereo))] +#[macro_export] +macro_rules! opencv_has_module_xstereo { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether OpenCV was built with or without the opencl feature +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let cuda_available = opencv::opencv_has_inherent_feature_cuda! { +/// { +/// true +/// } else { +/// false +/// } +/// }; +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_inherent_feature_cuda! { +/// use opencv::core::GpuMat; +/// } +/// ``` +#[cfg(ocvrs_has_inherent_feature_opencl)] +#[macro_export] +macro_rules! opencv_has_inherent_feature_opencl { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether OpenCV was built with or without the opencl feature +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let cuda_available = opencv::opencv_has_inherent_feature_cuda! { +/// { +/// true +/// } else { +/// false +/// } +/// }; +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_inherent_feature_cuda! { +/// use opencv::core::GpuMat; +/// } +/// ``` +#[cfg(not(ocvrs_has_inherent_feature_opencl))] +#[macro_export] +macro_rules! opencv_has_inherent_feature_opencl { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether OpenCV was built with or without the cuda feature +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let cuda_available = opencv::opencv_has_inherent_feature_cuda! { +/// { +/// true +/// } else { +/// false +/// } +/// }; +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_inherent_feature_cuda! { +/// use opencv::core::GpuMat; +/// } +/// ``` +#[cfg(ocvrs_has_inherent_feature_cuda)] +#[macro_export] +macro_rules! opencv_has_inherent_feature_cuda { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether OpenCV was built with or without the cuda feature +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let cuda_available = opencv::opencv_has_inherent_feature_cuda! { +/// { +/// true +/// } else { +/// false +/// } +/// }; +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_inherent_feature_cuda! { +/// use opencv::core::GpuMat; +/// } +/// ``` +#[cfg(not(ocvrs_has_inherent_feature_cuda))] +#[macro_export] +macro_rules! opencv_has_inherent_feature_cuda { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether OpenCV was built with or without the hfloat feature +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let cuda_available = opencv::opencv_has_inherent_feature_cuda! { +/// { +/// true +/// } else { +/// false +/// } +/// }; +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_inherent_feature_cuda! { +/// use opencv::core::GpuMat; +/// } +/// ``` +#[cfg(ocvrs_has_inherent_feature_hfloat)] +#[macro_export] +macro_rules! opencv_has_inherent_feature_hfloat { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether OpenCV was built with or without the hfloat feature +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let cuda_available = opencv::opencv_has_inherent_feature_cuda! { +/// { +/// true +/// } else { +/// false +/// } +/// }; +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_inherent_feature_cuda! { +/// use opencv::core::GpuMat; +/// } +/// ``` +#[cfg(not(ocvrs_has_inherent_feature_hfloat))] +#[macro_export] +macro_rules! opencv_has_inherent_feature_hfloat { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + +/// Conditional compilation macro based on whether OpenCV was built with or without the algorithm_hint feature +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let cuda_available = opencv::opencv_has_inherent_feature_cuda! { +/// { +/// true +/// } else { +/// false +/// } +/// }; +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_inherent_feature_cuda! { +/// use opencv::core::GpuMat; +/// } +/// ``` +#[cfg(ocvrs_has_inherent_feature_algorithm_hint)] +#[macro_export] +macro_rules! opencv_has_inherent_feature_algorithm_hint { + ($bl_pos:block else $bl_neg:block) => { $bl_pos }; + ($($tt:tt)*) => { $($tt)* }; +} + +/// Conditional compilation macro based on whether OpenCV was built with or without the algorithm_hint feature +/// +/// The macro has two forms: +/// 1. Two blocks, separated by the `else` keyword. The first block will be compiled if the OpenCV feature is enabled, the second +/// one — if it's not. Note that both blocks must have the same return type. +/// 2. Plain token tree, for usage on the item level, e.g., for `use` imports. The code inside will be compiled if the OpenCV +/// feature is enabled and completely skipped if it's not. +/// +/// # Examples +/// +/// Two blocks with `else`: +/// ``` +/// let cuda_available = opencv::opencv_has_inherent_feature_cuda! { +/// { +/// true +/// } else { +/// false +/// } +/// }; +/// ``` +/// +/// Plain token tree: +/// ``` +/// opencv::opencv_has_inherent_feature_cuda! { +/// use opencv::core::GpuMat; +/// } +/// ``` +#[cfg(not(ocvrs_has_inherent_feature_algorithm_hint))] +#[macro_export] +macro_rules! opencv_has_inherent_feature_algorithm_hint { + ($bl_pos:block else $bl_neg:block) => { $bl_neg }; + ($($tt:tt)*) => { }; +} + diff --git a/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs b/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs new file mode 100644 index 0000000..6a986ef --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/opencv/core.rs @@ -0,0 +1,29950 @@ +pub mod core { + //! # Core functionality + //! + //! The Core module is the backbone of OpenCV, offering fundamental data structures, matrix operations, + //! and utility functions that other modules depend on. It’s essential for handling image data, + //! performing mathematical computations, and managing memory efficiently within the OpenCV ecosystem. + //! # Basic structures + //! # Operations on arrays + //! # Asynchronous API + //! # XML/YAML/JSON Persistence + //! # Clustering + //! # Utility and system functions and macros + //! # Logging facilities + //! # SSE utilities + //! # NEON utilities + //! # VSX utilities + //! # Softfloat support + //! # Utility functions for OpenCV samples + //! # OpenGL interoperability + //! # Optimization Algorithms + //! # DirectX interoperability + //! # Eigen support + //! # OpenCL support + //! # Intel VA-API/OpenCL (CL-VA) interoperability + //! # Hardware Acceleration Layer + //! # Functions + //! # Interface + //! # Universal intrinsics + //! # Private implementation helpers + //! # Low-level API for external libraries / plugins + //! # Parallel Processing + //! # Parallel backends API + //! # Quaternion + use crate::mod_prelude::*; + use crate::{core, sys, types}; + pub mod prelude { + pub use super::{AlgorithmTrait, AlgorithmTraitConst, AsyncArrayTrait, AsyncArrayTraitConst, AsyncPromiseTrait, AsyncPromiseTraitConst, CommandLineParserTrait, CommandLineParserTraitConst, ConjGradSolverTrait, ConjGradSolverTraitConst, ContextTrait, ContextTraitConst, Context_UserContextTrait, Context_UserContextTraitConst, Detail_CheckContextTrait, Detail_CheckContextTraitConst, DeviceTrait, DeviceTraitConst, DownhillSolverTrait, DownhillSolverTraitConst, ExceptionTrait, ExceptionTraitConst, FileNodeIteratorTrait, FileNodeIteratorTraitConst, FileNodeTrait, FileNodeTraitConst, FileStorageTrait, FileStorageTraitConst, FormattedTrait, FormattedTraitConst, FormatterTrait, FormatterTraitConst, HammingTrait, HammingTraitConst, Image2DTrait, Image2DTraitConst, KernelArgTrait, KernelArgTraitConst, KernelTrait, KernelTraitConst, KeyPointTrait, KeyPointTraitConst, LDATrait, LDATraitConst, LogTagTrait, LogTagTraitConst, MatConstIteratorTrait, MatConstIteratorTraitConst, MatExprTrait, MatExprTraitConst, MatOpTrait, MatOpTraitConst, MatSizeTrait, MatSizeTraitConst, MatStepTrait, MatStepTraitConst, MatTrait, MatTraitConst, Matx_AddOpTrait, Matx_AddOpTraitConst, Matx_DivOpTrait, Matx_DivOpTraitConst, Matx_MatMulOpTrait, Matx_MatMulOpTraitConst, Matx_MulOpTrait, Matx_MulOpTraitConst, Matx_ScaleOpTrait, Matx_ScaleOpTraitConst, Matx_SubOpTrait, Matx_SubOpTraitConst, Matx_TOpTrait, Matx_TOpTraitConst, MinProblemSolverTrait, MinProblemSolverTraitConst, MinProblemSolver_FunctionTrait, MinProblemSolver_FunctionTraitConst, NodeDataTrait, NodeDataTraitConst, OpenCLExecutionContextTrait, OpenCLExecutionContextTraitConst, OriginalClassNameTrait, OriginalClassNameTraitConst, PCATrait, PCATraitConst, ParallelLoopBodyTrait, ParallelLoopBodyTraitConst, PlatformInfoTrait, PlatformInfoTraitConst, PlatformTrait, PlatformTraitConst, ProgramSourceTrait, ProgramSourceTraitConst, ProgramTrait, ProgramTraitConst, QueueTrait, QueueTraitConst, RNGTrait, RNGTraitConst, RNG_MT19937Trait, RNG_MT19937TraitConst, RangeTrait, RangeTraitConst, SVDTrait, SVDTraitConst, SparseMatConstIteratorTrait, SparseMatConstIteratorTraitConst, SparseMatIteratorTrait, SparseMatIteratorTraitConst, SparseMatTrait, SparseMatTraitConst, SparseMat_HdrTrait, SparseMat_HdrTraitConst, SparseMat_NodeTrait, SparseMat_NodeTraitConst, TickMeterTrait, TickMeterTraitConst, TimerTrait, TimerTraitConst, UMatDataTrait, UMatDataTraitConst, UMatTrait, UMatTraitConst, WriteStructContextTrait, WriteStructContextTraitConst, _InputArrayTrait, _InputArrayTraitConst, _InputOutputArrayTrait, _InputOutputArrayTraitConst, _OutputArrayTrait, _OutputArrayTraitConst}; + } + + pub const ACCESS_FAST: i32 = 67108864; + pub const ACCESS_MASK: i32 = 50331648; + pub const ACCESS_READ: i32 = 16777216; + pub const ACCESS_RW: i32 = 50331648; + pub const ACCESS_WRITE: i32 = 33554432; + /// Use generic portable implementation + pub const ALGO_HINT_ACCURATE: i32 = 1; + /// Allow alternative approximations to get faster implementation. Behaviour and result depends on a platform + pub const ALGO_HINT_APPROX: i32 = 2; + /// Default algorithm behaviour defined during OpenCV build + pub const ALGO_HINT_DEFAULT: i32 = 0; + /// `iiiiii|abcdefgh|iiiiiii` with some specified `i` + pub const BORDER_CONSTANT: i32 = 0; + /// same as BORDER_REFLECT_101 + pub const BORDER_DEFAULT: i32 = 4; + /// Interpolation restricted within the ROI boundaries. + pub const BORDER_ISOLATED: i32 = 16; + /// `fedcba|abcdefgh|hgfedcb` + pub const BORDER_REFLECT: i32 = 2; + /// same as BORDER_REFLECT_101 + pub const BORDER_REFLECT101: i32 = 4; + /// `gfedcb|abcdefgh|gfedcba` + pub const BORDER_REFLECT_101: i32 = 4; + /// `aaaaaa|abcdefgh|hhhhhhh` + pub const BORDER_REPLICATE: i32 = 1; + /// `uvwxyz|abcdefgh|ijklmno` - Treats outliers as transparent. + pub const BORDER_TRANSPARENT: i32 = 5; + /// `cdefgh|abcdefgh|abcdefg` + pub const BORDER_WRAP: i32 = 3; + /// incorrect input align + pub const BadAlign: i32 = -21; + pub const BadAlphaChannel: i32 = -18; + /// input COI is not supported + pub const BadCOI: i32 = -24; + pub const BadCallBack: i32 = -22; + pub const BadDataPtr: i32 = -12; + /// input image depth is not supported by the function + pub const BadDepth: i32 = -17; + /// image size is invalid + pub const BadImageSize: i32 = -10; + pub const BadModelOrChSeq: i32 = -14; + pub const BadNumChannel1U: i32 = -16; + /// bad number of channels, for example, some functions accept only single channel matrices. + pub const BadNumChannels: i32 = -15; + /// offset is invalid + pub const BadOffset: i32 = -11; + /// number of dimensions is out of range + pub const BadOrder: i32 = -19; + /// incorrect input origin + pub const BadOrigin: i32 = -20; + /// incorrect input roi + pub const BadROISize: i32 = -25; + /// image step is wrong, this may happen for a non-continuous matrix. + pub const BadStep: i32 = -13; + pub const BadTileSize: i32 = -23; + /// src1 is equal to src2. + pub const CMP_EQ: i32 = 0; + /// src1 is greater than or equal to src2. + pub const CMP_GE: i32 = 2; + /// src1 is greater than src2. + pub const CMP_GT: i32 = 1; + /// src1 is less than or equal to src2. + pub const CMP_LE: i32 = 4; + /// src1 is less than src2. + pub const CMP_LT: i32 = 3; + /// src1 is unequal to src2. + pub const CMP_NE: i32 = 5; + /// If the flag is + /// specified, all the input vectors are stored as columns of the samples matrix. mean should be a + /// single-column vector in this case. + pub const COVAR_COLS: i32 = 16; + /// The output covariance matrix is calculated as: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bscale%7D%20%20%20%5Ccdot%20%20%5B%20%20%5Ctexttt%7Bvects%7D%20%20%5B0%5D%2D%20%20%5Ctexttt%7Bmean%7D%20%20%2C%20%5Ctexttt%7Bvects%7D%20%20%5B1%5D%2D%20%20%5Ctexttt%7Bmean%7D%20%20%2C%2E%2E%2E%5D%20%20%5Ccdot%20%20%5B%20%5Ctexttt%7Bvects%7D%20%20%5B0%5D%2D%20%5Ctexttt%7Bmean%7D%20%20%2C%20%5Ctexttt%7Bvects%7D%20%20%5B1%5D%2D%20%5Ctexttt%7Bmean%7D%20%20%2C%2E%2E%2E%5D%5ET%2C) + /// covar will be a square matrix of the same size as the total number of elements in each input + /// vector. One and only one of [COVAR_SCRAMBLED] and [COVAR_NORMAL] must be specified. + pub const COVAR_NORMAL: i32 = 1; + /// If the flag is + /// specified, all the input vectors are stored as rows of the samples matrix. mean should be a + /// single-row vector in this case. + pub const COVAR_ROWS: i32 = 8; + /// If the flag is specified, the covariance matrix is scaled. In the + /// "normal" mode, scale is 1./nsamples . In the "scrambled" mode, scale is the reciprocal of the + /// total number of elements in each input vector. By default (if the flag is not specified), the + /// covariance matrix is not scaled ( scale=1 ). + pub const COVAR_SCALE: i32 = 4; + /// The output covariance matrix is calculated as: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bscale%7D%20%20%20%5Ccdot%20%20%5B%20%20%5Ctexttt%7Bvects%7D%20%20%5B0%5D%2D%20%20%5Ctexttt%7Bmean%7D%20%20%2C%20%5Ctexttt%7Bvects%7D%20%20%5B1%5D%2D%20%20%5Ctexttt%7Bmean%7D%20%20%2C%2E%2E%2E%5D%5ET%20%20%5Ccdot%20%20%5B%20%5Ctexttt%7Bvects%7D%20%20%5B0%5D%2D%20%5Ctexttt%7Bmean%7D%20%20%2C%20%5Ctexttt%7Bvects%7D%20%20%5B1%5D%2D%20%5Ctexttt%7Bmean%7D%20%20%2C%2E%2E%2E%5D%2C) + /// The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used + /// for fast PCA of a set of very large vectors (see, for example, the EigenFaces technique for + /// face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true + /// covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of + /// the "scrambled" covariance matrix. + pub const COVAR_SCRAMBLED: i32 = 0; + /// If the flag is specified, the function does not calculate mean from + /// the input vectors but, instead, uses the passed mean vector. This is useful if mean has been + /// pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In + /// this case, mean is not a mean vector of the input sub-set of vectors but rather the mean + /// vector of the whole set. + pub const COVAR_USE_AVG: i32 = 2; + pub const CPU_AVX: i32 = 10; + pub const CPU_AVX2: i32 = 11; + /// Cascade Lake with AVX-512F/CD/BW/DQ/VL/VNNI + pub const CPU_AVX512_CLX: i32 = 261; + /// Cannon Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI + pub const CPU_AVX512_CNL: i32 = 260; + /// Common instructions AVX-512F/CD for all CPUs that support AVX-512 + pub const CPU_AVX512_COMMON: i32 = 257; + /// Ice Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI/VNNI/VBMI2/BITALG/VPOPCNTDQ + pub const CPU_AVX512_ICL: i32 = 262; + /// Knights Landing with AVX-512F/CD/ER/PF + pub const CPU_AVX512_KNL: i32 = 258; + /// Knights Mill with AVX-512F/CD/ER/PF/4FMAPS/4VNNIW/VPOPCNTDQ + pub const CPU_AVX512_KNM: i32 = 259; + /// Skylake-X with AVX-512F/CD/BW/DQ/VL + pub const CPU_AVX512_SKX: i32 = 256; + pub const CPU_AVX_5124FMAPS: i32 = 27; + pub const CPU_AVX_5124VNNIW: i32 = 26; + pub const CPU_AVX_512BITALG: i32 = 24; + pub const CPU_AVX_512BW: i32 = 14; + pub const CPU_AVX_512CD: i32 = 15; + pub const CPU_AVX_512DQ: i32 = 16; + pub const CPU_AVX_512ER: i32 = 17; + pub const CPU_AVX_512F: i32 = 13; + pub const CPU_AVX_512IFMA: i32 = 18; + pub const CPU_AVX_512IFMA512: i32 = 18; + pub const CPU_AVX_512PF: i32 = 19; + pub const CPU_AVX_512VBMI: i32 = 20; + pub const CPU_AVX_512VBMI2: i32 = 22; + pub const CPU_AVX_512VL: i32 = 21; + pub const CPU_AVX_512VNNI: i32 = 23; + pub const CPU_AVX_512VPOPCNTDQ: i32 = 25; + pub const CPU_FMA3: i32 = 12; + pub const CPU_FP16: i32 = 9; + pub const CPU_LASX: i32 = 231; + pub const CPU_LSX: i32 = 230; + pub const CPU_MAX_FEATURE: i32 = 512; + pub const CPU_MMX: i32 = 1; + pub const CPU_MSA: i32 = 150; + pub const CPU_NEON: i32 = 100; + pub const CPU_NEON_BF16: i32 = 103; + pub const CPU_NEON_DOTPROD: i32 = 101; + pub const CPU_NEON_FP16: i32 = 102; + pub const CPU_POPCNT: i32 = 8; + pub const CPU_RISCVV: i32 = 170; + pub const CPU_RVV: i32 = 210; + pub const CPU_SSE: i32 = 2; + pub const CPU_SSE2: i32 = 3; + pub const CPU_SSE3: i32 = 4; + pub const CPU_SSE4_1: i32 = 6; + pub const CPU_SSE4_2: i32 = 7; + pub const CPU_SSSE3: i32 = 5; + pub const CPU_SVE: i32 = 104; + pub const CPU_VSX: i32 = 200; + pub const CPU_VSX3: i32 = 201; + pub const CV_16F: i32 = 7; + pub const CV_16FC1: i32 = CV_MAKETYPE(CV_16F,1); + pub const CV_16FC2: i32 = CV_MAKETYPE(CV_16F,2); + pub const CV_16FC3: i32 = CV_MAKETYPE(CV_16F,3); + pub const CV_16FC4: i32 = CV_MAKETYPE(CV_16F,4); + pub const CV_16S: i32 = 3; + pub const CV_16SC1: i32 = CV_MAKETYPE(CV_16S,1); + pub const CV_16SC2: i32 = CV_MAKETYPE(CV_16S,2); + pub const CV_16SC3: i32 = CV_MAKETYPE(CV_16S,3); + pub const CV_16SC4: i32 = CV_MAKETYPE(CV_16S,4); + pub const CV_16U: i32 = 2; + pub const CV_16UC1: i32 = CV_MAKETYPE(CV_16U,1); + pub const CV_16UC2: i32 = CV_MAKETYPE(CV_16U,2); + pub const CV_16UC3: i32 = CV_MAKETYPE(CV_16U,3); + pub const CV_16UC4: i32 = CV_MAKETYPE(CV_16U,4); + pub const CV_2PI: f64 = 6.283185307179586476925286766559; + pub const CV_32F: i32 = 5; + pub const CV_32FC1: i32 = CV_MAKETYPE(CV_32F,1); + pub const CV_32FC2: i32 = CV_MAKETYPE(CV_32F,2); + pub const CV_32FC3: i32 = CV_MAKETYPE(CV_32F,3); + pub const CV_32FC4: i32 = CV_MAKETYPE(CV_32F,4); + pub const CV_32S: i32 = 4; + pub const CV_32SC1: i32 = CV_MAKETYPE(CV_32S,1); + pub const CV_32SC2: i32 = CV_MAKETYPE(CV_32S,2); + pub const CV_32SC3: i32 = CV_MAKETYPE(CV_32S,3); + pub const CV_32SC4: i32 = CV_MAKETYPE(CV_32S,4); + pub const CV_64F: i32 = 6; + pub const CV_64FC1: i32 = CV_MAKETYPE(CV_64F,1); + pub const CV_64FC2: i32 = CV_MAKETYPE(CV_64F,2); + pub const CV_64FC3: i32 = CV_MAKETYPE(CV_64F,3); + pub const CV_64FC4: i32 = CV_MAKETYPE(CV_64F,4); + pub const CV_8S: i32 = 1; + pub const CV_8SC1: i32 = CV_MAKETYPE(CV_8S,1); + pub const CV_8SC2: i32 = CV_MAKETYPE(CV_8S,2); + pub const CV_8SC3: i32 = CV_MAKETYPE(CV_8S,3); + pub const CV_8SC4: i32 = CV_MAKETYPE(CV_8S,4); + pub const CV_8U: i32 = 0; + pub const CV_8UC1: i32 = CV_MAKETYPE(CV_8U,1); + pub const CV_8UC2: i32 = CV_MAKETYPE(CV_8U,2); + pub const CV_8UC3: i32 = CV_MAKETYPE(CV_8U,3); + pub const CV_8UC4: i32 = CV_MAKETYPE(CV_8U,4); + pub const CV_AVX: i32 = 0; + pub const CV_AVX2: i32 = 0; + pub const CV_AVX512_CLX: i32 = 0; + pub const CV_AVX512_CNL: i32 = 0; + pub const CV_AVX512_COMMON: i32 = 0; + pub const CV_AVX512_ICL: i32 = 0; + pub const CV_AVX512_KNL: i32 = 0; + pub const CV_AVX512_KNM: i32 = 0; + pub const CV_AVX512_SKX: i32 = 0; + pub const CV_AVX_5124FMAPS: i32 = 0; + pub const CV_AVX_5124VNNIW: i32 = 0; + pub const CV_AVX_512BITALG: i32 = 0; + pub const CV_AVX_512BW: i32 = 0; + pub const CV_AVX_512CD: i32 = 0; + pub const CV_AVX_512DQ: i32 = 0; + pub const CV_AVX_512ER: i32 = 0; + pub const CV_AVX_512F: i32 = 0; + pub const CV_AVX_512IFMA: i32 = 0; + pub const CV_AVX_512IFMA512: i32 = CV_AVX_512IFMA; + pub const CV_AVX_512PF: i32 = 0; + pub const CV_AVX_512VBMI: i32 = 0; + pub const CV_AVX_512VBMI2: i32 = 0; + pub const CV_AVX_512VL: i32 = 0; + pub const CV_AVX_512VNNI: i32 = 0; + pub const CV_AVX_512VPOPCNTDQ: i32 = 0; + pub const CV_CN_MAX: i32 = 512; + pub const CV_CN_SHIFT: i32 = 3; + pub const CV_CPU_AVX: i32 = 10; + pub const CV_CPU_AVX2: i32 = 11; + pub const CV_CPU_AVX512_CLX: i32 = 261; + pub const CV_CPU_AVX512_CNL: i32 = 260; + pub const CV_CPU_AVX512_COMMON: i32 = 257; + pub const CV_CPU_AVX512_ICL: i32 = 262; + pub const CV_CPU_AVX512_KNL: i32 = 258; + pub const CV_CPU_AVX512_KNM: i32 = 259; + pub const CV_CPU_AVX512_SKX: i32 = 256; + pub const CV_CPU_AVX_5124FMAPS: i32 = 27; + pub const CV_CPU_AVX_5124VNNIW: i32 = 26; + pub const CV_CPU_AVX_512BITALG: i32 = 24; + pub const CV_CPU_AVX_512BW: i32 = 14; + pub const CV_CPU_AVX_512CD: i32 = 15; + pub const CV_CPU_AVX_512DQ: i32 = 16; + pub const CV_CPU_AVX_512ER: i32 = 17; + pub const CV_CPU_AVX_512F: i32 = 13; + pub const CV_CPU_AVX_512IFMA: i32 = 18; + pub const CV_CPU_AVX_512IFMA512: i32 = 18; + pub const CV_CPU_AVX_512PF: i32 = 19; + pub const CV_CPU_AVX_512VBMI: i32 = 20; + pub const CV_CPU_AVX_512VBMI2: i32 = 22; + pub const CV_CPU_AVX_512VL: i32 = 21; + pub const CV_CPU_AVX_512VNNI: i32 = 23; + pub const CV_CPU_AVX_512VPOPCNTDQ: i32 = 25; + pub const CV_CPU_FMA3: i32 = 12; + pub const CV_CPU_FP16: i32 = 9; + pub const CV_CPU_LASX: i32 = 231; + pub const CV_CPU_LSX: i32 = 230; + pub const CV_CPU_MMX: i32 = 1; + pub const CV_CPU_MSA: i32 = 150; + pub const CV_CPU_NEON: i32 = 100; + pub const CV_CPU_NEON_BF16: i32 = 103; + pub const CV_CPU_NEON_DOTPROD: i32 = 101; + pub const CV_CPU_NEON_FP16: i32 = 102; + pub const CV_CPU_NONE: i32 = 0; + pub const CV_CPU_POPCNT: i32 = 8; + pub const CV_CPU_RISCVV: i32 = 170; + pub const CV_CPU_RVV: i32 = 210; + pub const CV_CPU_SSE: i32 = 2; + pub const CV_CPU_SSE2: i32 = 3; + pub const CV_CPU_SSE3: i32 = 4; + pub const CV_CPU_SSE4_1: i32 = 6; + pub const CV_CPU_SSE4_2: i32 = 7; + pub const CV_CPU_SSSE3: i32 = 5; + pub const CV_CPU_SVE: i32 = 104; + pub const CV_CPU_VSX: i32 = 200; + pub const CV_CPU_VSX3: i32 = 201; + pub const CV_CXX11: i32 = 1; + pub const CV_DEPTH_MAX: i32 = (1< + pub const _InputArray_EXPR: i32 = 393216; + pub const _InputArray_FIXED_SIZE: i32 = 1073741824; + pub const _InputArray_FIXED_TYPE: i32 = -2147483648; + pub const _InputArray_KIND_MASK: i32 = 2031616; + pub const _InputArray_KIND_SHIFT: i32 = 16; + pub const _InputArray_MAT: i32 = 65536; + pub const _InputArray_MATX: i32 = 131072; + pub const _InputArray_NONE: i32 = 0; + pub const _InputArray_OPENGL_BUFFER: i32 = 458752; + /// removed: + pub const _InputArray_STD_ARRAY: i32 = 917504; + pub const _InputArray_STD_ARRAY_MAT: i32 = 983040; + pub const _InputArray_STD_BOOL_VECTOR: i32 = 786432; + pub const _InputArray_STD_VECTOR: i32 = 196608; + pub const _InputArray_STD_VECTOR_CUDA_GPU_MAT: i32 = 851968; + pub const _InputArray_STD_VECTOR_MAT: i32 = 327680; + pub const _InputArray_STD_VECTOR_UMAT: i32 = 720896; + pub const _InputArray_STD_VECTOR_VECTOR: i32 = 262144; + pub const _InputArray_UMAT: i32 = 655360; + pub const _OutputArray_DEPTH_MASK_16F: i32 = 128; + pub const _OutputArray_DEPTH_MASK_16S: i32 = 8; + pub const _OutputArray_DEPTH_MASK_16U: i32 = 4; + pub const _OutputArray_DEPTH_MASK_32F: i32 = 32; + pub const _OutputArray_DEPTH_MASK_32S: i32 = 16; + pub const _OutputArray_DEPTH_MASK_64F: i32 = 64; + pub const _OutputArray_DEPTH_MASK_8S: i32 = 2; + pub const _OutputArray_DEPTH_MASK_8U: i32 = 1; + pub const _OutputArray_DEPTH_MASK_ALL: i32 = 127; + pub const _OutputArray_DEPTH_MASK_ALL_16F: i32 = 255; + pub const _OutputArray_DEPTH_MASK_ALL_BUT_8S: i32 = 125; + pub const _OutputArray_DEPTH_MASK_FLT: i32 = 96; + pub const __UMAT_USAGE_FLAGS_32BIT: i32 = 2147483647; + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum AccessFlag { + ACCESS_READ = 16777216, + ACCESS_WRITE = 33554432, + ACCESS_RW = 50331648, + // Duplicate, use ACCESS_RW instead + // ACCESS_MASK = 50331648, + ACCESS_FAST = 67108864, + } + + opencv_type_enum! { core::AccessFlag { ACCESS_READ, ACCESS_WRITE, ACCESS_RW, ACCESS_FAST } } + + /// ! Flags that allow to midify some functions behavior. Used as set of flags. + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum AlgorithmHint { + /// Default algorithm behaviour defined during OpenCV build + ALGO_HINT_DEFAULT = 0, + /// Use generic portable implementation + ALGO_HINT_ACCURATE = 1, + /// Allow alternative approximations to get faster implementation. Behaviour and result depends on a platform + ALGO_HINT_APPROX = 2, + } + + opencv_type_enum! { core::AlgorithmHint { ALGO_HINT_DEFAULT, ALGO_HINT_ACCURATE, ALGO_HINT_APPROX } } + + /// ! Various border types, image boundaries are denoted with the `|` character in the table below, when describing each method. + /// + /// The following examples show the result of the [copyMakeBorder] call according to different methods. + /// Input image is `6x4` (width x height) size and the [copyMakeBorder] function is used with a border size of 2 pixels + /// in each direction, giving a resulting image of `10x8` resolution. + /// + /// ```C++ + /// Input image: + /// [[ 0 1 2 3 4 5] + /// [ 6 7 8 9 10 11] + /// [12 13 14 15 16 17] + /// [18 19 20 21 22 23]] + /// + /// Border type: BORDER_CONSTANT (a constant value of 255 is used) + /// [[255 255 255 255 255 255 255 255 255 255] + /// [255 255 255 255 255 255 255 255 255 255] + /// [255 255 0 1 2 3 4 5 255 255] + /// [255 255 6 7 8 9 10 11 255 255] + /// [255 255 12 13 14 15 16 17 255 255] + /// [255 255 18 19 20 21 22 23 255 255] + /// [255 255 255 255 255 255 255 255 255 255] + /// [255 255 255 255 255 255 255 255 255 255]] + /// + /// Border type: BORDER_REPLICATE + /// [[ 0 0 0 1 2 3 4 5 5 5] + /// [ 0 0 0 1 2 3 4 5 5 5] + /// [ 0 0 0 1 2 3 4 5 5 5] + /// [ 6 6 6 7 8 9 10 11 11 11] + /// [12 12 12 13 14 15 16 17 17 17] + /// [18 18 18 19 20 21 22 23 23 23] + /// [18 18 18 19 20 21 22 23 23 23] + /// [18 18 18 19 20 21 22 23 23 23]] + /// + /// Border type: BORDER_REFLECT + /// [[ 7 6 6 7 8 9 10 11 11 10] + /// [ 1 0 0 1 2 3 4 5 5 4] + /// [ 1 0 0 1 2 3 4 5 5 4] + /// [ 7 6 6 7 8 9 10 11 11 10] + /// [13 12 12 13 14 15 16 17 17 16] + /// [19 18 18 19 20 21 22 23 23 22] + /// [19 18 18 19 20 21 22 23 23 22] + /// [13 12 12 13 14 15 16 17 17 16]] + /// + /// Border type: BORDER_WRAP + /// [[16 17 12 13 14 15 16 17 12 13] + /// [22 23 18 19 20 21 22 23 18 19] + /// [ 4 5 0 1 2 3 4 5 0 1] + /// [10 11 6 7 8 9 10 11 6 7] + /// [16 17 12 13 14 15 16 17 12 13] + /// [22 23 18 19 20 21 22 23 18 19] + /// [ 4 5 0 1 2 3 4 5 0 1] + /// [10 11 6 7 8 9 10 11 6 7]] + /// + /// Border type: BORDER_REFLECT_101 + /// [[14 13 12 13 14 15 16 17 16 15] + /// [ 8 7 6 7 8 9 10 11 10 9] + /// [ 2 1 0 1 2 3 4 5 4 3] + /// [ 8 7 6 7 8 9 10 11 10 9] + /// [14 13 12 13 14 15 16 17 16 15] + /// [20 19 18 19 20 21 22 23 22 21] + /// [14 13 12 13 14 15 16 17 16 15] + /// [ 8 7 6 7 8 9 10 11 10 9]] + /// ``` + /// ## See also + /// borderInterpolate, copyMakeBorder + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum BorderTypes { + /// `iiiiii|abcdefgh|iiiiiii` with some specified `i` + BORDER_CONSTANT = 0, + /// `aaaaaa|abcdefgh|hhhhhhh` + BORDER_REPLICATE = 1, + /// `fedcba|abcdefgh|hgfedcb` + BORDER_REFLECT = 2, + /// `cdefgh|abcdefgh|abcdefg` + BORDER_WRAP = 3, + /// `gfedcb|abcdefgh|gfedcba` + BORDER_REFLECT_101 = 4, + /// `uvwxyz|abcdefgh|ijklmno` - Treats outliers as transparent. + BORDER_TRANSPARENT = 5, + // same as BORDER_REFLECT_101 + // Duplicate, use BORDER_REFLECT_101 instead + // BORDER_REFLECT101 = 4, + // same as BORDER_REFLECT_101 + // Duplicate, use BORDER_REFLECT101 instead + // BORDER_DEFAULT = 4, + /// Interpolation restricted within the ROI boundaries. + BORDER_ISOLATED = 16, + } + + opencv_type_enum! { core::BorderTypes { BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT, BORDER_WRAP, BORDER_REFLECT_101, BORDER_TRANSPARENT, BORDER_ISOLATED } } + + /// comparison types + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum CmpTypes { + /// src1 is equal to src2. + CMP_EQ = 0, + /// src1 is greater than src2. + CMP_GT = 1, + /// src1 is greater than or equal to src2. + CMP_GE = 2, + /// src1 is less than src2. + CMP_LT = 3, + /// src1 is less than or equal to src2. + CMP_LE = 4, + /// src1 is unequal to src2. + CMP_NE = 5, + } + + opencv_type_enum! { core::CmpTypes { CMP_EQ, CMP_GT, CMP_GE, CMP_LT, CMP_LE, CMP_NE } } + + /// error codes + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum Code { + /// everything is ok + StsOk = 0, + /// pseudo error for back trace + StsBackTrace = -1, + /// unknown /unspecified error + StsError = -2, + /// internal error (bad state) + StsInternal = -3, + /// insufficient memory + StsNoMem = -4, + /// function arg/param is bad + StsBadArg = -5, + /// unsupported function + StsBadFunc = -6, + /// iteration didn't converge + StsNoConv = -7, + /// tracing + StsAutoTrace = -8, + /// image header is NULL + HeaderIsNull = -9, + /// image size is invalid + BadImageSize = -10, + /// offset is invalid + BadOffset = -11, + BadDataPtr = -12, + /// image step is wrong, this may happen for a non-continuous matrix. + BadStep = -13, + BadModelOrChSeq = -14, + /// bad number of channels, for example, some functions accept only single channel matrices. + BadNumChannels = -15, + BadNumChannel1U = -16, + /// input image depth is not supported by the function + BadDepth = -17, + BadAlphaChannel = -18, + /// number of dimensions is out of range + BadOrder = -19, + /// incorrect input origin + BadOrigin = -20, + /// incorrect input align + BadAlign = -21, + BadCallBack = -22, + BadTileSize = -23, + /// input COI is not supported + BadCOI = -24, + /// incorrect input roi + BadROISize = -25, + MaskIsTiled = -26, + /// null pointer + StsNullPtr = -27, + /// incorrect vector length + StsVecLengthErr = -28, + /// incorrect filter structure content + StsFilterStructContentErr = -29, + /// incorrect transform kernel content + StsKernelStructContentErr = -30, + /// incorrect filter offset value + StsFilterOffsetErr = -31, + /// the input/output structure size is incorrect + StsBadSize = -201, + /// division by zero + StsDivByZero = -202, + /// in-place operation is not supported + StsInplaceNotSupported = -203, + /// request can't be completed + StsObjectNotFound = -204, + /// formats of input/output arrays differ + StsUnmatchedFormats = -205, + /// flag is wrong or not supported + StsBadFlag = -206, + /// bad CvPoint + StsBadPoint = -207, + /// bad format of mask (neither 8uC1 nor 8sC1) + StsBadMask = -208, + /// sizes of input/output structures do not match + StsUnmatchedSizes = -209, + /// the data format/type is not supported by the function + StsUnsupportedFormat = -210, + /// some of parameters are out of range + StsOutOfRange = -211, + /// invalid syntax/structure of the parsed file + StsParseError = -212, + /// the requested function/feature is not implemented + StsNotImplemented = -213, + /// an allocated block has been corrupted + StsBadMemBlock = -214, + /// assertion failed + StsAssert = -215, + /// no CUDA support + GpuNotSupported = -216, + /// GPU API call error + GpuApiCallError = -217, + /// no OpenGL support + OpenGlNotSupported = -218, + /// OpenGL API call error + OpenGlApiCallError = -219, + /// OpenCL API call error + OpenCLApiCallError = -220, + OpenCLDoubleNotSupported = -221, + /// OpenCL initialization error + OpenCLInitError = -222, + OpenCLNoAMDBlasFft = -223, + } + + opencv_type_enum! { core::Code { StsOk, StsBackTrace, StsError, StsInternal, StsNoMem, StsBadArg, StsBadFunc, StsNoConv, StsAutoTrace, HeaderIsNull, BadImageSize, BadOffset, BadDataPtr, BadStep, BadModelOrChSeq, BadNumChannels, BadNumChannel1U, BadDepth, BadAlphaChannel, BadOrder, BadOrigin, BadAlign, BadCallBack, BadTileSize, BadCOI, BadROISize, MaskIsTiled, StsNullPtr, StsVecLengthErr, StsFilterStructContentErr, StsKernelStructContentErr, StsFilterOffsetErr, StsBadSize, StsDivByZero, StsInplaceNotSupported, StsObjectNotFound, StsUnmatchedFormats, StsBadFlag, StsBadPoint, StsBadMask, StsUnmatchedSizes, StsUnsupportedFormat, StsOutOfRange, StsParseError, StsNotImplemented, StsBadMemBlock, StsAssert, GpuNotSupported, GpuApiCallError, OpenGlNotSupported, OpenGlApiCallError, OpenCLApiCallError, OpenCLDoubleNotSupported, OpenCLInitError, OpenCLNoAMDBlasFft } } + + /// Covariation flags + #[repr(transparent)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub struct CovarFlags(i32); + + impl CovarFlags { + /// The output covariance matrix is calculated as: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bscale%7D%20%20%20%5Ccdot%20%20%5B%20%20%5Ctexttt%7Bvects%7D%20%20%5B0%5D%2D%20%20%5Ctexttt%7Bmean%7D%20%20%2C%20%5Ctexttt%7Bvects%7D%20%20%5B1%5D%2D%20%20%5Ctexttt%7Bmean%7D%20%20%2C%2E%2E%2E%5D%5ET%20%20%5Ccdot%20%20%5B%20%5Ctexttt%7Bvects%7D%20%20%5B0%5D%2D%20%5Ctexttt%7Bmean%7D%20%20%2C%20%5Ctexttt%7Bvects%7D%20%20%5B1%5D%2D%20%5Ctexttt%7Bmean%7D%20%20%2C%2E%2E%2E%5D%2C) + /// The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used + /// for fast PCA of a set of very large vectors (see, for example, the EigenFaces technique for + /// face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true + /// covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of + /// the "scrambled" covariance matrix. + pub const COVAR_SCRAMBLED: Self = Self(0); + /// The output covariance matrix is calculated as: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bscale%7D%20%20%20%5Ccdot%20%20%5B%20%20%5Ctexttt%7Bvects%7D%20%20%5B0%5D%2D%20%20%5Ctexttt%7Bmean%7D%20%20%2C%20%5Ctexttt%7Bvects%7D%20%20%5B1%5D%2D%20%20%5Ctexttt%7Bmean%7D%20%20%2C%2E%2E%2E%5D%20%20%5Ccdot%20%20%5B%20%5Ctexttt%7Bvects%7D%20%20%5B0%5D%2D%20%5Ctexttt%7Bmean%7D%20%20%2C%20%5Ctexttt%7Bvects%7D%20%20%5B1%5D%2D%20%5Ctexttt%7Bmean%7D%20%20%2C%2E%2E%2E%5D%5ET%2C) + /// covar will be a square matrix of the same size as the total number of elements in each input + /// vector. One and only one of [COVAR_SCRAMBLED] and [COVAR_NORMAL] must be specified. + pub const COVAR_NORMAL: Self = Self(1); + /// If the flag is specified, the function does not calculate mean from + /// the input vectors but, instead, uses the passed mean vector. This is useful if mean has been + /// pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In + /// this case, mean is not a mean vector of the input sub-set of vectors but rather the mean + /// vector of the whole set. + pub const COVAR_USE_AVG: Self = Self(2); + /// If the flag is specified, the covariance matrix is scaled. In the + /// "normal" mode, scale is 1./nsamples . In the "scrambled" mode, scale is the reciprocal of the + /// total number of elements in each input vector. By default (if the flag is not specified), the + /// covariance matrix is not scaled ( scale=1 ). + pub const COVAR_SCALE: Self = Self(4); + /// If the flag is + /// specified, all the input vectors are stored as rows of the samples matrix. mean should be a + /// single-row vector in this case. + pub const COVAR_ROWS: Self = Self(8); + /// If the flag is + /// specified, all the input vectors are stored as columns of the samples matrix. mean should be a + /// single-column vector in this case. + pub const COVAR_COLS: Self = Self(16); + } + + opencv_type_bitfield_enum! { core::CovarFlags { COVAR_SCRAMBLED, COVAR_NORMAL, COVAR_USE_AVG, COVAR_SCALE, COVAR_ROWS, COVAR_COLS } } + + /// Available CPU features. + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum CpuFeatures { + CPU_MMX = 1, + CPU_SSE = 2, + CPU_SSE2 = 3, + CPU_SSE3 = 4, + CPU_SSSE3 = 5, + CPU_SSE4_1 = 6, + CPU_SSE4_2 = 7, + CPU_POPCNT = 8, + CPU_FP16 = 9, + CPU_AVX = 10, + CPU_AVX2 = 11, + CPU_FMA3 = 12, + CPU_AVX_512F = 13, + CPU_AVX_512BW = 14, + CPU_AVX_512CD = 15, + CPU_AVX_512DQ = 16, + CPU_AVX_512ER = 17, + CPU_AVX_512IFMA512 = 18, + // Duplicate, use CPU_AVX_512IFMA512 instead + // CPU_AVX_512IFMA = 18, + CPU_AVX_512PF = 19, + CPU_AVX_512VBMI = 20, + CPU_AVX_512VL = 21, + CPU_AVX_512VBMI2 = 22, + CPU_AVX_512VNNI = 23, + CPU_AVX_512BITALG = 24, + CPU_AVX_512VPOPCNTDQ = 25, + CPU_AVX_5124VNNIW = 26, + CPU_AVX_5124FMAPS = 27, + CPU_NEON = 100, + CPU_NEON_DOTPROD = 101, + CPU_NEON_FP16 = 102, + CPU_NEON_BF16 = 103, + CPU_SVE = 104, + CPU_MSA = 150, + CPU_RISCVV = 170, + CPU_VSX = 200, + CPU_VSX3 = 201, + CPU_RVV = 210, + CPU_LSX = 230, + CPU_LASX = 231, + /// Skylake-X with AVX-512F/CD/BW/DQ/VL + CPU_AVX512_SKX = 256, + /// Common instructions AVX-512F/CD for all CPUs that support AVX-512 + CPU_AVX512_COMMON = 257, + /// Knights Landing with AVX-512F/CD/ER/PF + CPU_AVX512_KNL = 258, + /// Knights Mill with AVX-512F/CD/ER/PF/4FMAPS/4VNNIW/VPOPCNTDQ + CPU_AVX512_KNM = 259, + /// Cannon Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI + CPU_AVX512_CNL = 260, + /// Cascade Lake with AVX-512F/CD/BW/DQ/VL/VNNI + CPU_AVX512_CLX = 261, + /// Ice Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI/VNNI/VBMI2/BITALG/VPOPCNTDQ + CPU_AVX512_ICL = 262, + CPU_MAX_FEATURE = 512, + } + + opencv_type_enum! { core::CpuFeatures { CPU_MMX, CPU_SSE, CPU_SSE2, CPU_SSE3, CPU_SSSE3, CPU_SSE4_1, CPU_SSE4_2, CPU_POPCNT, CPU_FP16, CPU_AVX, CPU_AVX2, CPU_FMA3, CPU_AVX_512F, CPU_AVX_512BW, CPU_AVX_512CD, CPU_AVX_512DQ, CPU_AVX_512ER, CPU_AVX_512IFMA512, CPU_AVX_512PF, CPU_AVX_512VBMI, CPU_AVX_512VL, CPU_AVX_512VBMI2, CPU_AVX_512VNNI, CPU_AVX_512BITALG, CPU_AVX_512VPOPCNTDQ, CPU_AVX_5124VNNIW, CPU_AVX_5124FMAPS, CPU_NEON, CPU_NEON_DOTPROD, CPU_NEON_FP16, CPU_NEON_BF16, CPU_SVE, CPU_MSA, CPU_RISCVV, CPU_VSX, CPU_VSX3, CPU_RVV, CPU_LSX, CPU_LASX, CPU_AVX512_SKX, CPU_AVX512_COMMON, CPU_AVX512_KNL, CPU_AVX512_KNM, CPU_AVX512_CNL, CPU_AVX512_CLX, CPU_AVX512_ICL, CPU_MAX_FEATURE } } + + /// matrix decomposition types + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum DecompTypes { + /// Gaussian elimination with the optimal pivot element chosen. + DECOMP_LU = 0, + /// singular value decomposition (SVD) method; the system can be over-defined and/or the matrix + /// src1 can be singular + DECOMP_SVD = 1, + /// eigenvalue decomposition; the matrix src1 must be symmetrical + DECOMP_EIG = 2, + /// Cholesky ![inline formula](https://latex.codecogs.com/png.latex?LL%5ET) factorization; the matrix src1 must be symmetrical and positively + /// defined + DECOMP_CHOLESKY = 3, + /// QR factorization; the system can be over-defined and/or the matrix src1 can be singular + DECOMP_QR = 4, + /// while all the previous flags are mutually exclusive, this flag can be used together with + /// any of the previous; it means that the normal equations + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc1%7D%5ET%5Ccdot%5Ctexttt%7Bsrc1%7D%5Ccdot%5Ctexttt%7Bdst%7D%3D%5Ctexttt%7Bsrc1%7D%5ET%5Ctexttt%7Bsrc2%7D) are + /// solved instead of the original system + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc1%7D%5Ccdot%5Ctexttt%7Bdst%7D%3D%5Ctexttt%7Bsrc2%7D) + DECOMP_NORMAL = 16, + } + + opencv_type_enum! { core::DecompTypes { DECOMP_LU, DECOMP_SVD, DECOMP_EIG, DECOMP_CHOLESKY, DECOMP_QR, DECOMP_NORMAL } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum Detail_TestOp { + TEST_CUSTOM = 0, + TEST_EQ = 1, + TEST_NE = 2, + TEST_LE = 3, + TEST_LT = 4, + TEST_GE = 5, + TEST_GT = 6, + CV__LAST_TEST_OP = 7, + } + + opencv_type_enum! { core::Detail_TestOp { TEST_CUSTOM, TEST_EQ, TEST_NE, TEST_LE, TEST_LT, TEST_GE, TEST_GT, CV__LAST_TEST_OP } } + + #[repr(transparent)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub struct DftFlags(i32); + + impl DftFlags { + /// No flags are set, might not make sense for all enums + pub const NONE: Self = Self(0); + /// performs an inverse 1D or 2D transform instead of the default forward + /// transform. + pub const DFT_INVERSE: Self = Self(1); + /// scales the result: divide it by the number of array elements. Normally, it is + /// combined with DFT_INVERSE. + pub const DFT_SCALE: Self = Self(2); + /// performs a forward or inverse transform of every individual row of the input + /// matrix; this flag enables you to transform multiple vectors simultaneously and can be used to + /// decrease the overhead (which is sometimes several times larger than the processing itself) to + /// perform 3D and higher-dimensional transformations and so forth. + pub const DFT_ROWS: Self = Self(4); + /// performs a forward transformation of 1D or 2D real array; the result, + /// though being a complex array, has complex-conjugate symmetry (*CCS*, see the function + /// description below for details), and such an array can be packed into a real array of the same + /// size as input, which is the fastest option and which is what the function does by default; + /// however, you may wish to get a full complex array (for simpler spectrum analysis, and so on) - + /// pass the flag to enable the function to produce a full-size complex output array. + pub const DFT_COMPLEX_OUTPUT: Self = Self(16); + /// performs an inverse transformation of a 1D or 2D complex array; the + /// result is normally a complex array of the same size, however, if the input array has + /// conjugate-complex symmetry (for example, it is a result of forward transformation with + /// DFT_COMPLEX_OUTPUT flag), the output is a real array; while the function itself does not + /// check whether the input is symmetrical or not, you can pass the flag and then the function + /// will assume the symmetry and produce the real output array (note that when the input is packed + /// into a real array and inverse transformation is executed, the function treats the input as a + /// packed complex-conjugate symmetrical array, and the output will also be a real array). + pub const DFT_REAL_OUTPUT: Self = Self(32); + /// specifies that input is complex input. If this flag is set, the input must have 2 channels. + /// On the other hand, for backwards compatibility reason, if input has 2 channels, input is + /// already considered complex. + pub const DFT_COMPLEX_INPUT: Self = Self(64); + /// performs an inverse 1D or 2D transform instead of the default forward transform. + pub const DCT_INVERSE: Self = Self(1); + /// performs a forward or inverse transform of every individual row of the input + /// matrix. This flag enables you to transform multiple vectors simultaneously and can be used to + /// decrease the overhead (which is sometimes several times larger than the processing itself) to + /// perform 3D and higher-dimensional transforms and so forth. + pub const DCT_ROWS: Self = Self(4); + } + + opencv_type_bitfield_enum! { core::DftFlags { NONE, DFT_INVERSE, DFT_SCALE, DFT_ROWS, DFT_COMPLEX_OUTPUT, DFT_REAL_OUTPUT, DFT_COMPLEX_INPUT } } + + #[repr(transparent)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub struct FLAGS(i32); + + impl FLAGS { + pub const FLAGS_NONE: Self = Self(0); + pub const FLAGS_MAPPING: Self = Self(1); + pub const FLAGS_EXPAND_SAME_NAMES: Self = Self(2); + } + + opencv_type_bitfield_enum! { core::FLAGS { FLAGS_NONE, FLAGS_MAPPING, FLAGS_EXPAND_SAME_NAMES } } + + /// file storage mode + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum FileStorage_Mode { + /// value, open the file for reading + READ = 0, + /// value, open the file for writing + WRITE = 1, + /// value, open the file for appending + APPEND = 2, + /// < flag, read data from source or write data to the internal buffer (which is + /// returned by FileStorage::release) + MEMORY = 4, + /// mask for format flags + FORMAT_MASK = 56, + // flag, auto format + // Duplicate, use READ instead + // FORMAT_AUTO = 0, + /// flag, XML format + FORMAT_XML = 8, + /// flag, YAML format + FORMAT_YAML = 16, + /// flag, JSON format + FORMAT_JSON = 24, + /// flag, write rawdata in Base64 by default. (consider using WRITE_BASE64) + BASE64 = 64, + /// flag, enable both WRITE and BASE64 + WRITE_BASE64 = 65, + } + + opencv_type_enum! { core::FileStorage_Mode { READ, WRITE, APPEND, MEMORY, FORMAT_MASK, FORMAT_XML, FORMAT_YAML, FORMAT_JSON, BASE64, WRITE_BASE64 } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum FileStorage_State { + /// Initial or uninitialized state. + UNDEFINED = 0, + /// Expecting a value in the current position. + VALUE_EXPECTED = 1, + /// Expecting a key/name in the current position. + NAME_EXPECTED = 2, + /// Indicates being inside a map (a set of key-value pairs). + INSIDE_MAP = 4, + } + + opencv_type_enum! { core::FileStorage_State { UNDEFINED, VALUE_EXPECTED, NAME_EXPECTED, INSIDE_MAP } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum Formatter_FormatType { + FMT_DEFAULT = 0, + FMT_MATLAB = 1, + FMT_CSV = 2, + FMT_PYTHON = 3, + FMT_NUMPY = 4, + FMT_C = 5, + } + + opencv_type_enum! { core::Formatter_FormatType { FMT_DEFAULT, FMT_MATLAB, FMT_CSV, FMT_PYTHON, FMT_NUMPY, FMT_C } } + + /// generalized matrix multiplication flags + #[repr(transparent)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub struct GemmFlags(i32); + + impl GemmFlags { + /// No flags are set, might not make sense for all enums + pub const NONE: Self = Self(0); + /// transposes src1 + pub const GEMM_1_T: Self = Self(1); + /// transposes src2 + pub const GEMM_2_T: Self = Self(2); + /// transposes src3 + pub const GEMM_3_T: Self = Self(4); + } + + opencv_type_bitfield_enum! { core::GemmFlags { NONE, GEMM_1_T, GEMM_2_T, GEMM_3_T } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum IMPL { + IMPL_PLAIN = 0, + IMPL_IPP = 1, + IMPL_OPENCL = 2, + } + + opencv_type_enum! { core::IMPL { IMPL_PLAIN, IMPL_IPP, IMPL_OPENCL } } + + /// k-means flags + #[repr(transparent)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub struct KmeansFlags(i32); + + impl KmeansFlags { + /// Select random initial centers in each attempt. + pub const KMEANS_RANDOM_CENTERS: Self = Self(0); + /// Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007]. + pub const KMEANS_PP_CENTERS: Self = Self(2); + /// During the first (and possibly the only) attempt, use the + /// user-supplied labels instead of computing them from the initial centers. For the second and + /// further attempts, use the random or semi-random centers. Use one of KMEANS_\*_CENTERS flag + /// to specify the exact method. + pub const KMEANS_USE_INITIAL_LABELS: Self = Self(1); + } + + opencv_type_bitfield_enum! { core::KmeansFlags { KMEANS_RANDOM_CENTERS, KMEANS_PP_CENTERS, KMEANS_USE_INITIAL_LABELS } } + + /// Supported logging levels and their semantic + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum LogLevel { + /// for using in setLogVevel() call + LOG_LEVEL_SILENT = 0, + /// Fatal (critical) error (unrecoverable internal error) + LOG_LEVEL_FATAL = 1, + /// Error message + LOG_LEVEL_ERROR = 2, + /// Warning message + LOG_LEVEL_WARNING = 3, + /// Info message + LOG_LEVEL_INFO = 4, + /// Debug message. Disabled in the "Release" build. + LOG_LEVEL_DEBUG = 5, + /// Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build. + LOG_LEVEL_VERBOSE = 6, + ENUM_LOG_LEVEL_FORCE_INT = 2147483647, + } + + opencv_type_enum! { core::LogLevel { LOG_LEVEL_SILENT, LOG_LEVEL_FATAL, LOG_LEVEL_ERROR, LOG_LEVEL_WARNING, LOG_LEVEL_INFO, LOG_LEVEL_DEBUG, LOG_LEVEL_VERBOSE, ENUM_LOG_LEVEL_FORCE_INT } } + + /// norm types + /// + /// src1 and src2 denote input arrays. + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum NormTypes { + /// ![block formula](https://latex.codecogs.com/png.latex?%0Anorm%20%3D%20%20%5Cforkthree%0A%7B%5C%7C%5Ctexttt%7Bsrc1%7D%5C%7C%5F%7BL%5F%7B%5Cinfty%7D%7D%20%3D%20%20%5Cmax%20%5FI%20%7C%20%5Ctexttt%7Bsrc1%7D%20%28I%29%7C%7D%7Bif%20%20%5C%28%5Ctexttt%7BnormType%7D%20%3D%20%5Ctexttt%7BNORM%5FINF%7D%5C%29%20%7D%0A%7B%5C%7C%5Ctexttt%7Bsrc1%7D%2D%5Ctexttt%7Bsrc2%7D%5C%7C%5F%7BL%5F%7B%5Cinfty%7D%7D%20%3D%20%20%5Cmax%20%5FI%20%7C%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%2D%20%20%5Ctexttt%7Bsrc2%7D%20%28I%29%7C%7D%7Bif%20%20%5C%28%5Ctexttt%7BnormType%7D%20%3D%20%5Ctexttt%7BNORM%5FINF%7D%5C%29%20%7D%0A%7B%5Cfrac%7B%5C%7C%5Ctexttt%7Bsrc1%7D%2D%5Ctexttt%7Bsrc2%7D%5C%7C%5F%7BL%5F%7B%5Cinfty%7D%7D%20%20%20%20%7D%7B%5C%7C%5Ctexttt%7Bsrc2%7D%5C%7C%5F%7BL%5F%7B%5Cinfty%7D%7D%20%7D%7D%7Bif%20%20%5C%28%5Ctexttt%7BnormType%7D%20%3D%20%5Ctexttt%7BNORM%5FRELATIVE%20%7C%20NORM%5FINF%7D%5C%29%20%7D%0A) + NORM_INF = 1, + /// ![block formula](https://latex.codecogs.com/png.latex?%0Anorm%20%3D%20%20%5Cforkthree%0A%7B%5C%7C%20%5Ctexttt%7Bsrc1%7D%20%5C%7C%20%5F%7BL%5F1%7D%20%3D%20%20%5Csum%20%5FI%20%7C%20%5Ctexttt%7Bsrc1%7D%20%28I%29%7C%7D%7Bif%20%20%5C%28%5Ctexttt%7BnormType%7D%20%3D%20%5Ctexttt%7BNORM%5FL1%7D%5C%29%7D%0A%7B%20%5C%7C%20%5Ctexttt%7Bsrc1%7D%20%2D%20%5Ctexttt%7Bsrc2%7D%20%5C%7C%20%5F%7BL%5F1%7D%20%3D%20%20%5Csum%20%5FI%20%7C%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%2D%20%20%5Ctexttt%7Bsrc2%7D%20%28I%29%7C%7D%7Bif%20%20%5C%28%5Ctexttt%7BnormType%7D%20%3D%20%5Ctexttt%7BNORM%5FL1%7D%5C%29%20%7D%0A%7B%20%5Cfrac%7B%5C%7C%5Ctexttt%7Bsrc1%7D%2D%5Ctexttt%7Bsrc2%7D%5C%7C%5F%7BL%5F1%7D%20%7D%7B%5C%7C%5Ctexttt%7Bsrc2%7D%5C%7C%5F%7BL%5F1%7D%7D%20%7D%7Bif%20%20%5C%28%5Ctexttt%7BnormType%7D%20%3D%20%5Ctexttt%7BNORM%5FRELATIVE%20%7C%20NORM%5FL1%7D%5C%29%20%7D%0A) + NORM_L1 = 2, + /// ![block formula](https://latex.codecogs.com/png.latex?%0Anorm%20%3D%20%20%5Cforkthree%0A%7B%20%5C%7C%20%5Ctexttt%7Bsrc1%7D%20%5C%7C%20%5F%7BL%5F2%7D%20%3D%20%20%5Csqrt%7B%5Csum%5FI%20%5Ctexttt%7Bsrc1%7D%28I%29%5E2%7D%20%7D%7Bif%20%20%5C%28%5Ctexttt%7BnormType%7D%20%3D%20%5Ctexttt%7BNORM%5FL2%7D%5C%29%20%7D%0A%7B%20%5C%7C%20%5Ctexttt%7Bsrc1%7D%20%2D%20%5Ctexttt%7Bsrc2%7D%20%5C%7C%20%5F%7BL%5F2%7D%20%3D%20%20%5Csqrt%7B%5Csum%5FI%20%28%5Ctexttt%7Bsrc1%7D%28I%29%20%2D%20%5Ctexttt%7Bsrc2%7D%28I%29%29%5E2%7D%20%7D%7Bif%20%20%5C%28%5Ctexttt%7BnormType%7D%20%3D%20%5Ctexttt%7BNORM%5FL2%7D%5C%29%20%7D%0A%7B%20%5Cfrac%7B%5C%7C%5Ctexttt%7Bsrc1%7D%2D%5Ctexttt%7Bsrc2%7D%5C%7C%5F%7BL%5F2%7D%20%7D%7B%5C%7C%5Ctexttt%7Bsrc2%7D%5C%7C%5F%7BL%5F2%7D%7D%20%7D%7Bif%20%20%5C%28%5Ctexttt%7BnormType%7D%20%3D%20%5Ctexttt%7BNORM%5FRELATIVE%20%7C%20NORM%5FL2%7D%5C%29%20%7D%0A) + NORM_L2 = 4, + /// ![block formula](https://latex.codecogs.com/png.latex?%0Anorm%20%3D%20%20%5Cforkthree%0A%7B%20%5C%7C%20%5Ctexttt%7Bsrc1%7D%20%5C%7C%20%5F%7BL%5F2%7D%20%5E%7B2%7D%20%3D%20%5Csum%5FI%20%5Ctexttt%7Bsrc1%7D%28I%29%5E2%7D%20%7Bif%20%20%5C%28%5Ctexttt%7BnormType%7D%20%3D%20%5Ctexttt%7BNORM%5FL2SQR%7D%5C%29%7D%0A%7B%20%5C%7C%20%5Ctexttt%7Bsrc1%7D%20%2D%20%5Ctexttt%7Bsrc2%7D%20%5C%7C%20%5F%7BL%5F2%7D%20%5E%7B2%7D%20%3D%20%20%5Csum%5FI%20%28%5Ctexttt%7Bsrc1%7D%28I%29%20%2D%20%5Ctexttt%7Bsrc2%7D%28I%29%29%5E2%20%7D%7Bif%20%20%5C%28%5Ctexttt%7BnormType%7D%20%3D%20%5Ctexttt%7BNORM%5FL2SQR%7D%5C%29%20%7D%0A%7B%20%5Cleft%28%5Cfrac%7B%5C%7C%5Ctexttt%7Bsrc1%7D%2D%5Ctexttt%7Bsrc2%7D%5C%7C%5F%7BL%5F2%7D%20%7D%7B%5C%7C%5Ctexttt%7Bsrc2%7D%5C%7C%5F%7BL%5F2%7D%7D%5Cright%29%5E2%20%7D%7Bif%20%20%5C%28%5Ctexttt%7BnormType%7D%20%3D%20%5Ctexttt%7BNORM%5FRELATIVE%20%7C%20NORM%5FL2SQR%7D%5C%29%20%7D%0A) + NORM_L2SQR = 5, + /// In the case of one input array, calculates the Hamming distance of the array from zero, + /// In the case of two input arrays, calculates the Hamming distance between the arrays. + NORM_HAMMING = 6, + /// Similar to NORM_HAMMING, but in the calculation, each two bits of the input sequence will + /// be added and treated as a single bit to be used in the same calculation as NORM_HAMMING. + NORM_HAMMING2 = 7, + // bit-mask which can be used to separate norm type from norm flags + // Duplicate, use NORM_HAMMING2 instead + // NORM_TYPE_MASK = 7, + /// flag + NORM_RELATIVE = 8, + /// flag + NORM_MINMAX = 32, + } + + opencv_type_enum! { core::NormTypes { NORM_INF, NORM_L1, NORM_L2, NORM_L2SQR, NORM_HAMMING, NORM_HAMMING2, NORM_RELATIVE, NORM_MINMAX } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum OclVectorStrategy { + OCL_VECTOR_OWN = 0, + OCL_VECTOR_MAX = 1, + // Duplicate, use OCL_VECTOR_OWN instead + // OCL_VECTOR_DEFAULT = 0, + } + + opencv_type_enum! { core::OclVectorStrategy { OCL_VECTOR_OWN, OCL_VECTOR_MAX } } + + #[repr(transparent)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub struct PCA_Flags(i32); + + impl PCA_Flags { + /// indicates that the input samples are stored as matrix rows + pub const DATA_AS_ROW: Self = Self(0); + /// indicates that the input samples are stored as matrix columns + pub const DATA_AS_COL: Self = Self(1); + pub const USE_AVG: Self = Self(2); + } + + opencv_type_bitfield_enum! { core::PCA_Flags { DATA_AS_ROW, DATA_AS_COL, USE_AVG } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum Param { + INT = 0, + BOOLEAN = 1, + REAL = 2, + STRING = 3, + MAT = 4, + MAT_VECTOR = 5, + ALGORITHM = 6, + FLOAT = 7, + UNSIGNED_INT = 8, + UINT64 = 9, + UCHAR = 11, + SCALAR = 12, + } + + opencv_type_enum! { core::Param { INT, BOOLEAN, REAL, STRING, MAT, MAT_VECTOR, ALGORITHM, FLOAT, UNSIGNED_INT, UINT64, UCHAR, SCALAR } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum ReduceTypes { + /// the output is the sum of all rows/columns of the matrix. + REDUCE_SUM = 0, + /// the output is the mean vector of all rows/columns of the matrix. + REDUCE_AVG = 1, + /// the output is the maximum (column/row-wise) of all rows/columns of the matrix. + REDUCE_MAX = 2, + /// the output is the minimum (column/row-wise) of all rows/columns of the matrix. + REDUCE_MIN = 3, + /// the output is the sum of all squared rows/columns of the matrix. + REDUCE_SUM2 = 4, + } + + opencv_type_enum! { core::ReduceTypes { REDUCE_SUM, REDUCE_AVG, REDUCE_MAX, REDUCE_MIN, REDUCE_SUM2 } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum RotateFlags { + /// Rotate 90 degrees clockwise + ROTATE_90_CLOCKWISE = 0, + /// Rotate 180 degrees clockwise + ROTATE_180 = 1, + /// Rotate 270 degrees clockwise + ROTATE_90_COUNTERCLOCKWISE = 2, + } + + opencv_type_enum! { core::RotateFlags { ROTATE_90_CLOCKWISE, ROTATE_180, ROTATE_90_COUNTERCLOCKWISE } } + + #[repr(transparent)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub struct SVD_Flags(i32); + + impl SVD_Flags { + /// No flags are set, might not make sense for all enums + pub const NONE: Self = Self(0); + /// allow the algorithm to modify the decomposed matrix; it can save space and speed up + /// processing. currently ignored. + pub const MODIFY_A: Self = Self(1); + /// indicates that only a vector of singular values `w` is to be processed, while u and vt + /// will be set to empty matrices + pub const NO_UV: Self = Self(2); + /// when the matrix is not square, by default the algorithm produces u and vt matrices of + /// sufficiently large size for the further A reconstruction; if, however, FULL_UV flag is + /// specified, u and vt will be full-size square orthogonal matrices. + pub const FULL_UV: Self = Self(4); + } + + opencv_type_bitfield_enum! { core::SVD_Flags { NONE, MODIFY_A, NO_UV, FULL_UV } } + + /// return codes for cv::solveLP() function + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum SolveLPResult { + /// problem is feasible, but solver lost solution due to floating-point arithmetic errors + SOLVELP_LOST = -3, + /// problem is unbounded (target function can achieve arbitrary high values) + SOLVELP_UNBOUNDED = -2, + /// problem is unfeasible (there are no points that satisfy all the constraints imposed) + SOLVELP_UNFEASIBLE = -1, + /// there is only one maximum for target function + SOLVELP_SINGLE = 0, + /// there are multiple maxima for target function - the arbitrary one is returned + SOLVELP_MULTI = 1, + } + + opencv_type_enum! { core::SolveLPResult { SOLVELP_LOST, SOLVELP_UNBOUNDED, SOLVELP_UNFEASIBLE, SOLVELP_SINGLE, SOLVELP_MULTI } } + + #[repr(transparent)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub struct SortFlags(i32); + + impl SortFlags { + /// each matrix row is sorted independently + pub const SORT_EVERY_ROW: Self = Self(0); + /// each matrix column is sorted + /// independently; this flag and the previous one are + /// mutually exclusive. + pub const SORT_EVERY_COLUMN: Self = Self(1); + /// each matrix row is sorted in the ascending + /// order. + pub const SORT_ASCENDING: Self = Self(0); + /// each matrix row is sorted in the + /// descending order; this flag and the previous one are also + /// mutually exclusive. + pub const SORT_DESCENDING: Self = Self(16); + } + + opencv_type_bitfield_enum! { core::SortFlags { SORT_EVERY_ROW, SORT_EVERY_COLUMN, SORT_DESCENDING } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum TYPE { + TYPE_GENERAL = 0, + TYPE_MARKER = 1, + TYPE_WRAPPER = 2, + TYPE_FUN = 3, + } + + opencv_type_enum! { core::TYPE { TYPE_GENERAL, TYPE_MARKER, TYPE_WRAPPER, TYPE_FUN } } + + /// Criteria type, can be one of: COUNT, EPS or COUNT + EPS + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum TermCriteria_Type { + /// the maximum number of iterations or elements to compute + COUNT = 1, + // ditto + // Duplicate, use COUNT instead + // MAX_ITER = 1, + /// the desired accuracy or change in parameters at which the iterative algorithm stops + EPS = 2, + } + + opencv_type_enum! { core::TermCriteria_Type { COUNT, EPS } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum UMatData_MemoryFlag { + COPY_ON_MAP = 1, + HOST_COPY_OBSOLETE = 2, + DEVICE_COPY_OBSOLETE = 4, + TEMP_UMAT = 8, + TEMP_COPIED_UMAT = 24, + USER_ALLOCATED = 32, + DEVICE_MEM_MAPPED = 64, + ASYNC_CLEANUP = 128, + } + + opencv_type_enum! { core::UMatData_MemoryFlag { COPY_ON_MAP, HOST_COPY_OBSOLETE, DEVICE_COPY_OBSOLETE, TEMP_UMAT, TEMP_COPIED_UMAT, USER_ALLOCATED, DEVICE_MEM_MAPPED, ASYNC_CLEANUP } } + + /// Usage flags for allocator + /// + /// @warning All flags except `USAGE_DEFAULT` are experimental. + /// + /// @warning For the OpenCL allocator, `USAGE_ALLOCATE_SHARED_MEMORY` depends on + /// OpenCV's optional, experimental integration with OpenCL SVM. To enable this + /// integration, build OpenCV using the `WITH_OPENCL_SVM=ON` CMake option and, at + /// runtime, call `cv::ocl::Context::getDefault().setUseSVM(true);` or similar + /// code. Note that SVM is incompatible with OpenCL 1.x. + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum UMatUsageFlags { + USAGE_DEFAULT = 0, + USAGE_ALLOCATE_HOST_MEMORY = 1, + USAGE_ALLOCATE_DEVICE_MEMORY = 2, + USAGE_ALLOCATE_SHARED_MEMORY = 4, + __UMAT_USAGE_FLAGS_32BIT = 2147483647, + } + + opencv_type_enum! { core::UMatUsageFlags { USAGE_DEFAULT, USAGE_ALLOCATE_HOST_MEMORY, USAGE_ALLOCATE_DEVICE_MEMORY, USAGE_ALLOCATE_SHARED_MEMORY, __UMAT_USAGE_FLAGS_32BIT } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum _InputArray_KindFlag { + KIND_SHIFT = 16, + FIXED_TYPE = -2147483648, + FIXED_SIZE = 1073741824, + KIND_MASK = 2031616, + NONE = 0, + MAT = 65536, + MATX = 131072, + STD_VECTOR = 196608, + STD_VECTOR_VECTOR = 262144, + STD_VECTOR_MAT = 327680, + /// removed: + EXPR = 393216, + OPENGL_BUFFER = 458752, + CUDA_HOST_MEM = 524288, + CUDA_GPU_MAT = 589824, + UMAT = 655360, + STD_VECTOR_UMAT = 720896, + STD_BOOL_VECTOR = 786432, + STD_VECTOR_CUDA_GPU_MAT = 851968, + /// removed: + STD_ARRAY = 917504, + STD_ARRAY_MAT = 983040, + CUDA_GPU_MATND = 1048576, + } + + opencv_type_enum! { core::_InputArray_KindFlag { KIND_SHIFT, FIXED_TYPE, FIXED_SIZE, KIND_MASK, NONE, MAT, MATX, STD_VECTOR, STD_VECTOR_VECTOR, STD_VECTOR_MAT, EXPR, OPENGL_BUFFER, CUDA_HOST_MEM, CUDA_GPU_MAT, UMAT, STD_VECTOR_UMAT, STD_BOOL_VECTOR, STD_VECTOR_CUDA_GPU_MAT, STD_ARRAY, STD_ARRAY_MAT, CUDA_GPU_MATND } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum _OutputArray_DepthMask { + DEPTH_MASK_8U = 1, + DEPTH_MASK_8S = 2, + DEPTH_MASK_16U = 4, + DEPTH_MASK_16S = 8, + DEPTH_MASK_32S = 16, + DEPTH_MASK_32F = 32, + DEPTH_MASK_64F = 64, + DEPTH_MASK_16F = 128, + DEPTH_MASK_ALL = 127, + DEPTH_MASK_ALL_BUT_8S = 125, + DEPTH_MASK_ALL_16F = 255, + DEPTH_MASK_FLT = 96, + } + + opencv_type_enum! { core::_OutputArray_DepthMask { DEPTH_MASK_8U, DEPTH_MASK_8S, DEPTH_MASK_16U, DEPTH_MASK_16S, DEPTH_MASK_32S, DEPTH_MASK_32F, DEPTH_MASK_64F, DEPTH_MASK_16F, DEPTH_MASK_ALL, DEPTH_MASK_ALL_BUT_8S, DEPTH_MASK_ALL_16F, DEPTH_MASK_FLT } } + + pub type VADisplay = *mut c_void; + pub type VASurfaceID = u32; + pub type Affine3d = core::Affine3; + pub type Affine3f = core::Affine3; + pub type Hamming_ResultType = i32; + pub type Hamming_ValueType = u8; + pub type HammingLUT = core::Hamming; + pub type InputArray<'a> = &'a core::_InputArray; + pub type InputArrayOfArrays<'a> = core::InputArray<'a>; + pub type InputOutputArray<'a> = &'a core::_InputOutputArray; + pub type InputOutputArrayOfArrays<'a> = core::InputOutputArray<'a>; + pub type Mat1b = core::Mat_; + pub type Mat1d = core::Mat_; + pub type Mat1f = core::Mat_; + pub type Mat1i = core::Mat_; + pub type Mat1s = core::Mat_; + pub type Mat1w = core::Mat_; + pub type Mat2b = core::Mat_; + pub type Mat2d = core::Mat_; + pub type Mat2f = core::Mat_; + pub type Mat2i = core::Mat_; + pub type Mat2s = core::Mat_; + pub type Mat2w = core::Mat_; + pub type Mat3b = core::Mat_; + pub type Mat3d = core::Mat_; + pub type Mat3f = core::Mat_; + pub type Mat3i = core::Mat_; + pub type Mat3s = core::Mat_; + pub type Mat3w = core::Mat_; + pub type Mat4b = core::Mat_; + pub type Mat4d = core::Mat_; + pub type Mat4f = core::Mat_; + pub type Mat4i = core::Mat_; + pub type Mat4s = core::Mat_; + pub type Mat4w = core::Mat_; + pub type MatConstIterator_difference_type = ptrdiff_t; + pub type MatConstIterator_pointer = *mut *const u8; + pub type MatConstIterator_reference = *mut u8; + pub type MatConstIterator_value_type = *mut u8; + pub type Matx12d = core::Matx12; + pub type Matx12f = core::Matx12; + pub type Matx13d = core::Matx13; + pub type Matx13f = core::Matx13; + pub type Matx14d = core::Matx14; + pub type Matx14f = core::Matx14; + pub type Matx16d = core::Matx16; + pub type Matx16f = core::Matx16; + pub type Matx21d = core::Matx21; + pub type Matx21f = core::Matx21; + pub type Matx22d = core::Matx22; + pub type Matx22f = core::Matx22; + pub type Matx23d = core::Matx23; + pub type Matx23f = core::Matx23; + pub type Matx31d = core::Matx31; + pub type Matx31f = core::Matx31; + pub type Matx32d = core::Matx32; + pub type Matx32f = core::Matx32; + pub type Matx33d = core::Matx33; + pub type Matx33f = core::Matx33; + pub type Matx34d = core::Matx34; + pub type Matx34f = core::Matx34; + pub type Matx41d = core::Matx41; + pub type Matx41f = core::Matx41; + pub type Matx43d = core::Matx43; + pub type Matx43f = core::Matx43; + pub type Matx44d = core::Matx44; + pub type Matx44f = core::Matx44; + pub type Matx61d = core::Matx61; + pub type Matx61f = core::Matx61; + pub type Matx66d = core::Matx66; + pub type Matx66f = core::Matx66; + pub type OutputArray<'a> = &'a core::_OutputArray; + pub type OutputArrayOfArrays<'a> = core::OutputArray<'a>; + pub type Point = core::Point2i; + pub type Point2d = core::Point_; + pub type Point2f = core::Point_; + pub type Point2i = core::Point_; + pub type Point2l = core::Point_; + pub type Point3d = core::Point3_; + pub type Point3f = core::Point3_; + pub type Point3i = core::Point3_; + pub type Rect = core::Rect2i; + pub type Rect2d = core::Rect_; + pub type Rect2f = core::Rect_; + pub type Rect2i = core::Rect_; + pub type Scalar = core::Scalar_; + pub type Size = core::Size2i; + pub type Size2d = core::Size_; + pub type Size2f = core::Size_; + pub type Size2i = core::Size_; + pub type Size2l = core::Size_; + /// Shorter aliases for the most popular specializations of Vec + pub type Vec2b = core::VecN; + pub type Vec2d = core::VecN; + pub type Vec2f = core::VecN; + pub type Vec2i = core::VecN; + pub type Vec2s = core::VecN; + pub type Vec2w = core::VecN; + pub type Vec3b = core::VecN; + pub type Vec3d = core::VecN; + pub type Vec3f = core::VecN; + pub type Vec3i = core::VecN; + pub type Vec3s = core::VecN; + pub type Vec3w = core::VecN; + pub type Vec4b = core::VecN; + pub type Vec4d = core::VecN; + pub type Vec4f = core::VecN; + pub type Vec4i = core::VecN; + pub type Vec4s = core::VecN; + pub type Vec4w = core::VecN; + pub type Vec6d = core::VecN; + pub type Vec6f = core::VecN; + pub type Vec6i = core::VecN; + pub type Vec8i = core::VecN; + pub type ProgramSource_hash_t = u64; + /// Function pointer type for writeLogMessageEx. Used by replaceWriteLogMessageEx. + pub type WriteLogMessageExFuncType = Option ()>; + /// Function pointer type for writeLogMessage. Used by replaceWriteLogMessage. + pub type WriteLogMessageFuncType = Option ()>; + /// proxy for hal::Cholesky + #[inline] + pub fn cholesky(a: &mut f64, astep: size_t, m: i32, b: &mut f64, bstep: size_t, n: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Cholesky_doubleX_size_t_int_doubleX_size_t_int(a, astep, m, b, bstep, n, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// proxy for hal::Cholesky + #[inline] + pub fn cholesky_f32(a: &mut f32, astep: size_t, m: i32, b: &mut f32, bstep: size_t, n: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Cholesky_floatX_size_t_int_floatX_size_t_int(a, astep, m, b, bstep, n, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs a look-up table transform of an array. + /// + /// The function LUT fills the output array with values from the look-up table. Indices of the entries + /// are taken from the input array. That is, the function processes each element of src as follows: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%20%5Cleftarrow%20%5Ctexttt%7Blut%28src%28I%29%20%2B%20d%29%7D) + /// where + /// ![block formula](https://latex.codecogs.com/png.latex?d%20%3D%20%20%5Cforkthree%7B0%7D%7Bif%20%5C%28%5Ctexttt%7Bsrc%7D%5C%29%20has%20depth%20%5C%28%5Ctexttt%7BCV%5F8U%7D%5C%29%20or%20%5C%28%5Ctexttt%7BCV%5F16U%7D%5C%29%7D%7B128%7D%7Bif%20%5C%28%5Ctexttt%7Bsrc%7D%5C%29%20has%20depth%20%5C%28%5Ctexttt%7BCV%5F8S%7D%5C%29%7D%7B32768%7D%7Bif%20%5C%28%5Ctexttt%7Bsrc%7D%5C%29%20has%20depth%20%5C%28%5Ctexttt%7BCV%5F16S%7D%5C%29%7D) + /// ## Parameters + /// * src: input array of 8-bit or 16-bit integer elements. + /// * lut: look-up table of 256 elements (if src has depth CV_8U or CV_8S) or 65536 elements(if src has depth CV_16U or CV_16S); in case of multi-channel input array, the table should + /// either have a single channel (in this case the same table is used for all channels) or the same + /// number of channels as in the input array. + /// * dst: output array of the same size and number of channels as src, and the same depth as lut. + /// ## See also + /// convertScaleAbs, Mat::convertTo + #[inline] + pub fn lut(src: &impl ToInputArray, lut: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + input_array_arg!(lut); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_LUT_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), lut.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// proxy for hal::LU + #[inline] + pub fn lu(a: &mut f64, astep: size_t, m: i32, b: &mut f64, bstep: size_t, n: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LU_doubleX_size_t_int_doubleX_size_t_int(a, astep, m, b, bstep, n, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// proxy for hal::LU + #[inline] + pub fn lu_f32(a: &mut f32, astep: size_t, m: i32, b: &mut f32, bstep: size_t, n: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LU_floatX_size_t_int_floatX_size_t_int(a, astep, m, b, bstep, n, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the Mahalanobis distance between two vectors. + /// + /// The function cv::Mahalanobis calculates and returns the weighted distance between two vectors: + /// ![block formula](https://latex.codecogs.com/png.latex?d%28%20%5Ctexttt%7Bvec1%7D%20%2C%20%5Ctexttt%7Bvec2%7D%20%29%3D%20%5Csqrt%7B%5Csum%5F%7Bi%2Cj%7D%7B%5Ctexttt%7Bicovar%28i%2Cj%29%7D%5Ccdot%28%5Ctexttt%7Bvec1%7D%28I%29%2D%5Ctexttt%7Bvec2%7D%28I%29%29%5Ccdot%28%5Ctexttt%7Bvec1%28j%29%7D%2D%5Ctexttt%7Bvec2%28j%29%7D%29%7D%20%7D) + /// The covariance matrix may be calculated using the [calc_covar_matrix] function and then inverted using + /// the invert function (preferably using the [DECOMP_SVD] method, as the most accurate). + /// ## Parameters + /// * v1: first 1D input vector. + /// * v2: second 1D input vector. + /// * icovar: inverse covariance matrix. + #[inline] + pub fn mahalanobis(v1: &impl ToInputArray, v2: &impl ToInputArray, icovar: &impl ToInputArray) -> Result { + input_array_arg!(v1); + input_array_arg!(v2); + input_array_arg!(icovar); + return_send!(via ocvrs_return); + unsafe { sys::cv_Mahalanobis_const__InputArrayR_const__InputArrayR_const__InputArrayR(v1.as_raw__InputArray(), v2.as_raw__InputArray(), icovar.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// wrap PCA::backProject + #[inline] + pub fn pca_back_project(data: &impl ToInputArray, mean: &impl ToInputArray, eigenvectors: &impl ToInputArray, result: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(data); + input_array_arg!(mean); + input_array_arg!(eigenvectors); + output_array_arg!(result); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCABackProject_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(data.as_raw__InputArray(), mean.as_raw__InputArray(), eigenvectors.as_raw__InputArray(), result.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// wrap PCA::operator() + /// + /// ## Note + /// This alternative version of [pca_compute] function uses the following default values for its arguments: + /// * max_components: 0 + #[inline] + pub fn pca_compute_def(data: &impl ToInputArray, mean: &mut impl ToInputOutputArray, eigenvectors: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(data); + input_output_array_arg!(mean); + output_array_arg!(eigenvectors); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR(data.as_raw__InputArray(), mean.as_raw__InputOutputArray(), eigenvectors.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// wrap PCA::operator() and add eigenvalues output parameter + /// + /// ## Note + /// This alternative version of [pca_compute2] function uses the following default values for its arguments: + /// * max_components: 0 + #[inline] + pub fn pca_compute2_def(data: &impl ToInputArray, mean: &mut impl ToInputOutputArray, eigenvectors: &mut impl ToOutputArray, eigenvalues: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(data); + input_output_array_arg!(mean); + output_array_arg!(eigenvectors); + output_array_arg!(eigenvalues); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR(data.as_raw__InputArray(), mean.as_raw__InputOutputArray(), eigenvectors.as_raw__OutputArray(), eigenvalues.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// wrap PCA::operator() and add eigenvalues output parameter + #[inline] + pub fn pca_compute2_variance(data: &impl ToInputArray, mean: &mut impl ToInputOutputArray, eigenvectors: &mut impl ToOutputArray, eigenvalues: &mut impl ToOutputArray, retained_variance: f64) -> Result<()> { + input_array_arg!(data); + input_output_array_arg!(mean); + output_array_arg!(eigenvectors); + output_array_arg!(eigenvalues); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_double(data.as_raw__InputArray(), mean.as_raw__InputOutputArray(), eigenvectors.as_raw__OutputArray(), eigenvalues.as_raw__OutputArray(), retained_variance, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// wrap PCA::operator() and add eigenvalues output parameter + /// + /// ## C++ default parameters + /// * max_components: 0 + #[inline] + pub fn pca_compute2(data: &impl ToInputArray, mean: &mut impl ToInputOutputArray, eigenvectors: &mut impl ToOutputArray, eigenvalues: &mut impl ToOutputArray, max_components: i32) -> Result<()> { + input_array_arg!(data); + input_output_array_arg!(mean); + output_array_arg!(eigenvectors); + output_array_arg!(eigenvalues); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_int(data.as_raw__InputArray(), mean.as_raw__InputOutputArray(), eigenvectors.as_raw__OutputArray(), eigenvalues.as_raw__OutputArray(), max_components, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// wrap PCA::operator() + #[inline] + pub fn pca_compute_variance(data: &impl ToInputArray, mean: &mut impl ToInputOutputArray, eigenvectors: &mut impl ToOutputArray, retained_variance: f64) -> Result<()> { + input_array_arg!(data); + input_output_array_arg!(mean); + output_array_arg!(eigenvectors); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_double(data.as_raw__InputArray(), mean.as_raw__InputOutputArray(), eigenvectors.as_raw__OutputArray(), retained_variance, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// wrap PCA::operator() + /// + /// ## C++ default parameters + /// * max_components: 0 + #[inline] + pub fn pca_compute(data: &impl ToInputArray, mean: &mut impl ToInputOutputArray, eigenvectors: &mut impl ToOutputArray, max_components: i32) -> Result<()> { + input_array_arg!(data); + input_output_array_arg!(mean); + output_array_arg!(eigenvectors); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_int(data.as_raw__InputArray(), mean.as_raw__InputOutputArray(), eigenvectors.as_raw__OutputArray(), max_components, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// wrap PCA::project + #[inline] + pub fn pca_project(data: &impl ToInputArray, mean: &impl ToInputArray, eigenvectors: &impl ToInputArray, result: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(data); + input_array_arg!(mean); + input_array_arg!(eigenvectors); + output_array_arg!(result); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCAProject_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(data.as_raw__InputArray(), mean.as_raw__InputArray(), eigenvectors.as_raw__InputArray(), result.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric. + /// + /// This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB), + /// between two input arrays src1 and src2. The arrays must have the same type. + /// + /// The PSNR is calculated as follows: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Ctexttt%7BPSNR%7D%20%3D%2010%20%5Ccdot%20%5Clog%5F%7B10%7D%7B%5Cleft%28%20%5Cfrac%7BR%5E2%7D%7BMSE%7D%20%5Cright%29%20%7D%0A) + /// + /// where R is the maximum integer value of depth (e.g. 255 in the case of CV_8U data) + /// and MSE is the mean squared error between the two arrays. + /// + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size as src1. + /// * R: the maximum pixel value (255 by default) + /// + /// ## Note + /// This alternative version of [psnr] function uses the following default values for its arguments: + /// * r: 255. + #[inline] + pub fn psnr_def(src1: &impl ToInputArray, src2: &impl ToInputArray) -> Result { + input_array_arg!(src1); + input_array_arg!(src2); + return_send!(via ocvrs_return); + unsafe { sys::cv_PSNR_const__InputArrayR_const__InputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric. + /// + /// This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB), + /// between two input arrays src1 and src2. The arrays must have the same type. + /// + /// The PSNR is calculated as follows: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Ctexttt%7BPSNR%7D%20%3D%2010%20%5Ccdot%20%5Clog%5F%7B10%7D%7B%5Cleft%28%20%5Cfrac%7BR%5E2%7D%7BMSE%7D%20%5Cright%29%20%7D%0A) + /// + /// where R is the maximum integer value of depth (e.g. 255 in the case of CV_8U data) + /// and MSE is the mean squared error between the two arrays. + /// + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size as src1. + /// * R: the maximum pixel value (255 by default) + /// + /// ## C++ default parameters + /// * r: 255. + #[inline] + pub fn psnr(src1: &impl ToInputArray, src2: &impl ToInputArray, r: f64) -> Result { + input_array_arg!(src1); + input_array_arg!(src2); + return_send!(via ocvrs_return); + unsafe { sys::cv_PSNR_const__InputArrayR_const__InputArrayR_double(src1.as_raw__InputArray(), src2.as_raw__InputArray(), r, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// wrap SVD::backSubst + #[inline] + pub fn sv_back_subst(w: &impl ToInputArray, u: &impl ToInputArray, vt: &impl ToInputArray, rhs: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(w); + input_array_arg!(u); + input_array_arg!(vt); + input_array_arg!(rhs); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVBackSubst_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(w.as_raw__InputArray(), u.as_raw__InputArray(), vt.as_raw__InputArray(), rhs.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// wrap SVD::compute + /// + /// ## Note + /// This alternative version of [sv_decomp] function uses the following default values for its arguments: + /// * flags: 0 + #[inline] + pub fn sv_decomp_def(src: &impl ToInputArray, w: &mut impl ToOutputArray, u: &mut impl ToOutputArray, vt: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(w); + output_array_arg!(u); + output_array_arg!(vt); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVDecomp_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(src.as_raw__InputArray(), w.as_raw__OutputArray(), u.as_raw__OutputArray(), vt.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// wrap SVD::compute + /// + /// ## C++ default parameters + /// * flags: 0 + #[inline] + pub fn sv_decomp(src: &impl ToInputArray, w: &mut impl ToOutputArray, u: &mut impl ToOutputArray, vt: &mut impl ToOutputArray, flags: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(w); + output_array_arg!(u); + output_array_arg!(vt); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVDecomp_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), w.as_raw__OutputArray(), u.as_raw__OutputArray(), vt.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates an absolute value of each matrix element. + /// + /// abs is a meta-function that is expanded to one of absdiff or convertScaleAbs forms: + /// - C = abs(A-B) is equivalent to `absdiff(A, B, C)` + /// - C = abs(A) is equivalent to `absdiff(A, Scalar::all(0), C)` + /// - C = `Mat_ >(abs(A*alpha + beta))` is equivalent to `convertScaleAbs(A, C, alpha, + /// beta)` + /// + /// The output matrix has the same size and the same type as the input one except for the last case, + /// where C is depth=CV_8U . + /// ## Parameters + /// * m: matrix. + /// ## See also + /// [MatrixExpressions], absdiff, convertScaleAbs + /// + /// ## Overloaded parameters + /// + /// * e: matrix expression. + #[inline] + pub fn abs_matexpr(e: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_abs_const_MatExprR(e.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Calculates an absolute value of each matrix element. + /// + /// abs is a meta-function that is expanded to one of absdiff or convertScaleAbs forms: + /// - C = abs(A-B) is equivalent to `absdiff(A, B, C)` + /// - C = abs(A) is equivalent to `absdiff(A, Scalar::all(0), C)` + /// - C = `Mat_ >(abs(A*alpha + beta))` is equivalent to `convertScaleAbs(A, C, alpha, + /// beta)` + /// + /// The output matrix has the same size and the same type as the input one except for the last case, + /// where C is depth=CV_8U . + /// ## Parameters + /// * m: matrix. + /// ## See also + /// [MatrixExpressions], absdiff, convertScaleAbs + #[inline] + pub fn abs(m: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_abs_const_MatR(m.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Calculates the per-element absolute difference between two arrays or between an array and a scalar. + /// + /// The function cv::absdiff calculates: + /// * Absolute difference between two arrays when they have the same + /// size and type: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%7C%20%5Ctexttt%7Bsrc1%7D%28I%29%20%2D%20%20%5Ctexttt%7Bsrc2%7D%28I%29%7C%29) + /// * Absolute difference between an array and a scalar when the second + /// array is constructed from Scalar or has as many elements as the + /// number of channels in `src1`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%7C%20%5Ctexttt%7Bsrc1%7D%28I%29%20%2D%20%20%5Ctexttt%7Bsrc2%7D%20%7C%29) + /// * Absolute difference between a scalar and an array when the first + /// array is constructed from Scalar or has as many elements as the + /// number of channels in `src2`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%7C%20%5Ctexttt%7Bsrc1%7D%20%2D%20%20%5Ctexttt%7Bsrc2%7D%28I%29%20%7C%29) + /// where I is a multi-dimensional index of array elements. In case of + /// multi-channel arrays, each channel is processed independently. + /// + /// Note: Saturation is not applied when the arrays have the depth CV_32S. + /// You may even get a negative value in the case of overflow. + /// + /// Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array. + /// `absdiff(src,X)` means `absdiff(src,(X,X,X,X))`. + /// `absdiff(src,(X,))` means `absdiff(src,(X,0,0,0))`. + /// ## Parameters + /// * src1: first input array or a scalar. + /// * src2: second input array or a scalar. + /// * dst: output array that has the same size and type as input arrays. + /// ## See also + /// cv::abs(const Mat&) + #[inline] + pub fn absdiff(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_absdiff_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the weighted sum of two arrays. + /// + /// The function addWeighted calculates the weighted sum of two arrays as follows: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2A%20%5Ctexttt%7Balpha%7D%20%2B%20%20%5Ctexttt%7Bsrc2%7D%20%28I%29%2A%20%5Ctexttt%7Bbeta%7D%20%2B%20%20%5Ctexttt%7Bgamma%7D%20%29) + /// where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each + /// channel is processed independently. + /// The function can be replaced with a matrix expression: + /// ```C++ + /// dst = src1*alpha + src2*beta + gamma; + /// ``` + /// + /// + /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get + /// result of an incorrect sign in the case of overflow. + /// ## Parameters + /// * src1: first input array. + /// * alpha: weight of the first array elements. + /// * src2: second input array of the same size and channel number as src1. + /// * beta: weight of the second array elements. + /// * gamma: scalar added to each sum. + /// * dst: output array that has the same size and number of channels as the input arrays. + /// * dtype: optional depth of the output array; when both input arrays have the same depth, dtype + /// can be set to -1, which will be equivalent to src1.depth(). + /// ## See also + /// add, subtract, scaleAdd, Mat::convertTo + /// + /// ## Note + /// This alternative version of [add_weighted] function uses the following default values for its arguments: + /// * dtype: -1 + #[inline] + pub fn add_weighted_def(src1: &impl ToInputArray, alpha: f64, src2: &impl ToInputArray, beta: f64, gamma: f64, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_addWeighted_const__InputArrayR_double_const__InputArrayR_double_double_const__OutputArrayR(src1.as_raw__InputArray(), alpha, src2.as_raw__InputArray(), beta, gamma, dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the weighted sum of two arrays. + /// + /// The function addWeighted calculates the weighted sum of two arrays as follows: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2A%20%5Ctexttt%7Balpha%7D%20%2B%20%20%5Ctexttt%7Bsrc2%7D%20%28I%29%2A%20%5Ctexttt%7Bbeta%7D%20%2B%20%20%5Ctexttt%7Bgamma%7D%20%29) + /// where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each + /// channel is processed independently. + /// The function can be replaced with a matrix expression: + /// ```C++ + /// dst = src1*alpha + src2*beta + gamma; + /// ``` + /// + /// + /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get + /// result of an incorrect sign in the case of overflow. + /// ## Parameters + /// * src1: first input array. + /// * alpha: weight of the first array elements. + /// * src2: second input array of the same size and channel number as src1. + /// * beta: weight of the second array elements. + /// * gamma: scalar added to each sum. + /// * dst: output array that has the same size and number of channels as the input arrays. + /// * dtype: optional depth of the output array; when both input arrays have the same depth, dtype + /// can be set to -1, which will be equivalent to src1.depth(). + /// ## See also + /// add, subtract, scaleAdd, Mat::convertTo + /// + /// ## C++ default parameters + /// * dtype: -1 + #[inline] + pub fn add_weighted(src1: &impl ToInputArray, alpha: f64, src2: &impl ToInputArray, beta: f64, gamma: f64, dst: &mut impl ToOutputArray, dtype: i32) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_addWeighted_const__InputArrayR_double_const__InputArrayR_double_double_const__OutputArrayR_int(src1.as_raw__InputArray(), alpha, src2.as_raw__InputArray(), beta, gamma, dst.as_raw__OutputArray(), dtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the per-element sum of two arrays or an array and a scalar. + /// + /// The function add calculates: + /// - Sum of two arrays when both input arrays have the same size and the same number of channels: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%28I%29%20%2B%20%20%5Ctexttt%7Bsrc2%7D%28I%29%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// - Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of + /// elements as `src1.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%28I%29%20%2B%20%20%5Ctexttt%7Bsrc2%7D%20%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// - Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of + /// elements as `src2.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%20%2B%20%20%5Ctexttt%7Bsrc2%7D%28I%29%20%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each + /// channel is processed independently. + /// + /// The first function in the list above can be replaced with matrix expressions: + /// ```C++ + /// dst = src1 + src2; + /// dst += src1; // equivalent to add(dst, src1, dst); + /// ``` + /// + /// The input arrays and the output array can all have the same or different depths. For example, you + /// can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit + /// floating-point array. Depth of the output array is determined by the dtype parameter. In the second + /// and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can + /// be set to the default -1. In this case, the output array will have the same depth as the input + /// array, be it src1, src2 or both. + /// + /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get + /// result of an incorrect sign in the case of overflow. + /// + /// Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array. + /// `add(src,X)` means `add(src,(X,X,X,X))`. + /// `add(src,(X,))` means `add(src,(X,0,0,0))`. + /// ## Parameters + /// * src1: first input array or a scalar. + /// * src2: second input array or a scalar. + /// * dst: output array that has the same size and number of channels as the input array(s); the + /// depth is defined by dtype or src1/src2. + /// * mask: optional operation mask - 8-bit single channel array, that specifies elements of the + /// output array to be changed. + /// * dtype: optional depth of the output array (see the discussion below). + /// ## See also + /// subtract, addWeighted, scaleAdd, Mat::convertTo + /// + /// ## Note + /// This alternative version of [add] function uses the following default values for its arguments: + /// * mask: noArray() + /// * dtype: -1 + #[inline] + pub fn add_def(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_add_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the per-element sum of two arrays or an array and a scalar. + /// + /// The function add calculates: + /// - Sum of two arrays when both input arrays have the same size and the same number of channels: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%28I%29%20%2B%20%20%5Ctexttt%7Bsrc2%7D%28I%29%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// - Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of + /// elements as `src1.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%28I%29%20%2B%20%20%5Ctexttt%7Bsrc2%7D%20%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// - Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of + /// elements as `src2.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%20%2B%20%20%5Ctexttt%7Bsrc2%7D%28I%29%20%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each + /// channel is processed independently. + /// + /// The first function in the list above can be replaced with matrix expressions: + /// ```C++ + /// dst = src1 + src2; + /// dst += src1; // equivalent to add(dst, src1, dst); + /// ``` + /// + /// The input arrays and the output array can all have the same or different depths. For example, you + /// can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit + /// floating-point array. Depth of the output array is determined by the dtype parameter. In the second + /// and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can + /// be set to the default -1. In this case, the output array will have the same depth as the input + /// array, be it src1, src2 or both. + /// + /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get + /// result of an incorrect sign in the case of overflow. + /// + /// Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array. + /// `add(src,X)` means `add(src,(X,X,X,X))`. + /// `add(src,(X,))` means `add(src,(X,0,0,0))`. + /// ## Parameters + /// * src1: first input array or a scalar. + /// * src2: second input array or a scalar. + /// * dst: output array that has the same size and number of channels as the input array(s); the + /// depth is defined by dtype or src1/src2. + /// * mask: optional operation mask - 8-bit single channel array, that specifies elements of the + /// output array to be changed. + /// * dtype: optional depth of the output array (see the discussion below). + /// ## See also + /// subtract, addWeighted, scaleAdd, Mat::convertTo + /// + /// ## C++ default parameters + /// * mask: noArray() + /// * dtype: -1 + #[inline] + pub fn add(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray, mask: &impl ToInputArray, dtype: i32) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_add_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR_int(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), mask.as_raw__InputArray(), dtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// naive nearest neighbor finder + /// + /// see + /// @todo document + /// + /// ## Note + /// This alternative version of [batch_distance] function uses the following default values for its arguments: + /// * norm_type: NORM_L2 + /// * k: 0 + /// * mask: noArray() + /// * update: 0 + /// * crosscheck: false + #[inline] + pub fn batch_distance_def(src1: &impl ToInputArray, src2: &impl ToInputArray, dist: &mut impl ToOutputArray, dtype: i32, nidx: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dist); + output_array_arg!(nidx); + return_send!(via ocvrs_return); + unsafe { sys::cv_batchDistance_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dist.as_raw__OutputArray(), dtype, nidx.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// naive nearest neighbor finder + /// + /// see + /// @todo document + /// + /// ## C++ default parameters + /// * norm_type: NORM_L2 + /// * k: 0 + /// * mask: noArray() + /// * update: 0 + /// * crosscheck: false + #[inline] + pub fn batch_distance(src1: &impl ToInputArray, src2: &impl ToInputArray, dist: &mut impl ToOutputArray, dtype: i32, nidx: &mut impl ToOutputArray, norm_type: i32, k: i32, mask: &impl ToInputArray, update: i32, crosscheck: bool) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dist); + output_array_arg!(nidx); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_batchDistance_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_const__OutputArrayR_int_int_const__InputArrayR_int_bool(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dist.as_raw__OutputArray(), dtype, nidx.as_raw__OutputArray(), norm_type, k, mask.as_raw__InputArray(), update, crosscheck, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// computes bitwise conjunction of the two arrays (dst = src1 & src2) + /// Calculates the per-element bit-wise conjunction of two arrays or an + /// array and a scalar. + /// + /// The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for: + /// * Two arrays when src1 and src2 have the same size: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Cwedge%20%5Ctexttt%7Bsrc2%7D%20%28I%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// * An array and a scalar when src2 is constructed from Scalar or has + /// the same number of elements as `src1.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Cwedge%20%5Ctexttt%7Bsrc2%7D%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// * A scalar and an array when src1 is constructed from Scalar or has + /// the same number of elements as `src2.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%20%5Cwedge%20%5Ctexttt%7Bsrc2%7D%20%28I%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// In case of floating-point arrays, their machine-specific bit + /// representations (usually IEEE754-compliant) are used for the operation. + /// In case of multi-channel arrays, each channel is processed + /// independently. In the second and third cases above, the scalar is first + /// converted to the array type. + /// ## Parameters + /// * src1: first input array or a scalar. + /// * src2: second input array or a scalar. + /// * dst: output array that has the same size and type as the input + /// arrays. + /// * mask: optional operation mask, 8-bit single channel array, that + /// specifies elements of the output array to be changed. + /// + /// ## Note + /// This alternative version of [bitwise_and] function uses the following default values for its arguments: + /// * mask: noArray() + #[inline] + pub fn bitwise_and_def(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_bitwise_and_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// computes bitwise conjunction of the two arrays (dst = src1 & src2) + /// Calculates the per-element bit-wise conjunction of two arrays or an + /// array and a scalar. + /// + /// The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for: + /// * Two arrays when src1 and src2 have the same size: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Cwedge%20%5Ctexttt%7Bsrc2%7D%20%28I%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// * An array and a scalar when src2 is constructed from Scalar or has + /// the same number of elements as `src1.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Cwedge%20%5Ctexttt%7Bsrc2%7D%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// * A scalar and an array when src1 is constructed from Scalar or has + /// the same number of elements as `src2.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%20%5Cwedge%20%5Ctexttt%7Bsrc2%7D%20%28I%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// In case of floating-point arrays, their machine-specific bit + /// representations (usually IEEE754-compliant) are used for the operation. + /// In case of multi-channel arrays, each channel is processed + /// independently. In the second and third cases above, the scalar is first + /// converted to the array type. + /// ## Parameters + /// * src1: first input array or a scalar. + /// * src2: second input array or a scalar. + /// * dst: output array that has the same size and type as the input + /// arrays. + /// * mask: optional operation mask, 8-bit single channel array, that + /// specifies elements of the output array to be changed. + /// + /// ## C++ default parameters + /// * mask: noArray() + #[inline] + pub fn bitwise_and(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_bitwise_and_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Inverts every bit of an array. + /// + /// The function cv::bitwise_not calculates per-element bit-wise inversion of the input + /// array: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Cneg%20%5Ctexttt%7Bsrc%7D%20%28I%29) + /// In case of a floating-point input array, its machine-specific bit + /// representation (usually IEEE754-compliant) is used for the operation. In + /// case of multi-channel arrays, each channel is processed independently. + /// ## Parameters + /// * src: input array. + /// * dst: output array that has the same size and type as the input + /// array. + /// * mask: optional operation mask, 8-bit single channel array, that + /// specifies elements of the output array to be changed. + /// + /// ## Note + /// This alternative version of [bitwise_not] function uses the following default values for its arguments: + /// * mask: noArray() + #[inline] + pub fn bitwise_not_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_bitwise_not_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Inverts every bit of an array. + /// + /// The function cv::bitwise_not calculates per-element bit-wise inversion of the input + /// array: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Cneg%20%5Ctexttt%7Bsrc%7D%20%28I%29) + /// In case of a floating-point input array, its machine-specific bit + /// representation (usually IEEE754-compliant) is used for the operation. In + /// case of multi-channel arrays, each channel is processed independently. + /// ## Parameters + /// * src: input array. + /// * dst: output array that has the same size and type as the input + /// array. + /// * mask: optional operation mask, 8-bit single channel array, that + /// specifies elements of the output array to be changed. + /// + /// ## C++ default parameters + /// * mask: noArray() + #[inline] + pub fn bitwise_not(src: &impl ToInputArray, dst: &mut impl ToOutputArray, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_bitwise_not_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the per-element bit-wise disjunction of two arrays or an + /// array and a scalar. + /// + /// The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for: + /// * Two arrays when src1 and src2 have the same size: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Cvee%20%5Ctexttt%7Bsrc2%7D%20%28I%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// * An array and a scalar when src2 is constructed from Scalar or has + /// the same number of elements as `src1.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Cvee%20%5Ctexttt%7Bsrc2%7D%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// * A scalar and an array when src1 is constructed from Scalar or has + /// the same number of elements as `src2.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%20%5Cvee%20%5Ctexttt%7Bsrc2%7D%20%28I%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// In case of floating-point arrays, their machine-specific bit + /// representations (usually IEEE754-compliant) are used for the operation. + /// In case of multi-channel arrays, each channel is processed + /// independently. In the second and third cases above, the scalar is first + /// converted to the array type. + /// ## Parameters + /// * src1: first input array or a scalar. + /// * src2: second input array or a scalar. + /// * dst: output array that has the same size and type as the input + /// arrays. + /// * mask: optional operation mask, 8-bit single channel array, that + /// specifies elements of the output array to be changed. + /// + /// ## Note + /// This alternative version of [bitwise_or] function uses the following default values for its arguments: + /// * mask: noArray() + #[inline] + pub fn bitwise_or_def(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_bitwise_or_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the per-element bit-wise disjunction of two arrays or an + /// array and a scalar. + /// + /// The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for: + /// * Two arrays when src1 and src2 have the same size: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Cvee%20%5Ctexttt%7Bsrc2%7D%20%28I%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// * An array and a scalar when src2 is constructed from Scalar or has + /// the same number of elements as `src1.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Cvee%20%5Ctexttt%7Bsrc2%7D%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// * A scalar and an array when src1 is constructed from Scalar or has + /// the same number of elements as `src2.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%20%5Cvee%20%5Ctexttt%7Bsrc2%7D%20%28I%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// In case of floating-point arrays, their machine-specific bit + /// representations (usually IEEE754-compliant) are used for the operation. + /// In case of multi-channel arrays, each channel is processed + /// independently. In the second and third cases above, the scalar is first + /// converted to the array type. + /// ## Parameters + /// * src1: first input array or a scalar. + /// * src2: second input array or a scalar. + /// * dst: output array that has the same size and type as the input + /// arrays. + /// * mask: optional operation mask, 8-bit single channel array, that + /// specifies elements of the output array to be changed. + /// + /// ## C++ default parameters + /// * mask: noArray() + #[inline] + pub fn bitwise_or(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_bitwise_or_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the per-element bit-wise "exclusive or" operation on two + /// arrays or an array and a scalar. + /// + /// The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or" + /// operation for: + /// * Two arrays when src1 and src2 have the same size: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Coplus%20%5Ctexttt%7Bsrc2%7D%20%28I%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// * An array and a scalar when src2 is constructed from Scalar or has + /// the same number of elements as `src1.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Coplus%20%5Ctexttt%7Bsrc2%7D%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// * A scalar and an array when src1 is constructed from Scalar or has + /// the same number of elements as `src2.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%20%5Coplus%20%5Ctexttt%7Bsrc2%7D%20%28I%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// In case of floating-point arrays, their machine-specific bit + /// representations (usually IEEE754-compliant) are used for the operation. + /// In case of multi-channel arrays, each channel is processed + /// independently. In the 2nd and 3rd cases above, the scalar is first + /// converted to the array type. + /// ## Parameters + /// * src1: first input array or a scalar. + /// * src2: second input array or a scalar. + /// * dst: output array that has the same size and type as the input + /// arrays. + /// * mask: optional operation mask, 8-bit single channel array, that + /// specifies elements of the output array to be changed. + /// + /// ## Note + /// This alternative version of [bitwise_xor] function uses the following default values for its arguments: + /// * mask: noArray() + #[inline] + pub fn bitwise_xor_def(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_bitwise_xor_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the per-element bit-wise "exclusive or" operation on two + /// arrays or an array and a scalar. + /// + /// The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or" + /// operation for: + /// * Two arrays when src1 and src2 have the same size: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Coplus%20%5Ctexttt%7Bsrc2%7D%20%28I%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// * An array and a scalar when src2 is constructed from Scalar or has + /// the same number of elements as `src1.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Coplus%20%5Ctexttt%7Bsrc2%7D%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// * A scalar and an array when src1 is constructed from Scalar or has + /// the same number of elements as `src2.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%20%5Coplus%20%5Ctexttt%7Bsrc2%7D%20%28I%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%20%28I%29%20%5Cne0) + /// In case of floating-point arrays, their machine-specific bit + /// representations (usually IEEE754-compliant) are used for the operation. + /// In case of multi-channel arrays, each channel is processed + /// independently. In the 2nd and 3rd cases above, the scalar is first + /// converted to the array type. + /// ## Parameters + /// * src1: first input array or a scalar. + /// * src2: second input array or a scalar. + /// * dst: output array that has the same size and type as the input + /// arrays. + /// * mask: optional operation mask, 8-bit single channel array, that + /// specifies elements of the output array to be changed. + /// + /// ## C++ default parameters + /// * mask: noArray() + #[inline] + pub fn bitwise_xor(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_bitwise_xor_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes the source location of an extrapolated pixel. + /// + /// The function computes and returns the coordinate of a donor pixel corresponding to the specified + /// extrapolated pixel when using the specified extrapolation border mode. For example, if you use + /// cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and + /// want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img, it + /// looks like: + /// ```C++ + /// float val = img.at(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101), + /// borderInterpolate(-5, img.cols, cv::BORDER_WRAP)); + /// ``` + /// + /// Normally, the function is not called directly. It is used inside filtering functions and also in + /// copyMakeBorder. + /// ## Parameters + /// * p: 0-based coordinate of the extrapolated pixel along one of the axes, likely \<0 or \>= len + /// * len: Length of the array along the corresponding axis. + /// * borderType: Border type, one of the #BorderTypes, except for [BORDER_TRANSPARENT] and + /// #BORDER_ISOLATED. When borderType==#BORDER_CONSTANT, the function always returns -1, regardless + /// of p and len. + /// ## See also + /// copyMakeBorder + #[inline] + pub fn border_interpolate(p: i32, len: i32, border_type: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_borderInterpolate_int_int_int(p, len, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Broadcast the given Mat to the given shape. + /// ## Parameters + /// * src: input array + /// * shape: target shape. Should be a list of CV_32S numbers. Note that negative values are not supported. + /// * dst: output array that has the given shape + #[inline] + pub fn broadcast(src: &impl ToInputArray, shape: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + input_array_arg!(shape); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_broadcast_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), shape.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the covariance matrix of a set of vectors. + /// + /// The function cv::calcCovarMatrix calculates the covariance matrix and, optionally, the mean vector of + /// the set of input vectors. + /// ## Parameters + /// * samples: samples stored as separate matrices + /// * nsamples: number of samples + /// * covar: output covariance matrix of the type ctype and square size. + /// * mean: input or output (depending on the flags) array as the average value of the input vectors. + /// * flags: operation flags as a combination of [covar_flags] + /// * ctype: type of the matrixl; it equals 'CV_64F' by default. + /// ## See also + /// PCA, mulTransposed, Mahalanobis + /// @todo InputArrayOfArrays + /// + /// ## Overloaded parameters + /// + /// + /// Note: use [COVAR_ROWS] or [COVAR_COLS] flag + /// * samples: samples stored as rows/columns of a single matrix. + /// * covar: output covariance matrix of the type ctype and square size. + /// * mean: input or output (depending on the flags) array as the average value of the input vectors. + /// * flags: operation flags as a combination of [covar_flags] + /// * ctype: type of the matrixl; it equals 'CV_64F' by default. + /// + /// ## Note + /// This alternative version of [calc_covar_matrix] function uses the following default values for its arguments: + /// * ctype: CV_64F + #[inline] + pub fn calc_covar_matrix_def(samples: &impl ToInputArray, covar: &mut impl ToOutputArray, mean: &mut impl ToInputOutputArray, flags: i32) -> Result<()> { + input_array_arg!(samples); + output_array_arg!(covar); + input_output_array_arg!(mean); + return_send!(via ocvrs_return); + unsafe { sys::cv_calcCovarMatrix_const__InputArrayR_const__OutputArrayR_const__InputOutputArrayR_int(samples.as_raw__InputArray(), covar.as_raw__OutputArray(), mean.as_raw__InputOutputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the covariance matrix of a set of vectors. + /// + /// The function cv::calcCovarMatrix calculates the covariance matrix and, optionally, the mean vector of + /// the set of input vectors. + /// ## Parameters + /// * samples: samples stored as separate matrices + /// * nsamples: number of samples + /// * covar: output covariance matrix of the type ctype and square size. + /// * mean: input or output (depending on the flags) array as the average value of the input vectors. + /// * flags: operation flags as a combination of [covar_flags] + /// * ctype: type of the matrixl; it equals 'CV_64F' by default. + /// ## See also + /// PCA, mulTransposed, Mahalanobis + /// @todo InputArrayOfArrays + /// + /// ## Overloaded parameters + /// + /// + /// Note: use [COVAR_ROWS] or [COVAR_COLS] flag + /// * samples: samples stored as rows/columns of a single matrix. + /// * covar: output covariance matrix of the type ctype and square size. + /// * mean: input or output (depending on the flags) array as the average value of the input vectors. + /// * flags: operation flags as a combination of [covar_flags] + /// * ctype: type of the matrixl; it equals 'CV_64F' by default. + /// + /// ## C++ default parameters + /// * ctype: CV_64F + #[inline] + pub fn calc_covar_matrix(samples: &impl ToInputArray, covar: &mut impl ToOutputArray, mean: &mut impl ToInputOutputArray, flags: i32, ctype: i32) -> Result<()> { + input_array_arg!(samples); + output_array_arg!(covar); + input_output_array_arg!(mean); + return_send!(via ocvrs_return); + unsafe { sys::cv_calcCovarMatrix_const__InputArrayR_const__OutputArrayR_const__InputOutputArrayR_int_int(samples.as_raw__InputArray(), covar.as_raw__OutputArray(), mean.as_raw__InputOutputArray(), flags, ctype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the magnitude and angle of 2D vectors. + /// + /// The function cv::cartToPolar calculates either the magnitude, angle, or both + /// for every 2D vector (x(I),y(I)): + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20%5Ctexttt%7Bmagnitude%7D%20%28I%29%3D%20%5Csqrt%7B%5Ctexttt%7Bx%7D%28I%29%5E2%2B%5Ctexttt%7By%7D%28I%29%5E2%7D%20%2C%20%5C%5C%20%5Ctexttt%7Bangle%7D%20%28I%29%3D%20%5Ctexttt%7Batan2%7D%20%28%20%5Ctexttt%7By%7D%20%28I%29%2C%20%5Ctexttt%7Bx%7D%20%28I%29%29%5B%20%5Ccdot180%20%2F%20%5Cpi%20%5D%20%5Cend%7Barray%7D) + /// + /// The angles are calculated with accuracy about 0.3 degrees. For the point + /// (0,0), the angle is set to 0. + /// ## Parameters + /// * x: array of x-coordinates; this must be a single-precision or + /// double-precision floating-point array. + /// * y: array of y-coordinates, that must have the same size and same type as x. + /// * magnitude: output array of magnitudes of the same size and type as x. + /// * angle: output array of angles that has the same size and type as + /// x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees). + /// * angleInDegrees: a flag, indicating whether the angles are measured + /// in radians (which is by default), or in degrees. + /// ## See also + /// Sobel, Scharr + /// + /// ## Note + /// This alternative version of [cart_to_polar] function uses the following default values for its arguments: + /// * angle_in_degrees: false + #[inline] + pub fn cart_to_polar_def(x: &impl ToInputArray, y: &impl ToInputArray, magnitude: &mut impl ToOutputArray, angle: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(x); + input_array_arg!(y); + output_array_arg!(magnitude); + output_array_arg!(angle); + return_send!(via ocvrs_return); + unsafe { sys::cv_cartToPolar_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(x.as_raw__InputArray(), y.as_raw__InputArray(), magnitude.as_raw__OutputArray(), angle.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the magnitude and angle of 2D vectors. + /// + /// The function cv::cartToPolar calculates either the magnitude, angle, or both + /// for every 2D vector (x(I),y(I)): + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20%5Ctexttt%7Bmagnitude%7D%20%28I%29%3D%20%5Csqrt%7B%5Ctexttt%7Bx%7D%28I%29%5E2%2B%5Ctexttt%7By%7D%28I%29%5E2%7D%20%2C%20%5C%5C%20%5Ctexttt%7Bangle%7D%20%28I%29%3D%20%5Ctexttt%7Batan2%7D%20%28%20%5Ctexttt%7By%7D%20%28I%29%2C%20%5Ctexttt%7Bx%7D%20%28I%29%29%5B%20%5Ccdot180%20%2F%20%5Cpi%20%5D%20%5Cend%7Barray%7D) + /// + /// The angles are calculated with accuracy about 0.3 degrees. For the point + /// (0,0), the angle is set to 0. + /// ## Parameters + /// * x: array of x-coordinates; this must be a single-precision or + /// double-precision floating-point array. + /// * y: array of y-coordinates, that must have the same size and same type as x. + /// * magnitude: output array of magnitudes of the same size and type as x. + /// * angle: output array of angles that has the same size and type as + /// x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees). + /// * angleInDegrees: a flag, indicating whether the angles are measured + /// in radians (which is by default), or in degrees. + /// ## See also + /// Sobel, Scharr + /// + /// ## C++ default parameters + /// * angle_in_degrees: false + #[inline] + pub fn cart_to_polar(x: &impl ToInputArray, y: &impl ToInputArray, magnitude: &mut impl ToOutputArray, angle: &mut impl ToOutputArray, angle_in_degrees: bool) -> Result<()> { + input_array_arg!(x); + input_array_arg!(y); + output_array_arg!(magnitude); + output_array_arg!(angle); + return_send!(via ocvrs_return); + unsafe { sys::cv_cartToPolar_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_bool(x.as_raw__InputArray(), y.as_raw__InputArray(), magnitude.as_raw__OutputArray(), angle.as_raw__OutputArray(), angle_in_degrees, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns true if the specified feature is supported by the host hardware. + /// + /// The function returns true if the host hardware supports the specified feature. When user calls + /// setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until + /// setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code + /// in OpenCV. + /// ## Parameters + /// * feature: The feature of interest, one of cv::CpuFeatures + #[inline] + pub fn check_hardware_support(feature: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_checkHardwareSupport_int(feature, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Checks every element of an input array for invalid values. + /// + /// The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal \> + /// -DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and + /// maxVal. In case of multi-channel arrays, each channel is processed independently. If some values + /// are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the + /// function either returns false (when quiet=true) or throws an exception. + /// ## Parameters + /// * a: input array. + /// * quiet: a flag, indicating whether the functions quietly return false when the array elements + /// are out of range or they throw an exception. + /// * pos: optional output parameter, when not NULL, must be a pointer to array of src.dims + /// elements. + /// * minVal: inclusive lower boundary of valid values range. + /// * maxVal: exclusive upper boundary of valid values range. + /// + /// ## Note + /// This alternative version of [check_range] function uses the following default values for its arguments: + /// * quiet: true + /// * pos: 0 + /// * min_val: -DBL_MAX + /// * max_val: DBL_MAX + #[inline] + pub fn check_range_def(a: &impl ToInputArray) -> Result { + input_array_arg!(a); + return_send!(via ocvrs_return); + unsafe { sys::cv_checkRange_const__InputArrayR(a.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Checks every element of an input array for invalid values. + /// + /// The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal \> + /// -DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and + /// maxVal. In case of multi-channel arrays, each channel is processed independently. If some values + /// are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the + /// function either returns false (when quiet=true) or throws an exception. + /// ## Parameters + /// * a: input array. + /// * quiet: a flag, indicating whether the functions quietly return false when the array elements + /// are out of range or they throw an exception. + /// * pos: optional output parameter, when not NULL, must be a pointer to array of src.dims + /// elements. + /// * minVal: inclusive lower boundary of valid values range. + /// * maxVal: exclusive upper boundary of valid values range. + /// + /// ## C++ default parameters + /// * quiet: true + /// * pos: 0 + /// * min_val: -DBL_MAX + /// * max_val: DBL_MAX + #[inline] + pub fn check_range(a: &impl ToInputArray, quiet: bool, pos: &mut core::Point, min_val: f64, max_val: f64) -> Result { + input_array_arg!(a); + return_send!(via ocvrs_return); + unsafe { sys::cv_checkRange_const__InputArrayR_bool_PointX_double_double(a.as_raw__InputArray(), quiet, pos, min_val, max_val, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs the per-element comparison of two arrays or an array and scalar value. + /// + /// The function compares: + /// * Elements of two arrays when src1 and src2 have the same size: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5C%2C%5Ctexttt%7Bcmpop%7D%5C%2C%20%5Ctexttt%7Bsrc2%7D%20%28I%29) + /// * Elements of src1 with a scalar src2 when src2 is constructed from + /// Scalar or has a single element: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%28I%29%20%5C%2C%5Ctexttt%7Bcmpop%7D%5C%2C%20%20%5Ctexttt%7Bsrc2%7D) + /// * src1 with elements of src2 when src1 is constructed from Scalar or + /// has a single element: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bsrc1%7D%20%20%5C%2C%5Ctexttt%7Bcmpop%7D%5C%2C%20%5Ctexttt%7Bsrc2%7D%20%28I%29) + /// When the comparison result is true, the corresponding element of output + /// array is set to 255. The comparison operations can be replaced with the + /// equivalent matrix expressions: + /// ```C++ + /// Mat dst1 = src1 >= src2; + /// Mat dst2 = src1 < 8; + /// ... + /// ``` + /// + /// ## Parameters + /// * src1: first input array or a scalar; when it is an array, it must have a single channel. + /// * src2: second input array or a scalar; when it is an array, it must have a single channel. + /// * dst: output array of type ref CV_8U that has the same size and the same number of channels as + /// the input arrays. + /// * cmpop: a flag, that specifies correspondence between the arrays (cv::CmpTypes) + /// ## See also + /// checkRange, min, max, threshold + #[inline] + pub fn compare(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray, cmpop: i32) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_compare_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), cmpop, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Copies the lower or the upper half of a square matrix to its another half. + /// + /// The function cv::completeSymm copies the lower or the upper half of a square matrix to + /// its another half. The matrix diagonal remains unchanged: + /// - ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bm%7D%5F%7Bij%7D%3D%5Ctexttt%7Bm%7D%5F%7Bji%7D) for ![inline formula](https://latex.codecogs.com/png.latex?i%20%3E%20j) if + /// lowerToUpper=false + /// - ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bm%7D%5F%7Bij%7D%3D%5Ctexttt%7Bm%7D%5F%7Bji%7D) for ![inline formula](https://latex.codecogs.com/png.latex?i%20%3C%20j) if + /// lowerToUpper=true + /// + /// ## Parameters + /// * m: input-output floating-point square matrix. + /// * lowerToUpper: operation flag; if true, the lower half is copied to + /// the upper half. Otherwise, the upper half is copied to the lower half. + /// ## See also + /// flip, transpose + /// + /// ## Note + /// This alternative version of [complete_symm] function uses the following default values for its arguments: + /// * lower_to_upper: false + #[inline] + pub fn complete_symm_def(m: &mut impl ToInputOutputArray) -> Result<()> { + input_output_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_completeSymm_const__InputOutputArrayR(m.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Copies the lower or the upper half of a square matrix to its another half. + /// + /// The function cv::completeSymm copies the lower or the upper half of a square matrix to + /// its another half. The matrix diagonal remains unchanged: + /// - ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bm%7D%5F%7Bij%7D%3D%5Ctexttt%7Bm%7D%5F%7Bji%7D) for ![inline formula](https://latex.codecogs.com/png.latex?i%20%3E%20j) if + /// lowerToUpper=false + /// - ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bm%7D%5F%7Bij%7D%3D%5Ctexttt%7Bm%7D%5F%7Bji%7D) for ![inline formula](https://latex.codecogs.com/png.latex?i%20%3C%20j) if + /// lowerToUpper=true + /// + /// ## Parameters + /// * m: input-output floating-point square matrix. + /// * lowerToUpper: operation flag; if true, the lower half is copied to + /// the upper half. Otherwise, the upper half is copied to the lower half. + /// ## See also + /// flip, transpose + /// + /// ## C++ default parameters + /// * lower_to_upper: false + #[inline] + pub fn complete_symm(m: &mut impl ToInputOutputArray, lower_to_upper: bool) -> Result<()> { + input_output_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_completeSymm_const__InputOutputArrayR_bool(m.as_raw__InputOutputArray(), lower_to_upper, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Converts an array to half precision floating number. + /// + /// This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data. + /// There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or + /// CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error. + /// The format of half precision floating point is defined in IEEE 754-2008. + /// + /// ## Parameters + /// * src: input array. + /// * dst: output array. + /// + /// + /// **Deprecated**: Use Mat::convertTo with CV_16F instead. + #[deprecated = "Use Mat::convertTo with CV_16F instead."] + #[inline] + pub fn convert_fp16(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_convertFp16_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Scales, calculates absolute values, and converts the result to 8-bit. + /// + /// On each element of the input array, the function convertScaleAbs + /// performs three operations sequentially: scaling, taking an absolute + /// value, conversion to an unsigned 8-bit type: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Bsaturate%5C%5Fcast%3Cuchar%3E%7D%20%28%7C%20%5Ctexttt%7Bsrc%7D%20%28I%29%2A%20%5Ctexttt%7Balpha%7D%20%2B%20%20%5Ctexttt%7Bbeta%7D%20%7C%29) + /// In case of multi-channel arrays, the function processes each channel + /// independently. When the output is not 8-bit, the operation can be + /// emulated by calling the Mat::convertTo method (or by using matrix + /// expressions) and then by calculating an absolute value of the result. + /// For example: + /// ```C++ + /// Mat_ A(30,30); + /// randu(A, Scalar(-100), Scalar(100)); + /// Mat_ B = A*5 + 3; + /// B = abs(B); + /// // Mat_ B = abs(A*5+3) will also do the job, + /// // but it will allocate a temporary matrix + /// ``` + /// + /// ## Parameters + /// * src: input array. + /// * dst: output array. + /// * alpha: optional scale factor. + /// * beta: optional delta added to the scaled values. + /// ## See also + /// Mat::convertTo, cv::abs(const Mat&) + /// + /// ## Note + /// This alternative version of [convert_scale_abs] function uses the following default values for its arguments: + /// * alpha: 1 + /// * beta: 0 + #[inline] + pub fn convert_scale_abs_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_convertScaleAbs_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Scales, calculates absolute values, and converts the result to 8-bit. + /// + /// On each element of the input array, the function convertScaleAbs + /// performs three operations sequentially: scaling, taking an absolute + /// value, conversion to an unsigned 8-bit type: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Bsaturate%5C%5Fcast%3Cuchar%3E%7D%20%28%7C%20%5Ctexttt%7Bsrc%7D%20%28I%29%2A%20%5Ctexttt%7Balpha%7D%20%2B%20%20%5Ctexttt%7Bbeta%7D%20%7C%29) + /// In case of multi-channel arrays, the function processes each channel + /// independently. When the output is not 8-bit, the operation can be + /// emulated by calling the Mat::convertTo method (or by using matrix + /// expressions) and then by calculating an absolute value of the result. + /// For example: + /// ```C++ + /// Mat_ A(30,30); + /// randu(A, Scalar(-100), Scalar(100)); + /// Mat_ B = A*5 + 3; + /// B = abs(B); + /// // Mat_ B = abs(A*5+3) will also do the job, + /// // but it will allocate a temporary matrix + /// ``` + /// + /// ## Parameters + /// * src: input array. + /// * dst: output array. + /// * alpha: optional scale factor. + /// * beta: optional delta added to the scaled values. + /// ## See also + /// Mat::convertTo, cv::abs(const Mat&) + /// + /// ## C++ default parameters + /// * alpha: 1 + /// * beta: 0 + #[inline] + pub fn convert_scale_abs(src: &impl ToInputArray, dst: &mut impl ToOutputArray, alpha: f64, beta: f64) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_convertScaleAbs_const__InputArrayR_const__OutputArrayR_double_double(src.as_raw__InputArray(), dst.as_raw__OutputArray(), alpha, beta, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Forms a border around an image. + /// + /// The function copies the source image into the middle of the destination image. The areas to the + /// left, to the right, above and below the copied source image will be filled with extrapolated + /// pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but + /// what other more complex functions, including your own, may do to simplify image boundary handling. + /// + /// The function supports the mode when src is already in the middle of dst . In this case, the + /// function does not copy src itself but simply constructs the border, for example: + /// + /// ```C++ + /// // let border be the same in all directions + /// int border=2; + /// // constructs a larger image to fit both the image and the border + /// Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth()); + /// // select the middle part of it w/o copying data + /// Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows)); + /// // convert image from RGB to grayscale + /// cvtColor(rgb, gray, COLOR_RGB2GRAY); + /// // form a border in-place + /// copyMakeBorder(gray, gray_buf, border, border, + /// border, border, BORDER_REPLICATE); + /// // now do some custom filtering ... + /// ... + /// ``` + /// + /// + /// Note: When the source image is a part (ROI) of a bigger image, the function will try to use the + /// pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as + /// if src was not a ROI, use borderType | #BORDER_ISOLATED. + /// + /// ## Parameters + /// * src: Source image. + /// * dst: Destination image of the same type as src and the size Size(src.cols+left+right, + /// src.rows+top+bottom) . + /// * top: the top pixels + /// * bottom: the bottom pixels + /// * left: the left pixels + /// * right: Parameter specifying how many pixels in each direction from the source image rectangle + /// to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs + /// to be built. + /// * borderType: Border type. See borderInterpolate for details. + /// * value: Border value if borderType==BORDER_CONSTANT . + /// ## See also + /// borderInterpolate + /// + /// ## Note + /// This alternative version of [copy_make_border] function uses the following default values for its arguments: + /// * value: Scalar() + #[inline] + pub fn copy_make_border_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, top: i32, bottom: i32, left: i32, right: i32, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_copyMakeBorder_const__InputArrayR_const__OutputArrayR_int_int_int_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), top, bottom, left, right, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Forms a border around an image. + /// + /// The function copies the source image into the middle of the destination image. The areas to the + /// left, to the right, above and below the copied source image will be filled with extrapolated + /// pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but + /// what other more complex functions, including your own, may do to simplify image boundary handling. + /// + /// The function supports the mode when src is already in the middle of dst . In this case, the + /// function does not copy src itself but simply constructs the border, for example: + /// + /// ```C++ + /// // let border be the same in all directions + /// int border=2; + /// // constructs a larger image to fit both the image and the border + /// Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth()); + /// // select the middle part of it w/o copying data + /// Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows)); + /// // convert image from RGB to grayscale + /// cvtColor(rgb, gray, COLOR_RGB2GRAY); + /// // form a border in-place + /// copyMakeBorder(gray, gray_buf, border, border, + /// border, border, BORDER_REPLICATE); + /// // now do some custom filtering ... + /// ... + /// ``` + /// + /// + /// Note: When the source image is a part (ROI) of a bigger image, the function will try to use the + /// pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as + /// if src was not a ROI, use borderType | #BORDER_ISOLATED. + /// + /// ## Parameters + /// * src: Source image. + /// * dst: Destination image of the same type as src and the size Size(src.cols+left+right, + /// src.rows+top+bottom) . + /// * top: the top pixels + /// * bottom: the bottom pixels + /// * left: the left pixels + /// * right: Parameter specifying how many pixels in each direction from the source image rectangle + /// to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs + /// to be built. + /// * borderType: Border type. See borderInterpolate for details. + /// * value: Border value if borderType==BORDER_CONSTANT . + /// ## See also + /// borderInterpolate + /// + /// ## C++ default parameters + /// * value: Scalar() + #[inline] + pub fn copy_make_border(src: &impl ToInputArray, dst: &mut impl ToOutputArray, top: i32, bottom: i32, left: i32, right: i32, border_type: i32, value: core::Scalar) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_copyMakeBorder_const__InputArrayR_const__OutputArrayR_int_int_int_int_int_const_ScalarR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), top, bottom, left, right, border_type, &value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This is an overloaded member function, provided for convenience (python) + /// Copies the matrix to another one. + /// When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, the newly allocated matrix is initialized with all zeros before copying the data. + /// ## Parameters + /// * src: source matrix. + /// * dst: Destination matrix. If it does not have a proper size or type before the operation, it is + /// reallocated. + /// * mask: Operation mask of the same size as \*this. Its non-zero elements indicate which matrix + /// elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels. + #[inline] + pub fn copy_to(src: &impl ToInputArray, dst: &mut impl ToOutputArray, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_copyTo_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Counts non-zero array elements. + /// + /// The function returns the number of non-zero elements in src : + /// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5F%7BI%3A%20%5C%3B%20%5Ctexttt%7Bsrc%7D%20%28I%29%20%5Cne0%20%7D%201) + /// + /// The function do not work with multi-channel arrays. If you need to count non-zero array + /// elements across all the channels, use Mat::reshape first to reinterpret the array as + /// single-channel. Or you may extract the particular channel using either extractImageCOI, or + /// mixChannels, or split. + /// + /// + /// Note: + /// - If only whether there are non-zero elements is important, [hasNonZero] is helpful. + /// - If the location of non-zero array elements is important, [findNonZero] is helpful. + /// ## Parameters + /// * src: single-channel array. + /// ## See also + /// mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix + /// findNonZero, hasNonZero + #[inline] + pub fn count_non_zero(src: &impl ToInputArray) -> Result { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_countNonZero_const__InputArrayR(src.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes the cube root of an argument. + /// + /// The function cubeRoot computes ![inline formula](https://latex.codecogs.com/png.latex?%5Csqrt%5B3%5D%7B%5Ctexttt%7Bval%7D%7D). Negative arguments are handled correctly. + /// NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for + /// single-precision data. + /// ## Parameters + /// * val: A function argument. + #[inline] + pub fn cube_root(val: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_cubeRoot_float(val, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs a forward or inverse discrete Cosine transform of 1D or 2D array. + /// + /// The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D + /// floating-point array: + /// * Forward Cosine transform of a 1D vector of N elements: + /// ![block formula](https://latex.codecogs.com/png.latex?Y%20%3D%20C%5E%7B%28N%29%7D%20%20%5Ccdot%20X) + /// where + /// ![block formula](https://latex.codecogs.com/png.latex?C%5E%7B%28N%29%7D%5F%7Bjk%7D%3D%20%5Csqrt%7B%5Calpha%5Fj%2FN%7D%20%5Ccos%20%5Cleft%20%28%20%5Cfrac%7B%5Cpi%282k%2B1%29j%7D%7B2N%7D%20%5Cright%20%29) + /// and + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Calpha%5F0%3D1), ![inline formula](https://latex.codecogs.com/png.latex?%5Calpha%5Fj%3D2) for *j \> 0*. + /// * Inverse Cosine transform of a 1D vector of N elements: + /// ![block formula](https://latex.codecogs.com/png.latex?X%20%3D%20%20%5Cleft%20%28C%5E%7B%28N%29%7D%20%5Cright%20%29%5E%7B%2D1%7D%20%20%5Ccdot%20Y%20%3D%20%20%5Cleft%20%28C%5E%7B%28N%29%7D%20%5Cright%20%29%5ET%20%20%5Ccdot%20Y) + /// (since ![inline formula](https://latex.codecogs.com/png.latex?C%5E%7B%28N%29%7D) is an orthogonal matrix, ![inline formula](https://latex.codecogs.com/png.latex?C%5E%7B%28N%29%7D%20%5Ccdot%20%5Cleft%28C%5E%7B%28N%29%7D%5Cright%29%5ET%20%3D%20I) ) + /// * Forward 2D Cosine transform of M x N matrix: + /// ![block formula](https://latex.codecogs.com/png.latex?Y%20%3D%20C%5E%7B%28N%29%7D%20%20%5Ccdot%20X%20%20%5Ccdot%20%5Cleft%20%28C%5E%7B%28N%29%7D%20%5Cright%20%29%5ET) + /// * Inverse 2D Cosine transform of M x N matrix: + /// ![block formula](https://latex.codecogs.com/png.latex?X%20%3D%20%20%5Cleft%20%28C%5E%7B%28N%29%7D%20%5Cright%20%29%5ET%20%20%5Ccdot%20X%20%20%5Ccdot%20C%5E%7B%28N%29%7D) + /// + /// The function chooses the mode of operation by looking at the flags and size of the input array: + /// * If (flags & #DCT_INVERSE) == 0, the function does a forward 1D or 2D transform. Otherwise, it + /// is an inverse 1D or 2D transform. + /// * If (flags & #DCT_ROWS) != 0, the function performs a 1D transform of each row. + /// * If the array is a single column or a single row, the function performs a 1D transform. + /// * If none of the above is true, the function performs a 2D transform. + /// + /// + /// Note: Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you + /// can pad the array when necessary. + /// Also, the function performance depends very much, and not monotonically, on the array size (see + /// getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT + /// of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated as: + /// ```C++ + /// size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); } + /// N1 = getOptimalDCTSize(N); + /// ``` + /// + /// ## Parameters + /// * src: input floating-point array. + /// * dst: output array of the same size and type as src . + /// * flags: transformation flags as a combination of cv::DftFlags (DCT_*) + /// ## See also + /// dft, getOptimalDFTSize, idct + /// + /// ## Note + /// This alternative version of [dct] function uses the following default values for its arguments: + /// * flags: 0 + #[inline] + pub fn dct_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_dct_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs a forward or inverse discrete Cosine transform of 1D or 2D array. + /// + /// The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D + /// floating-point array: + /// * Forward Cosine transform of a 1D vector of N elements: + /// ![block formula](https://latex.codecogs.com/png.latex?Y%20%3D%20C%5E%7B%28N%29%7D%20%20%5Ccdot%20X) + /// where + /// ![block formula](https://latex.codecogs.com/png.latex?C%5E%7B%28N%29%7D%5F%7Bjk%7D%3D%20%5Csqrt%7B%5Calpha%5Fj%2FN%7D%20%5Ccos%20%5Cleft%20%28%20%5Cfrac%7B%5Cpi%282k%2B1%29j%7D%7B2N%7D%20%5Cright%20%29) + /// and + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Calpha%5F0%3D1), ![inline formula](https://latex.codecogs.com/png.latex?%5Calpha%5Fj%3D2) for *j \> 0*. + /// * Inverse Cosine transform of a 1D vector of N elements: + /// ![block formula](https://latex.codecogs.com/png.latex?X%20%3D%20%20%5Cleft%20%28C%5E%7B%28N%29%7D%20%5Cright%20%29%5E%7B%2D1%7D%20%20%5Ccdot%20Y%20%3D%20%20%5Cleft%20%28C%5E%7B%28N%29%7D%20%5Cright%20%29%5ET%20%20%5Ccdot%20Y) + /// (since ![inline formula](https://latex.codecogs.com/png.latex?C%5E%7B%28N%29%7D) is an orthogonal matrix, ![inline formula](https://latex.codecogs.com/png.latex?C%5E%7B%28N%29%7D%20%5Ccdot%20%5Cleft%28C%5E%7B%28N%29%7D%5Cright%29%5ET%20%3D%20I) ) + /// * Forward 2D Cosine transform of M x N matrix: + /// ![block formula](https://latex.codecogs.com/png.latex?Y%20%3D%20C%5E%7B%28N%29%7D%20%20%5Ccdot%20X%20%20%5Ccdot%20%5Cleft%20%28C%5E%7B%28N%29%7D%20%5Cright%20%29%5ET) + /// * Inverse 2D Cosine transform of M x N matrix: + /// ![block formula](https://latex.codecogs.com/png.latex?X%20%3D%20%20%5Cleft%20%28C%5E%7B%28N%29%7D%20%5Cright%20%29%5ET%20%20%5Ccdot%20X%20%20%5Ccdot%20C%5E%7B%28N%29%7D) + /// + /// The function chooses the mode of operation by looking at the flags and size of the input array: + /// * If (flags & #DCT_INVERSE) == 0, the function does a forward 1D or 2D transform. Otherwise, it + /// is an inverse 1D or 2D transform. + /// * If (flags & #DCT_ROWS) != 0, the function performs a 1D transform of each row. + /// * If the array is a single column or a single row, the function performs a 1D transform. + /// * If none of the above is true, the function performs a 2D transform. + /// + /// + /// Note: Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you + /// can pad the array when necessary. + /// Also, the function performance depends very much, and not monotonically, on the array size (see + /// getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT + /// of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated as: + /// ```C++ + /// size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); } + /// N1 = getOptimalDCTSize(N); + /// ``` + /// + /// ## Parameters + /// * src: input floating-point array. + /// * dst: output array of the same size and type as src . + /// * flags: transformation flags as a combination of cv::DftFlags (DCT_*) + /// ## See also + /// dft, getOptimalDFTSize, idct + /// + /// ## C++ default parameters + /// * flags: 0 + #[inline] + pub fn dct(src: &impl ToInputArray, dst: &mut impl ToOutputArray, flags: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_dct_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or "" + #[inline] + pub fn depth_to_string(depth: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_depthToString_int(depth, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn check_failed_mat_channels_1(v: i32, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_MatChannels_const_int_const_CheckContextR(v, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_mat_channels(v1: i32, v2: i32, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_MatChannels_const_int_const_int_const_CheckContextR(v1, v2, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_mat_depth_1(v: i32, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_MatDepth_const_int_const_CheckContextR(v, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_mat_depth(v1: i32, v2: i32, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_MatDepth_const_int_const_int_const_CheckContextR(v1, v2, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_mat_type_1(v: i32, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_MatType_const_int_const_CheckContextR(v, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_mat_type(v1: i32, v2: i32, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_MatType_const_int_const_int_const_CheckContextR(v1, v2, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_auto_10(v: core::Size_, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_auto_const_Size_LintG_const_CheckContextR(&v, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_auto_5(v1: core::Size_, v2: core::Size_, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_auto_const_Size_LintG_const_Size_LintG_const_CheckContextR(&v1, &v2, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_auto(v1: bool, v2: bool, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_auto_const_bool_const_bool_const_CheckContextR(v1, v2, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_auto_9(v: f64, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_auto_const_double_const_CheckContextR(v, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_auto_4(v1: f64, v2: f64, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_auto_const_double_const_double_const_CheckContextR(v1, v2, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_auto_8(v: f32, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_auto_const_float_const_CheckContextR(v, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_auto_3(v1: f32, v2: f32, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_auto_const_float_const_float_const_CheckContextR(v1, v2, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_auto_6(v: i32, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_auto_const_int_const_CheckContextR(v, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_auto_1(v1: i32, v2: i32, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_auto_const_int_const_int_const_CheckContextR(v1, v2, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_auto_7(v: size_t, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_auto_const_size_t_const_CheckContextR(v, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_auto_2(v1: size_t, v2: size_t, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_auto_const_size_t_const_size_t_const_CheckContextR(v1, v2, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_auto_11(v1: &str, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + extern_container_arg!(v1); + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_auto_const_stringR_const_CheckContextR(v1.opencv_as_extern(), ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_false(v: bool, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_false_const_bool_const_CheckContextR(v, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn check_failed_true(v: bool, ctx: &impl core::Detail_CheckContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_check_failed_true_const_bool_const_CheckContextR(v, ctx.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the determinant of a square floating-point matrix. + /// + /// The function cv::determinant calculates and returns the determinant of the + /// specified matrix. For small matrices ( mtx.cols=mtx.rows\<=3 ), the + /// direct method is used. For larger matrices, the function uses LU + /// factorization with partial pivoting. + /// + /// For symmetric positively-determined matrices, it is also possible to use + /// eigen decomposition to calculate the determinant. + /// ## Parameters + /// * mtx: input matrix that must have CV_32FC1 or CV_64FC1 type and + /// square size. + /// ## See also + /// trace, invert, solve, eigen, [MatrixExpressions] + #[inline] + pub fn determinant(mtx: &impl ToInputArray) -> Result { + input_array_arg!(mtx); + return_send!(via ocvrs_return); + unsafe { sys::cv_determinant_const__InputArrayR(mtx.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. + /// + /// The function cv::dft performs one of the following: + /// * Forward the Fourier transform of a 1D vector of N elements: + /// ![block formula](https://latex.codecogs.com/png.latex?Y%20%3D%20F%5E%7B%28N%29%7D%20%20%5Ccdot%20X%2C) + /// where ![inline formula](https://latex.codecogs.com/png.latex?F%5E%7B%28N%29%7D%5F%7Bjk%7D%3D%5Cexp%28%2D2%5Cpi%20i%20j%20k%2FN%29) and ![inline formula](https://latex.codecogs.com/png.latex?i%3D%5Csqrt%7B%2D1%7D) + /// * Inverse the Fourier transform of a 1D vector of N elements: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20X%27%3D%20%20%5Cleft%20%28F%5E%7B%28N%29%7D%20%5Cright%20%29%5E%7B%2D1%7D%20%20%5Ccdot%20Y%20%3D%20%20%5Cleft%20%28F%5E%7B%28N%29%7D%20%5Cright%20%29%5E%2A%20%20%5Ccdot%20y%20%20%5C%5C%20X%20%3D%20%281%2FN%29%20%20%5Ccdot%20X%2C%20%5Cend%7Barray%7D) + /// where ![inline formula](https://latex.codecogs.com/png.latex?F%5E%2A%3D%5Cleft%28%5Ctextrm%7BRe%7D%28F%5E%7B%28N%29%7D%29%2D%5Ctextrm%7BIm%7D%28F%5E%7B%28N%29%7D%29%5Cright%29%5ET) + /// * Forward the 2D Fourier transform of a M x N matrix: + /// ![block formula](https://latex.codecogs.com/png.latex?Y%20%3D%20F%5E%7B%28M%29%7D%20%20%5Ccdot%20X%20%20%5Ccdot%20F%5E%7B%28N%29%7D) + /// * Inverse the 2D Fourier transform of a M x N matrix: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20X%27%3D%20%20%5Cleft%20%28F%5E%7B%28M%29%7D%20%5Cright%20%29%5E%2A%20%20%5Ccdot%20Y%20%20%5Ccdot%20%5Cleft%20%28F%5E%7B%28N%29%7D%20%5Cright%20%29%5E%2A%20%5C%5C%20X%20%3D%20%20%5Cfrac%7B1%7D%7BM%20%5Ccdot%20N%7D%20%5Ccdot%20X%27%20%5Cend%7Barray%7D) + /// + /// In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input + /// spectrum of the inverse Fourier transform can be represented in a packed format called *CCS* + /// (complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here + /// is how 2D *CCS* spectrum looks: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20Re%20Y%5F%7B0%2C0%7D%20%26%20Re%20Y%5F%7B0%2C1%7D%20%26%20Im%20Y%5F%7B0%2C1%7D%20%26%20Re%20Y%5F%7B0%2C2%7D%20%26%20Im%20Y%5F%7B0%2C2%7D%20%26%20%20%5Ccdots%20%26%20Re%20Y%5F%7B0%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7B0%2CN%2F2%2D1%7D%20%26%20Re%20Y%5F%7B0%2CN%2F2%7D%20%20%5C%5C%20Re%20Y%5F%7B1%2C0%7D%20%26%20Re%20Y%5F%7B1%2C1%7D%20%26%20Im%20Y%5F%7B1%2C1%7D%20%26%20Re%20Y%5F%7B1%2C2%7D%20%26%20Im%20Y%5F%7B1%2C2%7D%20%26%20%20%5Ccdots%20%26%20Re%20Y%5F%7B1%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7B1%2CN%2F2%2D1%7D%20%26%20Re%20Y%5F%7B1%2CN%2F2%7D%20%20%5C%5C%20Im%20Y%5F%7B1%2C0%7D%20%26%20Re%20Y%5F%7B2%2C1%7D%20%26%20Im%20Y%5F%7B2%2C1%7D%20%26%20Re%20Y%5F%7B2%2C2%7D%20%26%20Im%20Y%5F%7B2%2C2%7D%20%26%20%20%5Ccdots%20%26%20Re%20Y%5F%7B2%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7B2%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7B1%2CN%2F2%7D%20%20%5C%5C%20%5Cdots%20%5C%5C%20Re%20Y%5F%7BM%2F2%2D1%2C0%7D%20%26%20%20Re%20Y%5F%7BM%2D3%2C1%7D%20%20%26%20Im%20Y%5F%7BM%2D3%2C1%7D%20%26%20%20%5Cdots%20%26%20Re%20Y%5F%7BM%2D3%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7BM%2D3%2CN%2F2%2D1%7D%26%20Re%20Y%5F%7BM%2F2%2D1%2CN%2F2%7D%20%20%5C%5C%20Im%20Y%5F%7BM%2F2%2D1%2C0%7D%20%26%20%20Re%20Y%5F%7BM%2D2%2C1%7D%20%20%26%20Im%20Y%5F%7BM%2D2%2C1%7D%20%26%20%20%5Cdots%20%26%20Re%20Y%5F%7BM%2D2%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7BM%2D2%2CN%2F2%2D1%7D%26%20Im%20Y%5F%7BM%2F2%2D1%2CN%2F2%7D%20%20%5C%5C%20Re%20Y%5F%7BM%2F2%2C0%7D%20%20%26%20%20Re%20Y%5F%7BM%2D1%2C1%7D%20%26%20%20Im%20Y%5F%7BM%2D1%2C1%7D%20%26%20%20%5Cdots%20%26%20Re%20Y%5F%7BM%2D1%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7BM%2D1%2CN%2F2%2D1%7D%26%20Re%20Y%5F%7BM%2F2%2CN%2F2%7D%20%5Cend%7Bbmatrix%7D) + /// + /// In case of 1D transform of a real vector, the output looks like the first row of the matrix above. + /// + /// So, the function chooses an operation mode depending on the flags and size of the input array: + /// * If [DFT_ROWS] is set or the input array has a single row or single column, the function + /// performs a 1D forward or inverse transform of each row of a matrix when [DFT_ROWS] is set. + /// Otherwise, it performs a 2D transform. + /// * If the input array is real and [DFT_INVERSE] is not set, the function performs a forward 1D or + /// 2D transform: + /// * When [DFT_COMPLEX_OUTPUT] is set, the output is a complex matrix of the same size as + /// input. + /// * When [DFT_COMPLEX_OUTPUT] is not set, the output is a real matrix of the same size as + /// input. In case of 2D transform, it uses the packed format as shown above. In case of a + /// single 1D transform, it looks like the first row of the matrix above. In case of + /// multiple 1D transforms (when using the [DFT_ROWS] flag), each row of the output matrix + /// looks like the first row of the matrix above. + /// * If the input array is complex and either [DFT_INVERSE] or [DFT_REAL_OUTPUT] are not set, the + /// output is a complex array of the same size as input. The function performs a forward or + /// inverse 1D or 2D transform of the whole input array or each row of the input array + /// independently, depending on the flags DFT_INVERSE and DFT_ROWS. + /// * When [DFT_INVERSE] is set and the input array is real, or it is complex but [DFT_REAL_OUTPUT] + /// is set, the output is a real array of the same size as input. The function performs a 1D or 2D + /// inverse transformation of the whole input array or each individual row, depending on the flags + /// [DFT_INVERSE] and #DFT_ROWS. + /// + /// If [DFT_SCALE] is set, the scaling is done after the transformation. + /// + /// Unlike dct, the function supports arrays of arbitrary size. But only those arrays are processed + /// efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the + /// current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize + /// method. + /// + /// The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays: + /// ```C++ + /// void convolveDFT(InputArray A, InputArray B, OutputArray C) + /// { + /// // reallocate the output array if needed + /// C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type()); + /// Size dftSize; + /// // calculate the size of DFT transform + /// dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1); + /// dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1); + /// + /// // allocate temporary buffers and initialize them with 0's + /// Mat tempA(dftSize, A.type(), Scalar::all(0)); + /// Mat tempB(dftSize, B.type(), Scalar::all(0)); + /// + /// // copy A and B to the top-left corners of tempA and tempB, respectively + /// Mat roiA(tempA, Rect(0,0,A.cols,A.rows)); + /// A.copyTo(roiA); + /// Mat roiB(tempB, Rect(0,0,B.cols,B.rows)); + /// B.copyTo(roiB); + /// + /// // now transform the padded A & B in-place; + /// // use "nonzeroRows" hint for faster processing + /// dft(tempA, tempA, 0, A.rows); + /// dft(tempB, tempB, 0, B.rows); + /// + /// // multiply the spectrums; + /// // the function handles packed spectrum representations well + /// mulSpectrums(tempA, tempB, tempA); + /// + /// // transform the product back from the frequency domain. + /// // Even though all the result rows will be non-zero, + /// // you need only the first C.rows of them, and thus you + /// // pass nonzeroRows == C.rows + /// dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows); + /// + /// // now copy the result back to C. + /// tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C); + /// + /// // all the temporary buffers will be deallocated automatically + /// } + /// ``` + /// + /// To optimize this sample, consider the following approaches: + /// * Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to + /// the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole + /// tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols) + /// rightmost columns of the matrices. + /// * This DFT-based convolution does not have to be applied to the whole big arrays, especially if B + /// is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts. + /// To do this, you need to split the output array C into multiple tiles. For each tile, estimate + /// which parts of A and B are required to calculate convolution in this tile. If the tiles in C are + /// too small, the speed will decrease a lot because of repeated work. In the ultimate case, when + /// each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution + /// algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and + /// there is also a slowdown because of bad cache locality. So, there is an optimal tile size + /// somewhere in the middle. + /// * If different tiles in C can be calculated in parallel and, thus, the convolution is done by + /// parts, the loop can be threaded. + /// + /// All of the above improvements have been implemented in [match_template] and [filter_2d] . Therefore, by + /// using them, you can get the performance even better than with the above theoretically optimal + /// implementation. Though, those two functions actually calculate cross-correlation, not convolution, + /// so you need to "flip" the second convolution operand B vertically and horizontally using flip . + /// + /// Note: + /// * An example using the discrete fourier transform can be found at + /// opencv_source_code/samples/cpp/dft.cpp + /// * (Python) An example using the dft functionality to perform Wiener deconvolution can be found + /// at opencv_source/samples/python/deconvolution.py + /// * (Python) An example rearranging the quadrants of a Fourier image can be found at + /// opencv_source/samples/python/dft.py + /// ## Parameters + /// * src: input array that could be real or complex. + /// * dst: output array whose size and type depends on the flags . + /// * flags: transformation flags, representing a combination of the [dft_flags] + /// * nonzeroRows: when the parameter is not zero, the function assumes that only the first + /// nonzeroRows rows of the input array ([DFT_INVERSE] is not set) or only the first nonzeroRows of the + /// output array ([DFT_INVERSE] is set) contain non-zeros, thus, the function can handle the rest of the + /// rows more efficiently and save some time; this technique is very useful for calculating array + /// cross-correlation or convolution using DFT. + /// ## See also + /// dct, getOptimalDFTSize, mulSpectrums, filter2D, matchTemplate, flip, cartToPolar, + /// magnitude, phase + /// + /// ## Note + /// This alternative version of [dft] function uses the following default values for its arguments: + /// * flags: 0 + /// * nonzero_rows: 0 + #[inline] + pub fn dft_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_dft_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. + /// + /// The function cv::dft performs one of the following: + /// * Forward the Fourier transform of a 1D vector of N elements: + /// ![block formula](https://latex.codecogs.com/png.latex?Y%20%3D%20F%5E%7B%28N%29%7D%20%20%5Ccdot%20X%2C) + /// where ![inline formula](https://latex.codecogs.com/png.latex?F%5E%7B%28N%29%7D%5F%7Bjk%7D%3D%5Cexp%28%2D2%5Cpi%20i%20j%20k%2FN%29) and ![inline formula](https://latex.codecogs.com/png.latex?i%3D%5Csqrt%7B%2D1%7D) + /// * Inverse the Fourier transform of a 1D vector of N elements: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20X%27%3D%20%20%5Cleft%20%28F%5E%7B%28N%29%7D%20%5Cright%20%29%5E%7B%2D1%7D%20%20%5Ccdot%20Y%20%3D%20%20%5Cleft%20%28F%5E%7B%28N%29%7D%20%5Cright%20%29%5E%2A%20%20%5Ccdot%20y%20%20%5C%5C%20X%20%3D%20%281%2FN%29%20%20%5Ccdot%20X%2C%20%5Cend%7Barray%7D) + /// where ![inline formula](https://latex.codecogs.com/png.latex?F%5E%2A%3D%5Cleft%28%5Ctextrm%7BRe%7D%28F%5E%7B%28N%29%7D%29%2D%5Ctextrm%7BIm%7D%28F%5E%7B%28N%29%7D%29%5Cright%29%5ET) + /// * Forward the 2D Fourier transform of a M x N matrix: + /// ![block formula](https://latex.codecogs.com/png.latex?Y%20%3D%20F%5E%7B%28M%29%7D%20%20%5Ccdot%20X%20%20%5Ccdot%20F%5E%7B%28N%29%7D) + /// * Inverse the 2D Fourier transform of a M x N matrix: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20X%27%3D%20%20%5Cleft%20%28F%5E%7B%28M%29%7D%20%5Cright%20%29%5E%2A%20%20%5Ccdot%20Y%20%20%5Ccdot%20%5Cleft%20%28F%5E%7B%28N%29%7D%20%5Cright%20%29%5E%2A%20%5C%5C%20X%20%3D%20%20%5Cfrac%7B1%7D%7BM%20%5Ccdot%20N%7D%20%5Ccdot%20X%27%20%5Cend%7Barray%7D) + /// + /// In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input + /// spectrum of the inverse Fourier transform can be represented in a packed format called *CCS* + /// (complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here + /// is how 2D *CCS* spectrum looks: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20Re%20Y%5F%7B0%2C0%7D%20%26%20Re%20Y%5F%7B0%2C1%7D%20%26%20Im%20Y%5F%7B0%2C1%7D%20%26%20Re%20Y%5F%7B0%2C2%7D%20%26%20Im%20Y%5F%7B0%2C2%7D%20%26%20%20%5Ccdots%20%26%20Re%20Y%5F%7B0%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7B0%2CN%2F2%2D1%7D%20%26%20Re%20Y%5F%7B0%2CN%2F2%7D%20%20%5C%5C%20Re%20Y%5F%7B1%2C0%7D%20%26%20Re%20Y%5F%7B1%2C1%7D%20%26%20Im%20Y%5F%7B1%2C1%7D%20%26%20Re%20Y%5F%7B1%2C2%7D%20%26%20Im%20Y%5F%7B1%2C2%7D%20%26%20%20%5Ccdots%20%26%20Re%20Y%5F%7B1%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7B1%2CN%2F2%2D1%7D%20%26%20Re%20Y%5F%7B1%2CN%2F2%7D%20%20%5C%5C%20Im%20Y%5F%7B1%2C0%7D%20%26%20Re%20Y%5F%7B2%2C1%7D%20%26%20Im%20Y%5F%7B2%2C1%7D%20%26%20Re%20Y%5F%7B2%2C2%7D%20%26%20Im%20Y%5F%7B2%2C2%7D%20%26%20%20%5Ccdots%20%26%20Re%20Y%5F%7B2%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7B2%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7B1%2CN%2F2%7D%20%20%5C%5C%20%5Cdots%20%5C%5C%20Re%20Y%5F%7BM%2F2%2D1%2C0%7D%20%26%20%20Re%20Y%5F%7BM%2D3%2C1%7D%20%20%26%20Im%20Y%5F%7BM%2D3%2C1%7D%20%26%20%20%5Cdots%20%26%20Re%20Y%5F%7BM%2D3%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7BM%2D3%2CN%2F2%2D1%7D%26%20Re%20Y%5F%7BM%2F2%2D1%2CN%2F2%7D%20%20%5C%5C%20Im%20Y%5F%7BM%2F2%2D1%2C0%7D%20%26%20%20Re%20Y%5F%7BM%2D2%2C1%7D%20%20%26%20Im%20Y%5F%7BM%2D2%2C1%7D%20%26%20%20%5Cdots%20%26%20Re%20Y%5F%7BM%2D2%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7BM%2D2%2CN%2F2%2D1%7D%26%20Im%20Y%5F%7BM%2F2%2D1%2CN%2F2%7D%20%20%5C%5C%20Re%20Y%5F%7BM%2F2%2C0%7D%20%20%26%20%20Re%20Y%5F%7BM%2D1%2C1%7D%20%26%20%20Im%20Y%5F%7BM%2D1%2C1%7D%20%26%20%20%5Cdots%20%26%20Re%20Y%5F%7BM%2D1%2CN%2F2%2D1%7D%20%26%20Im%20Y%5F%7BM%2D1%2CN%2F2%2D1%7D%26%20Re%20Y%5F%7BM%2F2%2CN%2F2%7D%20%5Cend%7Bbmatrix%7D) + /// + /// In case of 1D transform of a real vector, the output looks like the first row of the matrix above. + /// + /// So, the function chooses an operation mode depending on the flags and size of the input array: + /// * If [DFT_ROWS] is set or the input array has a single row or single column, the function + /// performs a 1D forward or inverse transform of each row of a matrix when [DFT_ROWS] is set. + /// Otherwise, it performs a 2D transform. + /// * If the input array is real and [DFT_INVERSE] is not set, the function performs a forward 1D or + /// 2D transform: + /// * When [DFT_COMPLEX_OUTPUT] is set, the output is a complex matrix of the same size as + /// input. + /// * When [DFT_COMPLEX_OUTPUT] is not set, the output is a real matrix of the same size as + /// input. In case of 2D transform, it uses the packed format as shown above. In case of a + /// single 1D transform, it looks like the first row of the matrix above. In case of + /// multiple 1D transforms (when using the [DFT_ROWS] flag), each row of the output matrix + /// looks like the first row of the matrix above. + /// * If the input array is complex and either [DFT_INVERSE] or [DFT_REAL_OUTPUT] are not set, the + /// output is a complex array of the same size as input. The function performs a forward or + /// inverse 1D or 2D transform of the whole input array or each row of the input array + /// independently, depending on the flags DFT_INVERSE and DFT_ROWS. + /// * When [DFT_INVERSE] is set and the input array is real, or it is complex but [DFT_REAL_OUTPUT] + /// is set, the output is a real array of the same size as input. The function performs a 1D or 2D + /// inverse transformation of the whole input array or each individual row, depending on the flags + /// [DFT_INVERSE] and #DFT_ROWS. + /// + /// If [DFT_SCALE] is set, the scaling is done after the transformation. + /// + /// Unlike dct, the function supports arrays of arbitrary size. But only those arrays are processed + /// efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the + /// current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize + /// method. + /// + /// The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays: + /// ```C++ + /// void convolveDFT(InputArray A, InputArray B, OutputArray C) + /// { + /// // reallocate the output array if needed + /// C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type()); + /// Size dftSize; + /// // calculate the size of DFT transform + /// dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1); + /// dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1); + /// + /// // allocate temporary buffers and initialize them with 0's + /// Mat tempA(dftSize, A.type(), Scalar::all(0)); + /// Mat tempB(dftSize, B.type(), Scalar::all(0)); + /// + /// // copy A and B to the top-left corners of tempA and tempB, respectively + /// Mat roiA(tempA, Rect(0,0,A.cols,A.rows)); + /// A.copyTo(roiA); + /// Mat roiB(tempB, Rect(0,0,B.cols,B.rows)); + /// B.copyTo(roiB); + /// + /// // now transform the padded A & B in-place; + /// // use "nonzeroRows" hint for faster processing + /// dft(tempA, tempA, 0, A.rows); + /// dft(tempB, tempB, 0, B.rows); + /// + /// // multiply the spectrums; + /// // the function handles packed spectrum representations well + /// mulSpectrums(tempA, tempB, tempA); + /// + /// // transform the product back from the frequency domain. + /// // Even though all the result rows will be non-zero, + /// // you need only the first C.rows of them, and thus you + /// // pass nonzeroRows == C.rows + /// dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows); + /// + /// // now copy the result back to C. + /// tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C); + /// + /// // all the temporary buffers will be deallocated automatically + /// } + /// ``` + /// + /// To optimize this sample, consider the following approaches: + /// * Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to + /// the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole + /// tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols) + /// rightmost columns of the matrices. + /// * This DFT-based convolution does not have to be applied to the whole big arrays, especially if B + /// is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts. + /// To do this, you need to split the output array C into multiple tiles. For each tile, estimate + /// which parts of A and B are required to calculate convolution in this tile. If the tiles in C are + /// too small, the speed will decrease a lot because of repeated work. In the ultimate case, when + /// each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution + /// algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and + /// there is also a slowdown because of bad cache locality. So, there is an optimal tile size + /// somewhere in the middle. + /// * If different tiles in C can be calculated in parallel and, thus, the convolution is done by + /// parts, the loop can be threaded. + /// + /// All of the above improvements have been implemented in [match_template] and [filter_2d] . Therefore, by + /// using them, you can get the performance even better than with the above theoretically optimal + /// implementation. Though, those two functions actually calculate cross-correlation, not convolution, + /// so you need to "flip" the second convolution operand B vertically and horizontally using flip . + /// + /// Note: + /// * An example using the discrete fourier transform can be found at + /// opencv_source_code/samples/cpp/dft.cpp + /// * (Python) An example using the dft functionality to perform Wiener deconvolution can be found + /// at opencv_source/samples/python/deconvolution.py + /// * (Python) An example rearranging the quadrants of a Fourier image can be found at + /// opencv_source/samples/python/dft.py + /// ## Parameters + /// * src: input array that could be real or complex. + /// * dst: output array whose size and type depends on the flags . + /// * flags: transformation flags, representing a combination of the [dft_flags] + /// * nonzeroRows: when the parameter is not zero, the function assumes that only the first + /// nonzeroRows rows of the input array ([DFT_INVERSE] is not set) or only the first nonzeroRows of the + /// output array ([DFT_INVERSE] is set) contain non-zeros, thus, the function can handle the rest of the + /// rows more efficiently and save some time; this technique is very useful for calculating array + /// cross-correlation or convolution using DFT. + /// ## See also + /// dct, getOptimalDFTSize, mulSpectrums, filter2D, matchTemplate, flip, cartToPolar, + /// magnitude, phase + /// + /// ## C++ default parameters + /// * flags: 0 + /// * nonzero_rows: 0 + #[inline] + pub fn dft(src: &impl ToInputArray, dst: &mut impl ToOutputArray, flags: i32, nonzero_rows: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_dft_const__InputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), flags, nonzero_rows, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Parameters + /// * pD3D10Texture2D: - source D3D10 texture + /// * dst: - destination OutputArray + #[inline] + pub fn convert_from_d3d10_texture_2d(p_d3d10_texture_2d: &mut impl core::ID3D10Texture2DTrait, dst: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_convertFromD3D10Texture2D_ID3D10Texture2DX_const__OutputArrayR(p_d3d10_texture_2d.as_raw_mut_ID3D10Texture2D(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Parameters + /// * pD3D11Texture2D: - source D3D11 texture + /// * dst: - destination OutputArray + #[inline] + pub fn convert_from_d3d11_texture_2d(p_d3d11_texture_2d: &mut impl core::ID3D11Texture2DTrait, dst: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_convertFromD3D11Texture2D_ID3D11Texture2DX_const__OutputArrayR(p_d3d11_texture_2d.as_raw_mut_ID3D11Texture2D(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Parameters + /// * pDirect3DSurface9: - source D3D10 texture + /// * dst: - destination OutputArray + /// * surfaceSharedHandle: - shared handle + /// + /// ## Note + /// This alternative version of [convert_from_direct_3d_surface9] function uses the following default values for its arguments: + /// * surface_shared_handle: NULL + #[inline] + pub fn convert_from_direct_3d_surface9_def(p_direct_3d_surface9: &mut impl core::IDirect3DSurface9Trait, dst: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_convertFromDirect3DSurface9_IDirect3DSurface9X_const__OutputArrayR(p_direct_3d_surface9.as_raw_mut_IDirect3DSurface9(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Parameters + /// * pDirect3DSurface9: - source D3D10 texture + /// * dst: - destination OutputArray + /// * surfaceSharedHandle: - shared handle + /// + /// ## C++ default parameters + /// * surface_shared_handle: NULL + #[inline] + pub unsafe fn convert_from_direct_3d_surface9(p_direct_3d_surface9: &mut impl core::IDirect3DSurface9Trait, dst: &mut impl ToOutputArray, surface_shared_handle: *mut c_void) -> Result<()> { + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_convertFromDirect3DSurface9_IDirect3DSurface9X_const__OutputArrayR_voidX(p_direct_3d_surface9.as_raw_mut_IDirect3DSurface9(), dst.as_raw__OutputArray(), surface_shared_handle, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Parameters + /// * src: - source InputArray + /// * pD3D10Texture2D: - destination D3D10 texture + #[inline] + pub fn convert_to_d3d10_texture_2d(src: &impl ToInputArray, p_d3d10_texture_2d: &mut impl core::ID3D10Texture2DTrait) -> Result<()> { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_convertToD3D10Texture2D_const__InputArrayR_ID3D10Texture2DX(src.as_raw__InputArray(), p_d3d10_texture_2d.as_raw_mut_ID3D10Texture2D(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Parameters + /// * src: - source InputArray + /// * pD3D11Texture2D: - destination D3D11 texture + #[inline] + pub fn convert_to_d3d11_texture_2d(src: &impl ToInputArray, p_d3d11_texture_2d: &mut impl core::ID3D11Texture2DTrait) -> Result<()> { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_convertToD3D11Texture2D_const__InputArrayR_ID3D11Texture2DX(src.as_raw__InputArray(), p_d3d11_texture_2d.as_raw_mut_ID3D11Texture2D(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Parameters + /// * src: - source InputArray + /// * pDirect3DSurface9: - destination D3D10 texture + /// * surfaceSharedHandle: - shared handle + /// + /// ## Note + /// This alternative version of [convert_to_direct_3d_surface9] function uses the following default values for its arguments: + /// * surface_shared_handle: NULL + #[inline] + pub fn convert_to_direct_3d_surface9_def(src: &impl ToInputArray, p_direct_3d_surface9: &mut impl core::IDirect3DSurface9Trait) -> Result<()> { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_convertToDirect3DSurface9_const__InputArrayR_IDirect3DSurface9X(src.as_raw__InputArray(), p_direct_3d_surface9.as_raw_mut_IDirect3DSurface9(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Parameters + /// * src: - source InputArray + /// * pDirect3DSurface9: - destination D3D10 texture + /// * surfaceSharedHandle: - shared handle + /// + /// ## C++ default parameters + /// * surface_shared_handle: NULL + #[inline] + pub unsafe fn convert_to_direct_3d_surface9(src: &impl ToInputArray, p_direct_3d_surface9: &mut impl core::IDirect3DSurface9Trait, surface_shared_handle: *mut c_void) -> Result<()> { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_convertToDirect3DSurface9_const__InputArrayR_IDirect3DSurface9X_voidX(src.as_raw__InputArray(), p_direct_3d_surface9.as_raw_mut_IDirect3DSurface9(), surface_shared_handle, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Get OpenCV type from DirectX type + /// ## Parameters + /// * iD3DFORMAT: - enum D3DTYPE for D3D9 + /// ## Returns + /// OpenCV type or -1 if there is no equivalent + #[inline] + pub fn get_type_from_d3d_format(id_3d_format: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_getTypeFromD3DFORMAT_const_int(id_3d_format, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Get OpenCV type from DirectX type + /// ## Parameters + /// * iDXGI_FORMAT: - enum DXGI_FORMAT for D3D10/D3D11 + /// ## Returns + /// OpenCV type or -1 if there is no equivalent + #[inline] + pub fn get_type_from_dxgi_format(i_dxgi_format: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_getTypeFromDXGI_FORMAT_const_int(i_dxgi_format, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Parameters + /// * pD3D10Device: - pointer to D3D10 device + /// ## Returns + /// Returns reference to OpenCL Context + #[inline] + pub fn initialize_context_from_d3d10_device(p_d3d10_device: &mut impl core::ID3D10DeviceTrait) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_ocl_initializeContextFromD3D10Device_ID3D10DeviceX(p_d3d10_device.as_raw_mut_ID3D10Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Context::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Parameters + /// * pD3D11Device: - pointer to D3D11 device + /// ## Returns + /// Returns reference to OpenCL Context + #[inline] + pub fn initialize_context_from_d3d11_device(p_d3d11_device: &mut impl core::ID3D11DeviceTrait) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_ocl_initializeContextFromD3D11Device_ID3D11DeviceX(p_d3d11_device.as_raw_mut_ID3D11Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Context::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Parameters + /// * pDirect3DDevice9Ex: - pointer to Direct3DDevice9Ex device + /// ## Returns + /// Returns reference to OpenCL Context + #[inline] + pub fn initialize_context_from_direct_3d_device9_ex(p_direct_3d_device9_ex: &mut impl core::IDirect3DDevice9ExTrait) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_ocl_initializeContextFromDirect3DDevice9Ex_IDirect3DDevice9ExX(p_direct_3d_device9_ex.as_raw_mut_IDirect3DDevice9Ex(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Context::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Parameters + /// * pDirect3DDevice9: - pointer to Direct3Device9 device + /// ## Returns + /// Returns reference to OpenCL Context + #[inline] + pub fn initialize_context_from_direct_3d_device9(p_direct_3d_device9: &mut impl core::IDirect3DDevice9Trait) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_directx_ocl_initializeContextFromDirect3DDevice9_IDirect3DDevice9X(p_direct_3d_device9.as_raw_mut_IDirect3DDevice9(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Context::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Performs per-element division of two arrays or a scalar by an array. + /// + /// The function cv::divide divides one array by another: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%28I%29%20%3D%20saturate%28src1%28I%29%2Ascale%2Fsrc2%28I%29%29%7D) + /// or a scalar by an array when there is no src1 : + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%28I%29%20%3D%20saturate%28scale%2Fsrc2%28I%29%29%7D) + /// + /// Different channels of multi-channel arrays are processed independently. + /// + /// For integer types when src2(I) is zero, dst(I) will also be zero. + /// + /// + /// Note: In case of floating point data there is no special defined behavior for zero src2(I) values. + /// Regular floating-point division is used. + /// Expect correct IEEE-754 behaviour for floating-point data (with NaN, Inf result values). + /// + /// + /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get + /// result of an incorrect sign in the case of overflow. + /// + /// Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array. + /// `divide(src,X)` means `divide(src,(X,X,X,X))`. + /// `divide(src,(X,))` means `divide(src,(X,0,0,0))`. + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and type as src1. + /// * scale: scalar factor. + /// * dst: output array of the same size and type as src2. + /// * dtype: optional depth of the output array; if -1, dst will have depth src2.depth(), but in + /// case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). + /// ## See also + /// multiply, add, subtract + /// + /// ## Note + /// This alternative version of [divide2] function uses the following default values for its arguments: + /// * scale: 1 + /// * dtype: -1 + #[inline] + pub fn divide2_def(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_divide_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs per-element division of two arrays or a scalar by an array. + /// + /// The function cv::divide divides one array by another: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%28I%29%20%3D%20saturate%28src1%28I%29%2Ascale%2Fsrc2%28I%29%29%7D) + /// or a scalar by an array when there is no src1 : + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%28I%29%20%3D%20saturate%28scale%2Fsrc2%28I%29%29%7D) + /// + /// Different channels of multi-channel arrays are processed independently. + /// + /// For integer types when src2(I) is zero, dst(I) will also be zero. + /// + /// + /// Note: In case of floating point data there is no special defined behavior for zero src2(I) values. + /// Regular floating-point division is used. + /// Expect correct IEEE-754 behaviour for floating-point data (with NaN, Inf result values). + /// + /// + /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get + /// result of an incorrect sign in the case of overflow. + /// + /// Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array. + /// `divide(src,X)` means `divide(src,(X,X,X,X))`. + /// `divide(src,(X,))` means `divide(src,(X,0,0,0))`. + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and type as src1. + /// * scale: scalar factor. + /// * dst: output array of the same size and type as src2. + /// * dtype: optional depth of the output array; if -1, dst will have depth src2.depth(), but in + /// case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). + /// ## See also + /// multiply, add, subtract + /// + /// ## C++ default parameters + /// * scale: 1 + /// * dtype: -1 + #[inline] + pub fn divide2(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray, scale: f64, dtype: i32) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_divide_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double_int(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), scale, dtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs per-element division of two arrays or a scalar by an array. + /// + /// The function cv::divide divides one array by another: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%28I%29%20%3D%20saturate%28src1%28I%29%2Ascale%2Fsrc2%28I%29%29%7D) + /// or a scalar by an array when there is no src1 : + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%28I%29%20%3D%20saturate%28scale%2Fsrc2%28I%29%29%7D) + /// + /// Different channels of multi-channel arrays are processed independently. + /// + /// For integer types when src2(I) is zero, dst(I) will also be zero. + /// + /// + /// Note: In case of floating point data there is no special defined behavior for zero src2(I) values. + /// Regular floating-point division is used. + /// Expect correct IEEE-754 behaviour for floating-point data (with NaN, Inf result values). + /// + /// + /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get + /// result of an incorrect sign in the case of overflow. + /// + /// Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array. + /// `divide(src,X)` means `divide(src,(X,X,X,X))`. + /// `divide(src,(X,))` means `divide(src,(X,0,0,0))`. + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and type as src1. + /// * scale: scalar factor. + /// * dst: output array of the same size and type as src2. + /// * dtype: optional depth of the output array; if -1, dst will have depth src2.depth(), but in + /// case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). + /// ## See also + /// multiply, add, subtract + /// + /// ## Overloaded parameters + /// + /// + /// ## Note + /// This alternative version of [divide] function uses the following default values for its arguments: + /// * dtype: -1 + #[inline] + pub fn divide_def(scale: f64, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_divide_double_const__InputArrayR_const__OutputArrayR(scale, src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs per-element division of two arrays or a scalar by an array. + /// + /// The function cv::divide divides one array by another: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%28I%29%20%3D%20saturate%28src1%28I%29%2Ascale%2Fsrc2%28I%29%29%7D) + /// or a scalar by an array when there is no src1 : + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%28I%29%20%3D%20saturate%28scale%2Fsrc2%28I%29%29%7D) + /// + /// Different channels of multi-channel arrays are processed independently. + /// + /// For integer types when src2(I) is zero, dst(I) will also be zero. + /// + /// + /// Note: In case of floating point data there is no special defined behavior for zero src2(I) values. + /// Regular floating-point division is used. + /// Expect correct IEEE-754 behaviour for floating-point data (with NaN, Inf result values). + /// + /// + /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get + /// result of an incorrect sign in the case of overflow. + /// + /// Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array. + /// `divide(src,X)` means `divide(src,(X,X,X,X))`. + /// `divide(src,(X,))` means `divide(src,(X,0,0,0))`. + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and type as src1. + /// * scale: scalar factor. + /// * dst: output array of the same size and type as src2. + /// * dtype: optional depth of the output array; if -1, dst will have depth src2.depth(), but in + /// case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). + /// ## See also + /// multiply, add, subtract + /// + /// ## Overloaded parameters + /// + /// ## C++ default parameters + /// * dtype: -1 + #[inline] + pub fn divide(scale: f64, src2: &impl ToInputArray, dst: &mut impl ToOutputArray, dtype: i32) -> Result<()> { + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_divide_double_const__InputArrayR_const__OutputArrayR_int(scale, src2.as_raw__InputArray(), dst.as_raw__OutputArray(), dtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only). + /// + /// + /// Note: Assumes real eigenvalues. + /// + /// The function calculates eigenvalues and eigenvectors (optional) of the square matrix src: + /// ```C++ + /// src*eigenvectors.row(i).t() = eigenvalues.at(i)*eigenvectors.row(i).t() + /// ``` + /// + /// + /// ## Parameters + /// * src: input matrix (CV_32FC1 or CV_64FC1 type). + /// * eigenvalues: output vector of eigenvalues (type is the same type as src). + /// * eigenvectors: output matrix of eigenvectors (type is the same type as src). The eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues. + /// ## See also + /// eigen + #[inline] + pub fn eigen_non_symmetric(src: &impl ToInputArray, eigenvalues: &mut impl ToOutputArray, eigenvectors: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(eigenvalues); + output_array_arg!(eigenvectors); + return_send!(via ocvrs_return); + unsafe { sys::cv_eigenNonSymmetric_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src.as_raw__InputArray(), eigenvalues.as_raw__OutputArray(), eigenvectors.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates eigenvalues and eigenvectors of a symmetric matrix. + /// + /// The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric + /// matrix src: + /// ```C++ + /// src*eigenvectors.row(i).t() = eigenvalues.at(i)*eigenvectors.row(i).t() + /// ``` + /// + /// + /// + /// Note: Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix. + /// + /// ## Parameters + /// * src: input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical + /// (src ^T^ == src). + /// * eigenvalues: output vector of eigenvalues of the same type as src; the eigenvalues are stored + /// in the descending order. + /// * eigenvectors: output matrix of eigenvectors; it has the same size and type as src; the + /// eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding + /// eigenvalues. + /// ## See also + /// eigenNonSymmetric, completeSymm, PCA + /// + /// ## Note + /// This alternative version of [eigen] function uses the following default values for its arguments: + /// * eigenvectors: noArray() + #[inline] + pub fn eigen_def(src: &impl ToInputArray, eigenvalues: &mut impl ToOutputArray) -> Result { + input_array_arg!(src); + output_array_arg!(eigenvalues); + return_send!(via ocvrs_return); + unsafe { sys::cv_eigen_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), eigenvalues.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates eigenvalues and eigenvectors of a symmetric matrix. + /// + /// The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric + /// matrix src: + /// ```C++ + /// src*eigenvectors.row(i).t() = eigenvalues.at(i)*eigenvectors.row(i).t() + /// ``` + /// + /// + /// + /// Note: Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix. + /// + /// ## Parameters + /// * src: input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical + /// (src ^T^ == src). + /// * eigenvalues: output vector of eigenvalues of the same type as src; the eigenvalues are stored + /// in the descending order. + /// * eigenvectors: output matrix of eigenvectors; it has the same size and type as src; the + /// eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding + /// eigenvalues. + /// ## See also + /// eigenNonSymmetric, completeSymm, PCA + /// + /// ## C++ default parameters + /// * eigenvectors: noArray() + #[inline] + pub fn eigen(src: &impl ToInputArray, eigenvalues: &mut impl ToOutputArray, eigenvectors: &mut impl ToOutputArray) -> Result { + input_array_arg!(src); + output_array_arg!(eigenvalues); + output_array_arg!(eigenvectors); + return_send!(via ocvrs_return); + unsafe { sys::cv_eigen_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src.as_raw__InputArray(), eigenvalues.as_raw__OutputArray(), eigenvectors.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ! Signals an error and raises the exception. + /// + /// By default the function prints information about the error to stderr, + /// then it either stops if cv::setBreakOnError() had been called before or raises the exception. + /// It is possible to alternate error processing by using #redirectError(). + /// ## Parameters + /// * exc: the exception raisen. + /// + /// **Deprecated**: drop this version + #[deprecated = "drop this version"] + #[inline] + pub fn error_1(exc: &impl core::ExceptionTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_error_const_ExceptionR(exc.as_raw_Exception(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ! Signals an error and raises the exception. + /// + /// By default the function prints information about the error to stderr, + /// then it either stops if setBreakOnError() had been called before or raises the exception. + /// It is possible to alternate error processing by using redirectError(). + /// ## Parameters + /// * code: - error code (Error::Code) + /// * err: - error description + /// * func: - function name. Available only when the compiler supports getting it + /// * file: - source file name where the error has occurred + /// * line: - line number in the source file where the error has occurred + /// ## See also + /// CV_Error, CV_Error_, CV_Assert, CV_DbgAssert + #[inline] + pub fn error(code: i32, err: &str, func: &str, file: &str, line: i32) -> Result<()> { + extern_container_arg!(err); + extern_container_arg!(func); + extern_container_arg!(file); + return_send!(via ocvrs_return); + unsafe { sys::cv_error_int_const_StringR_const_charX_const_charX_int(code, err.opencv_as_extern(), func.opencv_as_extern(), file.opencv_as_extern(), line, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the exponent of every array element. + /// + /// The function cv::exp calculates the exponent of every element of the input + /// array: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%5BI%5D%20%3D%20e%5E%7B%20src%28I%29%20%7D) + /// + /// The maximum relative error is about 7e-6 for single-precision input and + /// less than 1e-10 for double-precision input. Currently, the function + /// converts denormalized values to zeros on output. Special values (NaN, + /// Inf) are not handled. + /// ## Parameters + /// * src: input array. + /// * dst: output array of the same size and type as src. + /// ## See also + /// log, cartToPolar, polarToCart, phase, pow, sqrt, magnitude + #[inline] + pub fn exp(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_exp_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Extracts a single channel from src (coi is 0-based index) + /// ## Parameters + /// * src: input array + /// * dst: output array + /// * coi: index of channel to extract + /// ## See also + /// mixChannels, split + #[inline] + pub fn extract_channel(src: &impl ToInputArray, dst: &mut impl ToOutputArray, coi: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_extractChannel_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), coi, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the angle of a 2D vector in degrees. + /// + /// The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured + /// in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees. + /// ## Parameters + /// * x: x-coordinate of the vector. + /// * y: y-coordinate of the vector. + #[inline] + pub fn fast_atan2(y: f32, x: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_fastAtan2_float_float(y, x, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the list of locations of non-zero pixels + /// + /// Given a binary matrix (likely returned from an operation such + /// as threshold(), compare(), >, ==, etc, return all of + /// the non-zero indices as a cv::Mat or std::vector (x,y) + /// For example: + /// ```C++ + /// cv::Mat binaryImage; // input, binary image + /// cv::Mat locations; // output, locations of non-zero pixels + /// cv::findNonZero(binaryImage, locations); + /// + /// // access pixel coordinates + /// Point pnt = locations.at(i); + /// ``` + /// + /// or + /// ```C++ + /// cv::Mat binaryImage; // input, binary image + /// vector locations; // output, locations of non-zero pixels + /// cv::findNonZero(binaryImage, locations); + /// + /// // access pixel coordinates + /// Point pnt = locations[i]; + /// ``` + /// + /// + /// The function do not work with multi-channel arrays. If you need to find non-zero + /// elements across all the channels, use Mat::reshape first to reinterpret the array as + /// single-channel. Or you may extract the particular channel using either extractImageCOI, or + /// mixChannels, or split. + /// + /// + /// Note: + /// - If only count of non-zero array elements is important, [countNonZero] is helpful. + /// - If only whether there are non-zero elements is important, [hasNonZero] is helpful. + /// ## Parameters + /// * src: single-channel array + /// * idx: the output array, type of cv::Mat or std::vector, corresponding to non-zero indices in the input + /// ## See also + /// countNonZero, hasNonZero + #[inline] + pub fn find_non_zero(src: &impl ToInputArray, idx: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(idx); + return_send!(via ocvrs_return); + unsafe { sys::cv_findNonZero_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), idx.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Flips a n-dimensional at given axis + /// ## Parameters + /// * src: input array + /// * dst: output array that has the same shape of src + /// * axis: axis that performs a flip on. 0 <= axis < src.dims. + #[inline] + pub fn flip_nd(src: &impl ToInputArray, dst: &mut impl ToOutputArray, axis: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_flipND_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), axis, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Flips a 2D array around vertical, horizontal, or both axes. + /// + /// The function cv::flip flips the array in one of three different ways (row + /// and column indices are 0-based): + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%5F%7Bij%7D%20%3D%0A%5Cleft%5C%7B%0A%5Cbegin%7Barray%7D%7Bl%20l%7D%0A%5Ctexttt%7Bsrc%7D%20%5F%7B%5Ctexttt%7Bsrc%2Erows%7D%2Di%2D1%2Cj%7D%20%26%20if%5C%3B%20%20%5Ctexttt%7BflipCode%7D%20%3D%200%20%5C%5C%0A%5Ctexttt%7Bsrc%7D%20%5F%7Bi%2C%20%5Ctexttt%7Bsrc%2Ecols%7D%20%2Dj%2D1%7D%20%26%20if%5C%3B%20%20%5Ctexttt%7BflipCode%7D%20%3E%200%20%5C%5C%0A%5Ctexttt%7Bsrc%7D%20%5F%7B%20%5Ctexttt%7Bsrc%2Erows%7D%20%2Di%2D1%2C%20%5Ctexttt%7Bsrc%2Ecols%7D%20%2Dj%2D1%7D%20%26%20if%5C%3B%20%5Ctexttt%7BflipCode%7D%20%3C%200%20%5C%5C%0A%5Cend%7Barray%7D%0A%5Cright%2E) + /// The example scenarios of using the function are the following: + /// * Vertical flipping of the image (flipCode == 0) to switch between + /// top-left and bottom-left image origin. This is a typical operation + /// in video processing on Microsoft Windows\* OS. + /// * Horizontal flipping of the image with the subsequent horizontal + /// shift and absolute difference calculation to check for a + /// vertical-axis symmetry (flipCode \> 0). + /// * Simultaneous horizontal and vertical flipping of the image with + /// the subsequent shift and absolute difference calculation to check + /// for a central symmetry (flipCode \< 0). + /// * Reversing the order of point arrays (flipCode \> 0 or + /// flipCode == 0). + /// ## Parameters + /// * src: input array. + /// * dst: output array of the same size and type as src. + /// * flipCode: a flag to specify how to flip the array; 0 means + /// flipping around the x-axis and positive value (for example, 1) means + /// flipping around y-axis. Negative value (for example, -1) means flipping + /// around both axes. + /// ## See also + /// transpose, repeat, completeSymm + #[inline] + pub fn flip(src: &impl ToInputArray, dst: &mut impl ToOutputArray, flip_code: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_flip_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), flip_code, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs generalized matrix multiplication. + /// + /// The function cv::gemm performs generalized matrix multiplication similar to the + /// gemm functions in BLAS level 3. For example, + /// `gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)` + /// corresponds to + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%20%5Ctexttt%7Balpha%7D%20%5Ccdot%20%5Ctexttt%7Bsrc1%7D%20%5ET%20%20%5Ccdot%20%5Ctexttt%7Bsrc2%7D%20%2B%20%20%5Ctexttt%7Bbeta%7D%20%5Ccdot%20%5Ctexttt%7Bsrc3%7D%20%5ET) + /// + /// In case of complex (two-channel) data, performed a complex matrix + /// multiplication. + /// + /// The function can be replaced with a matrix expression. For example, the + /// above call can be replaced with: + /// ```C++ + /// dst = alpha*src1.t()*src2 + beta*src3.t(); + /// ``` + /// + /// ## Parameters + /// * src1: first multiplied input matrix that could be real(CV_32FC1, + /// CV_64FC1) or complex(CV_32FC2, CV_64FC2). + /// * src2: second multiplied input matrix of the same type as src1. + /// * alpha: weight of the matrix product. + /// * src3: third optional delta matrix added to the matrix product; it + /// should have the same type as src1 and src2. + /// * beta: weight of src3. + /// * dst: output matrix; it has the proper size and the same type as + /// input matrices. + /// * flags: operation flags (cv::GemmFlags) + /// ## See also + /// mulTransposed, transform + /// + /// ## Note + /// This alternative version of [gemm] function uses the following default values for its arguments: + /// * flags: 0 + #[inline] + pub fn gemm_def(src1: &impl ToInputArray, src2: &impl ToInputArray, alpha: f64, src3: &impl ToInputArray, beta: f64, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + input_array_arg!(src3); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_gemm_const__InputArrayR_const__InputArrayR_double_const__InputArrayR_double_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), alpha, src3.as_raw__InputArray(), beta, dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs generalized matrix multiplication. + /// + /// The function cv::gemm performs generalized matrix multiplication similar to the + /// gemm functions in BLAS level 3. For example, + /// `gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)` + /// corresponds to + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%20%5Ctexttt%7Balpha%7D%20%5Ccdot%20%5Ctexttt%7Bsrc1%7D%20%5ET%20%20%5Ccdot%20%5Ctexttt%7Bsrc2%7D%20%2B%20%20%5Ctexttt%7Bbeta%7D%20%5Ccdot%20%5Ctexttt%7Bsrc3%7D%20%5ET) + /// + /// In case of complex (two-channel) data, performed a complex matrix + /// multiplication. + /// + /// The function can be replaced with a matrix expression. For example, the + /// above call can be replaced with: + /// ```C++ + /// dst = alpha*src1.t()*src2 + beta*src3.t(); + /// ``` + /// + /// ## Parameters + /// * src1: first multiplied input matrix that could be real(CV_32FC1, + /// CV_64FC1) or complex(CV_32FC2, CV_64FC2). + /// * src2: second multiplied input matrix of the same type as src1. + /// * alpha: weight of the matrix product. + /// * src3: third optional delta matrix added to the matrix product; it + /// should have the same type as src1 and src2. + /// * beta: weight of src3. + /// * dst: output matrix; it has the proper size and the same type as + /// input matrices. + /// * flags: operation flags (cv::GemmFlags) + /// ## See also + /// mulTransposed, transform + /// + /// ## C++ default parameters + /// * flags: 0 + #[inline] + pub fn gemm(src1: &impl ToInputArray, src2: &impl ToInputArray, alpha: f64, src3: &impl ToInputArray, beta: f64, dst: &mut impl ToOutputArray, flags: i32) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + input_array_arg!(src3); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_gemm_const__InputArrayR_const__InputArrayR_double_const__InputArrayR_double_const__OutputArrayR_int(src1.as_raw__InputArray(), src2.as_raw__InputArray(), alpha, src3.as_raw__InputArray(), beta, dst.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns full configuration time cmake output. + /// + /// Returned value is raw cmake output including version control system revision, compiler version, + /// compiler flags, enabled modules and third party libraries, etc. Output format depends on target + /// architecture. + #[inline] + pub fn get_build_information() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getBuildInformation(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns list of CPU features enabled during compilation. + /// + /// Returned value is a string containing space separated list of CPU features with following markers: + /// + /// - no markers - baseline features + /// - prefix `*` - features enabled in dispatcher + /// - suffix `?` - features enabled but not available in HW + /// + /// Example: `SSE SSE2 SSE3 *SSE4.1 *SSE4.2 *FP16 *AVX *AVX2 *AVX512-SKX?` + #[inline] + pub fn get_cpu_features_line() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getCPUFeaturesLine(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns the number of CPU ticks. + /// + /// The function returns the current number of CPU ticks on some architectures (such as x86, x64, + /// PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for + /// very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU + /// systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU + /// with its own counter. So, theoretically (and practically) the subsequent calls to the function do + /// not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU + /// frequency depending on the load, the number of CPU clocks spent in some code cannot be directly + /// converted to time units. Therefore, getTickCount is generally a preferable solution for measuring + /// execution time. + #[inline] + pub fn get_cpu_tick_count() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getCPUTickCount(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ! Returns AlgorithmHint defined during OpenCV compilation. Defines [ALGO_HINT_DEFAULT] behavior. + #[inline] + pub fn get_default_algorithm_hint() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getDefaultAlgorithmHint(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn get_elem_size(typ: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getElemSize_int(typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns feature name by ID + /// + /// Returns empty string if feature is not defined + #[inline] + pub fn get_hardware_feature_name(feature: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getHardwareFeatureName_int(feature, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns the number of threads used by OpenCV for parallel regions. + /// + /// Always returns 1 if OpenCV is built without threading support. + /// + /// The exact meaning of return value depends on the threading framework used by OpenCV library: + /// - `TBB` - The number of threads, that OpenCV will try to use for parallel regions. If there is + /// any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns + /// default number of threads used by TBB library. + /// - `OpenMP` - An upper bound on the number of threads that could be used to form a new team. + /// - `Concurrency` - The number of threads, that OpenCV will try to use for parallel regions. + /// - `GCD` - Unsupported; returns the GCD thread pool limit (512) for compatibility. + /// - `C=` - The number of threads, that OpenCV will try to use for parallel regions, if before + /// called setNumThreads with threads \> 0, otherwise returns the number of logical CPUs, + /// available for the process. + /// ## See also + /// setNumThreads, getThreadNum + #[inline] + pub fn get_num_threads() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getNumThreads(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the number of logical CPUs available for the process. + #[inline] + pub fn get_number_of_cpus() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getNumberOfCPUs(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the optimal DFT size for a given vector size. + /// + /// DFT performance is not a monotonic function of a vector size. Therefore, when you calculate + /// convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to + /// pad the input data with zeros to get a bit larger array that can be transformed much faster than the + /// original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process. + /// Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2) + /// are also processed quite efficiently. + /// + /// The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize + /// so that the DFT of a vector of size N can be processed efficiently. In the current implementation N + /// = 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r. + /// + /// The function returns a negative number if vecsize is too large (very close to INT_MAX ). + /// + /// While the function cannot be used directly to estimate the optimal vector size for DCT transform + /// (since the current DCT implementation supports only even-size vectors), it can be easily processed + /// as getOptimalDFTSize((vecsize+1)/2)\*2. + /// ## Parameters + /// * vecsize: vector size. + /// ## See also + /// dft, dct, idft, idct, mulSpectrums + #[inline] + pub fn get_optimal_dft_size(vecsize: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getOptimalDFTSize_int(vecsize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the index of the currently executed thread within the current parallel region. Always + /// returns 0 if called outside of parallel region. + /// + /// + /// **Deprecated**: Current implementation doesn't corresponding to this documentation. + /// + /// The exact meaning of the return value depends on the threading framework used by OpenCV library: + /// - `TBB` - Unsupported with current 4.1 TBB release. Maybe will be supported in future. + /// - `OpenMP` - The thread number, within the current team, of the calling thread. + /// - `Concurrency` - An ID for the virtual processor that the current context is executing on (0 + /// for master thread and unique number for others, but not necessary 1,2,3,...). + /// - `GCD` - System calling thread's ID. Never returns 0 inside parallel region. + /// - `C=` - The index of the current parallel task. + /// ## See also + /// setNumThreads, getNumThreads + #[deprecated = "Current implementation doesn't corresponding to this documentation."] + #[inline] + pub fn get_thread_num() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getThreadNum(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the number of ticks. + /// + /// The function returns the number of ticks after the certain event (for example, when the machine was + /// turned on). It can be used to initialize RNG or to measure a function execution time by reading the + /// tick count before and after the function call. + /// ## See also + /// getTickFrequency, TickMeter + #[inline] + pub fn get_tick_count() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getTickCount(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the number of ticks per second. + /// + /// The function returns the number of ticks per second. That is, the following code computes the + /// execution time in seconds: + /// ```C++ + /// double t = (double)getTickCount(); + /// // do something ... + /// t = ((double)getTickCount() - t)/getTickFrequency(); + /// ``` + /// ## See also + /// getTickCount, TickMeter + #[inline] + pub fn get_tick_frequency() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getTickFrequency(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns major library version + #[inline] + pub fn get_version_major() -> i32 { + let ret = unsafe { sys::cv_getVersionMajor() }; + ret + } + + /// Returns minor library version + #[inline] + pub fn get_version_minor() -> i32 { + let ret = unsafe { sys::cv_getVersionMinor() }; + ret + } + + /// Returns revision field of the library version + #[inline] + pub fn get_version_revision() -> i32 { + let ret = unsafe { sys::cv_getVersionRevision() }; + ret + } + + /// Returns library version string + /// + /// For example "3.4.1-dev". + /// ## See also + /// getMajorVersion, getMinorVersion, getRevisionVersion + #[inline] + pub fn get_version_string() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getVersionString(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Searches for files matching the specified pattern in a directory. + /// + /// This function searches for files that match a given pattern (e.g., `*.jpg`) + /// in the specified directory. The search can be limited to the directory itself + /// or be recursive, including subdirectories. + /// + /// ## Parameters + /// * pattern: The file search pattern, which can include wildcards like `*` + /// (for matching multiple characters) or `?` (for matching a single character). + /// + /// * result: Output vector where the file paths matching the search + /// pattern will be stored. + /// * recursive: (optional) Boolean flag indicating whether to search + /// subdirectories recursively. If true, the search will include all subdirectories. + /// The default value is `false`. + /// + /// ## Note + /// This alternative version of [glob] function uses the following default values for its arguments: + /// * recursive: false + #[inline] + pub fn glob_def(pattern: &str, result: &mut core::Vector) -> Result<()> { + extern_container_arg!(pattern); + return_send!(via ocvrs_return); + unsafe { sys::cv_glob_String_vectorLStringGR(pattern.opencv_as_extern(), result.as_raw_mut_VectorOfString(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Searches for files matching the specified pattern in a directory. + /// + /// This function searches for files that match a given pattern (e.g., `*.jpg`) + /// in the specified directory. The search can be limited to the directory itself + /// or be recursive, including subdirectories. + /// + /// ## Parameters + /// * pattern: The file search pattern, which can include wildcards like `*` + /// (for matching multiple characters) or `?` (for matching a single character). + /// + /// * result: Output vector where the file paths matching the search + /// pattern will be stored. + /// * recursive: (optional) Boolean flag indicating whether to search + /// subdirectories recursively. If true, the search will include all subdirectories. + /// The default value is `false`. + /// + /// ## C++ default parameters + /// * recursive: false + #[inline] + pub fn glob(pattern: &str, result: &mut core::Vector, recursive: bool) -> Result<()> { + extern_container_arg!(pattern); + return_send!(via ocvrs_return); + unsafe { sys::cv_glob_String_vectorLStringGR_bool(pattern.opencv_as_extern(), result.as_raw_mut_VectorOfString(), recursive, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Checks for the presence of at least one non-zero array element. + /// + /// The function returns whether there are non-zero elements in src + /// + /// The function do not work with multi-channel arrays. If you need to check non-zero array + /// elements across all the channels, use Mat::reshape first to reinterpret the array as + /// single-channel. Or you may extract the particular channel using either extractImageCOI, or + /// mixChannels, or split. + /// + /// + /// Note: + /// - If the location of non-zero array elements is important, [findNonZero] is helpful. + /// - If the count of non-zero array elements is important, [countNonZero] is helpful. + /// ## Parameters + /// * src: single-channel array. + /// ## See also + /// mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix + /// findNonZero, countNonZero + #[inline] + pub fn has_non_zero(src: &impl ToInputArray) -> Result { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_hasNonZero_const__InputArrayR(src.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Check if use of OpenVX is possible + #[inline] + pub fn have_openvx() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_haveOpenVX(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies horizontal concatenation to given matrices. + /// + /// The function horizontally concatenates two or more cv::Mat matrices (with the same number of rows). + /// ```C++ + /// cv::Mat matArray[] = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), + /// cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), + /// cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; + /// + /// cv::Mat out; + /// cv::hconcat( matArray, 3, out ); + /// //out: + /// //[1, 2, 3; + /// // 1, 2, 3; + /// // 1, 2, 3; + /// // 1, 2, 3] + /// ``` + /// + /// ## Parameters + /// * src: input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. + /// * nsrc: number of matrices in src. + /// * dst: output array. It has the same number of rows and depth as the src, and the sum of cols of the src. + /// ## See also + /// cv::vconcat(const Mat*, size_t, OutputArray), cv::vconcat(InputArrayOfArrays, OutputArray) and cv::vconcat(InputArray, InputArray, OutputArray) + /// + /// ## Overloaded parameters + /// + /// ```C++ + /// cv::Mat_ A = (cv::Mat_(3, 2) << 1, 4, + /// 2, 5, + /// 3, 6); + /// cv::Mat_ B = (cv::Mat_(3, 2) << 7, 10, + /// 8, 11, + /// 9, 12); + /// + /// cv::Mat C; + /// cv::hconcat(A, B, C); + /// //C: + /// //[1, 4, 7, 10; + /// // 2, 5, 8, 11; + /// // 3, 6, 9, 12] + /// ``` + /// + /// * src1: first input array to be considered for horizontal concatenation. + /// * src2: second input array to be considered for horizontal concatenation. + /// * dst: output array. It has the same number of rows and depth as the src1 and src2, and the sum of cols of the src1 and src2. + #[inline] + pub fn hconcat2(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_hconcat_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies horizontal concatenation to given matrices. + /// + /// The function horizontally concatenates two or more cv::Mat matrices (with the same number of rows). + /// ```C++ + /// cv::Mat matArray[] = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), + /// cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), + /// cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; + /// + /// cv::Mat out; + /// cv::hconcat( matArray, 3, out ); + /// //out: + /// //[1, 2, 3; + /// // 1, 2, 3; + /// // 1, 2, 3; + /// // 1, 2, 3] + /// ``` + /// + /// ## Parameters + /// * src: input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. + /// * nsrc: number of matrices in src. + /// * dst: output array. It has the same number of rows and depth as the src, and the sum of cols of the src. + /// ## See also + /// cv::vconcat(const Mat*, size_t, OutputArray), cv::vconcat(InputArrayOfArrays, OutputArray) and cv::vconcat(InputArray, InputArray, OutputArray) + /// + /// ## Overloaded parameters + /// + /// ```C++ + /// std::vector matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), + /// cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), + /// cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; + /// + /// cv::Mat out; + /// cv::hconcat( matrices, out ); + /// //out: + /// //[1, 2, 3; + /// // 1, 2, 3; + /// // 1, 2, 3; + /// // 1, 2, 3] + /// ``` + /// + /// * src: input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. + /// * dst: output array. It has the same number of rows and depth as the src, and the sum of cols of the src. + /// same depth. + #[inline] + pub fn hconcat(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_hconcat_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the inverse Discrete Cosine Transform of a 1D or 2D array. + /// + /// idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE). + /// ## Parameters + /// * src: input floating-point single-channel array. + /// * dst: output array of the same size and type as src. + /// * flags: operation flags. + /// ## See also + /// dct, dft, idft, getOptimalDFTSize + /// + /// ## Note + /// This alternative version of [idct] function uses the following default values for its arguments: + /// * flags: 0 + #[inline] + pub fn idct_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_idct_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the inverse Discrete Cosine Transform of a 1D or 2D array. + /// + /// idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE). + /// ## Parameters + /// * src: input floating-point single-channel array. + /// * dst: output array of the same size and type as src. + /// * flags: operation flags. + /// ## See also + /// dct, dft, idft, getOptimalDFTSize + /// + /// ## C++ default parameters + /// * flags: 0 + #[inline] + pub fn idct(src: &impl ToInputArray, dst: &mut impl ToOutputArray, flags: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_idct_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. + /// + /// idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) . + /// + /// Note: None of dft and idft scales the result by default. So, you should pass [DFT_SCALE] to one of + /// dft or idft explicitly to make these transforms mutually inverse. + /// ## See also + /// dft, dct, idct, mulSpectrums, getOptimalDFTSize + /// ## Parameters + /// * src: input floating-point real or complex array. + /// * dst: output array whose size and type depend on the flags. + /// * flags: operation flags (see dft and #DftFlags). + /// * nonzeroRows: number of dst rows to process; the rest of the rows have undefined content (see + /// the convolution sample in dft description. + /// + /// ## Note + /// This alternative version of [idft] function uses the following default values for its arguments: + /// * flags: 0 + /// * nonzero_rows: 0 + #[inline] + pub fn idft_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_idft_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. + /// + /// idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) . + /// + /// Note: None of dft and idft scales the result by default. So, you should pass [DFT_SCALE] to one of + /// dft or idft explicitly to make these transforms mutually inverse. + /// ## See also + /// dft, dct, idct, mulSpectrums, getOptimalDFTSize + /// ## Parameters + /// * src: input floating-point real or complex array. + /// * dst: output array whose size and type depend on the flags. + /// * flags: operation flags (see dft and #DftFlags). + /// * nonzeroRows: number of dst rows to process; the rest of the rows have undefined content (see + /// the convolution sample in dft description. + /// + /// ## C++ default parameters + /// * flags: 0 + /// * nonzero_rows: 0 + #[inline] + pub fn idft(src: &impl ToInputArray, dst: &mut impl ToOutputArray, flags: i32, nonzero_rows: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_idft_const__InputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), flags, nonzero_rows, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Checks if array elements lie between the elements of two other arrays. + /// + /// The function checks the range as follows: + /// * For every element of a single-channel input array: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Blowerb%7D%20%28I%29%5F0%20%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28I%29%5F0%20%5Cleq%20%20%5Ctexttt%7Bupperb%7D%20%28I%29%5F0) + /// * For two-channel arrays: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Blowerb%7D%20%28I%29%5F0%20%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28I%29%5F0%20%5Cleq%20%20%5Ctexttt%7Bupperb%7D%20%28I%29%5F0%20%20%5Cland%20%5Ctexttt%7Blowerb%7D%20%28I%29%5F1%20%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28I%29%5F1%20%5Cleq%20%20%5Ctexttt%7Bupperb%7D%20%28I%29%5F1) + /// * and so forth. + /// + /// That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the + /// specified 1D, 2D, 3D, ... box and 0 otherwise. + /// + /// When the lower and/or upper boundary parameters are scalars, the indexes + /// (I) at lowerb and upperb in the above formulas should be omitted. + /// ## Parameters + /// * src: first input array. + /// * lowerb: inclusive lower boundary array or a scalar. + /// * upperb: inclusive upper boundary array or a scalar. + /// * dst: output array of the same size as src and CV_8U type. + #[inline] + pub fn in_range(src: &impl ToInputArray, lowerb: &impl ToInputArray, upperb: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + input_array_arg!(lowerb); + input_array_arg!(upperb); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_inRange_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), lowerb.as_raw__InputArray(), upperb.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Inserts a single channel to dst (coi is 0-based index) + /// ## Parameters + /// * src: input array + /// * dst: output array + /// * coi: index of channel for insertion + /// ## See also + /// mixChannels, merge + #[inline] + pub fn insert_channel(src: &impl ToInputArray, dst: &mut impl ToInputOutputArray, coi: i32) -> Result<()> { + input_array_arg!(src); + input_output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_insertChannel_const__InputArrayR_const__InputOutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), coi, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn get_flags() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_instr_getFlags(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn reset_trace() -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_instr_resetTrace(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn set_flags(mode_flags: core::FLAGS) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_instr_setFlags_FLAGS(mode_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn set_use_instrumentation(flag: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_instr_setUseInstrumentation_bool(flag, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn use_instrumentation() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_instr_useInstrumentation(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the inverse or pseudo-inverse of a matrix. + /// + /// The function cv::invert inverts the matrix src and stores the result in dst + /// . When the matrix src is singular or non-square, the function calculates + /// the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is + /// minimal, where I is an identity matrix. + /// + /// In case of the [DECOMP_LU] method, the function returns non-zero value if + /// the inverse has been successfully calculated and 0 if src is singular. + /// + /// In case of the [DECOMP_SVD] method, the function returns the inverse + /// condition number of src (the ratio of the smallest singular value to the + /// largest singular value) and 0 if src is singular. The SVD method + /// calculates a pseudo-inverse matrix if src is singular. + /// + /// Similarly to #DECOMP_LU, the method [DECOMP_CHOLESKY] works only with + /// non-singular square matrices that should also be symmetrical and + /// positively defined. In this case, the function stores the inverted + /// matrix in dst and returns non-zero. Otherwise, it returns 0. + /// + /// ## Parameters + /// * src: input floating-point M x N matrix. + /// * dst: output matrix of N x M size and the same type as src. + /// * flags: inversion method (cv::DecompTypes) + /// ## See also + /// solve, SVD + /// + /// ## Note + /// This alternative version of [invert] function uses the following default values for its arguments: + /// * flags: DECOMP_LU + #[inline] + pub fn invert_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_invert_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the inverse or pseudo-inverse of a matrix. + /// + /// The function cv::invert inverts the matrix src and stores the result in dst + /// . When the matrix src is singular or non-square, the function calculates + /// the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is + /// minimal, where I is an identity matrix. + /// + /// In case of the [DECOMP_LU] method, the function returns non-zero value if + /// the inverse has been successfully calculated and 0 if src is singular. + /// + /// In case of the [DECOMP_SVD] method, the function returns the inverse + /// condition number of src (the ratio of the smallest singular value to the + /// largest singular value) and 0 if src is singular. The SVD method + /// calculates a pseudo-inverse matrix if src is singular. + /// + /// Similarly to #DECOMP_LU, the method [DECOMP_CHOLESKY] works only with + /// non-singular square matrices that should also be symmetrical and + /// positively defined. In this case, the function stores the inverted + /// matrix in dst and returns non-zero. Otherwise, it returns 0. + /// + /// ## Parameters + /// * src: input floating-point M x N matrix. + /// * dst: output matrix of N x M size and the same type as src. + /// * flags: inversion method (cv::DecompTypes) + /// ## See also + /// solve, SVD + /// + /// ## C++ default parameters + /// * flags: DECOMP_LU + #[inline] + pub fn invert(src: &impl ToInputArray, dst: &mut impl ToOutputArray, flags: i32) -> Result { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_invert_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn get_ipp_error_location() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ipp_getIppErrorLocation(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn get_ipp_features() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ipp_getIppFeatures(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn get_ipp_status() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ipp_getIppStatus(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn get_ipp_version() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ipp_getIppVersion(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [set_ipp_status] function uses the following default values for its arguments: + /// * funcname: NULL + /// * filename: NULL + /// * line: 0 + #[inline] + pub fn set_ipp_status_def(status: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ipp_setIppStatus_int(status, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * funcname: NULL + /// * filename: NULL + /// * line: 0 + #[inline] + pub fn set_ipp_status(status: i32, funcname: &str, filename: &str, line: i32) -> Result<()> { + extern_container_arg!(funcname); + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_ipp_setIppStatus_int_const_charX_const_charX_int(status, funcname.opencv_as_extern(), filename.opencv_as_extern(), line, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn set_use_ipp_not_exact(flag: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ipp_setUseIPP_NotExact_bool(flag, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn set_use_ipp(flag: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ipp_setUseIPP_bool(flag, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn use_ipp() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ipp_useIPP(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn use_ipp_not_exact() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ipp_useIPP_NotExact(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds centers of clusters and groups input samples around the clusters. + /// + /// The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters + /// and groups the input samples around the clusters. As an output, ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BbestLabels%7D%5Fi) contains a + /// 0-based cluster index for the sample stored in the ![inline formula](https://latex.codecogs.com/png.latex?i%5E%7Bth%7D) row of the samples matrix. + /// + /// + /// Note: + /// * (Python) An example on k-means clustering can be found at + /// opencv_source_code/samples/python/kmeans.py + /// ## Parameters + /// * data: Data for clustering. An array of N-Dimensional points with float coordinates is needed. + /// Examples of this array can be: + /// * Mat points(count, 2, CV_32F); + /// * Mat points(count, 1, CV_32FC2); + /// * Mat points(1, count, CV_32FC2); + /// * std::vector\ points(sampleCount); + /// * K: Number of clusters to split the set by. + /// * bestLabels: Input/output integer array that stores the cluster indices for every sample. + /// * criteria: The algorithm termination criteria, that is, the maximum number of iterations and/or + /// the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster + /// centers moves by less than criteria.epsilon on some iteration, the algorithm stops. + /// * attempts: Flag to specify the number of times the algorithm is executed using different + /// initial labellings. The algorithm returns the labels that yield the best compactness (see the last + /// function parameter). + /// * flags: Flag that can take values of cv::KmeansFlags + /// * centers: Output matrix of the cluster centers, one row per each cluster center. + /// ## Returns + /// The function returns the compactness measure that is computed as + /// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5Fi%20%20%5C%7C%20%5Ctexttt%7Bsamples%7D%20%5Fi%20%2D%20%20%5Ctexttt%7Bcenters%7D%20%5F%7B%20%5Ctexttt%7Blabels%7D%20%5Fi%7D%20%5C%7C%20%5E2) + /// after every attempt. The best (minimum) value is chosen and the corresponding labels and the + /// compactness value are returned by the function. Basically, you can use only the core of the + /// function, set the number of attempts to 1, initialize labels each time using a custom algorithm, + /// pass them with the ( flags = [KMEANS_USE_INITIAL_LABELS] ) flag, and then choose the best + /// (most-compact) clustering. + /// + /// ## Note + /// This alternative version of [kmeans] function uses the following default values for its arguments: + /// * centers: noArray() + #[inline] + pub fn kmeans_def(data: &impl ToInputArray, k: i32, best_labels: &mut impl ToInputOutputArray, criteria: core::TermCriteria, attempts: i32, flags: i32) -> Result { + input_array_arg!(data); + input_output_array_arg!(best_labels); + return_send!(via ocvrs_return); + unsafe { sys::cv_kmeans_const__InputArrayR_int_const__InputOutputArrayR_TermCriteria_int_int(data.as_raw__InputArray(), k, best_labels.as_raw__InputOutputArray(), &criteria, attempts, flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds centers of clusters and groups input samples around the clusters. + /// + /// The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters + /// and groups the input samples around the clusters. As an output, ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BbestLabels%7D%5Fi) contains a + /// 0-based cluster index for the sample stored in the ![inline formula](https://latex.codecogs.com/png.latex?i%5E%7Bth%7D) row of the samples matrix. + /// + /// + /// Note: + /// * (Python) An example on k-means clustering can be found at + /// opencv_source_code/samples/python/kmeans.py + /// ## Parameters + /// * data: Data for clustering. An array of N-Dimensional points with float coordinates is needed. + /// Examples of this array can be: + /// * Mat points(count, 2, CV_32F); + /// * Mat points(count, 1, CV_32FC2); + /// * Mat points(1, count, CV_32FC2); + /// * std::vector\ points(sampleCount); + /// * K: Number of clusters to split the set by. + /// * bestLabels: Input/output integer array that stores the cluster indices for every sample. + /// * criteria: The algorithm termination criteria, that is, the maximum number of iterations and/or + /// the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster + /// centers moves by less than criteria.epsilon on some iteration, the algorithm stops. + /// * attempts: Flag to specify the number of times the algorithm is executed using different + /// initial labellings. The algorithm returns the labels that yield the best compactness (see the last + /// function parameter). + /// * flags: Flag that can take values of cv::KmeansFlags + /// * centers: Output matrix of the cluster centers, one row per each cluster center. + /// ## Returns + /// The function returns the compactness measure that is computed as + /// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5Fi%20%20%5C%7C%20%5Ctexttt%7Bsamples%7D%20%5Fi%20%2D%20%20%5Ctexttt%7Bcenters%7D%20%5F%7B%20%5Ctexttt%7Blabels%7D%20%5Fi%7D%20%5C%7C%20%5E2) + /// after every attempt. The best (minimum) value is chosen and the corresponding labels and the + /// compactness value are returned by the function. Basically, you can use only the core of the + /// function, set the number of attempts to 1, initialize labels each time using a custom algorithm, + /// pass them with the ( flags = [KMEANS_USE_INITIAL_LABELS] ) flag, and then choose the best + /// (most-compact) clustering. + /// + /// ## C++ default parameters + /// * centers: noArray() + #[inline] + pub fn kmeans(data: &impl ToInputArray, k: i32, best_labels: &mut impl ToInputOutputArray, criteria: core::TermCriteria, attempts: i32, flags: i32, centers: &mut impl ToOutputArray) -> Result { + input_array_arg!(data); + input_output_array_arg!(best_labels); + output_array_arg!(centers); + return_send!(via ocvrs_return); + unsafe { sys::cv_kmeans_const__InputArrayR_int_const__InputOutputArrayR_TermCriteria_int_int_const__OutputArrayR(data.as_raw__InputArray(), k, best_labels.as_raw__InputOutputArray(), &criteria, attempts, flags, centers.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the natural logarithm of every array element. + /// + /// The function cv::log calculates the natural logarithm of every element of the input array: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Clog%20%28%5Ctexttt%7Bsrc%7D%28I%29%29%20) + /// + /// Output on zero, negative and special (NaN, Inf) values is undefined. + /// + /// ## Parameters + /// * src: input array. + /// * dst: output array of the same size and type as src . + /// ## See also + /// exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude + #[inline] + pub fn log(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_log_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the magnitude of 2D vectors. + /// + /// The function cv::magnitude calculates the magnitude of 2D vectors formed + /// from the corresponding elements of x and y arrays: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Csqrt%7B%5Ctexttt%7Bx%7D%28I%29%5E2%20%2B%20%5Ctexttt%7By%7D%28I%29%5E2%7D) + /// ## Parameters + /// * x: floating-point array of x-coordinates of the vectors. + /// * y: floating-point array of y-coordinates of the vectors; it must + /// have the same size as x. + /// * magnitude: output array of the same size and type as x. + /// ## See also + /// cartToPolar, polarToCart, phase, sqrt + #[inline] + pub fn magnitude(x: &impl ToInputArray, y: &impl ToInputArray, magnitude: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(x); + input_array_arg!(y); + output_array_arg!(magnitude); + return_send!(via ocvrs_return); + unsafe { sys::cv_magnitude_const__InputArrayR_const__InputArrayR_const__OutputArrayR(x.as_raw__InputArray(), y.as_raw__InputArray(), magnitude.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn max_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_max_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Calculates per-element maximum of two arrays or an array and a scalar. + /// + /// The function cv::max calculates the per-element maximum of two arrays: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Cmax%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2C%20%5Ctexttt%7Bsrc2%7D%20%28I%29%29) + /// or array and a scalar: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Cmax%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2C%20%5Ctexttt%7Bvalue%7D%20%29) + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and type as src1 . + /// * dst: output array of the same size and type as src1. + /// ## See also + /// min, compare, inRange, minMaxLoc, [MatrixExpressions] + /// + /// ## Overloaded parameters + /// + /// needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) + #[inline] + pub fn max_mat_to(src1: &impl core::MatTraitConst, src2: &impl core::MatTraitConst, dst: &mut impl core::MatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_max_const_MatR_const_MatR_MatR(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn max_mat_f64(a: &impl core::MatTraitConst, s: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_max_const_MatR_double(a.as_raw_Mat(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Calculates per-element maximum of two arrays or an array and a scalar. + /// + /// The function cv::max calculates the per-element maximum of two arrays: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Cmax%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2C%20%5Ctexttt%7Bsrc2%7D%20%28I%29%29) + /// or array and a scalar: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Cmax%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2C%20%5Ctexttt%7Bvalue%7D%20%29) + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and type as src1 . + /// * dst: output array of the same size and type as src1. + /// ## See also + /// min, compare, inRange, minMaxLoc, [MatrixExpressions] + /// + /// ## Overloaded parameters + /// + /// needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) + #[inline] + pub fn max_umat_to(src1: &impl core::UMatTraitConst, src2: &impl core::UMatTraitConst, dst: &mut impl core::UMatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_max_const_UMatR_const_UMatR_UMatR(src1.as_raw_UMat(), src2.as_raw_UMat(), dst.as_raw_mut_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates per-element maximum of two arrays or an array and a scalar. + /// + /// The function cv::max calculates the per-element maximum of two arrays: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Cmax%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2C%20%5Ctexttt%7Bsrc2%7D%20%28I%29%29) + /// or array and a scalar: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Cmax%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2C%20%5Ctexttt%7Bvalue%7D%20%29) + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and type as src1 . + /// * dst: output array of the same size and type as src1. + /// ## See also + /// min, compare, inRange, minMaxLoc, [MatrixExpressions] + #[inline] + pub fn max(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_max_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn max_f64_mat(s: f64, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_max_double_const_MatR(s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Calculates a mean and standard deviation of array elements. + /// + /// The function cv::meanStdDev calculates the mean and the standard deviation M + /// of array elements independently for each channel and returns it via the + /// output parameters: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20N%20%3D%20%20%5Csum%20%5F%7BI%2C%20%5Ctexttt%7Bmask%7D%20%28I%29%20%20%5Cne%200%7D%201%20%5C%5C%20%5Ctexttt%7Bmean%7D%20%5Fc%20%3D%20%20%5Cfrac%7B%5Csum%5F%7B%20I%3A%20%5C%3B%20%5Ctexttt%7Bmask%7D%28I%29%20%5Cne%200%7D%20%5Ctexttt%7Bsrc%7D%20%28I%29%5Fc%7D%7BN%7D%20%5C%5C%20%5Ctexttt%7Bstddev%7D%20%5Fc%20%3D%20%20%5Csqrt%7B%5Cfrac%7B%5Csum%5F%7B%20I%3A%20%5C%3B%20%5Ctexttt%7Bmask%7D%28I%29%20%5Cne%200%7D%20%5Cleft%20%28%20%5Ctexttt%7Bsrc%7D%20%28I%29%5Fc%20%2D%20%20%5Ctexttt%7Bmean%7D%20%5Fc%20%5Cright%20%29%5E2%7D%7BN%7D%7D%20%5Cend%7Barray%7D) + /// When all the mask elements are 0's, the function returns + /// mean=stddev=Scalar::all(0). + /// + /// Note: The calculated standard deviation is only the diagonal of the + /// complete normalized covariance matrix. If the full matrix is needed, you + /// can reshape the multi-channel array M x N to the single-channel array + /// M\*N x mtx.channels() (only possible when the matrix is continuous) and + /// then pass the matrix to calcCovarMatrix . + /// ## Parameters + /// * src: input array that should have from 1 to 4 channels so that the results can be stored in + /// Scalar_ 's. + /// * mean: output parameter: calculated mean value. + /// * stddev: output parameter: calculated standard deviation. + /// * mask: optional operation mask. + /// ## See also + /// countNonZero, mean, norm, minMaxLoc, calcCovarMatrix + /// + /// ## Note + /// This alternative version of [mean_std_dev] function uses the following default values for its arguments: + /// * mask: noArray() + #[inline] + pub fn mean_std_dev_def(src: &impl ToInputArray, mean: &mut impl ToOutputArray, stddev: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(mean); + output_array_arg!(stddev); + return_send!(via ocvrs_return); + unsafe { sys::cv_meanStdDev_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src.as_raw__InputArray(), mean.as_raw__OutputArray(), stddev.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates a mean and standard deviation of array elements. + /// + /// The function cv::meanStdDev calculates the mean and the standard deviation M + /// of array elements independently for each channel and returns it via the + /// output parameters: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20N%20%3D%20%20%5Csum%20%5F%7BI%2C%20%5Ctexttt%7Bmask%7D%20%28I%29%20%20%5Cne%200%7D%201%20%5C%5C%20%5Ctexttt%7Bmean%7D%20%5Fc%20%3D%20%20%5Cfrac%7B%5Csum%5F%7B%20I%3A%20%5C%3B%20%5Ctexttt%7Bmask%7D%28I%29%20%5Cne%200%7D%20%5Ctexttt%7Bsrc%7D%20%28I%29%5Fc%7D%7BN%7D%20%5C%5C%20%5Ctexttt%7Bstddev%7D%20%5Fc%20%3D%20%20%5Csqrt%7B%5Cfrac%7B%5Csum%5F%7B%20I%3A%20%5C%3B%20%5Ctexttt%7Bmask%7D%28I%29%20%5Cne%200%7D%20%5Cleft%20%28%20%5Ctexttt%7Bsrc%7D%20%28I%29%5Fc%20%2D%20%20%5Ctexttt%7Bmean%7D%20%5Fc%20%5Cright%20%29%5E2%7D%7BN%7D%7D%20%5Cend%7Barray%7D) + /// When all the mask elements are 0's, the function returns + /// mean=stddev=Scalar::all(0). + /// + /// Note: The calculated standard deviation is only the diagonal of the + /// complete normalized covariance matrix. If the full matrix is needed, you + /// can reshape the multi-channel array M x N to the single-channel array + /// M\*N x mtx.channels() (only possible when the matrix is continuous) and + /// then pass the matrix to calcCovarMatrix . + /// ## Parameters + /// * src: input array that should have from 1 to 4 channels so that the results can be stored in + /// Scalar_ 's. + /// * mean: output parameter: calculated mean value. + /// * stddev: output parameter: calculated standard deviation. + /// * mask: optional operation mask. + /// ## See also + /// countNonZero, mean, norm, minMaxLoc, calcCovarMatrix + /// + /// ## C++ default parameters + /// * mask: noArray() + #[inline] + pub fn mean_std_dev(src: &impl ToInputArray, mean: &mut impl ToOutputArray, stddev: &mut impl ToOutputArray, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(mean); + output_array_arg!(stddev); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_meanStdDev_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__InputArrayR(src.as_raw__InputArray(), mean.as_raw__OutputArray(), stddev.as_raw__OutputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates an average (mean) of array elements. + /// + /// The function cv::mean calculates the mean value M of array elements, + /// independently for each channel, and return it: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20N%20%3D%20%20%5Csum%20%5F%7BI%3A%20%5C%3B%20%5Ctexttt%7Bmask%7D%20%28I%29%20%5Cne%200%7D%201%20%5C%5C%20M%5Fc%20%3D%20%20%5Cleft%20%28%20%5Csum%20%5F%7BI%3A%20%5C%3B%20%5Ctexttt%7Bmask%7D%20%28I%29%20%5Cne%200%7D%7B%20%5Ctexttt%7Bmtx%7D%20%28I%29%5Fc%7D%20%5Cright%20%29%2FN%20%5Cend%7Barray%7D) + /// When all the mask elements are 0's, the function returns Scalar::all(0) + /// ## Parameters + /// * src: input array that should have from 1 to 4 channels so that the result can be stored in + /// Scalar_ . + /// * mask: optional operation mask. + /// ## See also + /// countNonZero, meanStdDev, norm, minMaxLoc + /// + /// ## Note + /// This alternative version of [mean] function uses the following default values for its arguments: + /// * mask: noArray() + #[inline] + pub fn mean_def(src: &impl ToInputArray) -> Result { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_mean_const__InputArrayR(src.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates an average (mean) of array elements. + /// + /// The function cv::mean calculates the mean value M of array elements, + /// independently for each channel, and return it: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20N%20%3D%20%20%5Csum%20%5F%7BI%3A%20%5C%3B%20%5Ctexttt%7Bmask%7D%20%28I%29%20%5Cne%200%7D%201%20%5C%5C%20M%5Fc%20%3D%20%20%5Cleft%20%28%20%5Csum%20%5F%7BI%3A%20%5C%3B%20%5Ctexttt%7Bmask%7D%20%28I%29%20%5Cne%200%7D%7B%20%5Ctexttt%7Bmtx%7D%20%28I%29%5Fc%7D%20%5Cright%20%29%2FN%20%5Cend%7Barray%7D) + /// When all the mask elements are 0's, the function returns Scalar::all(0) + /// ## Parameters + /// * src: input array that should have from 1 to 4 channels so that the result can be stored in + /// Scalar_ . + /// * mask: optional operation mask. + /// ## See also + /// countNonZero, meanStdDev, norm, minMaxLoc + /// + /// ## C++ default parameters + /// * mask: noArray() + #[inline] + pub fn mean(src: &impl ToInputArray, mask: &impl ToInputArray) -> Result { + input_array_arg!(src); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_mean_const__InputArrayR_const__InputArrayR(src.as_raw__InputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates one multi-channel array out of several single-channel ones. + /// + /// The function cv::merge merges several arrays to make a single multi-channel array. That is, each + /// element of the output array will be a concatenation of the elements of the input arrays, where + /// elements of i-th input array are treated as mv[i].channels()-element vectors. + /// + /// The function cv::split does the reverse operation. If you need to shuffle channels in some other + /// advanced way, use cv::mixChannels. + /// + /// The following example shows how to merge 3 single channel matrices into a single 3-channel matrix. + /// [example](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_merge.cpp#L1) + /// + /// ## Parameters + /// * mv: input array of matrices to be merged; all the matrices in mv must have the same + /// size and the same depth. + /// * count: number of input matrices when mv is a plain C array; it must be greater than zero. + /// * dst: output array of the same size and the same depth as mv[0]; The number of channels will + /// be equal to the parameter count. + /// ## See also + /// mixChannels, split, Mat::reshape + /// + /// ## Overloaded parameters + /// + /// * mv: input vector of matrices to be merged; all the matrices in mv must have the same + /// size and the same depth. + /// * dst: output array of the same size and the same depth as mv[0]; The number of channels will + /// be the total number of channels in the matrix array. + #[inline] + pub fn merge(mv: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(mv); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_merge_const__InputArrayR_const__OutputArrayR(mv.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the global minimum and maximum in an array + /// + /// The function cv::minMaxIdx finds the minimum and maximum element values and their positions. The + /// extremums are searched across the whole array or, if mask is not an empty array, in the specified + /// array region. In case of a sparse matrix, the minimum is found among non-zero elements + /// only. Multi-channel input is supported without mask and extremums indexes (should be nullptr). + /// + /// Note: When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is + /// a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2 + /// dimensions, i.e. single-column matrix is Mx1 matrix (and therefore minIdx/maxIdx will be + /// (i1,0)/(i2,0)) and single-row matrix is 1xN matrix (and therefore minIdx/maxIdx will be + /// (0,j1)/(0,j2)). + /// ## Parameters + /// * src: input single-channel array. + /// * minVal: pointer to the returned minimum value; NULL is used if not required. + /// * maxVal: pointer to the returned maximum value; NULL is used if not required. + /// * minIdx: pointer to the returned minimum location (in nD case); NULL is used if not required; + /// Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element + /// in each dimension are stored there sequentially. + /// * maxIdx: pointer to the returned maximum location (in nD case). NULL is used if not required. + /// * mask: specified array region + /// + /// ## Note + /// This alternative version of [min_max_idx] function uses the following default values for its arguments: + /// * max_val: 0 + /// * min_idx: 0 + /// * max_idx: 0 + /// * mask: noArray() + #[inline] + pub fn min_max_idx_def(src: &impl ToInputArray, min_val: Option<&mut f64>) -> Result<()> { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_minMaxIdx_const__InputArrayR_doubleX(src.as_raw__InputArray(), min_val.map_or(::core::ptr::null_mut(), |min_val| min_val), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the global minimum and maximum in an array + /// + /// The function cv::minMaxIdx finds the minimum and maximum element values and their positions. The + /// extremums are searched across the whole array or, if mask is not an empty array, in the specified + /// array region. In case of a sparse matrix, the minimum is found among non-zero elements + /// only. Multi-channel input is supported without mask and extremums indexes (should be nullptr). + /// + /// Note: When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is + /// a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2 + /// dimensions, i.e. single-column matrix is Mx1 matrix (and therefore minIdx/maxIdx will be + /// (i1,0)/(i2,0)) and single-row matrix is 1xN matrix (and therefore minIdx/maxIdx will be + /// (0,j1)/(0,j2)). + /// ## Parameters + /// * src: input single-channel array. + /// * minVal: pointer to the returned minimum value; NULL is used if not required. + /// * maxVal: pointer to the returned maximum value; NULL is used if not required. + /// * minIdx: pointer to the returned minimum location (in nD case); NULL is used if not required; + /// Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element + /// in each dimension are stored there sequentially. + /// * maxIdx: pointer to the returned maximum location (in nD case). NULL is used if not required. + /// * mask: specified array region + /// + /// ## C++ default parameters + /// * max_val: 0 + /// * min_idx: 0 + /// * max_idx: 0 + /// * mask: noArray() + #[inline] + pub fn min_max_idx(src: &impl ToInputArray, min_val: Option<&mut f64>, max_val: Option<&mut f64>, min_idx: Option<&mut i32>, max_idx: Option<&mut i32>, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_minMaxIdx_const__InputArrayR_doubleX_doubleX_intX_intX_const__InputArrayR(src.as_raw__InputArray(), min_val.map_or(::core::ptr::null_mut(), |min_val| min_val), max_val.map_or(::core::ptr::null_mut(), |max_val| max_val), min_idx.map_or(::core::ptr::null_mut(), |min_idx| min_idx), max_idx.map_or(::core::ptr::null_mut(), |max_idx| max_idx), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the global minimum and maximum in an array. + /// + /// The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The + /// extrema are searched across the whole array or, if mask is not an empty array, in the specified + /// array region. + /// + /// In C++, if the input is multi-channel, you should omit the minLoc, maxLoc, and mask arguments + /// (i.e. leave them as NULL, NULL, and noArray() respectively). These arguments are not + /// supported for multi-channel input arrays. If working with multi-channel input and you + /// need the minLoc, maxLoc, or mask arguments, then use Mat::reshape first to reinterpret + /// the array as single-channel. Alternatively, you can extract the particular channel using either + /// extractImageCOI, mixChannels, or split. + /// + /// In Python, multi-channel input is not supported at all due to a limitation in the + /// binding generation process (there is no way to set minLoc and maxLoc to NULL). A + /// workaround is to operate on each channel individually or to use NumPy to achieve the same + /// functionality. + /// + /// ## Parameters + /// * src: input single-channel array. + /// * minVal: pointer to the returned minimum value; NULL is used if not required. + /// * maxVal: pointer to the returned maximum value; NULL is used if not required. + /// * minLoc: pointer to the returned minimum location (in 2D case); NULL is used if not required. + /// * maxLoc: pointer to the returned maximum location (in 2D case); NULL is used if not required. + /// * mask: optional mask used to select a sub-array. + /// ## See also + /// max, min, reduceArgMin, reduceArgMax, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape + /// + /// ## Overloaded parameters + /// + /// * a: input single-channel array. + /// * minVal: pointer to the returned minimum value; NULL is used if not required. + /// * maxVal: pointer to the returned maximum value; NULL is used if not required. + /// * minIdx: pointer to the returned minimum location (in nD case); NULL is used if not required; + /// Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element + /// in each dimension are stored there sequentially. + /// * maxIdx: pointer to the returned maximum location (in nD case). NULL is used if not required. + /// + /// ## Note + /// This alternative version of [min_max_loc_sparse] function uses the following default values for its arguments: + /// * min_idx: 0 + /// * max_idx: 0 + #[inline] + pub fn min_max_loc_sparse_def(a: &impl core::SparseMatTraitConst, min_val: Option<&mut f64>, max_val: Option<&mut f64>) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_minMaxLoc_const_SparseMatR_doubleX_doubleX(a.as_raw_SparseMat(), min_val.map_or(::core::ptr::null_mut(), |min_val| min_val), max_val.map_or(::core::ptr::null_mut(), |max_val| max_val), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the global minimum and maximum in an array. + /// + /// The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The + /// extrema are searched across the whole array or, if mask is not an empty array, in the specified + /// array region. + /// + /// In C++, if the input is multi-channel, you should omit the minLoc, maxLoc, and mask arguments + /// (i.e. leave them as NULL, NULL, and noArray() respectively). These arguments are not + /// supported for multi-channel input arrays. If working with multi-channel input and you + /// need the minLoc, maxLoc, or mask arguments, then use Mat::reshape first to reinterpret + /// the array as single-channel. Alternatively, you can extract the particular channel using either + /// extractImageCOI, mixChannels, or split. + /// + /// In Python, multi-channel input is not supported at all due to a limitation in the + /// binding generation process (there is no way to set minLoc and maxLoc to NULL). A + /// workaround is to operate on each channel individually or to use NumPy to achieve the same + /// functionality. + /// + /// ## Parameters + /// * src: input single-channel array. + /// * minVal: pointer to the returned minimum value; NULL is used if not required. + /// * maxVal: pointer to the returned maximum value; NULL is used if not required. + /// * minLoc: pointer to the returned minimum location (in 2D case); NULL is used if not required. + /// * maxLoc: pointer to the returned maximum location (in 2D case); NULL is used if not required. + /// * mask: optional mask used to select a sub-array. + /// ## See also + /// max, min, reduceArgMin, reduceArgMax, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape + /// + /// ## Overloaded parameters + /// + /// * a: input single-channel array. + /// * minVal: pointer to the returned minimum value; NULL is used if not required. + /// * maxVal: pointer to the returned maximum value; NULL is used if not required. + /// * minIdx: pointer to the returned minimum location (in nD case); NULL is used if not required; + /// Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element + /// in each dimension are stored there sequentially. + /// * maxIdx: pointer to the returned maximum location (in nD case). NULL is used if not required. + /// + /// ## C++ default parameters + /// * min_idx: 0 + /// * max_idx: 0 + #[inline] + pub fn min_max_loc_sparse(a: &impl core::SparseMatTraitConst, min_val: Option<&mut f64>, max_val: Option<&mut f64>, min_idx: Option<&mut i32>, max_idx: Option<&mut i32>) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_minMaxLoc_const_SparseMatR_doubleX_doubleX_intX_intX(a.as_raw_SparseMat(), min_val.map_or(::core::ptr::null_mut(), |min_val| min_val), max_val.map_or(::core::ptr::null_mut(), |max_val| max_val), min_idx.map_or(::core::ptr::null_mut(), |min_idx| min_idx), max_idx.map_or(::core::ptr::null_mut(), |max_idx| max_idx), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the global minimum and maximum in an array. + /// + /// The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The + /// extrema are searched across the whole array or, if mask is not an empty array, in the specified + /// array region. + /// + /// In C++, if the input is multi-channel, you should omit the minLoc, maxLoc, and mask arguments + /// (i.e. leave them as NULL, NULL, and noArray() respectively). These arguments are not + /// supported for multi-channel input arrays. If working with multi-channel input and you + /// need the minLoc, maxLoc, or mask arguments, then use Mat::reshape first to reinterpret + /// the array as single-channel. Alternatively, you can extract the particular channel using either + /// extractImageCOI, mixChannels, or split. + /// + /// In Python, multi-channel input is not supported at all due to a limitation in the + /// binding generation process (there is no way to set minLoc and maxLoc to NULL). A + /// workaround is to operate on each channel individually or to use NumPy to achieve the same + /// functionality. + /// + /// ## Parameters + /// * src: input single-channel array. + /// * minVal: pointer to the returned minimum value; NULL is used if not required. + /// * maxVal: pointer to the returned maximum value; NULL is used if not required. + /// * minLoc: pointer to the returned minimum location (in 2D case); NULL is used if not required. + /// * maxLoc: pointer to the returned maximum location (in 2D case); NULL is used if not required. + /// * mask: optional mask used to select a sub-array. + /// ## See also + /// max, min, reduceArgMin, reduceArgMax, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape + /// + /// ## Note + /// This alternative version of [min_max_loc] function uses the following default values for its arguments: + /// * max_val: 0 + /// * min_loc: 0 + /// * max_loc: 0 + /// * mask: noArray() + #[inline] + pub fn min_max_loc_def(src: &impl ToInputArray, min_val: Option<&mut f64>) -> Result<()> { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_minMaxLoc_const__InputArrayR_doubleX(src.as_raw__InputArray(), min_val.map_or(::core::ptr::null_mut(), |min_val| min_val), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the global minimum and maximum in an array. + /// + /// The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The + /// extrema are searched across the whole array or, if mask is not an empty array, in the specified + /// array region. + /// + /// In C++, if the input is multi-channel, you should omit the minLoc, maxLoc, and mask arguments + /// (i.e. leave them as NULL, NULL, and noArray() respectively). These arguments are not + /// supported for multi-channel input arrays. If working with multi-channel input and you + /// need the minLoc, maxLoc, or mask arguments, then use Mat::reshape first to reinterpret + /// the array as single-channel. Alternatively, you can extract the particular channel using either + /// extractImageCOI, mixChannels, or split. + /// + /// In Python, multi-channel input is not supported at all due to a limitation in the + /// binding generation process (there is no way to set minLoc and maxLoc to NULL). A + /// workaround is to operate on each channel individually or to use NumPy to achieve the same + /// functionality. + /// + /// ## Parameters + /// * src: input single-channel array. + /// * minVal: pointer to the returned minimum value; NULL is used if not required. + /// * maxVal: pointer to the returned maximum value; NULL is used if not required. + /// * minLoc: pointer to the returned minimum location (in 2D case); NULL is used if not required. + /// * maxLoc: pointer to the returned maximum location (in 2D case); NULL is used if not required. + /// * mask: optional mask used to select a sub-array. + /// ## See also + /// max, min, reduceArgMin, reduceArgMax, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape + /// + /// ## C++ default parameters + /// * max_val: 0 + /// * min_loc: 0 + /// * max_loc: 0 + /// * mask: noArray() + #[inline] + pub fn min_max_loc(src: &impl ToInputArray, min_val: Option<&mut f64>, max_val: Option<&mut f64>, min_loc: Option<&mut core::Point>, max_loc: Option<&mut core::Point>, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_minMaxLoc_const__InputArrayR_doubleX_doubleX_PointX_PointX_const__InputArrayR(src.as_raw__InputArray(), min_val.map_or(::core::ptr::null_mut(), |min_val| min_val), max_val.map_or(::core::ptr::null_mut(), |max_val| max_val), min_loc.map_or(::core::ptr::null_mut(), |min_loc| min_loc), max_loc.map_or(::core::ptr::null_mut(), |max_loc| max_loc), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn min_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_min_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Calculates per-element minimum of two arrays or an array and a scalar. + /// + /// The function cv::min calculates the per-element minimum of two arrays: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Cmin%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2C%20%5Ctexttt%7Bsrc2%7D%20%28I%29%29) + /// or array and a scalar: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Cmin%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2C%20%5Ctexttt%7Bvalue%7D%20%29) + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and type as src1. + /// * dst: output array of the same size and type as src1. + /// ## See also + /// max, compare, inRange, minMaxLoc + /// + /// ## Overloaded parameters + /// + /// needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) + #[inline] + pub fn min_mat_to(src1: &impl core::MatTraitConst, src2: &impl core::MatTraitConst, dst: &mut impl core::MatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_min_const_MatR_const_MatR_MatR(src1.as_raw_Mat(), src2.as_raw_Mat(), dst.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn min_mat_f64(a: &impl core::MatTraitConst, s: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_min_const_MatR_double(a.as_raw_Mat(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Calculates per-element minimum of two arrays or an array and a scalar. + /// + /// The function cv::min calculates the per-element minimum of two arrays: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Cmin%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2C%20%5Ctexttt%7Bsrc2%7D%20%28I%29%29) + /// or array and a scalar: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Cmin%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2C%20%5Ctexttt%7Bvalue%7D%20%29) + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and type as src1. + /// * dst: output array of the same size and type as src1. + /// ## See also + /// max, compare, inRange, minMaxLoc + /// + /// ## Overloaded parameters + /// + /// needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) + #[inline] + pub fn min_umat_to(src1: &impl core::UMatTraitConst, src2: &impl core::UMatTraitConst, dst: &mut impl core::UMatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_min_const_UMatR_const_UMatR_UMatR(src1.as_raw_UMat(), src2.as_raw_UMat(), dst.as_raw_mut_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates per-element minimum of two arrays or an array and a scalar. + /// + /// The function cv::min calculates the per-element minimum of two arrays: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Cmin%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2C%20%5Ctexttt%7Bsrc2%7D%20%28I%29%29) + /// or array and a scalar: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Cmin%20%28%20%5Ctexttt%7Bsrc1%7D%20%28I%29%2C%20%5Ctexttt%7Bvalue%7D%20%29) + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and type as src1. + /// * dst: output array of the same size and type as src1. + /// ## See also + /// max, compare, inRange, minMaxLoc + #[inline] + pub fn min(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_min_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn min_f64_mat(s: f64, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_min_double_const_MatR(s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Copies specified channels from input arrays to the specified channels of + /// output arrays. + /// + /// The function cv::mixChannels provides an advanced mechanism for shuffling image channels. + /// + /// cv::split,cv::merge,cv::extractChannel,cv::insertChannel and some forms of cv::cvtColor are partial cases of cv::mixChannels. + /// + /// In the example below, the code splits a 4-channel BGRA image into a 3-channel BGR (with B and R + /// channels swapped) and a separate alpha-channel image: + /// ```C++ + /// Mat bgra( 100, 100, CV_8UC4, Scalar(255,0,0,255) ); + /// Mat bgr( bgra.rows, bgra.cols, CV_8UC3 ); + /// Mat alpha( bgra.rows, bgra.cols, CV_8UC1 ); + /// + /// // forming an array of matrices is a quite efficient operation, + /// // because the matrix data is not copied, only the headers + /// Mat out[] = { bgr, alpha }; + /// // bgra[0] -> bgr[2], bgra[1] -> bgr[1], + /// // bgra[2] -> bgr[0], bgra[3] -> alpha[0] + /// int from_to[] = { 0,2, 1,1, 2,0, 3,3 }; + /// mixChannels( &bgra, 1, out, 2, from_to, 4 ); + /// ``` + /// + /// + /// Note: Unlike many other new-style C++ functions in OpenCV (see the introduction section and + /// Mat::create ), cv::mixChannels requires the output arrays to be pre-allocated before calling the + /// function. + /// ## Parameters + /// * src: input array or vector of matrices; all of the matrices must have the same size and the + /// same depth. + /// * nsrcs: number of matrices in `src`. + /// * dst: output array or vector of matrices; all the matrices **must be allocated**; their size and + /// depth must be the same as in `src[0]`. + /// * ndsts: number of matrices in `dst`. + /// * fromTo: array of index pairs specifying which channels are copied and where; fromTo[k\*2] is + /// a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in + /// dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to + /// src[0].channels()-1, the second input image channels are indexed from src[0].channels() to + /// src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image + /// channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is + /// filled with zero . + /// * npairs: number of index pairs in `fromTo`. + /// ## See also + /// split, merge, extractChannel, insertChannel, cvtColor + /// + /// ## Overloaded parameters + /// + /// * src: input array or vector of matrices; all of the matrices must have the same size and the + /// same depth. + /// * dst: output array or vector of matrices; all the matrices **must be allocated**; their size and + /// depth must be the same as in src[0]. + /// * fromTo: array of index pairs specifying which channels are copied and where; fromTo[k\*2] is + /// a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in + /// dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to + /// src[0].channels()-1, the second input image channels are indexed from src[0].channels() to + /// src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image + /// channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is + /// filled with zero . + /// * npairs: number of index pairs in fromTo. + #[inline] + pub fn mix_channels(src: &impl ToInputArray, dst: &mut impl ToInputOutputArray, from_to: &[i32]) -> Result<()> { + input_array_arg!(src); + input_output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_mixChannels_const__InputArrayR_const__InputOutputArrayR_const_intX_size_t(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), from_to.as_ptr(), from_to.len() / 2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Copies specified channels from input arrays to the specified channels of + /// output arrays. + /// + /// The function cv::mixChannels provides an advanced mechanism for shuffling image channels. + /// + /// cv::split,cv::merge,cv::extractChannel,cv::insertChannel and some forms of cv::cvtColor are partial cases of cv::mixChannels. + /// + /// In the example below, the code splits a 4-channel BGRA image into a 3-channel BGR (with B and R + /// channels swapped) and a separate alpha-channel image: + /// ```C++ + /// Mat bgra( 100, 100, CV_8UC4, Scalar(255,0,0,255) ); + /// Mat bgr( bgra.rows, bgra.cols, CV_8UC3 ); + /// Mat alpha( bgra.rows, bgra.cols, CV_8UC1 ); + /// + /// // forming an array of matrices is a quite efficient operation, + /// // because the matrix data is not copied, only the headers + /// Mat out[] = { bgr, alpha }; + /// // bgra[0] -> bgr[2], bgra[1] -> bgr[1], + /// // bgra[2] -> bgr[0], bgra[3] -> alpha[0] + /// int from_to[] = { 0,2, 1,1, 2,0, 3,3 }; + /// mixChannels( &bgra, 1, out, 2, from_to, 4 ); + /// ``` + /// + /// + /// Note: Unlike many other new-style C++ functions in OpenCV (see the introduction section and + /// Mat::create ), cv::mixChannels requires the output arrays to be pre-allocated before calling the + /// function. + /// ## Parameters + /// * src: input array or vector of matrices; all of the matrices must have the same size and the + /// same depth. + /// * nsrcs: number of matrices in `src`. + /// * dst: output array or vector of matrices; all the matrices **must be allocated**; their size and + /// depth must be the same as in `src[0]`. + /// * ndsts: number of matrices in `dst`. + /// * fromTo: array of index pairs specifying which channels are copied and where; fromTo[k\*2] is + /// a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in + /// dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to + /// src[0].channels()-1, the second input image channels are indexed from src[0].channels() to + /// src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image + /// channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is + /// filled with zero . + /// * npairs: number of index pairs in `fromTo`. + /// ## See also + /// split, merge, extractChannel, insertChannel, cvtColor + /// + /// ## Overloaded parameters + /// + /// * src: input array or vector of matrices; all of the matrices must have the same size and the + /// same depth. + /// * dst: output array or vector of matrices; all the matrices **must be allocated**; their size and + /// depth must be the same as in src[0]. + /// * fromTo: array of index pairs specifying which channels are copied and where; fromTo[k\*2] is + /// a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in + /// dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to + /// src[0].channels()-1, the second input image channels are indexed from src[0].channels() to + /// src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image + /// channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is + /// filled with zero . + #[inline] + pub fn mix_channels_vec(src: &impl ToInputArray, dst: &mut impl ToInputOutputArray, from_to: &core::Vector) -> Result<()> { + input_array_arg!(src); + input_output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_mixChannels_const__InputArrayR_const__InputOutputArrayR_const_vectorLintGR(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), from_to.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs the per-element multiplication of two Fourier spectrums. + /// + /// The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex + /// matrices that are results of a real or complex Fourier transform. + /// + /// The function, together with dft and idft, may be used to calculate convolution (pass conjB=false ) + /// or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are + /// simply multiplied (per element) with an optional conjugation of the second-array elements. When the + /// arrays are real, they are assumed to be CCS-packed (see dft for details). + /// ## Parameters + /// * a: first input array. + /// * b: second input array of the same size and type as src1 . + /// * c: output array of the same size and type as src1 . + /// * flags: operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that + /// each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value. + /// * conjB: optional flag that conjugates the second input array before the multiplication (true) + /// or not (false). + /// + /// ## Note + /// This alternative version of [mul_spectrums] function uses the following default values for its arguments: + /// * conj_b: false + #[inline] + pub fn mul_spectrums_def(a: &impl ToInputArray, b: &impl ToInputArray, c: &mut impl ToOutputArray, flags: i32) -> Result<()> { + input_array_arg!(a); + input_array_arg!(b); + output_array_arg!(c); + return_send!(via ocvrs_return); + unsafe { sys::cv_mulSpectrums_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(a.as_raw__InputArray(), b.as_raw__InputArray(), c.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs the per-element multiplication of two Fourier spectrums. + /// + /// The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex + /// matrices that are results of a real or complex Fourier transform. + /// + /// The function, together with dft and idft, may be used to calculate convolution (pass conjB=false ) + /// or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are + /// simply multiplied (per element) with an optional conjugation of the second-array elements. When the + /// arrays are real, they are assumed to be CCS-packed (see dft for details). + /// ## Parameters + /// * a: first input array. + /// * b: second input array of the same size and type as src1 . + /// * c: output array of the same size and type as src1 . + /// * flags: operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that + /// each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value. + /// * conjB: optional flag that conjugates the second input array before the multiplication (true) + /// or not (false). + /// + /// ## C++ default parameters + /// * conj_b: false + #[inline] + pub fn mul_spectrums(a: &impl ToInputArray, b: &impl ToInputArray, c: &mut impl ToOutputArray, flags: i32, conj_b: bool) -> Result<()> { + input_array_arg!(a); + input_array_arg!(b); + output_array_arg!(c); + return_send!(via ocvrs_return); + unsafe { sys::cv_mulSpectrums_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_bool(a.as_raw__InputArray(), b.as_raw__InputArray(), c.as_raw__OutputArray(), flags, conj_b, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the product of a matrix and its transposition. + /// + /// The function cv::mulTransposed calculates the product of src and its + /// transposition: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%5Ctexttt%7Bscale%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2D%20%5Ctexttt%7Bdelta%7D%20%29%5ET%20%28%20%5Ctexttt%7Bsrc%7D%20%2D%20%5Ctexttt%7Bdelta%7D%20%29) + /// if aTa=true, and + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%5Ctexttt%7Bscale%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2D%20%5Ctexttt%7Bdelta%7D%20%29%20%28%20%5Ctexttt%7Bsrc%7D%20%2D%20%5Ctexttt%7Bdelta%7D%20%29%5ET) + /// otherwise. The function is used to calculate the covariance matrix. With + /// zero delta, it can be used as a faster substitute for general matrix + /// product A\*B when B=A' + /// ## Parameters + /// * src: input single-channel matrix. Note that unlike gemm, the + /// function can multiply not only floating-point matrices. + /// * dst: output square matrix. + /// * aTa: Flag specifying the multiplication ordering. See the + /// description below. + /// * delta: Optional delta matrix subtracted from src before the + /// multiplication. When the matrix is empty ( delta=noArray() ), it is + /// assumed to be zero, that is, nothing is subtracted. If it has the same + /// size as src, it is simply subtracted. Otherwise, it is "repeated" (see + /// repeat ) to cover the full src and then subtracted. Type of the delta + /// matrix, when it is not empty, must be the same as the type of created + /// output matrix. See the dtype parameter description below. + /// * scale: Optional scale factor for the matrix product. + /// * dtype: Optional type of the output matrix. When it is negative, + /// the output matrix will have the same type as src . Otherwise, it will be + /// type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F . + /// ## See also + /// calcCovarMatrix, gemm, repeat, reduce + /// + /// ## Note + /// This alternative version of [mul_transposed] function uses the following default values for its arguments: + /// * delta: noArray() + /// * scale: 1 + /// * dtype: -1 + #[inline] + pub fn mul_transposed_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, a_ta: bool) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_mulTransposed_const__InputArrayR_const__OutputArrayR_bool(src.as_raw__InputArray(), dst.as_raw__OutputArray(), a_ta, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the product of a matrix and its transposition. + /// + /// The function cv::mulTransposed calculates the product of src and its + /// transposition: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%5Ctexttt%7Bscale%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2D%20%5Ctexttt%7Bdelta%7D%20%29%5ET%20%28%20%5Ctexttt%7Bsrc%7D%20%2D%20%5Ctexttt%7Bdelta%7D%20%29) + /// if aTa=true, and + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%5Ctexttt%7Bscale%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2D%20%5Ctexttt%7Bdelta%7D%20%29%20%28%20%5Ctexttt%7Bsrc%7D%20%2D%20%5Ctexttt%7Bdelta%7D%20%29%5ET) + /// otherwise. The function is used to calculate the covariance matrix. With + /// zero delta, it can be used as a faster substitute for general matrix + /// product A\*B when B=A' + /// ## Parameters + /// * src: input single-channel matrix. Note that unlike gemm, the + /// function can multiply not only floating-point matrices. + /// * dst: output square matrix. + /// * aTa: Flag specifying the multiplication ordering. See the + /// description below. + /// * delta: Optional delta matrix subtracted from src before the + /// multiplication. When the matrix is empty ( delta=noArray() ), it is + /// assumed to be zero, that is, nothing is subtracted. If it has the same + /// size as src, it is simply subtracted. Otherwise, it is "repeated" (see + /// repeat ) to cover the full src and then subtracted. Type of the delta + /// matrix, when it is not empty, must be the same as the type of created + /// output matrix. See the dtype parameter description below. + /// * scale: Optional scale factor for the matrix product. + /// * dtype: Optional type of the output matrix. When it is negative, + /// the output matrix will have the same type as src . Otherwise, it will be + /// type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F . + /// ## See also + /// calcCovarMatrix, gemm, repeat, reduce + /// + /// ## C++ default parameters + /// * delta: noArray() + /// * scale: 1 + /// * dtype: -1 + #[inline] + pub fn mul_transposed(src: &impl ToInputArray, dst: &mut impl ToOutputArray, a_ta: bool, delta: &impl ToInputArray, scale: f64, dtype: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(delta); + return_send!(via ocvrs_return); + unsafe { sys::cv_mulTransposed_const__InputArrayR_const__OutputArrayR_bool_const__InputArrayR_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), a_ta, delta.as_raw__InputArray(), scale, dtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the per-element scaled product of two arrays. + /// + /// The function multiply calculates the per-element product of two arrays: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bscale%7D%20%5Ccdot%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Ccdot%20%5Ctexttt%7Bsrc2%7D%20%28I%29%29) + /// + /// There is also a [MatrixExpressions] -friendly variant of the first function. See Mat::mul . + /// + /// For a not-per-element matrix product, see gemm . + /// + /// + /// Note: Saturation is not applied when the output array has the depth + /// CV_32S. You may even get result of an incorrect sign in the case of + /// overflow. + /// + /// Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array. + /// `multiply(src,X)` means `multiply(src,(X,X,X,X))`. + /// `multiply(src,(X,))` means `multiply(src,(X,0,0,0))`. + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and the same type as src1. + /// * dst: output array of the same size and type as src1. + /// * scale: optional scale factor. + /// * dtype: optional depth of the output array + /// ## See also + /// add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare, + /// Mat::convertTo + /// + /// ## Note + /// This alternative version of [multiply] function uses the following default values for its arguments: + /// * scale: 1 + /// * dtype: -1 + #[inline] + pub fn multiply_def(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_multiply_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the per-element scaled product of two arrays. + /// + /// The function multiply calculates the per-element product of two arrays: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bscale%7D%20%5Ccdot%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%20%5Ccdot%20%5Ctexttt%7Bsrc2%7D%20%28I%29%29) + /// + /// There is also a [MatrixExpressions] -friendly variant of the first function. See Mat::mul . + /// + /// For a not-per-element matrix product, see gemm . + /// + /// + /// Note: Saturation is not applied when the output array has the depth + /// CV_32S. You may even get result of an incorrect sign in the case of + /// overflow. + /// + /// Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array. + /// `multiply(src,X)` means `multiply(src,(X,X,X,X))`. + /// `multiply(src,(X,))` means `multiply(src,(X,0,0,0))`. + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and the same type as src1. + /// * dst: output array of the same size and type as src1. + /// * scale: optional scale factor. + /// * dtype: optional depth of the output array + /// ## See also + /// add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare, + /// Mat::convertTo + /// + /// ## C++ default parameters + /// * scale: 1 + /// * dtype: -1 + #[inline] + pub fn multiply(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray, scale: f64, dtype: i32) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_multiply_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double_int(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), scale, dtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns an empty InputArray or OutputArray. + /// + /// This function is used to provide an "empty" or "null" array when certain functions + /// take optional input or output arrays that you don't want to provide. + /// + /// Many OpenCV functions accept optional arguments as `cv::InputArray` or `cv::OutputArray`. + /// When you don't want to pass any data for these optional parameters, you can use `cv::noArray()` + /// to indicate that you are omitting them. + /// + /// ## Returns + /// An empty `cv::InputArray` or `cv::OutputArray` that can be used as a placeholder. + /// + /// + /// Note: This is often used when a function has optional arrays, and you do not want to + /// provide a specific input or output array. + /// ## See also + /// cv::InputArray, cv::OutputArray + #[inline] + pub fn no_array() -> core::_InputOutputArray { + let ret = unsafe { sys::cv_noArray() }; + let ret = unsafe { core::_InputOutputArray::opencv_from_extern(ret) }; + ret + } + + /// Calculates an absolute difference norm or a relative difference norm. + /// + /// This version of cv::norm calculates the absolute difference norm + /// or the relative difference norm of arrays src1 and src2. + /// The type of norm to calculate is specified using #NormTypes. + /// + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and the same type as src1. + /// * normType: type of the norm (see #NormTypes). + /// * mask: optional operation mask; it must have the same size as src1 and CV_8UC1 type. + /// + /// ## Overloaded parameters + /// + /// * src: first input array. + /// * normType: type of the norm (see #NormTypes). + #[inline] + pub fn norm_sparse(src: &impl core::SparseMatTraitConst, norm_type: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_norm_const_SparseMatR_int(src.as_raw_SparseMat(), norm_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the absolute norm of an array. + /// + /// This version of [norm] calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes. + /// + /// As example for one array consider the function ![inline formula](https://latex.codecogs.com/png.latex?r%28x%29%3D%20%5Cbegin%7Bpmatrix%7D%20x%20%5C%5C%201%2Dx%20%5Cend%7Bpmatrix%7D%2C%20x%20%5Cin%20%5B%2D1%3B1%5D). + /// The ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F%7B1%7D%2C%20L%5F%7B2%7D%20) and ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F%7B%5Cinfty%7D%20) norm for the sample value ![inline formula](https://latex.codecogs.com/png.latex?r%28%2D1%29%20%3D%20%5Cbegin%7Bpmatrix%7D%20%2D1%20%5C%5C%202%20%5Cend%7Bpmatrix%7D) + /// is calculated as follows + /// \f{align*} + /// \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\ + /// \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\ + /// \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2 + /// \f} + /// and for ![inline formula](https://latex.codecogs.com/png.latex?r%280%2E5%29%20%3D%20%5Cbegin%7Bpmatrix%7D%200%2E5%20%5C%5C%200%2E5%20%5Cend%7Bpmatrix%7D) the calculation is + /// \f{align*} + /// \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\ + /// \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\ + /// \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5. + /// \f} + /// The following graphic shows all values for the three norm functions ![inline formula](https://latex.codecogs.com/png.latex?%5C%7C%20r%28x%29%20%5C%7C%5F%7BL%5F1%7D%2C%20%5C%7C%20r%28x%29%20%5C%7C%5F%7BL%5F2%7D) and ![inline formula](https://latex.codecogs.com/png.latex?%5C%7C%20r%28x%29%20%5C%7C%5F%7BL%5F%5Cinfty%7D). + /// It is notable that the ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F%7B1%7D%20) norm forms the upper and the ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F%7B%5Cinfty%7D%20) norm forms the lower border for the example function ![inline formula](https://latex.codecogs.com/png.latex?%20r%28x%29%20). + /// ![Graphs for the different norm functions from the above example](https://docs.opencv.org/4.13.0/NormTypes_OneArray_1-2-INF.png) + /// + /// When the mask parameter is specified and it is not empty, the norm is + /// + /// If normType is not specified, [NORM_L2] is used. + /// calculated only over the region specified by the mask. + /// + /// Multi-channel input arrays are treated as single-channel arrays, that is, + /// the results for all channels are combined. + /// + /// Hamming norms can only be calculated with CV_8U depth arrays. + /// + /// ## Parameters + /// * src1: first input array. + /// * normType: type of the norm (see #NormTypes). + /// * mask: optional operation mask; it must have the same size as src1 and CV_8UC1 type. + /// + /// ## Note + /// This alternative version of [norm] function uses the following default values for its arguments: + /// * norm_type: NORM_L2 + /// * mask: noArray() + #[inline] + pub fn norm_def(src1: &impl ToInputArray) -> Result { + input_array_arg!(src1); + return_send!(via ocvrs_return); + unsafe { sys::cv_norm_const__InputArrayR(src1.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates an absolute difference norm or a relative difference norm. + /// + /// This version of cv::norm calculates the absolute difference norm + /// or the relative difference norm of arrays src1 and src2. + /// The type of norm to calculate is specified using #NormTypes. + /// + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and the same type as src1. + /// * normType: type of the norm (see #NormTypes). + /// * mask: optional operation mask; it must have the same size as src1 and CV_8UC1 type. + /// + /// ## Note + /// This alternative version of [norm2] function uses the following default values for its arguments: + /// * norm_type: NORM_L2 + /// * mask: noArray() + #[inline] + pub fn norm2_def(src1: &impl ToInputArray, src2: &impl ToInputArray) -> Result { + input_array_arg!(src1); + input_array_arg!(src2); + return_send!(via ocvrs_return); + unsafe { sys::cv_norm_const__InputArrayR_const__InputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates an absolute difference norm or a relative difference norm. + /// + /// This version of cv::norm calculates the absolute difference norm + /// or the relative difference norm of arrays src1 and src2. + /// The type of norm to calculate is specified using #NormTypes. + /// + /// ## Parameters + /// * src1: first input array. + /// * src2: second input array of the same size and the same type as src1. + /// * normType: type of the norm (see #NormTypes). + /// * mask: optional operation mask; it must have the same size as src1 and CV_8UC1 type. + /// + /// ## C++ default parameters + /// * norm_type: NORM_L2 + /// * mask: noArray() + #[inline] + pub fn norm2(src1: &impl ToInputArray, src2: &impl ToInputArray, norm_type: i32, mask: &impl ToInputArray) -> Result { + input_array_arg!(src1); + input_array_arg!(src2); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_norm_const__InputArrayR_const__InputArrayR_int_const__InputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), norm_type, mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the absolute norm of an array. + /// + /// This version of [norm] calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes. + /// + /// As example for one array consider the function ![inline formula](https://latex.codecogs.com/png.latex?r%28x%29%3D%20%5Cbegin%7Bpmatrix%7D%20x%20%5C%5C%201%2Dx%20%5Cend%7Bpmatrix%7D%2C%20x%20%5Cin%20%5B%2D1%3B1%5D). + /// The ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F%7B1%7D%2C%20L%5F%7B2%7D%20) and ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F%7B%5Cinfty%7D%20) norm for the sample value ![inline formula](https://latex.codecogs.com/png.latex?r%28%2D1%29%20%3D%20%5Cbegin%7Bpmatrix%7D%20%2D1%20%5C%5C%202%20%5Cend%7Bpmatrix%7D) + /// is calculated as follows + /// \f{align*} + /// \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\ + /// \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\ + /// \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2 + /// \f} + /// and for ![inline formula](https://latex.codecogs.com/png.latex?r%280%2E5%29%20%3D%20%5Cbegin%7Bpmatrix%7D%200%2E5%20%5C%5C%200%2E5%20%5Cend%7Bpmatrix%7D) the calculation is + /// \f{align*} + /// \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\ + /// \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\ + /// \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5. + /// \f} + /// The following graphic shows all values for the three norm functions ![inline formula](https://latex.codecogs.com/png.latex?%5C%7C%20r%28x%29%20%5C%7C%5F%7BL%5F1%7D%2C%20%5C%7C%20r%28x%29%20%5C%7C%5F%7BL%5F2%7D) and ![inline formula](https://latex.codecogs.com/png.latex?%5C%7C%20r%28x%29%20%5C%7C%5F%7BL%5F%5Cinfty%7D). + /// It is notable that the ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F%7B1%7D%20) norm forms the upper and the ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F%7B%5Cinfty%7D%20) norm forms the lower border for the example function ![inline formula](https://latex.codecogs.com/png.latex?%20r%28x%29%20). + /// ![Graphs for the different norm functions from the above example](https://docs.opencv.org/4.13.0/NormTypes_OneArray_1-2-INF.png) + /// + /// When the mask parameter is specified and it is not empty, the norm is + /// + /// If normType is not specified, [NORM_L2] is used. + /// calculated only over the region specified by the mask. + /// + /// Multi-channel input arrays are treated as single-channel arrays, that is, + /// the results for all channels are combined. + /// + /// Hamming norms can only be calculated with CV_8U depth arrays. + /// + /// ## Parameters + /// * src1: first input array. + /// * normType: type of the norm (see #NormTypes). + /// * mask: optional operation mask; it must have the same size as src1 and CV_8UC1 type. + /// + /// ## C++ default parameters + /// * norm_type: NORM_L2 + /// * mask: noArray() + #[inline] + pub fn norm(src1: &impl ToInputArray, norm_type: i32, mask: &impl ToInputArray) -> Result { + input_array_arg!(src1); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_norm_const__InputArrayR_int_const__InputArrayR(src1.as_raw__InputArray(), norm_type, mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Normalizes the norm or value range of an array. + /// + /// The function cv::normalize normalizes scale and shift the input array elements so that + /// ![block formula](https://latex.codecogs.com/png.latex?%5C%7C%20%5Ctexttt%7Bdst%7D%20%5C%7C%20%5F%7BL%5Fp%7D%3D%20%5Ctexttt%7Balpha%7D) + /// (where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cmin%20%5FI%20%20%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Balpha%7D%20%2C%20%5C%2C%20%5C%2C%20%5Cmax%20%5FI%20%20%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Bbeta%7D) + /// + /// when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be + /// normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this + /// sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or + /// min-max but modify the whole array, you can use norm and Mat::convertTo. + /// + /// In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, + /// the range transformation for sparse matrices is not allowed since it can shift the zero level. + /// + /// Possible usage with some positive example data: + /// ```C++ + /// vector positiveData = { 2.0, 8.0, 10.0 }; + /// vector normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; + /// + /// // Norm to probability (total count) + /// // sum(numbers) = 20.0 + /// // 2.0 0.1 (2.0/20.0) + /// // 8.0 0.4 (8.0/20.0) + /// // 10.0 0.5 (10.0/20.0) + /// normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); + /// + /// // Norm to unit vector: ||positiveData|| = 1.0 + /// // 2.0 0.15 + /// // 8.0 0.62 + /// // 10.0 0.77 + /// normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); + /// + /// // Norm to max element + /// // 2.0 0.2 (2.0/10.0) + /// // 8.0 0.8 (8.0/10.0) + /// // 10.0 1.0 (10.0/10.0) + /// normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); + /// + /// // Norm to range [0.0;1.0] + /// // 2.0 0.0 (shift to left border) + /// // 8.0 0.75 (6.0/8.0) + /// // 10.0 1.0 (shift to right border) + /// normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX); + /// ``` + /// + /// + /// + /// Note: Due to rounding issues, min-max normalization can result in values outside provided boundaries. + /// If exact range conformity is needed, following workarounds can be used: + /// - use double floating point precision (dtype = CV_64F) + /// - manually clip values (`cv::max(res, left_bound, res)`, `cv::min(res, right_bound, res)` or `np.clip`) + /// + /// ## Parameters + /// * src: input array. + /// * dst: output array of the same size as src . + /// * alpha: norm value to normalize to or the lower range boundary in case of the range + /// normalization. + /// * beta: upper range boundary in case of the range normalization; it is not used for the norm + /// normalization. + /// * norm_type: normalization type (see cv::NormTypes). + /// * dtype: when negative, the output array has the same type as src; otherwise, it has the same + /// number of channels as src and the depth =CV_MAT_DEPTH(dtype). + /// * mask: optional operation mask. + /// ## See also + /// norm, Mat::convertTo, SparseMat::convertTo + /// + /// ## Overloaded parameters + /// + /// * src: input array. + /// * dst: output array of the same size as src . + /// * alpha: norm value to normalize to or the lower range boundary in case of the range + /// normalization. + /// * normType: normalization type (see cv::NormTypes). + #[inline] + pub fn normalize_sparse(src: &impl core::SparseMatTraitConst, dst: &mut impl core::SparseMatTrait, alpha: f64, norm_type: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_normalize_const_SparseMatR_SparseMatR_double_int(src.as_raw_SparseMat(), dst.as_raw_mut_SparseMat(), alpha, norm_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Normalizes the norm or value range of an array. + /// + /// The function cv::normalize normalizes scale and shift the input array elements so that + /// ![block formula](https://latex.codecogs.com/png.latex?%5C%7C%20%5Ctexttt%7Bdst%7D%20%5C%7C%20%5F%7BL%5Fp%7D%3D%20%5Ctexttt%7Balpha%7D) + /// (where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cmin%20%5FI%20%20%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Balpha%7D%20%2C%20%5C%2C%20%5C%2C%20%5Cmax%20%5FI%20%20%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Bbeta%7D) + /// + /// when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be + /// normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this + /// sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or + /// min-max but modify the whole array, you can use norm and Mat::convertTo. + /// + /// In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, + /// the range transformation for sparse matrices is not allowed since it can shift the zero level. + /// + /// Possible usage with some positive example data: + /// ```C++ + /// vector positiveData = { 2.0, 8.0, 10.0 }; + /// vector normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; + /// + /// // Norm to probability (total count) + /// // sum(numbers) = 20.0 + /// // 2.0 0.1 (2.0/20.0) + /// // 8.0 0.4 (8.0/20.0) + /// // 10.0 0.5 (10.0/20.0) + /// normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); + /// + /// // Norm to unit vector: ||positiveData|| = 1.0 + /// // 2.0 0.15 + /// // 8.0 0.62 + /// // 10.0 0.77 + /// normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); + /// + /// // Norm to max element + /// // 2.0 0.2 (2.0/10.0) + /// // 8.0 0.8 (8.0/10.0) + /// // 10.0 1.0 (10.0/10.0) + /// normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); + /// + /// // Norm to range [0.0;1.0] + /// // 2.0 0.0 (shift to left border) + /// // 8.0 0.75 (6.0/8.0) + /// // 10.0 1.0 (shift to right border) + /// normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX); + /// ``` + /// + /// + /// + /// Note: Due to rounding issues, min-max normalization can result in values outside provided boundaries. + /// If exact range conformity is needed, following workarounds can be used: + /// - use double floating point precision (dtype = CV_64F) + /// - manually clip values (`cv::max(res, left_bound, res)`, `cv::min(res, right_bound, res)` or `np.clip`) + /// + /// ## Parameters + /// * src: input array. + /// * dst: output array of the same size as src . + /// * alpha: norm value to normalize to or the lower range boundary in case of the range + /// normalization. + /// * beta: upper range boundary in case of the range normalization; it is not used for the norm + /// normalization. + /// * norm_type: normalization type (see cv::NormTypes). + /// * dtype: when negative, the output array has the same type as src; otherwise, it has the same + /// number of channels as src and the depth =CV_MAT_DEPTH(dtype). + /// * mask: optional operation mask. + /// ## See also + /// norm, Mat::convertTo, SparseMat::convertTo + /// + /// ## Note + /// This alternative version of [normalize] function uses the following default values for its arguments: + /// * alpha: 1 + /// * beta: 0 + /// * norm_type: NORM_L2 + /// * dtype: -1 + /// * mask: noArray() + #[inline] + pub fn normalize_def(src: &impl ToInputArray, dst: &mut impl ToInputOutputArray) -> Result<()> { + input_array_arg!(src); + input_output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_normalize_const__InputArrayR_const__InputOutputArrayR(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Normalizes the norm or value range of an array. + /// + /// The function cv::normalize normalizes scale and shift the input array elements so that + /// ![block formula](https://latex.codecogs.com/png.latex?%5C%7C%20%5Ctexttt%7Bdst%7D%20%5C%7C%20%5F%7BL%5Fp%7D%3D%20%5Ctexttt%7Balpha%7D) + /// (where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cmin%20%5FI%20%20%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Balpha%7D%20%2C%20%5C%2C%20%5C%2C%20%5Cmax%20%5FI%20%20%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Bbeta%7D) + /// + /// when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be + /// normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this + /// sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or + /// min-max but modify the whole array, you can use norm and Mat::convertTo. + /// + /// In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, + /// the range transformation for sparse matrices is not allowed since it can shift the zero level. + /// + /// Possible usage with some positive example data: + /// ```C++ + /// vector positiveData = { 2.0, 8.0, 10.0 }; + /// vector normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; + /// + /// // Norm to probability (total count) + /// // sum(numbers) = 20.0 + /// // 2.0 0.1 (2.0/20.0) + /// // 8.0 0.4 (8.0/20.0) + /// // 10.0 0.5 (10.0/20.0) + /// normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); + /// + /// // Norm to unit vector: ||positiveData|| = 1.0 + /// // 2.0 0.15 + /// // 8.0 0.62 + /// // 10.0 0.77 + /// normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); + /// + /// // Norm to max element + /// // 2.0 0.2 (2.0/10.0) + /// // 8.0 0.8 (8.0/10.0) + /// // 10.0 1.0 (10.0/10.0) + /// normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); + /// + /// // Norm to range [0.0;1.0] + /// // 2.0 0.0 (shift to left border) + /// // 8.0 0.75 (6.0/8.0) + /// // 10.0 1.0 (shift to right border) + /// normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX); + /// ``` + /// + /// + /// + /// Note: Due to rounding issues, min-max normalization can result in values outside provided boundaries. + /// If exact range conformity is needed, following workarounds can be used: + /// - use double floating point precision (dtype = CV_64F) + /// - manually clip values (`cv::max(res, left_bound, res)`, `cv::min(res, right_bound, res)` or `np.clip`) + /// + /// ## Parameters + /// * src: input array. + /// * dst: output array of the same size as src . + /// * alpha: norm value to normalize to or the lower range boundary in case of the range + /// normalization. + /// * beta: upper range boundary in case of the range normalization; it is not used for the norm + /// normalization. + /// * norm_type: normalization type (see cv::NormTypes). + /// * dtype: when negative, the output array has the same type as src; otherwise, it has the same + /// number of channels as src and the depth =CV_MAT_DEPTH(dtype). + /// * mask: optional operation mask. + /// ## See also + /// norm, Mat::convertTo, SparseMat::convertTo + /// + /// ## C++ default parameters + /// * alpha: 1 + /// * beta: 0 + /// * norm_type: NORM_L2 + /// * dtype: -1 + /// * mask: noArray() + #[inline] + pub fn normalize(src: &impl ToInputArray, dst: &mut impl ToInputOutputArray, alpha: f64, beta: f64, norm_type: i32, dtype: i32, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + input_output_array_arg!(dst); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_normalize_const__InputArrayR_const__InputOutputArrayR_double_double_int_int_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), alpha, beta, norm_type, dtype, mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Attaches OpenCL context to OpenCV + /// + /// Note: + /// OpenCV will check if available OpenCL platform has platformName name, then assign context to + /// OpenCV and call `clRetainContext` function. The deviceID device will be used as target device and + /// new command queue will be created. + /// ## Parameters + /// * platformName: name of OpenCL platform to attach, this string is used to check if platform is available to OpenCV at runtime + /// * platformID: ID of platform attached context was created for + /// * context: OpenCL context to be attached to OpenCV + /// * deviceID: ID of device, must be created from attached context + #[inline] + pub unsafe fn attach_context(platform_name: &str, platform_id: *mut c_void, context: *mut c_void, device_id: *mut c_void) -> Result<()> { + extern_container_arg!(platform_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_attachContext_const_StringR_voidX_voidX_voidX(platform_name.opencv_as_extern(), platform_id, context, device_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn build_options_add_matrix_description(build_options: &mut String, name: &str, _m: &impl ToInputArray) -> Result<()> { + string_arg_output_send!(via build_options_via); + extern_container_arg!(name); + input_array_arg!(_m); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_buildOptionsAddMatrixDescription_StringR_const_StringR_const__InputArrayR(&mut build_options_via, name.opencv_as_extern(), _m.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + string_arg_output_receive!(build_options_via => build_options); + Ok(ret) + } + + /// ## Note + /// This alternative version of [check_optimal_vector_width] function uses the following default values for its arguments: + /// * src2: noArray() + /// * src3: noArray() + /// * src4: noArray() + /// * src5: noArray() + /// * src6: noArray() + /// * src7: noArray() + /// * src8: noArray() + /// * src9: noArray() + /// * strat: OCL_VECTOR_DEFAULT + #[inline] + pub fn check_optimal_vector_width_def(vector_widths: &i32, src1: &impl ToInputArray) -> Result { + input_array_arg!(src1); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_checkOptimalVectorWidth_const_intX_const__InputArrayR(vector_widths, src1.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * src2: noArray() + /// * src3: noArray() + /// * src4: noArray() + /// * src5: noArray() + /// * src6: noArray() + /// * src7: noArray() + /// * src8: noArray() + /// * src9: noArray() + /// * strat: OCL_VECTOR_DEFAULT + #[inline] + pub fn check_optimal_vector_width(vector_widths: &i32, src1: &impl ToInputArray, src2: &impl ToInputArray, src3: &impl ToInputArray, src4: &impl ToInputArray, src5: &impl ToInputArray, src6: &impl ToInputArray, src7: &impl ToInputArray, src8: &impl ToInputArray, src9: &impl ToInputArray, strat: core::OclVectorStrategy) -> Result { + input_array_arg!(src1); + input_array_arg!(src2); + input_array_arg!(src3); + input_array_arg!(src4); + input_array_arg!(src5); + input_array_arg!(src6); + input_array_arg!(src7); + input_array_arg!(src8); + input_array_arg!(src9); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_checkOptimalVectorWidth_const_intX_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_OclVectorStrategy(vector_widths, src1.as_raw__InputArray(), src2.as_raw__InputArray(), src3.as_raw__InputArray(), src4.as_raw__InputArray(), src5.as_raw__InputArray(), src6.as_raw__InputArray(), src7.as_raw__InputArray(), src8.as_raw__InputArray(), src9.as_raw__InputArray(), strat, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Convert OpenCL buffer to UMat + /// + /// Note: + /// OpenCL buffer (cl_mem_buffer) should contain 2D image data, compatible with OpenCV. Memory + /// content is not copied from `clBuffer` to UMat. Instead, buffer handle assigned to UMat and + /// `clRetainMemObject` is called. + /// ## Parameters + /// * cl_mem_buffer: source clBuffer handle + /// * step: num of bytes in single row + /// * rows: number of rows + /// * cols: number of cols + /// * type: OpenCV type of image + /// * dst: destination UMat + #[inline] + pub unsafe fn convert_from_buffer(cl_mem_buffer: *mut c_void, step: size_t, rows: i32, cols: i32, typ: i32, dst: &mut impl core::UMatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_convertFromBuffer_voidX_size_t_int_int_int_UMatR(cl_mem_buffer, step, rows, cols, typ, dst.as_raw_mut_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Convert OpenCL image2d_t to UMat + /// + /// Note: + /// OpenCL `image2d_t` (cl_mem_image), should be compatible with OpenCV UMat formats. Memory content + /// is copied from image to UMat with `clEnqueueCopyImageToBuffer` function. + /// ## Parameters + /// * cl_mem_image: source image2d_t handle + /// * dst: destination UMat + #[inline] + pub unsafe fn convert_from_image(cl_mem_image: *mut c_void, dst: &mut impl core::UMatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_convertFromImage_voidX_UMatR(cl_mem_image, dst.as_raw_mut_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn convert_type_str(sdepth: i32, ddepth: i32, cn: i32, buf: &mut String) -> Result { + string_arg_output_send!(via buf_via); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_convertTypeStr_int_int_int_charX(sdepth, ddepth, cn, &mut buf_via, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + string_arg_output_receive!(buf_via => buf); + Ok(ret) + } + + #[inline] + pub fn convert_type_str_1(sdepth: i32, ddepth: i32, cn: i32, buf: &mut String, buf_size: size_t) -> Result { + string_arg_output_send!(via buf_via); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_convertTypeStr_int_int_int_charX_size_t(sdepth, ddepth, cn, &mut buf_via, buf_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + string_arg_output_receive!(buf_via => buf); + Ok(ret) + } + + #[inline] + pub fn finish() -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_finish(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn get_opencl_error_string(error_code: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_getOpenCLErrorString_int(error_code, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn get_platfoms_info(platform_info: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_getPlatfomsInfo_vectorLPlatformInfoGR(platform_info.as_raw_mut_VectorOfPlatformInfo(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn have_amd_blas() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_haveAmdBlas(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn have_amd_fft() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_haveAmdFft(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn have_opencl() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_haveOpenCL(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn have_svm() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_haveSVM(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [kernel_to_str] function uses the following default values for its arguments: + /// * ddepth: -1 + /// * name: NULL + #[inline] + pub fn kernel_to_str_def(_kernel: &impl ToInputArray) -> Result { + input_array_arg!(_kernel); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_kernelToStr_const__InputArrayR(_kernel.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * ddepth: -1 + /// * name: NULL + #[inline] + pub fn kernel_to_str(_kernel: &impl ToInputArray, ddepth: i32, name: &str) -> Result { + input_array_arg!(_kernel); + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_kernelToStr_const__InputArrayR_int_const_charX(_kernel.as_raw__InputArray(), ddepth, name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn memop_type_to_str(t: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_memopTypeToStr_int(t, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [predict_optimal_vector_width_max] function uses the following default values for its arguments: + /// * src2: noArray() + /// * src3: noArray() + /// * src4: noArray() + /// * src5: noArray() + /// * src6: noArray() + /// * src7: noArray() + /// * src8: noArray() + /// * src9: noArray() + #[inline] + pub fn predict_optimal_vector_width_max_def(src1: &impl ToInputArray) -> Result { + input_array_arg!(src1); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_predictOptimalVectorWidthMax_const__InputArrayR(src1.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * src2: noArray() + /// * src3: noArray() + /// * src4: noArray() + /// * src5: noArray() + /// * src6: noArray() + /// * src7: noArray() + /// * src8: noArray() + /// * src9: noArray() + #[inline] + pub fn predict_optimal_vector_width_max(src1: &impl ToInputArray, src2: &impl ToInputArray, src3: &impl ToInputArray, src4: &impl ToInputArray, src5: &impl ToInputArray, src6: &impl ToInputArray, src7: &impl ToInputArray, src8: &impl ToInputArray, src9: &impl ToInputArray) -> Result { + input_array_arg!(src1); + input_array_arg!(src2); + input_array_arg!(src3); + input_array_arg!(src4); + input_array_arg!(src5); + input_array_arg!(src6); + input_array_arg!(src7); + input_array_arg!(src8); + input_array_arg!(src9); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_predictOptimalVectorWidthMax_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), src3.as_raw__InputArray(), src4.as_raw__InputArray(), src5.as_raw__InputArray(), src6.as_raw__InputArray(), src7.as_raw__InputArray(), src8.as_raw__InputArray(), src9.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [predict_optimal_vector_width] function uses the following default values for its arguments: + /// * src2: noArray() + /// * src3: noArray() + /// * src4: noArray() + /// * src5: noArray() + /// * src6: noArray() + /// * src7: noArray() + /// * src8: noArray() + /// * src9: noArray() + /// * strat: OCL_VECTOR_DEFAULT + #[inline] + pub fn predict_optimal_vector_width_def(src1: &impl ToInputArray) -> Result { + input_array_arg!(src1); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_predictOptimalVectorWidth_const__InputArrayR(src1.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * src2: noArray() + /// * src3: noArray() + /// * src4: noArray() + /// * src5: noArray() + /// * src6: noArray() + /// * src7: noArray() + /// * src8: noArray() + /// * src9: noArray() + /// * strat: OCL_VECTOR_DEFAULT + #[inline] + pub fn predict_optimal_vector_width(src1: &impl ToInputArray, src2: &impl ToInputArray, src3: &impl ToInputArray, src4: &impl ToInputArray, src5: &impl ToInputArray, src6: &impl ToInputArray, src7: &impl ToInputArray, src8: &impl ToInputArray, src9: &impl ToInputArray, strat: core::OclVectorStrategy) -> Result { + input_array_arg!(src1); + input_array_arg!(src2); + input_array_arg!(src3); + input_array_arg!(src4); + input_array_arg!(src5); + input_array_arg!(src6); + input_array_arg!(src7); + input_array_arg!(src8); + input_array_arg!(src9); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_predictOptimalVectorWidth_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_OclVectorStrategy(src1.as_raw__InputArray(), src2.as_raw__InputArray(), src3.as_raw__InputArray(), src4.as_raw__InputArray(), src5.as_raw__InputArray(), src6.as_raw__InputArray(), src7.as_raw__InputArray(), src8.as_raw__InputArray(), src9.as_raw__InputArray(), strat, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn set_use_opencl(flag: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_setUseOpenCL_bool(flag, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn type_to_str(t: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_typeToStr_int(t, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn use_opencl() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_useOpenCL(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn vecop_type_to_str(t: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_vecopTypeToStr_int(t, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn add_matexpr_matexpr(e1: &impl core::MatExprTraitConst, e2: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorA_const_MatExprR_const_MatExprR(e1.as_raw_MatExpr(), e2.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn add_matexpr_mat(e: &impl core::MatExprTraitConst, m: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorA_const_MatExprR_const_MatR(e.as_raw_MatExpr(), m.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn add_matexpr_scalar(e: &impl core::MatExprTraitConst, s: core::Scalar) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorA_const_MatExprR_const_ScalarR(e.as_raw_MatExpr(), &s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn add_mat_matexpr(m: &impl core::MatTraitConst, e: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorA_const_MatR_const_MatExprR(m.as_raw_Mat(), e.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// @relates cv::MatExpr + #[inline] + pub fn add_mat_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorA_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn add_mat_scalar(a: &impl core::MatTraitConst, s: core::Scalar) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorA_const_MatR_const_ScalarR(a.as_raw_Mat(), &s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn add_scalar_matexpr(s: core::Scalar, e: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorA_const_ScalarR_const_MatExprR(&s, e.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn add_scalar_mat(s: core::Scalar, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorA_const_ScalarR_const_MatR(&s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn div_matexpr_matexpr(e1: &impl core::MatExprTraitConst, e2: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorD_const_MatExprR_const_MatExprR(e1.as_raw_MatExpr(), e2.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn div_matexpr_mat(e: &impl core::MatExprTraitConst, m: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorD_const_MatExprR_const_MatR(e.as_raw_MatExpr(), m.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn div_matexpr_f64(e: &impl core::MatExprTraitConst, s: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorD_const_MatExprR_double(e.as_raw_MatExpr(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn div_mat_matexpr(m: &impl core::MatTraitConst, e: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorD_const_MatR_const_MatExprR(m.as_raw_Mat(), e.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn div_mat_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorD_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn div_mat_f64(a: &impl core::MatTraitConst, s: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorD_const_MatR_double(a.as_raw_Mat(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn div_f64_matexpr(s: f64, e: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorD_double_const_MatExprR(s, e.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn div_f64_mat(s: f64, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorD_double_const_MatR(s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn equals_filenodeiterator_filenodeiterator(it1: &impl core::FileNodeIteratorTraitConst, it2: &impl core::FileNodeIteratorTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorEQ_const_FileNodeIteratorR_const_FileNodeIteratorR(it1.as_raw_FileNodeIterator(), it2.as_raw_FileNodeIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn equals_mat_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorEQ_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn equals_mat_f64(a: &impl core::MatTraitConst, s: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorEQ_const_MatR_double(a.as_raw_Mat(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn equals_f64_mat(s: f64, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorEQ_double_const_MatR(s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn greater_than_or_equal_mat_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorGE_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn greater_than_or_equal_mat_f64(a: &impl core::MatTraitConst, s: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorGE_const_MatR_double(a.as_raw_Mat(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn greater_than_or_equal_f64_mat(s: f64, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorGE_double_const_MatR(s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn greater_than_mat_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorG_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn greater_than_mat_f64(a: &impl core::MatTraitConst, s: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorG_const_MatR_double(a.as_raw_Mat(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn greater_than_f64_mat(s: f64, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorG_double_const_MatR(s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn less_than_or_equal_mat_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorLE_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn less_than_or_equal_mat_f64(a: &impl core::MatTraitConst, s: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorLE_const_MatR_double(a.as_raw_Mat(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn less_than_or_equal_f64_mat(s: f64, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorLE_double_const_MatR(s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn less_than_mat_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorL_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn less_than_mat_f64(a: &impl core::MatTraitConst, s: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorL_const_MatR_double(a.as_raw_Mat(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn less_than_f64_mat(s: f64, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorL_double_const_MatR(s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn not_equals_filenodeiterator_filenodeiterator(it1: &impl core::FileNodeIteratorTraitConst, it2: &impl core::FileNodeIteratorTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorNE_const_FileNodeIteratorR_const_FileNodeIteratorR(it1.as_raw_FileNodeIterator(), it2.as_raw_FileNodeIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn not_equals_mat_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorNE_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn not_equals_mat_f64(a: &impl core::MatTraitConst, s: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorNE_const_MatR_double(a.as_raw_Mat(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn not_equals_f64_mat(s: f64, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorNE_double_const_MatR(s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn negate(m: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorNOTB_const_MatR(m.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn or_mat_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorOR_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn or_mat_scalar(a: &impl core::MatTraitConst, s: core::Scalar) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorOR_const_MatR_const_ScalarR(a.as_raw_Mat(), &s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn or_scalar_mat(s: core::Scalar, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorOR_const_ScalarR_const_MatR(&s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn and_mat_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorR_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn and_mat_scalar(a: &impl core::MatTraitConst, s: core::Scalar) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorR_const_MatR_const_ScalarR(a.as_raw_Mat(), &s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn and_scalar_mat(s: core::Scalar, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorR_const_ScalarR_const_MatR(&s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn sub_matexpr(e: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorS_const_MatExprR(e.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn sub_matexpr_matexpr(e1: &impl core::MatExprTraitConst, e2: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorS_const_MatExprR_const_MatExprR(e1.as_raw_MatExpr(), e2.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn sub_matexpr_mat(e: &impl core::MatExprTraitConst, m: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorS_const_MatExprR_const_MatR(e.as_raw_MatExpr(), m.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn sub_matexpr_scalar(e: &impl core::MatExprTraitConst, s: core::Scalar) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorS_const_MatExprR_const_ScalarR(e.as_raw_MatExpr(), &s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn sub_mat(m: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorS_const_MatR(m.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn sub_mat_matexpr(m: &impl core::MatTraitConst, e: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorS_const_MatR_const_MatExprR(m.as_raw_Mat(), e.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn sub_mat_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorS_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn sub_mat_scalar(a: &impl core::MatTraitConst, s: core::Scalar) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorS_const_MatR_const_ScalarR(a.as_raw_Mat(), &s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn sub_scalar_matexpr(s: core::Scalar, e: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorS_const_ScalarR_const_MatExprR(&s, e.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn sub_scalar_mat(s: core::Scalar, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorS_const_ScalarR_const_MatR(&s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn xor_mat_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorXOR_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn xor_mat_scalar(a: &impl core::MatTraitConst, s: core::Scalar) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorXOR_const_MatR_const_ScalarR(a.as_raw_Mat(), &s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn xor_scalar_mat(s: core::Scalar, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorXOR_const_ScalarR_const_MatR(&s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn mul_matexpr_matexpr(e1: &impl core::MatExprTraitConst, e2: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorX_const_MatExprR_const_MatExprR(e1.as_raw_MatExpr(), e2.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn mul_matexpr_mat(e: &impl core::MatExprTraitConst, m: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorX_const_MatExprR_const_MatR(e.as_raw_MatExpr(), m.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn mul_matexpr_f64(e: &impl core::MatExprTraitConst, s: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorX_const_MatExprR_double(e.as_raw_MatExpr(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn mul_mat_matexpr(m: &impl core::MatTraitConst, e: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorX_const_MatR_const_MatExprR(m.as_raw_Mat(), e.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn mul_mat_mat(a: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorX_const_MatR_const_MatR(a.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn mul_mat_f64(a: &impl core::MatTraitConst, s: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorX_const_MatR_double(a.as_raw_Mat(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn mul_f64_matexpr(s: f64, e: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorX_double_const_MatExprR(s, e.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn mul_f64_mat(s: f64, a: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_operatorX_double_const_MatR(s, a.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Parallel data processor + /// + /// @ingroup core_parallel + /// + /// ## Note + /// This alternative version of [parallel_for_] function uses the following default values for its arguments: + /// * nstripes: -1. + #[inline] + pub fn parallel_for__def(range: &impl core::RangeTraitConst, body: &impl core::ParallelLoopBodyTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_parallel_for__const_RangeR_const_ParallelLoopBodyR(range.as_raw_Range(), body.as_raw_ParallelLoopBody(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Parallel data processor + /// + /// @ingroup core_parallel + /// + /// ## C++ default parameters + /// * nstripes: -1. + #[inline] + pub fn parallel_for_(range: &impl core::RangeTraitConst, body: &impl core::ParallelLoopBodyTraitConst, nstripes: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_parallel_for__const_RangeR_const_ParallelLoopBodyR_double(range.as_raw_Range(), body.as_raw_ParallelLoopBody(), nstripes, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Replaces NaNs (Not-a-Number values) in a matrix with the specified value. + /// + /// This function modifies the input matrix in-place. + /// The input matrix must be of type `CV_32F` or `CV_64F`; other types are not supported. + /// + /// ## Parameters + /// * a: Input/output matrix (CV_32F or CV_64F type). + /// * val: Value used to replace NaNs (defaults to 0). + /// + /// ## Note + /// This alternative version of [patch_na_ns] function uses the following default values for its arguments: + /// * val: 0 + #[inline] + pub fn patch_na_ns_def(a: &mut impl ToInputOutputArray) -> Result<()> { + input_output_array_arg!(a); + return_send!(via ocvrs_return); + unsafe { sys::cv_patchNaNs_const__InputOutputArrayR(a.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Replaces NaNs (Not-a-Number values) in a matrix with the specified value. + /// + /// This function modifies the input matrix in-place. + /// The input matrix must be of type `CV_32F` or `CV_64F`; other types are not supported. + /// + /// ## Parameters + /// * a: Input/output matrix (CV_32F or CV_64F type). + /// * val: Value used to replace NaNs (defaults to 0). + /// + /// ## C++ default parameters + /// * val: 0 + #[inline] + pub fn patch_na_ns(a: &mut impl ToInputOutputArray, val: f64) -> Result<()> { + input_output_array_arg!(a); + return_send!(via ocvrs_return); + unsafe { sys::cv_patchNaNs_const__InputOutputArrayR_double(a.as_raw__InputOutputArray(), val, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs the perspective matrix transformation of vectors. + /// + /// The function cv::perspectiveTransform transforms every element of src by + /// treating it as a 2D or 3D vector, in the following way: + /// ![block formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%2C%20z%29%20%20%5Crightarrow%20%28x%27%2Fw%2C%20y%27%2Fw%2C%20z%27%2Fw%29) + /// where + /// ![block formula](https://latex.codecogs.com/png.latex?%28x%27%2C%20y%27%2C%20z%27%2C%20w%27%29%20%3D%20%20%5Ctexttt%7Bmat%7D%20%5Ccdot%20%5Cbegin%7Bbmatrix%7D%20x%20%26%20y%20%26%20z%20%26%201%20%20%5Cend%7Bbmatrix%7D) + /// and + /// ![block formula](https://latex.codecogs.com/png.latex?w%20%3D%20%20%5Cleft%5C%7B%20%5Cbegin%7Barray%7D%7Bl%20l%7D%20w%27%20%26%20%5Cmbox%7Bif%20%5C%28w%27%20%5Cne%200%5C%29%7D%5C%5C%20%5Cinfty%20%26%20%5Cmbox%7Botherwise%7D%5C%5C%20%5Cend%7Barray%7D%20%5Cright%2E) + /// + /// Here a 3D vector transformation is shown. In case of a 2D vector + /// transformation, the z component is omitted. + /// + /// + /// Note: The function transforms a sparse set of 2D or 3D vectors. If you + /// want to transform an image using perspective transformation, use + /// warpPerspective . If you have an inverse problem, that is, you want to + /// compute the most probable perspective transformation out of several + /// pairs of corresponding points, you can use getPerspectiveTransform or + /// findHomography . + /// ## Parameters + /// * src: input two-channel or three-channel floating-point array; each + /// element is a 2D/3D vector to be transformed. + /// * dst: output array of the same size and type as src. + /// * m: 3x3 or 4x4 floating-point transformation matrix. + /// ## See also + /// transform, warpPerspective, getPerspectiveTransform, findHomography + #[inline] + pub fn perspective_transform(src: &impl ToInputArray, dst: &mut impl ToOutputArray, m: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_perspectiveTransform_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), m.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the rotation angle of 2D vectors. + /// + /// The function cv::phase calculates the rotation angle of each 2D vector that + /// is formed from the corresponding elements of x and y : + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bangle%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Batan2%7D%20%28%20%5Ctexttt%7By%7D%20%28I%29%2C%20%5Ctexttt%7Bx%7D%20%28I%29%29) + /// + /// The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 , + /// the corresponding angle(I) is set to 0. + /// ## Parameters + /// * x: input floating-point array of x-coordinates of 2D vectors. + /// * y: input array of y-coordinates of 2D vectors; it must have the + /// same size and the same type as x. + /// * angle: output array of vector angles; it has the same size and + /// same type as x . + /// * angleInDegrees: when true, the function calculates the angle in + /// degrees, otherwise, they are measured in radians. + /// + /// ## Note + /// This alternative version of [phase] function uses the following default values for its arguments: + /// * angle_in_degrees: false + #[inline] + pub fn phase_def(x: &impl ToInputArray, y: &impl ToInputArray, angle: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(x); + input_array_arg!(y); + output_array_arg!(angle); + return_send!(via ocvrs_return); + unsafe { sys::cv_phase_const__InputArrayR_const__InputArrayR_const__OutputArrayR(x.as_raw__InputArray(), y.as_raw__InputArray(), angle.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the rotation angle of 2D vectors. + /// + /// The function cv::phase calculates the rotation angle of each 2D vector that + /// is formed from the corresponding elements of x and y : + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bangle%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Batan2%7D%20%28%20%5Ctexttt%7By%7D%20%28I%29%2C%20%5Ctexttt%7Bx%7D%20%28I%29%29) + /// + /// The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 , + /// the corresponding angle(I) is set to 0. + /// ## Parameters + /// * x: input floating-point array of x-coordinates of 2D vectors. + /// * y: input array of y-coordinates of 2D vectors; it must have the + /// same size and the same type as x. + /// * angle: output array of vector angles; it has the same size and + /// same type as x . + /// * angleInDegrees: when true, the function calculates the angle in + /// degrees, otherwise, they are measured in radians. + /// + /// ## C++ default parameters + /// * angle_in_degrees: false + #[inline] + pub fn phase(x: &impl ToInputArray, y: &impl ToInputArray, angle: &mut impl ToOutputArray, angle_in_degrees: bool) -> Result<()> { + input_array_arg!(x); + input_array_arg!(y); + output_array_arg!(angle); + return_send!(via ocvrs_return); + unsafe { sys::cv_phase_const__InputArrayR_const__InputArrayR_const__OutputArrayR_bool(x.as_raw__InputArray(), y.as_raw__InputArray(), angle.as_raw__OutputArray(), angle_in_degrees, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates x and y coordinates of 2D vectors from their magnitude and angle. + /// + /// The function cv::polarToCart calculates the Cartesian coordinates of each 2D + /// vector represented by the corresponding elements of magnitude and angle: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20%5Ctexttt%7Bx%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bmagnitude%7D%20%28I%29%20%5Ccos%20%28%20%5Ctexttt%7Bangle%7D%20%28I%29%29%20%5C%5C%20%5Ctexttt%7By%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bmagnitude%7D%20%28I%29%20%5Csin%20%28%20%5Ctexttt%7Bangle%7D%20%28I%29%29%20%5C%5C%20%5Cend%7Barray%7D) + /// + /// The relative accuracy of the estimated coordinates is about 1e-6. + /// ## Parameters + /// * magnitude: input floating-point array of magnitudes of 2D vectors; + /// it can be an empty matrix (=Mat()), in this case, the function assumes + /// that all the magnitudes are =1; if it is not empty, it must have the + /// same size and type as angle. + /// * angle: input floating-point array of angles of 2D vectors. + /// * x: output array of x-coordinates of 2D vectors; it has the same + /// size and type as angle. + /// * y: output array of y-coordinates of 2D vectors; it has the same + /// size and type as angle. + /// * angleInDegrees: when true, the input angles are measured in + /// degrees, otherwise, they are measured in radians. + /// ## See also + /// cartToPolar, magnitude, phase, exp, log, pow, sqrt + /// + /// ## Note + /// This alternative version of [polar_to_cart] function uses the following default values for its arguments: + /// * angle_in_degrees: false + #[inline] + pub fn polar_to_cart_def(magnitude: &impl ToInputArray, angle: &impl ToInputArray, x: &mut impl ToOutputArray, y: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(magnitude); + input_array_arg!(angle); + output_array_arg!(x); + output_array_arg!(y); + return_send!(via ocvrs_return); + unsafe { sys::cv_polarToCart_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(magnitude.as_raw__InputArray(), angle.as_raw__InputArray(), x.as_raw__OutputArray(), y.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates x and y coordinates of 2D vectors from their magnitude and angle. + /// + /// The function cv::polarToCart calculates the Cartesian coordinates of each 2D + /// vector represented by the corresponding elements of magnitude and angle: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20%5Ctexttt%7Bx%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bmagnitude%7D%20%28I%29%20%5Ccos%20%28%20%5Ctexttt%7Bangle%7D%20%28I%29%29%20%5C%5C%20%5Ctexttt%7By%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bmagnitude%7D%20%28I%29%20%5Csin%20%28%20%5Ctexttt%7Bangle%7D%20%28I%29%29%20%5C%5C%20%5Cend%7Barray%7D) + /// + /// The relative accuracy of the estimated coordinates is about 1e-6. + /// ## Parameters + /// * magnitude: input floating-point array of magnitudes of 2D vectors; + /// it can be an empty matrix (=Mat()), in this case, the function assumes + /// that all the magnitudes are =1; if it is not empty, it must have the + /// same size and type as angle. + /// * angle: input floating-point array of angles of 2D vectors. + /// * x: output array of x-coordinates of 2D vectors; it has the same + /// size and type as angle. + /// * y: output array of y-coordinates of 2D vectors; it has the same + /// size and type as angle. + /// * angleInDegrees: when true, the input angles are measured in + /// degrees, otherwise, they are measured in radians. + /// ## See also + /// cartToPolar, magnitude, phase, exp, log, pow, sqrt + /// + /// ## C++ default parameters + /// * angle_in_degrees: false + #[inline] + pub fn polar_to_cart(magnitude: &impl ToInputArray, angle: &impl ToInputArray, x: &mut impl ToOutputArray, y: &mut impl ToOutputArray, angle_in_degrees: bool) -> Result<()> { + input_array_arg!(magnitude); + input_array_arg!(angle); + output_array_arg!(x); + output_array_arg!(y); + return_send!(via ocvrs_return); + unsafe { sys::cv_polarToCart_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_bool(magnitude.as_raw__InputArray(), angle.as_raw__InputArray(), x.as_raw__OutputArray(), y.as_raw__OutputArray(), angle_in_degrees, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Raises every array element to a power. + /// + /// The function cv::pow raises every element of the input array to power : + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Cfork%7B%5Ctexttt%7Bsrc%7D%28I%29%5E%7Bpower%7D%7D%7Bif%20%5C%28%5Ctexttt%7Bpower%7D%5C%29%20is%20integer%7D%7B%7C%5Ctexttt%7Bsrc%7D%28I%29%7C%5E%7Bpower%7D%7D%7Botherwise%7D) + /// + /// So, for a non-integer power exponent, the absolute values of input array + /// elements are used. However, it is possible to get true values for + /// negative values using some extra operations. In the example below, + /// computing the 5th root of array src shows: + /// ```C++ + /// Mat mask = src < 0; + /// pow(src, 1./5, dst); + /// subtract(Scalar::all(0), dst, dst, mask); + /// ``` + /// + /// For some values of power, such as integer values, 0.5 and -0.5, + /// specialized faster algorithms are used. + /// + /// Special values (NaN, Inf) are not handled. + /// ## Parameters + /// * src: input array. + /// * power: exponent of power. + /// * dst: output array of the same size and type as src. + /// ## See also + /// sqrt, exp, log, cartToPolar, polarToCart + #[inline] + pub fn pow(src: &impl ToInputArray, power: f64, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_pow_const__InputArrayR_double_const__OutputArrayR(src.as_raw__InputArray(), power, dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fills the array with normally distributed random numbers. + /// + /// The function cv::randn fills the matrix dst with normally distributed random numbers with the specified + /// mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the + /// value range of the output array data type. + /// ## Parameters + /// * dst: output array of random numbers; the array must be pre-allocated and have 1 to 4 channels. + /// * mean: mean value (expectation) of the generated random numbers. + /// * stddev: standard deviation of the generated random numbers; it can be either a vector (in + /// which case a diagonal standard deviation matrix is assumed) or a square matrix. + /// ## See also + /// RNG, randu + #[inline] + pub fn randn(dst: &mut impl ToInputOutputArray, mean: &impl ToInputArray, stddev: &impl ToInputArray) -> Result<()> { + input_output_array_arg!(dst); + input_array_arg!(mean); + input_array_arg!(stddev); + return_send!(via ocvrs_return); + unsafe { sys::cv_randn_const__InputOutputArrayR_const__InputArrayR_const__InputArrayR(dst.as_raw__InputOutputArray(), mean.as_raw__InputArray(), stddev.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Generates a single uniformly-distributed random number or an array of random numbers. + /// + /// Non-template variant of the function fills the matrix dst with uniformly-distributed + /// random numbers from the specified range: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Blow%7D%20%5Fc%20%20%5Cleq%20%5Ctexttt%7Bdst%7D%20%28I%29%5Fc%20%3C%20%20%5Ctexttt%7Bhigh%7D%20%5Fc) + /// ## Parameters + /// * dst: output array of random numbers; the array must be pre-allocated. + /// * low: inclusive lower boundary of the generated random numbers. + /// * high: exclusive upper boundary of the generated random numbers. + /// ## See also + /// RNG, randn, theRNG + #[inline] + pub fn randu(dst: &mut impl ToInputOutputArray, low: &impl ToInputArray, high: &impl ToInputArray) -> Result<()> { + input_output_array_arg!(dst); + input_array_arg!(low); + input_array_arg!(high); + return_send!(via ocvrs_return); + unsafe { sys::cv_randu_const__InputOutputArrayR_const__InputArrayR_const__InputArrayR(dst.as_raw__InputOutputArray(), low.as_raw__InputArray(), high.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn read_dmatch(node: &impl core::FileNodeTraitConst, value: &mut core::DMatch, default_value: core::DMatch) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_read_const_FileNodeR_DMatchR_const_DMatchR(node.as_raw_FileNode(), value, &default_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn read_keypoint(node: &impl core::FileNodeTraitConst, value: &mut impl core::KeyPointTrait, default_value: &impl core::KeyPointTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_read_const_FileNodeR_KeyPointR_const_KeyPointR(node.as_raw_FileNode(), value.as_raw_mut_KeyPoint(), default_value.as_raw_KeyPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [read_mat] function uses the following default values for its arguments: + /// * default_mat: Mat() + #[inline] + pub fn read_mat_def(node: &impl core::FileNodeTraitConst, mat: &mut impl core::MatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_read_const_FileNodeR_MatR(node.as_raw_FileNode(), mat.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * default_mat: Mat() + #[inline] + pub fn read_mat(node: &impl core::FileNodeTraitConst, mat: &mut impl core::MatTrait, default_mat: &impl core::MatTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_read_const_FileNodeR_MatR_const_MatR(node.as_raw_FileNode(), mat.as_raw_mut_Mat(), default_mat.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [read_sparsemat] function uses the following default values for its arguments: + /// * default_mat: SparseMat() + #[inline] + pub fn read_sparsemat_def(node: &impl core::FileNodeTraitConst, mat: &mut impl core::SparseMatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_read_const_FileNodeR_SparseMatR(node.as_raw_FileNode(), mat.as_raw_mut_SparseMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * default_mat: SparseMat() + #[inline] + pub fn read_sparsemat(node: &impl core::FileNodeTraitConst, mat: &mut impl core::SparseMatTrait, default_mat: &impl core::SparseMatTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_read_const_FileNodeR_SparseMatR_const_SparseMatR(node.as_raw_FileNode(), mat.as_raw_mut_SparseMat(), default_mat.as_raw_SparseMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn read_f64(node: &impl core::FileNodeTraitConst, value: &mut f64, default_value: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_read_const_FileNodeR_doubleR_double(node.as_raw_FileNode(), value, default_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn read_f32(node: &impl core::FileNodeTraitConst, value: &mut f32, default_value: f32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_read_const_FileNodeR_floatR_float(node.as_raw_FileNode(), value, default_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn read(node: &impl core::FileNodeTraitConst, value: &mut i64, default_value: i64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_read_const_FileNodeR_int64_tR_int64_t(node.as_raw_FileNode(), value, default_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn read_i32(node: &impl core::FileNodeTraitConst, value: &mut i32, default_value: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_read_const_FileNodeR_intR_int(node.as_raw_FileNode(), value, default_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn read_str(node: &impl core::FileNodeTraitConst, value: &mut String, default_value: &str) -> Result<()> { + string_arg_output_send!(via value_via); + extern_container_arg!(default_value); + return_send!(via ocvrs_return); + unsafe { sys::cv_read_const_FileNodeR_stringR_const_stringR(node.as_raw_FileNode(), &mut value_via, default_value.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + string_arg_output_receive!(value_via => value); + Ok(ret) + } + + #[inline] + pub fn read_dmatch_vec_legacy(node: &impl core::FileNodeTraitConst, matches: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_read_const_FileNodeR_vectorLDMatchGR(node.as_raw_FileNode(), matches.as_raw_mut_VectorOfDMatch(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn read_keypoint_vec_legacy(node: &impl core::FileNodeTraitConst, keypoints: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_read_const_FileNodeR_vectorLKeyPointGR(node.as_raw_FileNode(), keypoints.as_raw_mut_VectorOfKeyPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds out if there is any intersection between two rectangles + /// + /// mainly useful for language bindings + /// ## Parameters + /// * a: First rectangle + /// * b: Second rectangle + /// ## Returns + /// the area of the intersection + #[inline] + pub fn rectangle_intersection_area(a: core::Rect2d, b: core::Rect2d) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_rectangleIntersectionArea_const_Rect2dR_const_Rect2dR(&a, &b, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds indices of max elements along provided axis + /// + /// + /// Note: + /// - If input or output array is not continuous, this function will create an internal copy. + /// - NaN handling is left unspecified, see patchNaNs(). + /// - The returned index is always in bounds of input matrix. + /// + /// ## Parameters + /// * src: input single-channel array. + /// * dst: output array of type CV_32SC1 with the same dimensionality as src, + /// except for axis being reduced - it should be set to 1. + /// * lastIndex: whether to get the index of first or last occurrence of max. + /// * axis: axis to reduce along. + /// ## See also + /// reduceArgMin, minMaxLoc, min, max, compare, reduce + /// + /// ## Note + /// This alternative version of [reduce_arg_max] function uses the following default values for its arguments: + /// * last_index: false + #[inline] + pub fn reduce_arg_max_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, axis: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_reduceArgMax_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), axis, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds indices of max elements along provided axis + /// + /// + /// Note: + /// - If input or output array is not continuous, this function will create an internal copy. + /// - NaN handling is left unspecified, see patchNaNs(). + /// - The returned index is always in bounds of input matrix. + /// + /// ## Parameters + /// * src: input single-channel array. + /// * dst: output array of type CV_32SC1 with the same dimensionality as src, + /// except for axis being reduced - it should be set to 1. + /// * lastIndex: whether to get the index of first or last occurrence of max. + /// * axis: axis to reduce along. + /// ## See also + /// reduceArgMin, minMaxLoc, min, max, compare, reduce + /// + /// ## C++ default parameters + /// * last_index: false + #[inline] + pub fn reduce_arg_max(src: &impl ToInputArray, dst: &mut impl ToOutputArray, axis: i32, last_index: bool) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_reduceArgMax_const__InputArrayR_const__OutputArrayR_int_bool(src.as_raw__InputArray(), dst.as_raw__OutputArray(), axis, last_index, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds indices of min elements along provided axis + /// + /// + /// Note: + /// - If input or output array is not continuous, this function will create an internal copy. + /// - NaN handling is left unspecified, see patchNaNs(). + /// - The returned index is always in bounds of input matrix. + /// + /// ## Parameters + /// * src: input single-channel array. + /// * dst: output array of type CV_32SC1 with the same dimensionality as src, + /// except for axis being reduced - it should be set to 1. + /// * lastIndex: whether to get the index of first or last occurrence of min. + /// * axis: axis to reduce along. + /// ## See also + /// reduceArgMax, minMaxLoc, min, max, compare, reduce + /// + /// ## Note + /// This alternative version of [reduce_arg_min] function uses the following default values for its arguments: + /// * last_index: false + #[inline] + pub fn reduce_arg_min_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, axis: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_reduceArgMin_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), axis, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds indices of min elements along provided axis + /// + /// + /// Note: + /// - If input or output array is not continuous, this function will create an internal copy. + /// - NaN handling is left unspecified, see patchNaNs(). + /// - The returned index is always in bounds of input matrix. + /// + /// ## Parameters + /// * src: input single-channel array. + /// * dst: output array of type CV_32SC1 with the same dimensionality as src, + /// except for axis being reduced - it should be set to 1. + /// * lastIndex: whether to get the index of first or last occurrence of min. + /// * axis: axis to reduce along. + /// ## See also + /// reduceArgMax, minMaxLoc, min, max, compare, reduce + /// + /// ## C++ default parameters + /// * last_index: false + #[inline] + pub fn reduce_arg_min(src: &impl ToInputArray, dst: &mut impl ToOutputArray, axis: i32, last_index: bool) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_reduceArgMin_const__InputArrayR_const__OutputArrayR_int_bool(src.as_raw__InputArray(), dst.as_raw__OutputArray(), axis, last_index, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Reduces a matrix to a vector. + /// + /// The function [reduce] reduces the matrix to a vector by treating the matrix rows/columns as a set of + /// 1D vectors and performing the specified operation on the vectors until a single row/column is + /// obtained. For example, the function can be used to compute horizontal and vertical projections of a + /// raster image. In case of [REDUCE_MAX] and #REDUCE_MIN, the output image should have the same type as the source one. + /// In case of #REDUCE_SUM, [REDUCE_SUM2] and #REDUCE_AVG, the output may have a larger element bit-depth to preserve accuracy. + /// And multi-channel arrays are also supported in these two reduction modes. + /// + /// The following code demonstrates its usage for a single channel matrix. + /// [example](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_reduce.cpp#L1) + /// + /// And the following code demonstrates its usage for a two-channel matrix. + /// [example2](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_reduce.cpp#L1) + /// + /// ## Parameters + /// * src: input 2D matrix. + /// * dst: output vector. Its size and type is defined by dim and dtype parameters. + /// * dim: dimension index along which the matrix is reduced. 0 means that the matrix is reduced to + /// a single row. 1 means that the matrix is reduced to a single column. + /// * rtype: reduction operation that could be one of [reduce_types] + /// * dtype: when negative, the output vector will have the same type as the input matrix, + /// otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()). + /// ## See also + /// repeat, reduceArgMin, reduceArgMax + /// + /// ## Note + /// This alternative version of [reduce] function uses the following default values for its arguments: + /// * dtype: -1 + #[inline] + pub fn reduce_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, dim: i32, rtype: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_reduce_const__InputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), dim, rtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Reduces a matrix to a vector. + /// + /// The function [reduce] reduces the matrix to a vector by treating the matrix rows/columns as a set of + /// 1D vectors and performing the specified operation on the vectors until a single row/column is + /// obtained. For example, the function can be used to compute horizontal and vertical projections of a + /// raster image. In case of [REDUCE_MAX] and #REDUCE_MIN, the output image should have the same type as the source one. + /// In case of #REDUCE_SUM, [REDUCE_SUM2] and #REDUCE_AVG, the output may have a larger element bit-depth to preserve accuracy. + /// And multi-channel arrays are also supported in these two reduction modes. + /// + /// The following code demonstrates its usage for a single channel matrix. + /// [example](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_reduce.cpp#L1) + /// + /// And the following code demonstrates its usage for a two-channel matrix. + /// [example2](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_reduce.cpp#L1) + /// + /// ## Parameters + /// * src: input 2D matrix. + /// * dst: output vector. Its size and type is defined by dim and dtype parameters. + /// * dim: dimension index along which the matrix is reduced. 0 means that the matrix is reduced to + /// a single row. 1 means that the matrix is reduced to a single column. + /// * rtype: reduction operation that could be one of [reduce_types] + /// * dtype: when negative, the output vector will have the same type as the input matrix, + /// otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()). + /// ## See also + /// repeat, reduceArgMin, reduceArgMax + /// + /// ## C++ default parameters + /// * dtype: -1 + #[inline] + pub fn reduce(src: &impl ToInputArray, dst: &mut impl ToOutputArray, dim: i32, rtype: i32, dtype: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_reduce_const__InputArrayR_const__OutputArrayR_int_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), dim, rtype, dtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fills the output array with repeated copies of the input array. + /// + /// The function cv::repeat duplicates the input array one or more times along each of the two axes: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%5F%7Bij%7D%3D%20%5Ctexttt%7Bsrc%7D%20%5F%7Bi%5Cmod%20src%2Erows%2C%20%5C%3B%20j%5Cmod%20src%2Ecols%20%7D) + /// The second variant of the function is more convenient to use with [MatrixExpressions]. + /// ## Parameters + /// * src: input array to replicate. + /// * ny: Flag to specify how many times the `src` is repeated along the + /// vertical axis. + /// * nx: Flag to specify how many times the `src` is repeated along the + /// horizontal axis. + /// * dst: output array of the same type as `src`. + /// ## See also + /// cv::reduce + /// + /// ## Overloaded parameters + /// + /// * src: input array to replicate. + /// * ny: Flag to specify how many times the `src` is repeated along the + /// vertical axis. + /// * nx: Flag to specify how many times the `src` is repeated along the + /// horizontal axis. + #[inline] + pub fn repeat(src: &impl core::MatTraitConst, ny: i32, nx: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_repeat_const_MatR_int_int(src.as_raw_Mat(), ny, nx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Fills the output array with repeated copies of the input array. + /// + /// The function cv::repeat duplicates the input array one or more times along each of the two axes: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%5F%7Bij%7D%3D%20%5Ctexttt%7Bsrc%7D%20%5F%7Bi%5Cmod%20src%2Erows%2C%20%5C%3B%20j%5Cmod%20src%2Ecols%20%7D) + /// The second variant of the function is more convenient to use with [MatrixExpressions]. + /// ## Parameters + /// * src: input array to replicate. + /// * ny: Flag to specify how many times the `src` is repeated along the + /// vertical axis. + /// * nx: Flag to specify how many times the `src` is repeated along the + /// horizontal axis. + /// * dst: output array of the same type as `src`. + /// ## See also + /// cv::reduce + #[inline] + pub fn repeat_to(src: &impl ToInputArray, ny: i32, nx: i32, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_repeat_const__InputArrayR_int_int_const__OutputArrayR(src.as_raw__InputArray(), ny, nx, dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Rotates a 2D array in multiples of 90 degrees. + /// The function cv::rotate rotates the array in one of three different ways: + /// * Rotate by 90 degrees clockwise (rotateCode = ROTATE_90_CLOCKWISE). + /// * Rotate by 180 degrees clockwise (rotateCode = ROTATE_180). + /// * Rotate by 270 degrees clockwise (rotateCode = ROTATE_90_COUNTERCLOCKWISE). + /// ## Parameters + /// * src: input array. + /// * dst: output array of the same type as src. The size is the same with ROTATE_180, + /// and the rows and cols are switched for ROTATE_90_CLOCKWISE and ROTATE_90_COUNTERCLOCKWISE. + /// * rotateCode: an enum to specify how to rotate the array; see the enum [rotate_flags] + /// ## See also + /// transpose, repeat, completeSymm, flip, RotateFlags + #[inline] + pub fn rotate(src: &impl ToInputArray, dst: &mut impl ToOutputArray, rotate_code: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_rotate_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), rotate_code, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Override search data path by adding new search location + /// + /// Use this only to override default behavior + /// Passed paths are used in LIFO order. + /// + /// ## Parameters + /// * path: Path to used samples data + #[inline] + pub fn add_samples_data_search_path(path: &str) -> Result<()> { + extern_container_arg!(path); + return_send!(via ocvrs_return); + unsafe { sys::cv_samples_addSamplesDataSearchPath_const_StringR(path.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Append samples search data sub directory + /// + /// General usage is to add OpenCV modules name (`/modules//samples/data` -> `/samples/data` + `modules//samples/data`). + /// Passed subdirectories are used in LIFO order. + /// + /// ## Parameters + /// * subdir: samples data sub directory + #[inline] + pub fn add_samples_data_search_sub_directory(subdir: &str) -> Result<()> { + extern_container_arg!(subdir); + return_send!(via ocvrs_return); + unsafe { sys::cv_samples_addSamplesDataSearchSubDirectory_const_StringR(subdir.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [find_file_or_keep] function uses the following default values for its arguments: + /// * silent_mode: false + #[inline] + pub fn find_file_or_keep_def(relative_path: &str) -> Result { + extern_container_arg!(relative_path); + return_send!(via ocvrs_return); + unsafe { sys::cv_samples_findFileOrKeep_const_StringR(relative_path.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * silent_mode: false + #[inline] + pub fn find_file_or_keep(relative_path: &str, silent_mode: bool) -> Result { + extern_container_arg!(relative_path); + return_send!(via ocvrs_return); + unsafe { sys::cv_samples_findFileOrKeep_const_StringR_bool(relative_path.opencv_as_extern(), silent_mode, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Try to find requested data file + /// + /// Search directories: + /// + /// 1. Directories passed via `addSamplesDataSearchPath()` + /// 2. OPENCV_SAMPLES_DATA_PATH_HINT environment variable + /// 3. OPENCV_SAMPLES_DATA_PATH environment variable + /// If parameter value is not empty and nothing is found then stop searching. + /// 4. Detects build/install path based on: + /// a. current working directory (CWD) + /// b. and/or binary module location (opencv_core/opencv_world, doesn't work with static linkage) + /// 5. Scan `/{,data,samples/data}` directories if build directory is detected or the current directory is in source tree. + /// 6. Scan `/share/OpenCV` directory if install directory is detected. + /// ## See also + /// cv::utils::findDataFile + /// + /// ## Parameters + /// * relative_path: Relative path to data file + /// * required: Specify "file not found" handling. + /// If true, function prints information message and raises cv::Exception. + /// If false, function returns empty result + /// * silentMode: Disables messages + /// ## Returns + /// Returns path (absolute or relative to the current directory) or empty string if file is not found + /// + /// ## Note + /// This alternative version of [find_file] function uses the following default values for its arguments: + /// * required: true + /// * silent_mode: false + #[inline] + pub fn find_file_def(relative_path: &str) -> Result { + extern_container_arg!(relative_path); + return_send!(via ocvrs_return); + unsafe { sys::cv_samples_findFile_const_StringR(relative_path.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Try to find requested data file + /// + /// Search directories: + /// + /// 1. Directories passed via `addSamplesDataSearchPath()` + /// 2. OPENCV_SAMPLES_DATA_PATH_HINT environment variable + /// 3. OPENCV_SAMPLES_DATA_PATH environment variable + /// If parameter value is not empty and nothing is found then stop searching. + /// 4. Detects build/install path based on: + /// a. current working directory (CWD) + /// b. and/or binary module location (opencv_core/opencv_world, doesn't work with static linkage) + /// 5. Scan `/{,data,samples/data}` directories if build directory is detected or the current directory is in source tree. + /// 6. Scan `/share/OpenCV` directory if install directory is detected. + /// ## See also + /// cv::utils::findDataFile + /// + /// ## Parameters + /// * relative_path: Relative path to data file + /// * required: Specify "file not found" handling. + /// If true, function prints information message and raises cv::Exception. + /// If false, function returns empty result + /// * silentMode: Disables messages + /// ## Returns + /// Returns path (absolute or relative to the current directory) or empty string if file is not found + /// + /// ## C++ default parameters + /// * required: true + /// * silent_mode: false + #[inline] + pub fn find_file(relative_path: &str, required: bool, silent_mode: bool) -> Result { + extern_container_arg!(relative_path); + return_send!(via ocvrs_return); + unsafe { sys::cv_samples_findFile_const_StringR_bool_bool(relative_path.opencv_as_extern(), required, silent_mode, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Calculates the sum of a scaled array and another array. + /// + /// The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY + /// or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates + /// the sum of a scaled array and another array: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%3D%20%5Ctexttt%7Bscale%7D%20%5Ccdot%20%5Ctexttt%7Bsrc1%7D%20%28I%29%20%2B%20%20%5Ctexttt%7Bsrc2%7D%20%28I%29) + /// The function can also be emulated with a matrix expression, for example: + /// ```C++ + /// Mat A(3, 3, CV_64F); + /// ... + /// A.row(0) = A.row(1)*2 + A.row(2); + /// ``` + /// + /// ## Parameters + /// * src1: first input array. + /// * alpha: scale factor for the first array. + /// * src2: second input array of the same size and type as src1. + /// * dst: output array of the same size and type as src1. + /// ## See also + /// add, addWeighted, subtract, Mat::dot, Mat::convertTo + #[inline] + pub fn scale_add(src1: &impl ToInputArray, alpha: f64, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_scaleAdd_const__InputArrayR_double_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), alpha, src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets/resets the break-on-error mode. + /// + /// When the break-on-error mode is set, the default error handler issues a hardware exception, which + /// can make debugging more convenient. + /// + /// \return the previous state + #[inline] + pub fn set_break_on_error(flag: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_setBreakOnError_bool(flag, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Initializes a scaled identity matrix. + /// + /// The function cv::setIdentity initializes a scaled identity matrix: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bmtx%7D%20%28i%2Cj%29%3D%20%5Cfork%7B%5Ctexttt%7Bvalue%7D%7D%7B%20if%20%5C%28i%3Dj%5C%29%7D%7B0%7D%7Botherwise%7D) + /// + /// The function can also be emulated using the matrix initializers and the + /// matrix expressions: + /// ```C++ + /// Mat A = Mat::eye(4, 3, CV_32F)*5; + /// // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]] + /// ``` + /// + /// ## Parameters + /// * mtx: matrix to initialize (not necessarily square). + /// * s: value to assign to diagonal elements. + /// ## See also + /// Mat::zeros, Mat::ones, Mat::setTo, Mat::operator= + /// + /// ## Note + /// This alternative version of [set_identity] function uses the following default values for its arguments: + /// * s: Scalar(1) + #[inline] + pub fn set_identity_def(mtx: &mut impl ToInputOutputArray) -> Result<()> { + input_output_array_arg!(mtx); + return_send!(via ocvrs_return); + unsafe { sys::cv_setIdentity_const__InputOutputArrayR(mtx.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Initializes a scaled identity matrix. + /// + /// The function cv::setIdentity initializes a scaled identity matrix: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bmtx%7D%20%28i%2Cj%29%3D%20%5Cfork%7B%5Ctexttt%7Bvalue%7D%7D%7B%20if%20%5C%28i%3Dj%5C%29%7D%7B0%7D%7Botherwise%7D) + /// + /// The function can also be emulated using the matrix initializers and the + /// matrix expressions: + /// ```C++ + /// Mat A = Mat::eye(4, 3, CV_32F)*5; + /// // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]] + /// ``` + /// + /// ## Parameters + /// * mtx: matrix to initialize (not necessarily square). + /// * s: value to assign to diagonal elements. + /// ## See also + /// Mat::zeros, Mat::ones, Mat::setTo, Mat::operator= + /// + /// ## C++ default parameters + /// * s: Scalar(1) + #[inline] + pub fn set_identity(mtx: &mut impl ToInputOutputArray, s: core::Scalar) -> Result<()> { + input_output_array_arg!(mtx); + return_send!(via ocvrs_return); + unsafe { sys::cv_setIdentity_const__InputOutputArrayR_const_ScalarR(mtx.as_raw__InputOutputArray(), &s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// OpenCV will try to set the number of threads for subsequent parallel regions. + /// + /// If threads == 1, OpenCV will disable threading optimizations and run all it's functions + /// sequentially. Passing threads \< 0 will reset threads number to system default. + /// The function is not thread-safe. It must not be called in parallel region or concurrent threads. + /// + /// OpenCV will try to run its functions with specified threads number, but some behaviour differs from + /// framework: + /// * `TBB` - User-defined parallel constructions will run with the same threads number, if + /// another is not specified. If later on user creates his own scheduler, OpenCV will use it. + /// * `OpenMP` - No special defined behaviour. + /// * `Concurrency` - If threads == 1, OpenCV will disable threading optimizations and run its + /// functions sequentially. + /// * `GCD` - Supports only values \<= 0. + /// * `C=` - No special defined behaviour. + /// ## Parameters + /// * nthreads: Number of threads used by OpenCV. + /// ## See also + /// getNumThreads, getThreadNum + #[inline] + pub fn set_num_threads(nthreads: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_setNumThreads_int(nthreads, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets state of default random number generator. + /// + /// The function cv::setRNGSeed sets state of default random number generator to custom value. + /// ## Parameters + /// * seed: new state for default random number generator + /// ## See also + /// RNG, randu, randn + #[inline] + pub fn set_rng_seed(seed: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_setRNGSeed_int(seed, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Enable/disable use of OpenVX + #[inline] + pub fn set_use_openvx(flag: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_setUseOpenVX_bool(flag, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Enables or disables the optimized code. + /// + /// The function can be used to dynamically turn on and off optimized dispatched code (code that uses SSE4.2, AVX/AVX2, + /// and other instructions on the platforms that support it). It sets a global flag that is further + /// checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only + /// safe to call the function on the very top level in your application where you can be sure that no + /// other OpenCV function is currently executed. + /// + /// By default, the optimized code is enabled unless you disable it in CMake. The current status can be + /// retrieved using useOptimized. + /// ## Parameters + /// * onoff: The boolean flag specifying whether the optimized code should be used (onoff=true) + /// or not (onoff=false). + #[inline] + pub fn set_use_optimized(onoff: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_setUseOptimized_bool(onoff, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the real roots of a cubic equation. + /// + /// The function solveCubic finds the real roots of a cubic equation: + /// * if coeffs is a 4-element vector: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bcoeffs%7D%20%5B0%5D%20x%5E3%20%2B%20%20%5Ctexttt%7Bcoeffs%7D%20%5B1%5D%20x%5E2%20%2B%20%20%5Ctexttt%7Bcoeffs%7D%20%5B2%5D%20x%20%2B%20%20%5Ctexttt%7Bcoeffs%7D%20%5B3%5D%20%3D%200) + /// * if coeffs is a 3-element vector: + /// ![block formula](https://latex.codecogs.com/png.latex?x%5E3%20%2B%20%20%5Ctexttt%7Bcoeffs%7D%20%5B0%5D%20x%5E2%20%2B%20%20%5Ctexttt%7Bcoeffs%7D%20%5B1%5D%20x%20%2B%20%20%5Ctexttt%7Bcoeffs%7D%20%5B2%5D%20%3D%200) + /// + /// The roots are stored in the roots array. + /// ## Parameters + /// * coeffs: equation coefficients, an array of 3 or 4 elements. + /// * roots: output array of real roots that has 0, 1, 2 or 3 elements. + /// ## Returns + /// number of real roots. It can be -1 (all real numbers), 0, 1, 2 or 3. + #[inline] + pub fn solve_cubic(coeffs: &impl ToInputArray, roots: &mut impl ToOutputArray) -> Result { + input_array_arg!(coeffs); + output_array_arg!(roots); + return_send!(via ocvrs_return); + unsafe { sys::cv_solveCubic_const__InputArrayR_const__OutputArrayR(coeffs.as_raw__InputArray(), roots.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method). + /// + /// What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cmbox%7BMaximize%20%7D%20c%5Ccdot%20x%5C%5C%0A%20%5Cmbox%7BSubject%20to%3A%7D%5C%5C%0A%20Ax%5Cleq%20b%5C%5C%0A%20x%5Cgeq%200) + /// + /// Where ![inline formula](https://latex.codecogs.com/png.latex?c) is fixed `1`-by-`n` row-vector, ![inline formula](https://latex.codecogs.com/png.latex?A) is fixed `m`-by-`n` matrix, ![inline formula](https://latex.codecogs.com/png.latex?b) is fixed `m`-by-`1` + /// column vector and ![inline formula](https://latex.codecogs.com/png.latex?x) is an arbitrary `n`-by-`1` column vector, which satisfies the constraints. + /// + /// Simplex algorithm is one of many algorithms that are designed to handle this sort of problems + /// efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve + /// any problem written as above in polynomial time, while simplex method degenerates to exponential + /// time for some special cases), it is well-studied, easy to implement and is shown to work well for + /// real-life purposes. + /// + /// The particular implementation is taken almost verbatim from **Introduction to Algorithms, third + /// edition** by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the + /// Bland's rule is used to prevent cycling. + /// + /// ## Parameters + /// * Func: This row-vector corresponds to ![inline formula](https://latex.codecogs.com/png.latex?c) in the LP problem formulation (see above). It should + /// contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, + /// in the latter case it is understood to correspond to ![inline formula](https://latex.codecogs.com/png.latex?c%5ET). + /// * Constr: `m`-by-`n+1` matrix, whose rightmost column corresponds to ![inline formula](https://latex.codecogs.com/png.latex?b) in formulation above + /// and the remaining to ![inline formula](https://latex.codecogs.com/png.latex?A). It should contain 32- or 64-bit floating point numbers. + /// * z: The solution will be returned here as a column-vector - it corresponds to ![inline formula](https://latex.codecogs.com/png.latex?c) in the + /// formulation above. It will contain 64-bit floating point numbers. + /// * constr_eps: allowed numeric disparity for constraints + /// ## Returns + /// One of cv::SolveLPResult + /// + /// ## Overloaded parameters + #[inline] + pub fn solve_lp_1(func: &impl ToInputArray, constr: &impl ToInputArray, z: &mut impl ToOutputArray) -> Result { + input_array_arg!(func); + input_array_arg!(constr); + output_array_arg!(z); + return_send!(via ocvrs_return); + unsafe { sys::cv_solveLP_const__InputArrayR_const__InputArrayR_const__OutputArrayR(func.as_raw__InputArray(), constr.as_raw__InputArray(), z.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method). + /// + /// What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cmbox%7BMaximize%20%7D%20c%5Ccdot%20x%5C%5C%0A%20%5Cmbox%7BSubject%20to%3A%7D%5C%5C%0A%20Ax%5Cleq%20b%5C%5C%0A%20x%5Cgeq%200) + /// + /// Where ![inline formula](https://latex.codecogs.com/png.latex?c) is fixed `1`-by-`n` row-vector, ![inline formula](https://latex.codecogs.com/png.latex?A) is fixed `m`-by-`n` matrix, ![inline formula](https://latex.codecogs.com/png.latex?b) is fixed `m`-by-`1` + /// column vector and ![inline formula](https://latex.codecogs.com/png.latex?x) is an arbitrary `n`-by-`1` column vector, which satisfies the constraints. + /// + /// Simplex algorithm is one of many algorithms that are designed to handle this sort of problems + /// efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve + /// any problem written as above in polynomial time, while simplex method degenerates to exponential + /// time for some special cases), it is well-studied, easy to implement and is shown to work well for + /// real-life purposes. + /// + /// The particular implementation is taken almost verbatim from **Introduction to Algorithms, third + /// edition** by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the + /// Bland's rule is used to prevent cycling. + /// + /// ## Parameters + /// * Func: This row-vector corresponds to ![inline formula](https://latex.codecogs.com/png.latex?c) in the LP problem formulation (see above). It should + /// contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, + /// in the latter case it is understood to correspond to ![inline formula](https://latex.codecogs.com/png.latex?c%5ET). + /// * Constr: `m`-by-`n+1` matrix, whose rightmost column corresponds to ![inline formula](https://latex.codecogs.com/png.latex?b) in formulation above + /// and the remaining to ![inline formula](https://latex.codecogs.com/png.latex?A). It should contain 32- or 64-bit floating point numbers. + /// * z: The solution will be returned here as a column-vector - it corresponds to ![inline formula](https://latex.codecogs.com/png.latex?c) in the + /// formulation above. It will contain 64-bit floating point numbers. + /// * constr_eps: allowed numeric disparity for constraints + /// ## Returns + /// One of cv::SolveLPResult + #[inline] + pub fn solve_lp(func: &impl ToInputArray, constr: &impl ToInputArray, z: &mut impl ToOutputArray, constr_eps: f64) -> Result { + input_array_arg!(func); + input_array_arg!(constr); + output_array_arg!(z); + return_send!(via ocvrs_return); + unsafe { sys::cv_solveLP_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double(func.as_raw__InputArray(), constr.as_raw__InputArray(), z.as_raw__OutputArray(), constr_eps, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the real or complex roots of a polynomial equation. + /// + /// The function cv::solvePoly finds real and complex roots of a polynomial equation: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bcoeffs%7D%20%5Bn%5D%20x%5E%7Bn%7D%20%2B%20%20%5Ctexttt%7Bcoeffs%7D%20%5Bn%2D1%5D%20x%5E%7Bn%2D1%7D%20%2B%20%2E%2E%2E%20%2B%20%20%5Ctexttt%7Bcoeffs%7D%20%5B1%5D%20x%20%2B%20%20%5Ctexttt%7Bcoeffs%7D%20%5B0%5D%20%3D%200) + /// ## Parameters + /// * coeffs: array of polynomial coefficients. + /// * roots: output (complex) array of roots. + /// * maxIters: maximum number of iterations the algorithm does. + /// + /// ## Note + /// This alternative version of [solve_poly] function uses the following default values for its arguments: + /// * max_iters: 300 + #[inline] + pub fn solve_poly_def(coeffs: &impl ToInputArray, roots: &mut impl ToOutputArray) -> Result { + input_array_arg!(coeffs); + output_array_arg!(roots); + return_send!(via ocvrs_return); + unsafe { sys::cv_solvePoly_const__InputArrayR_const__OutputArrayR(coeffs.as_raw__InputArray(), roots.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the real or complex roots of a polynomial equation. + /// + /// The function cv::solvePoly finds real and complex roots of a polynomial equation: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bcoeffs%7D%20%5Bn%5D%20x%5E%7Bn%7D%20%2B%20%20%5Ctexttt%7Bcoeffs%7D%20%5Bn%2D1%5D%20x%5E%7Bn%2D1%7D%20%2B%20%2E%2E%2E%20%2B%20%20%5Ctexttt%7Bcoeffs%7D%20%5B1%5D%20x%20%2B%20%20%5Ctexttt%7Bcoeffs%7D%20%5B0%5D%20%3D%200) + /// ## Parameters + /// * coeffs: array of polynomial coefficients. + /// * roots: output (complex) array of roots. + /// * maxIters: maximum number of iterations the algorithm does. + /// + /// ## C++ default parameters + /// * max_iters: 300 + #[inline] + pub fn solve_poly(coeffs: &impl ToInputArray, roots: &mut impl ToOutputArray, max_iters: i32) -> Result { + input_array_arg!(coeffs); + output_array_arg!(roots); + return_send!(via ocvrs_return); + unsafe { sys::cv_solvePoly_const__InputArrayR_const__OutputArrayR_int(coeffs.as_raw__InputArray(), roots.as_raw__OutputArray(), max_iters, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Solves one or more linear systems or least-squares problems. + /// + /// The function cv::solve solves a linear system or least-squares problem (the + /// latter is possible with SVD or QR methods, or by specifying the flag + /// [DECOMP_NORMAL] ): + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%20%5Carg%20%5Cmin%20%5FX%20%5C%7C%20%5Ctexttt%7Bsrc1%7D%20%5Ccdot%20%5Ctexttt%7BX%7D%20%2D%20%20%5Ctexttt%7Bsrc2%7D%20%5C%7C) + /// + /// If [DECOMP_LU] or [DECOMP_CHOLESKY] method is used, the function returns 1 + /// if src1 (or ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc1%7D%5ET%5Ctexttt%7Bsrc1%7D) ) is non-singular. Otherwise, + /// it returns 0. In the latter case, dst is not valid. Other methods find a + /// pseudo-solution in case of a singular left-hand side part. + /// + /// + /// Note: If you want to find a unity-norm solution of an under-defined + /// singular system ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc1%7D%5Ccdot%5Ctexttt%7Bdst%7D%3D0) , the function solve + /// will not do the work. Use SVD::solveZ instead. + /// + /// ## Parameters + /// * src1: input matrix on the left-hand side of the system. + /// * src2: input matrix on the right-hand side of the system. + /// * dst: output solution. + /// * flags: solution (matrix inversion) method (#DecompTypes) + /// ## See also + /// invert, SVD, eigen + /// + /// ## Note + /// This alternative version of [solve] function uses the following default values for its arguments: + /// * flags: DECOMP_LU + #[inline] + pub fn solve_def(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_solve_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Solves one or more linear systems or least-squares problems. + /// + /// The function cv::solve solves a linear system or least-squares problem (the + /// latter is possible with SVD or QR methods, or by specifying the flag + /// [DECOMP_NORMAL] ): + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%20%5Carg%20%5Cmin%20%5FX%20%5C%7C%20%5Ctexttt%7Bsrc1%7D%20%5Ccdot%20%5Ctexttt%7BX%7D%20%2D%20%20%5Ctexttt%7Bsrc2%7D%20%5C%7C) + /// + /// If [DECOMP_LU] or [DECOMP_CHOLESKY] method is used, the function returns 1 + /// if src1 (or ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc1%7D%5ET%5Ctexttt%7Bsrc1%7D) ) is non-singular. Otherwise, + /// it returns 0. In the latter case, dst is not valid. Other methods find a + /// pseudo-solution in case of a singular left-hand side part. + /// + /// + /// Note: If you want to find a unity-norm solution of an under-defined + /// singular system ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc1%7D%5Ccdot%5Ctexttt%7Bdst%7D%3D0) , the function solve + /// will not do the work. Use SVD::solveZ instead. + /// + /// ## Parameters + /// * src1: input matrix on the left-hand side of the system. + /// * src2: input matrix on the right-hand side of the system. + /// * dst: output solution. + /// * flags: solution (matrix inversion) method (#DecompTypes) + /// ## See also + /// invert, SVD, eigen + /// + /// ## C++ default parameters + /// * flags: DECOMP_LU + #[inline] + pub fn solve(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray, flags: i32) -> Result { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_solve_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sorts each row or each column of a matrix. + /// + /// The function cv::sortIdx sorts each matrix row or each matrix column in the + /// ascending or descending order. So you should pass two operation flags to + /// get desired behaviour. Instead of reordering the elements themselves, it + /// stores the indices of sorted elements in the output array. For example: + /// ```C++ + /// Mat A = Mat::eye(3,3,CV_32F), B; + /// sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING); + /// // B will probably contain + /// // (because of equal elements in A some permutations are possible): + /// // [[1, 2, 0], [0, 2, 1], [0, 1, 2]] + /// ``` + /// + /// ## Parameters + /// * src: input single-channel array. + /// * dst: output integer array of the same size as src. + /// * flags: operation flags that could be a combination of cv::SortFlags + /// ## See also + /// sort, randShuffle + #[inline] + pub fn sort_idx(src: &impl ToInputArray, dst: &mut impl ToOutputArray, flags: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_sortIdx_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sorts each row or each column of a matrix. + /// + /// The function cv::sort sorts each matrix row or each matrix column in + /// ascending or descending order. So you should pass two operation flags to + /// get desired behaviour. If you want to sort matrix rows or columns + /// lexicographically, you can use STL std::sort generic function with the + /// proper comparison predicate. + /// + /// ## Parameters + /// * src: input single-channel array. + /// * dst: output array of the same size and type as src. + /// * flags: operation flags, a combination of [sort_flags] + /// ## See also + /// sortIdx, randShuffle + #[inline] + pub fn sort(src: &impl ToInputArray, dst: &mut impl ToOutputArray, flags: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_sort_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Divides a multi-channel array into several single-channel arrays. + /// + /// The function cv::split splits a multi-channel array into separate single-channel arrays: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bmv%7D%20%5Bc%5D%28I%29%20%3D%20%20%5Ctexttt%7Bsrc%7D%20%28I%29%5Fc) + /// If you need to extract a single channel or do some other sophisticated channel permutation, use + /// mixChannels. + /// + /// The following example demonstrates how to split a 3-channel matrix into 3 single channel matrices. + /// [example](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_split.cpp#L1) + /// + /// ## Parameters + /// * src: input multi-channel array. + /// * mvbegin: output array; the number of arrays must match src.channels(); the arrays themselves are + /// reallocated, if needed. + /// ## See also + /// merge, mixChannels, cvtColor + #[inline] + pub fn split_slice(src: &impl core::MatTraitConst, mvbegin: &mut impl core::MatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_split_const_MatR_MatX(src.as_raw_Mat(), mvbegin.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Divides a multi-channel array into several single-channel arrays. + /// + /// The function cv::split splits a multi-channel array into separate single-channel arrays: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bmv%7D%20%5Bc%5D%28I%29%20%3D%20%20%5Ctexttt%7Bsrc%7D%20%28I%29%5Fc) + /// If you need to extract a single channel or do some other sophisticated channel permutation, use + /// mixChannels. + /// + /// The following example demonstrates how to split a 3-channel matrix into 3 single channel matrices. + /// [example](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_split.cpp#L1) + /// + /// ## Parameters + /// * src: input multi-channel array. + /// * mvbegin: output array; the number of arrays must match src.channels(); the arrays themselves are + /// reallocated, if needed. + /// ## See also + /// merge, mixChannels, cvtColor + /// + /// ## Overloaded parameters + /// + /// * m: input multi-channel array. + /// * mv: output vector of arrays; the arrays themselves are reallocated, if needed. + #[inline] + pub fn split(m: &impl ToInputArray, mv: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(m); + output_array_arg!(mv); + return_send!(via ocvrs_return); + unsafe { sys::cv_split_const__InputArrayR_const__OutputArrayR(m.as_raw__InputArray(), mv.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates a square root of array elements. + /// + /// The function cv::sqrt calculates a square root of each input array element. + /// In case of multi-channel arrays, each channel is processed + /// independently. The accuracy is approximately the same as of the built-in + /// std::sqrt . + /// ## Parameters + /// * src: input floating-point array. + /// * dst: output array of the same size and type as src. + #[inline] + pub fn sqrt(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_sqrt_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the per-element difference between two arrays or array and a scalar. + /// + /// The function subtract calculates: + /// - Difference between two arrays, when both input arrays have the same size and the same number of + /// channels: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%28I%29%20%2D%20%20%5Ctexttt%7Bsrc2%7D%28I%29%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// - Difference between an array and a scalar, when src2 is constructed from Scalar or has the same + /// number of elements as `src1.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%28I%29%20%2D%20%20%5Ctexttt%7Bsrc2%7D%20%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// - Difference between a scalar and an array, when src1 is constructed from Scalar or has the same + /// number of elements as `src2.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%20%2D%20%20%5Ctexttt%7Bsrc2%7D%28I%29%20%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// - The reverse difference between a scalar and an array in the case of `SubRS`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc2%7D%20%2D%20%20%5Ctexttt%7Bsrc1%7D%28I%29%20%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each + /// channel is processed independently. + /// + /// The first function in the list above can be replaced with matrix expressions: + /// ```C++ + /// dst = src1 - src2; + /// dst -= src1; // equivalent to subtract(dst, src1, dst); + /// ``` + /// + /// The input arrays and the output array can all have the same or different depths. For example, you + /// can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of + /// the output array is determined by dtype parameter. In the second and third cases above, as well as + /// in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this + /// case the output array will have the same depth as the input array, be it src1, src2 or both. + /// + /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get + /// result of an incorrect sign in the case of overflow. + /// + /// Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array. + /// `subtract(src,X)` means `subtract(src,(X,X,X,X))`. + /// `subtract(src,(X,))` means `subtract(src,(X,0,0,0))`. + /// ## Parameters + /// * src1: first input array or a scalar. + /// * src2: second input array or a scalar. + /// * dst: output array of the same size and the same number of channels as the input array. + /// * mask: optional operation mask; this is an 8-bit single channel array that specifies elements + /// of the output array to be changed. + /// * dtype: optional depth of the output array + /// ## See also + /// add, addWeighted, scaleAdd, Mat::convertTo + /// + /// ## Note + /// This alternative version of [subtract] function uses the following default values for its arguments: + /// * mask: noArray() + /// * dtype: -1 + #[inline] + pub fn subtract_def(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_subtract_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the per-element difference between two arrays or array and a scalar. + /// + /// The function subtract calculates: + /// - Difference between two arrays, when both input arrays have the same size and the same number of + /// channels: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%28I%29%20%2D%20%20%5Ctexttt%7Bsrc2%7D%28I%29%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// - Difference between an array and a scalar, when src2 is constructed from Scalar or has the same + /// number of elements as `src1.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%28I%29%20%2D%20%20%5Ctexttt%7Bsrc2%7D%20%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// - Difference between a scalar and an array, when src1 is constructed from Scalar or has the same + /// number of elements as `src2.channels()`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc1%7D%20%2D%20%20%5Ctexttt%7Bsrc2%7D%28I%29%20%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// - The reverse difference between a scalar and an array in the case of `SubRS`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28I%29%20%3D%20%20%5Ctexttt%7Bsaturate%7D%20%28%20%5Ctexttt%7Bsrc2%7D%20%2D%20%20%5Ctexttt%7Bsrc1%7D%28I%29%20%29%20%5Cquad%20%5Ctexttt%7Bif%20mask%7D%28I%29%20%5Cne0) + /// where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each + /// channel is processed independently. + /// + /// The first function in the list above can be replaced with matrix expressions: + /// ```C++ + /// dst = src1 - src2; + /// dst -= src1; // equivalent to subtract(dst, src1, dst); + /// ``` + /// + /// The input arrays and the output array can all have the same or different depths. For example, you + /// can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of + /// the output array is determined by dtype parameter. In the second and third cases above, as well as + /// in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this + /// case the output array will have the same depth as the input array, be it src1, src2 or both. + /// + /// Note: Saturation is not applied when the output array has the depth CV_32S. You may even get + /// result of an incorrect sign in the case of overflow. + /// + /// Note: (Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array. + /// `subtract(src,X)` means `subtract(src,(X,X,X,X))`. + /// `subtract(src,(X,))` means `subtract(src,(X,0,0,0))`. + /// ## Parameters + /// * src1: first input array or a scalar. + /// * src2: second input array or a scalar. + /// * dst: output array of the same size and the same number of channels as the input array. + /// * mask: optional operation mask; this is an 8-bit single channel array that specifies elements + /// of the output array to be changed. + /// * dtype: optional depth of the output array + /// ## See also + /// add, addWeighted, scaleAdd, Mat::convertTo + /// + /// ## C++ default parameters + /// * mask: noArray() + /// * dtype: -1 + #[inline] + pub fn subtract(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray, mask: &impl ToInputArray, dtype: i32) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_subtract_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR_int(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), mask.as_raw__InputArray(), dtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the sum of array elements. + /// + /// The function cv::sum calculates and returns the sum of array elements, + /// independently for each channel. + /// ## Parameters + /// * src: input array that must have from 1 to 4 channels. + /// ## See also + /// countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce + #[inline] + pub fn sum_elems(src: &impl ToInputArray) -> Result { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_sum_const__InputArrayR(src.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Swaps two matrices + #[inline] + pub fn swap(a: &mut impl core::MatTrait, b: &mut impl core::MatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_swap_MatR_MatR(a.as_raw_mut_Mat(), b.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Swaps two matrices + /// + /// ## Overloaded parameters + #[inline] + pub fn swap_umat(a: &mut impl core::UMatTrait, b: &mut impl core::UMatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_swap_UMatR_UMatR(a.as_raw_mut_UMat(), b.as_raw_mut_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Generates a unique temporary file name. + /// + /// This function generates a full, unique file path for a temporary file, + /// which can be used to create temporary files for various purposes. + /// + /// ## Parameters + /// * suffix: (optional) The desired file extension or suffix for the temporary file (e.g., ".png", ".txt"). + /// If no suffix is provided (suffix = 0), the file will not have a specific extension. + /// + /// ## Returns + /// cv::String A full unique path for the temporary file. + /// + /// + /// Note: + /// - The function does not create the file, it only generates the name. + /// - The file name is unique for the system session. + /// - Works cross-platform (Windows, Linux, macOS). + /// + /// ## Note + /// This alternative version of [tempfile] function uses the following default values for its arguments: + /// * suffix: 0 + #[inline] + pub fn tempfile_def() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_tempfile(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Generates a unique temporary file name. + /// + /// This function generates a full, unique file path for a temporary file, + /// which can be used to create temporary files for various purposes. + /// + /// ## Parameters + /// * suffix: (optional) The desired file extension or suffix for the temporary file (e.g., ".png", ".txt"). + /// If no suffix is provided (suffix = 0), the file will not have a specific extension. + /// + /// ## Returns + /// cv::String A full unique path for the temporary file. + /// + /// + /// Note: + /// - The function does not create the file, it only generates the name. + /// - The file name is unique for the system session. + /// - Works cross-platform (Windows, Linux, macOS). + /// + /// ## C++ default parameters + /// * suffix: 0 + #[inline] + pub fn tempfile(suffix: &str) -> Result { + extern_container_arg!(suffix); + return_send!(via ocvrs_return); + unsafe { sys::cv_tempfile_const_charX(suffix.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ! Signals an error and terminate application. + /// + /// By default the function prints information about the error to stderr, then it terminates application + /// with std::terminate. The function is designed for invariants check in functions and methods with + /// noexcept attribute. + /// ## Parameters + /// * code: - error code (Error::Code) + /// * err: - error description + /// * func: - function name. Available only when the compiler supports getting it + /// * file: - source file name where the error has occurred + /// * line: - line number in the source file where the error has occurred + /// ## See also + /// CV_AssertTerminate + #[inline] + pub fn terminate(code: i32, err: &str, func: &str, file: &str, line: i32) { + extern_container_arg!(nofail err); + extern_container_arg!(nofail func); + extern_container_arg!(nofail file); + let ret = unsafe { sys::cv_terminate_int_const_StringR_const_charX_const_charX_int(code, err.opencv_as_extern(), func.opencv_as_extern(), file.opencv_as_extern(), line) }; + ret + } + + /// Returns the trace of a matrix. + /// + /// The function cv::trace returns the sum of the diagonal elements of the + /// matrix mtx . + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cmathrm%7Btr%7D%20%28%20%5Ctexttt%7Bmtx%7D%20%29%20%3D%20%20%5Csum%20%5Fi%20%20%5Ctexttt%7Bmtx%7D%20%28i%2Ci%29) + /// ## Parameters + /// * mtx: input matrix. + #[inline] + pub fn trace(mtx: &impl ToInputArray) -> Result { + input_array_arg!(mtx); + return_send!(via ocvrs_return); + unsafe { sys::cv_trace_const__InputArrayR(mtx.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs the matrix transformation of every array element. + /// + /// The function cv::transform performs the matrix transformation of every + /// element of the array src and stores the results in dst : + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bm%7D%20%5Ccdot%20%5Ctexttt%7Bsrc%7D%20%28I%29) + /// (when m.cols=src.channels() ), or + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28I%29%20%3D%20%20%5Ctexttt%7Bm%7D%20%5Ccdot%20%5B%20%5Ctexttt%7Bsrc%7D%20%28I%29%3B%201%5D) + /// (when m.cols=src.channels()+1 ) + /// + /// Every element of the N -channel array src is interpreted as N -element + /// vector that is transformed using the M x N or M x (N+1) matrix m to + /// M-element vector - the corresponding element of the output array dst . + /// + /// The function may be used for geometrical transformation of + /// N -dimensional points, arbitrary linear color space transformation (such + /// as various kinds of RGB to YUV transforms), shuffling the image + /// channels, and so forth. + /// ## Parameters + /// * src: input array that must have as many channels (1 to 4) as + /// m.cols or m.cols-1. + /// * dst: output array of the same size and depth as src; it has as + /// many channels as m.rows. + /// * m: transformation 2x2 or 2x3 floating-point matrix. + /// ## See also + /// perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective + #[inline] + pub fn transform(src: &impl ToInputArray, dst: &mut impl ToOutputArray, m: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_transform_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), m.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Transpose for n-dimensional matrices. + /// + /// + /// Note: Input should be continuous single-channel matrix. + /// ## Parameters + /// * src: input array. + /// * order: a permutation of [0,1,..,N-1] where N is the number of axes of src. + /// The i'th axis of dst will correspond to the axis numbered order[i] of the input. + /// * dst: output array of the same type as src. + #[inline] + pub fn transpose_nd(src: &impl ToInputArray, order: &core::Vector, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_transposeND_const__InputArrayR_const_vectorLintGR_const__OutputArrayR(src.as_raw__InputArray(), order.as_raw_VectorOfi32(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Transposes a matrix. + /// + /// The function cv::transpose transposes the matrix src : + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28i%2Cj%29%20%3D%20%20%5Ctexttt%7Bsrc%7D%20%28j%2Ci%29) + /// + /// Note: No complex conjugation is done in case of a complex matrix. It + /// should be done separately if needed. + /// ## Parameters + /// * src: input array. + /// * dst: output array of the same type as src. + #[inline] + pub fn transpose(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_transpose_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or "" + #[inline] + pub fn type_to_string(typ: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_typeToString_int(typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Check if use of OpenVX is enabled + #[inline] + pub fn use_openvx() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_useOpenVX(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the status of optimized code usage. + /// + /// The function returns true if the optimized code is enabled. Otherwise, it returns false. + #[inline] + pub fn use_optimized() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_useOptimized(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [copy_mat_and_dump_named_arguments] function uses the following default values for its arguments: + /// * params: FunctionParams() + #[inline] + pub fn copy_mat_and_dump_named_arguments_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_copyMatAndDumpNamedArguments_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * params: FunctionParams() + #[inline] + pub fn copy_mat_and_dump_named_arguments(src: &impl ToInputArray, dst: &mut impl ToOutputArray, params: core::FunctionParams) -> Result { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_copyMatAndDumpNamedArguments_const__InputArrayR_const__OutputArrayR_const_FunctionParamsR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ¶ms, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_bool(argument: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpBool_bool(argument, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_c_string(argument: &str) -> Result { + extern_container_arg!(argument); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpCString_const_charX(argument.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_double(argument: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpDouble_double(argument, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_float(argument: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpFloat_float(argument, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_input_array_of_arrays(argument: &impl ToInputArray) -> Result { + input_array_arg!(argument); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpInputArrayOfArrays_const__InputArrayR(argument.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_input_array(argument: &impl ToInputArray) -> Result { + input_array_arg!(argument); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpInputArray_const__InputArrayR(argument.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_input_output_array_of_arrays(argument: &mut impl ToInputOutputArray) -> Result { + input_output_array_arg!(argument); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpInputOutputArrayOfArrays_const__InputOutputArrayR(argument.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_input_output_array(argument: &mut impl ToInputOutputArray) -> Result { + input_output_array_arg!(argument); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpInputOutputArray_const__InputOutputArrayR(argument.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_int64(argument: i64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpInt64_int64_t(argument, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_int(argument: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpInt_int(argument, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_range(argument: &impl core::RangeTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpRange_const_RangeR(argument.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_rect(argument: core::Rect) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpRect_const_RectR(&argument, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_rotated_rect(argument: core::RotatedRect) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpRotatedRect_const_RotatedRectR(&argument, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_size_t(argument: size_t) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpSizeT_size_t(argument, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_string(argument: &str) -> Result { + extern_container_arg!(argument); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpString_const_StringR(argument.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_term_criteria(argument: core::TermCriteria) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpTermCriteria_const_TermCriteriaR(&argument, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [dump_vec2i] function uses the following default values for its arguments: + /// * value: cv::Vec2i(42,24) + #[inline] + pub fn dump_vec2i_def() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpVec2i(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * value: cv::Vec2i(42,24) + #[inline] + pub fn dump_vec2i(value: core::Vec2i) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpVec2i_const_Vec2i(&value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_vector_of_double(vec: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpVectorOfDouble_const_vectorLdoubleGR(vec.as_raw_VectorOff64(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_vector_of_int(vec: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpVectorOfInt_const_vectorLintGR(vec.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn dump_vector_of_rect(vec: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_dumpVectorOfRect_const_vectorLRectGR(vec.as_raw_VectorOfRect(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn get_cache_directory_for_downloads() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_fs_getCacheDirectoryForDownloads(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn generate_vector_of_int(len: size_t, vec: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_generateVectorOfInt_size_t_vectorLintGR(len, vec.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn generate_vector_of_mat(len: size_t, rows: i32, cols: i32, dtype: i32, vec: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_generateVectorOfMat_size_t_int_int_int_vectorLMatGR(len, rows, cols, dtype, vec.as_raw_mut_VectorOfMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn generate_vector_of_rect(len: size_t, vec: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_generateVectorOfRect_size_t_vectorLRectGR(len, vec.as_raw_mut_VectorOfRect(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn get_thread_id() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_getThreadID(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Get global logging level + #[inline] + pub fn get_log_level() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_logging_getLogLevel(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn get_log_tag_level(tag: &str) -> Result { + extern_container_arg!(tag); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_logging_getLogTagLevel_const_charX(tag.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Get global log tag + #[inline] + pub fn get_global_log_tag() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_logging_internal_getGlobalLogTag(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::LogTag::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Replaces the OpenCV writeLogMessageEx function with a user-defined function. + /// + /// Note: The user-defined function must have the same signature as writeLogMessage. + /// + /// Note: The user-defined function must accept arguments that can be potentially null. + /// + /// Note: The user-defined function must be thread-safe, as OpenCV logging may be called + /// from multiple threads. + /// + /// Note: The user-defined function must not perform any action that can trigger + /// deadlocks or infinite loop. Many OpenCV functions are not re-entrant. + /// + /// Note: Once replaced, logs will not go through any of the OpenCV logging functions + /// such as writeLogMessage or writeLogMessageEx, until their respective restore + /// methods are called. + /// + /// Note: To restore, call this function with a nullptr. + #[inline] + pub fn replace_write_log_message_ex(f: core::WriteLogMessageExFuncType) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_logging_internal_replaceWriteLogMessageEx_WriteLogMessageExFuncType(f, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Replaces the OpenCV writeLogMessage function with a user-defined function. + /// + /// Note: The user-defined function must have the same signature as writeLogMessage. + /// + /// Note: The user-defined function must accept arguments that can be potentially null. + /// + /// Note: The user-defined function must be thread-safe, as OpenCV logging may be called + /// from multiple threads. + /// + /// Note: The user-defined function must not perform any action that can trigger + /// deadlocks or infinite loop. Many OpenCV functions are not re-entrant. + /// + /// Note: Once replaced, logs will not go through the OpenCV writeLogMessage function. + /// + /// Note: To restore, call this function with a nullptr. + #[inline] + pub fn replace_write_log_message(f: core::WriteLogMessageFuncType) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_logging_internal_replaceWriteLogMessage_WriteLogMessageFuncType(f, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Write log message + #[inline] + pub fn write_log_message_ex(log_level: core::LogLevel, tag: &str, file: &str, line: i32, func: &str, message: &str) -> Result<()> { + extern_container_arg!(tag); + extern_container_arg!(file); + extern_container_arg!(func); + extern_container_arg!(message); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_logging_internal_writeLogMessageEx_LogLevel_const_charX_const_charX_int_const_charX_const_charX(log_level, tag.opencv_as_extern(), file.opencv_as_extern(), line, func.opencv_as_extern(), message.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Write log message + #[inline] + pub fn write_log_message(log_level: core::LogLevel, message: &str) -> Result<()> { + extern_container_arg!(message); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_logging_internal_writeLogMessage_LogLevel_const_charX(log_level, message.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn register_log_tag(plogtag: &mut impl core::LogTagTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_logging_registerLogTag_LogTagX(plogtag.as_raw_mut_LogTag(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Set global logging level + /// ## Returns + /// previous logging level + #[inline] + pub fn set_log_level(log_level: core::LogLevel) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_logging_setLogLevel_LogLevel(log_level, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn set_log_tag_level(tag: &str, level: core::LogLevel) -> Result<()> { + extern_container_arg!(tag); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_logging_setLogTagLevel_const_charX_LogLevel(tag.opencv_as_extern(), level, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn test_echo_boolean_function(flag: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_nested_testEchoBooleanFunction_bool(flag, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn test_async_array(argument: &impl ToInputArray) -> Result { + input_array_arg!(argument); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_testAsyncArray_const__InputArrayR(argument.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::AsyncArray::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn test_async_exception() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_testAsyncException(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::AsyncArray::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn test_overload_resolution_1(rect: core::Rect) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_testOverloadResolution_const_RectR(&rect, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// @cond IGNORED + /// + /// ## Note + /// This alternative version of [test_overload_resolution] function uses the following default values for its arguments: + /// * point: Point(42,24) + #[inline] + pub fn test_overload_resolution_def(value: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_testOverloadResolution_int(value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// @cond IGNORED + /// + /// ## C++ default parameters + /// * point: Point(42,24) + #[inline] + pub fn test_overload_resolution(value: i32, point: core::Point) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_testOverloadResolution_int_const_PointR(value, &point, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn test_overwrite_native_method(argument: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_testOverwriteNativeMethod_int(argument, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn test_raise_general_exception() -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_testRaiseGeneralException(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [test_reserved_keyword_conversion] function uses the following default values for its arguments: + /// * lambda: 2 + /// * from: 3 + #[inline] + pub fn test_reserved_keyword_conversion_def(positional_argument: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_testReservedKeywordConversion_int(positional_argument, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * lambda: 2 + /// * from: 3 + #[inline] + pub fn test_reserved_keyword_conversion(positional_argument: i32, lambda: i32, from: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_testReservedKeywordConversion_int_int_int(positional_argument, lambda, from, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn test_rotated_rect_vector(x: f32, y: f32, w: f32, h: f32, angle: f32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_testRotatedRectVector_float_float_float_float_float(x, y, w, h, angle, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn test_rotated_rect(x: f32, y: f32, w: f32, h: f32, angle: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_testRotatedRect_float_float_float_float_float(x, y, w, h, angle, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Converts VASurfaceID object to OutputArray. + /// ## Parameters + /// * display: - VADisplay object. + /// * surface: - source VASurfaceID object. + /// * size: - size of image represented by VASurfaceID object. + /// * dst: - destination OutputArray. + #[inline] + pub unsafe fn convert_from_va_surface(display: core::VADisplay, surface: core::VASurfaceID, size: core::Size, dst: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_va_intel_convertFromVASurface_VADisplay_VASurfaceID_Size_const__OutputArrayR(display, surface, &size, dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Converts InputArray to VASurfaceID object. + /// ## Parameters + /// * display: - VADisplay object. + /// * src: - source InputArray. + /// * surface: - destination VASurfaceID object. + /// * size: - size of image represented by VASurfaceID object. + #[inline] + pub unsafe fn convert_to_va_surface(display: core::VADisplay, src: &impl ToInputArray, surface: core::VASurfaceID, size: core::Size) -> Result<()> { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_va_intel_convertToVASurface_VADisplay_const__InputArrayR_VASurfaceID_Size(display, src.as_raw__InputArray(), surface, &size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates OpenCL context from VA. + /// ## Parameters + /// * display: - VADisplay for which CL interop should be established. + /// * tryInterop: - try to set up for interoperability, if true; set up for use slow copy if false. + /// ## Returns + /// Returns reference to OpenCL Context + /// + /// ## Note + /// This alternative version of [initialize_context_from_va] function uses the following default values for its arguments: + /// * try_interop: true + #[inline] + pub unsafe fn initialize_context_from_va_def(display: core::VADisplay) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_va_intel_ocl_initializeContextFromVA_VADisplay(display, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Context::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates OpenCL context from VA. + /// ## Parameters + /// * display: - VADisplay for which CL interop should be established. + /// * tryInterop: - try to set up for interoperability, if true; set up for use slow copy if false. + /// ## Returns + /// Returns reference to OpenCL Context + /// + /// ## C++ default parameters + /// * try_interop: true + #[inline] + pub unsafe fn initialize_context_from_va(display: core::VADisplay, try_interop: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_va_intel_ocl_initializeContextFromVA_VADisplay_bool(display, try_interop, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Context::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Applies vertical concatenation to given matrices. + /// + /// The function vertically concatenates two or more cv::Mat matrices (with the same number of cols). + /// ```C++ + /// cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), + /// cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), + /// cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; + /// + /// cv::Mat out; + /// cv::vconcat( matArray, 3, out ); + /// //out: + /// //[1, 1, 1, 1; + /// // 2, 2, 2, 2; + /// // 3, 3, 3, 3] + /// ``` + /// + /// ## Parameters + /// * src: input array or vector of matrices. all of the matrices must have the same number of cols and the same depth. + /// * nsrc: number of matrices in src. + /// * dst: output array. It has the same number of cols and depth as the src, and the sum of rows of the src. + /// ## See also + /// cv::hconcat(const Mat*, size_t, OutputArray), cv::hconcat(InputArrayOfArrays, OutputArray) and cv::hconcat(InputArray, InputArray, OutputArray) + /// + /// ## Overloaded parameters + /// + /// ```C++ + /// cv::Mat_ A = (cv::Mat_(3, 2) << 1, 7, + /// 2, 8, + /// 3, 9); + /// cv::Mat_ B = (cv::Mat_(3, 2) << 4, 10, + /// 5, 11, + /// 6, 12); + /// + /// cv::Mat C; + /// cv::vconcat(A, B, C); + /// //C: + /// //[1, 7; + /// // 2, 8; + /// // 3, 9; + /// // 4, 10; + /// // 5, 11; + /// // 6, 12] + /// ``` + /// + /// * src1: first input array to be considered for vertical concatenation. + /// * src2: second input array to be considered for vertical concatenation. + /// * dst: output array. It has the same number of cols and depth as the src1 and src2, and the sum of rows of the src1 and src2. + #[inline] + pub fn vconcat2(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_vconcat_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies vertical concatenation to given matrices. + /// + /// The function vertically concatenates two or more cv::Mat matrices (with the same number of cols). + /// ```C++ + /// cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), + /// cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), + /// cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; + /// + /// cv::Mat out; + /// cv::vconcat( matArray, 3, out ); + /// //out: + /// //[1, 1, 1, 1; + /// // 2, 2, 2, 2; + /// // 3, 3, 3, 3] + /// ``` + /// + /// ## Parameters + /// * src: input array or vector of matrices. all of the matrices must have the same number of cols and the same depth. + /// * nsrc: number of matrices in src. + /// * dst: output array. It has the same number of cols and depth as the src, and the sum of rows of the src. + /// ## See also + /// cv::hconcat(const Mat*, size_t, OutputArray), cv::hconcat(InputArrayOfArrays, OutputArray) and cv::hconcat(InputArray, InputArray, OutputArray) + /// + /// ## Overloaded parameters + /// + /// ```C++ + /// std::vector matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), + /// cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), + /// cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; + /// + /// cv::Mat out; + /// cv::vconcat( matrices, out ); + /// //out: + /// //[1, 1, 1, 1; + /// // 2, 2, 2, 2; + /// // 3, 3, 3, 3] + /// ``` + /// + /// * src: input array or vector of matrices. all of the matrices must have the same number of cols and the same depth + /// * dst: output array. It has the same number of cols and depth as the src, and the sum of rows of the src. + /// same depth. + #[inline] + pub fn vconcat(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_vconcat_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn write_scalar_str(fs: &mut impl core::FileStorageTrait, value: &str) -> Result<()> { + extern_container_arg!(value); + return_send!(via ocvrs_return); + unsafe { sys::cv_writeScalar_FileStorageR_const_StringR(fs.as_raw_mut_FileStorage(), value.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn write_scalar_f64(fs: &mut impl core::FileStorageTrait, value: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_writeScalar_FileStorageR_double(fs.as_raw_mut_FileStorage(), value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn write_scalar_f32(fs: &mut impl core::FileStorageTrait, value: f32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_writeScalar_FileStorageR_float(fs.as_raw_mut_FileStorage(), value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn write_scalar_i32(fs: &mut impl core::FileStorageTrait, value: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_writeScalar_FileStorageR_int(fs.as_raw_mut_FileStorage(), value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn write_scalar(fs: &mut impl core::FileStorageTrait, value: i64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_writeScalar_FileStorageR_int64_t(fs.as_raw_mut_FileStorage(), value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn write_mat(fs: &mut impl core::FileStorageTrait, name: &str, value: &impl core::MatTraitConst) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_write_FileStorageR_const_StringR_const_MatR(fs.as_raw_mut_FileStorage(), name.opencv_as_extern(), value.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn write_sparsemat(fs: &mut impl core::FileStorageTrait, name: &str, value: &impl core::SparseMatTraitConst) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_write_FileStorageR_const_StringR_const_SparseMatR(fs.as_raw_mut_FileStorage(), name.opencv_as_extern(), value.as_raw_SparseMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn write_str(fs: &mut impl core::FileStorageTrait, name: &str, value: &str) -> Result<()> { + extern_container_arg!(name); + extern_container_arg!(value); + return_send!(via ocvrs_return); + unsafe { sys::cv_write_FileStorageR_const_StringR_const_StringR(fs.as_raw_mut_FileStorage(), name.opencv_as_extern(), value.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn write_dmatch_vec(fs: &mut impl core::FileStorageTrait, name: &str, value: &core::Vector) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_write_FileStorageR_const_StringR_const_vectorLDMatchGR(fs.as_raw_mut_FileStorage(), name.opencv_as_extern(), value.as_raw_VectorOfDMatch(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn write_keypoint_vec(fs: &mut impl core::FileStorageTrait, name: &str, value: &core::Vector) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_write_FileStorageR_const_StringR_const_vectorLKeyPointGR(fs.as_raw_mut_FileStorage(), name.opencv_as_extern(), value.as_raw_VectorOfKeyPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn write_f64(fs: &mut impl core::FileStorageTrait, name: &str, value: f64) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_write_FileStorageR_const_StringR_double(fs.as_raw_mut_FileStorage(), name.opencv_as_extern(), value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn write_f32(fs: &mut impl core::FileStorageTrait, name: &str, value: f32) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_write_FileStorageR_const_StringR_float(fs.as_raw_mut_FileStorage(), name.opencv_as_extern(), value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// //////////////// XML & YAML I/O implementation ////////////////// + #[inline] + pub fn write_i32(fs: &mut impl core::FileStorageTrait, name: &str, value: i32) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_write_FileStorageR_const_StringR_int(fs.as_raw_mut_FileStorage(), name.opencv_as_extern(), value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn write(fs: &mut impl core::FileStorageTrait, name: &str, value: i64) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_write_FileStorageR_const_StringR_int64_t(fs.as_raw_mut_FileStorage(), name.opencv_as_extern(), value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This is a base class for all more or less complex algorithms in OpenCV + /// + /// especially for classes of algorithms, for which there can be multiple implementations. The examples + /// are stereo correspondence (for which there are algorithms like block matching, semi-global block + /// matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians + /// models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck + /// etc.). + /// + /// Here is example of SimpleBlobDetector use in your application via Algorithm interface: + /// [Algorithm](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_various.cpp#L1) + pub struct Algorithm { + ptr: *mut c_void, + } + + opencv_type_boxed! { Algorithm } + + impl Drop for Algorithm { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Algorithm_delete(self.as_raw_mut_Algorithm()) }; + } + } + + unsafe impl Send for Algorithm {} + + /// Constant methods for [core::Algorithm] + pub trait AlgorithmTraitConst { + fn as_raw_Algorithm(&self) -> *const c_void; + + /// Stores algorithm parameters in a file storage + #[inline] + fn write(&self, fs: &mut impl core::FileStorageTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Algorithm_write_const_FileStorageR(self.as_raw_Algorithm(), fs.as_raw_mut_FileStorage(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Stores algorithm parameters in a file storage + /// + /// ## Overloaded parameters + #[inline] + fn write_1(&self, fs: &mut impl core::FileStorageTrait, name: &str) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_Algorithm_write_const_FileStorageR_const_StringR(self.as_raw_Algorithm(), fs.as_raw_mut_FileStorage(), name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// @deprecated + /// + /// ## C++ default parameters + /// * name: String() + #[inline] + fn write_with_name(&self, fs: &core::Ptr, name: &str) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_Algorithm_write_const_const_PtrLFileStorageGR_const_StringR(self.as_raw_Algorithm(), fs.as_raw_PtrOfFileStorage(), name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// + /// **Deprecated**: ## Note + /// This alternative version of [AlgorithmTraitConst::write_with_name] function uses the following default values for its arguments: + /// * name: String() + #[deprecated = "## Note"] + #[inline] + fn write_with_name_def(&self, fs: &core::Ptr) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Algorithm_write_const_const_PtrLFileStorageGR(self.as_raw_Algorithm(), fs.as_raw_PtrOfFileStorage(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Algorithm_empty_const(self.as_raw_Algorithm(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Saves the algorithm to a file. + /// In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). + #[inline] + fn save(&self, filename: &str) -> Result<()> { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_Algorithm_save_const_const_StringR(self.as_raw_Algorithm(), filename.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the algorithm string identifier. + /// This string is used as top level xml/yml node tag when the object is saved to a file or string. + #[inline] + fn get_default_name(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Algorithm_getDefaultName_const(self.as_raw_Algorithm(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Mutable methods for [core::Algorithm] + pub trait AlgorithmTrait: core::AlgorithmTraitConst { + fn as_raw_mut_Algorithm(&mut self) -> *mut c_void; + + /// Clears the algorithm state + #[inline] + fn clear(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Algorithm_clear(self.as_raw_mut_Algorithm(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Reads algorithm parameters from a file storage + #[inline] + fn read(&mut self, fn_: &impl core::FileNodeTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Algorithm_read_const_FileNodeR(self.as_raw_mut_Algorithm(), fn_.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for Algorithm { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Algorithm") + .finish() + } + } + + boxed_cast_descendant! { Algorithm, core::ConjGradSolver, cv_Algorithm_to_ConjGradSolver } + + boxed_cast_descendant! { Algorithm, core::DownhillSolver, cv_Algorithm_to_DownhillSolver } + + boxed_cast_descendant! { Algorithm, core::MinProblemSolver, cv_Algorithm_to_MinProblemSolver } + + impl core::AlgorithmTraitConst for Algorithm { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for Algorithm { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Algorithm, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + /// Returns result of asynchronous operations + /// + /// Object has attached asynchronous state. + /// Assignment operator doesn't clone asynchronous state (it is shared between all instances). + /// + /// Result can be fetched via get() method only once. + pub struct AsyncArray { + ptr: *mut c_void, + } + + opencv_type_boxed! { AsyncArray } + + impl Drop for AsyncArray { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_AsyncArray_delete(self.as_raw_mut_AsyncArray()) }; + } + } + + unsafe impl Send for AsyncArray {} + + impl AsyncArray { + #[inline] + pub fn default() -> core::AsyncArray { + let ret = unsafe { sys::cv_AsyncArray_AsyncArray() }; + let ret = unsafe { core::AsyncArray::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn copy(o: &impl core::AsyncArrayTraitConst) -> core::AsyncArray { + let ret = unsafe { sys::cv_AsyncArray_AsyncArray_const_AsyncArrayR(o.as_raw_AsyncArray()) }; + let ret = unsafe { core::AsyncArray::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn copy_mut(mut o: core::AsyncArray) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_AsyncArray_AsyncArray_AsyncArrayRR(o.as_raw_mut_AsyncArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::AsyncArray::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::AsyncArray] + pub trait AsyncArrayTraitConst { + fn as_raw_AsyncArray(&self) -> *const c_void; + + /// Fetch the result. + /// ## Parameters + /// * dst:[out] destination array + /// + /// Waits for result until container has valid result. + /// Throws exception if exception was stored as a result. + /// + /// Throws exception on invalid container state. + /// + /// + /// Note: Result or stored exception can be fetched only once. + #[inline] + fn get(&self, dst: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_AsyncArray_get_const_const__OutputArrayR(self.as_raw_AsyncArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Retrieving the result with timeout + /// ## Parameters + /// * dst:[out] destination array + /// * timeoutNs: timeout in nanoseconds, -1 for infinite wait + /// + /// ## Returns + /// true if result is ready, false if the timeout has expired + /// + /// + /// Note: Result or stored exception can be fetched only once. + #[inline] + fn get_with_timeout(&self, dst: &mut impl ToOutputArray, timeout_ns: i64) -> Result { + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_AsyncArray_get_const_const__OutputArrayR_int64_t(self.as_raw_AsyncArray(), dst.as_raw__OutputArray(), timeout_ns, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_with_timeout_f64(&self, dst: &mut impl ToOutputArray, timeout_ns: f64) -> Result { + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_AsyncArray_get_const_const__OutputArrayR_double(self.as_raw_AsyncArray(), dst.as_raw__OutputArray(), timeout_ns, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn wait_for(&self, timeout_ns: i64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_AsyncArray_wait_for_const_int64_t(self.as_raw_AsyncArray(), timeout_ns, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn wait_for_f64(&self, timeout_ns: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_AsyncArray_wait_for_const_double(self.as_raw_AsyncArray(), timeout_ns, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn valid(&self) -> bool { + let ret = unsafe { sys::cv_AsyncArray_valid_const(self.as_raw_AsyncArray()) }; + ret + } + + } + + /// Mutable methods for [core::AsyncArray] + pub trait AsyncArrayTrait: core::AsyncArrayTraitConst { + fn as_raw_mut_AsyncArray(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, o: &impl core::AsyncArrayTraitConst) { + let ret = unsafe { sys::cv_AsyncArray_operatorST_const_AsyncArrayR(self.as_raw_mut_AsyncArray(), o.as_raw_AsyncArray()) }; + ret + } + + #[inline] + fn release(&mut self) { + let ret = unsafe { sys::cv_AsyncArray_release(self.as_raw_mut_AsyncArray()) }; + ret + } + + #[inline] + fn set_1(&mut self, mut o: core::AsyncArray) { + let ret = unsafe { sys::cv_AsyncArray_operatorST_AsyncArrayRR(self.as_raw_mut_AsyncArray(), o.as_raw_mut_AsyncArray()) }; + ret + } + + } + + impl Default for AsyncArray { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for AsyncArray { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("AsyncArray") + .finish() + } + } + + impl core::AsyncArrayTraitConst for AsyncArray { + #[inline] fn as_raw_AsyncArray(&self) -> *const c_void { self.as_raw() } + } + + impl core::AsyncArrayTrait for AsyncArray { + #[inline] fn as_raw_mut_AsyncArray(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AsyncArray, core::AsyncArrayTraitConst, as_raw_AsyncArray, core::AsyncArrayTrait, as_raw_mut_AsyncArray } + + /// Provides result of asynchronous operations + pub struct AsyncPromise { + ptr: *mut c_void, + } + + opencv_type_boxed! { AsyncPromise } + + impl Drop for AsyncPromise { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_AsyncPromise_delete(self.as_raw_mut_AsyncPromise()) }; + } + } + + unsafe impl Send for AsyncPromise {} + + impl AsyncPromise { + #[inline] + pub fn default() -> core::AsyncPromise { + let ret = unsafe { sys::cv_AsyncPromise_AsyncPromise() }; + let ret = unsafe { core::AsyncPromise::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn copy(o: &impl core::AsyncPromiseTraitConst) -> core::AsyncPromise { + let ret = unsafe { sys::cv_AsyncPromise_AsyncPromise_const_AsyncPromiseR(o.as_raw_AsyncPromise()) }; + let ret = unsafe { core::AsyncPromise::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn copy_mut(mut o: core::AsyncPromise) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_AsyncPromise_AsyncPromise_AsyncPromiseRR(o.as_raw_mut_AsyncPromise(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::AsyncPromise::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::AsyncPromise] + pub trait AsyncPromiseTraitConst { + fn as_raw_AsyncPromise(&self) -> *const c_void; + + #[inline] + fn _get_impl(&self) -> *mut c_void { + let ret = unsafe { sys::cv_AsyncPromise__getImpl_const(self.as_raw_AsyncPromise()) }; + ret + } + + } + + /// Mutable methods for [core::AsyncPromise] + pub trait AsyncPromiseTrait: core::AsyncPromiseTraitConst { + fn as_raw_mut_AsyncPromise(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, o: &impl core::AsyncPromiseTraitConst) { + let ret = unsafe { sys::cv_AsyncPromise_operatorST_const_AsyncPromiseR(self.as_raw_mut_AsyncPromise(), o.as_raw_AsyncPromise()) }; + ret + } + + #[inline] + fn release(&mut self) { + let ret = unsafe { sys::cv_AsyncPromise_release(self.as_raw_mut_AsyncPromise()) }; + ret + } + + /// Returns associated AsyncArray + /// + /// Note: Can be called once + #[inline] + fn get_array_result(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_AsyncPromise_getArrayResult(self.as_raw_mut_AsyncPromise(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::AsyncArray::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Stores asynchronous result. + /// ## Parameters + /// * value: result + #[inline] + fn set_value(&mut self, value: &impl ToInputArray) -> Result<()> { + input_array_arg!(value); + return_send!(via ocvrs_return); + unsafe { sys::cv_AsyncPromise_setValue_const__InputArrayR(self.as_raw_mut_AsyncPromise(), value.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Stores exception. + /// ## Parameters + /// * exception: exception to be raised in AsyncArray + #[inline] + fn set_exception(&mut self, exception: &impl core::ExceptionTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_AsyncPromise_setException_const_ExceptionR(self.as_raw_mut_AsyncPromise(), exception.as_raw_Exception(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_1(&mut self, mut o: core::AsyncPromise) { + let ret = unsafe { sys::cv_AsyncPromise_operatorST_AsyncPromiseRR(self.as_raw_mut_AsyncPromise(), o.as_raw_mut_AsyncPromise()) }; + ret + } + + } + + impl Default for AsyncPromise { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for AsyncPromise { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("AsyncPromise") + .finish() + } + } + + impl core::AsyncPromiseTraitConst for AsyncPromise { + #[inline] fn as_raw_AsyncPromise(&self) -> *const c_void { self.as_raw() } + } + + impl core::AsyncPromiseTrait for AsyncPromise { + #[inline] fn as_raw_mut_AsyncPromise(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AsyncPromise, core::AsyncPromiseTraitConst, as_raw_AsyncPromise, core::AsyncPromiseTrait, as_raw_mut_AsyncPromise } + + /// Designed for command line parsing + /// + /// The sample below demonstrates how to use CommandLineParser: + /// ```C++ + /// CommandLineParser parser(argc, argv, keys); + /// parser.about("Application name v1.0.0"); + /// + /// if (parser.has("help")) + /// { + /// parser.printMessage(); + /// return 0; + /// } + /// + /// int N = parser.get("N"); + /// double fps = parser.get("fps"); + /// String path = parser.get("path"); + /// + /// use_time_stamp = parser.has("timestamp"); + /// + /// String img1 = parser.get(0); + /// String img2 = parser.get(1); + /// + /// int repeat = parser.get(2); + /// + /// if (!parser.check()) + /// { + /// parser.printErrors(); + /// return 0; + /// } + /// ``` + /// + /// + /// ### Keys syntax + /// + /// The keys parameter is a string containing several blocks, each one is enclosed in curly braces and + /// describes one argument. Each argument contains three parts separated by the `|` symbol: + /// + /// -# argument names is a list of option synonyms separated by standard space characters ' ' (to mark argument as positional, prefix it with the `@` symbol) + /// -# default value will be used if the argument was not provided (can be empty) + /// -# help message (can be empty) + /// + /// For example: + /// + /// ```C++ + /// const String keys = + /// "{help h usage ? | | print this message }" + /// "{@image1 | | image1 for compare }" + /// "{@image2 || image2 for compare }" + /// "{@repeat |1 | number }" + /// "{path |. | path to file }" + /// "{fps | -1.0 | fps for output video }" + /// "{N count |100 | count of objects }" + /// "{ts timestamp | | use time stamp }" + /// ; + /// } + /// ``` + /// + /// + /// Note that there are no default values for `help` and `timestamp` so we can check their presence using the `has()` method. + /// Arguments with default values are considered to be always present. Use the `get()` method in these cases to check their + /// actual value instead. + /// Note that whitespace characters other than standard spaces are considered part of the string. + /// Additionally, leading and trailing standard spaces around the help messages are ignored. + /// + /// String keys like `get("@image1")` return the empty string `""` by default - even with an empty default value. + /// Use the special `` default value to enforce that the returned string must not be empty. (like in `get("@image2")`) + /// + /// ### Usage + /// + /// For the described keys: + /// + /// ```C++ + /// # Good call (3 positional parameters: image1, image2 and repeat; N is 200, ts is true) + /// $ ./app -N=200 1.png 2.jpg 19 -ts + /// + /// # Bad call + /// $ ./app -fps=aaa + /// ERRORS: + /// Parameter "fps": can not convert: [aaa] to [double] + /// ``` + /// + pub struct CommandLineParser { + ptr: *mut c_void, + } + + opencv_type_boxed! { CommandLineParser } + + impl Drop for CommandLineParser { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_CommandLineParser_delete(self.as_raw_mut_CommandLineParser()) }; + } + } + + unsafe impl Send for CommandLineParser {} + + impl CommandLineParser { + /// Constructor + /// + /// Initializes command line parser object + /// + /// ## Parameters + /// * argc: number of command line arguments (from main()) + /// * argv: array of command line arguments (from main()) + /// * keys: string describing acceptable command line parameters (see class description for syntax) + #[inline] + pub fn new(argv: &[&str], keys: &str) -> Result { + string_array_arg!(argv); + extern_container_arg!(keys); + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_CommandLineParser_int_const_charXX_const_StringR(argv.len().try_into()?, argv.as_ptr(), keys.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::CommandLineParser::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Copy constructor + #[inline] + pub fn copy(parser: &impl core::CommandLineParserTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_CommandLineParser_const_CommandLineParserR(parser.as_raw_CommandLineParser(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::CommandLineParser::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::CommandLineParser] + pub trait CommandLineParserTraitConst { + fn as_raw_CommandLineParser(&self) -> *const c_void; + + /// Returns application path + /// + /// This method returns the path to the executable from the command line (`argv[0]`). + /// + /// For example, if the application has been started with such a command: + /// ```C++ + /// $ ./bin/my-executable + /// ``` + /// + /// this method will return `./bin`. + #[inline] + fn get_path_to_application(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_getPathToApplication_const(self.as_raw_CommandLineParser(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Access arguments by name + /// + /// Returns argument converted to selected type. If the argument is not known or can not be + /// converted to selected type, the error flag is set (can be checked with [check]). + /// + /// For example, define: + /// ```C++ + /// String keys = "{N count||}"; + /// ``` + /// + /// + /// Call: + /// ```C++ + /// $ ./my-app -N=20 + /// # or + /// $ ./my-app --count=20 + /// ``` + /// + /// + /// Access: + /// ```C++ + /// int N = parser.get("N"); + /// ``` + /// + /// + /// ## Parameters + /// * name: name of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// + /// Note: You can access positional arguments by their `@`-prefixed name: + /// ```C++ + /// parser.get("@image"); + /// ``` + /// + /// + /// ## C++ default parameters + /// * space_delete: true + #[inline] + fn get_bool(&self, name: &str, space_delete: bool) -> Result { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_bool_const_const_StringR_bool(self.as_raw_CommandLineParser(), name.opencv_as_extern(), space_delete, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access arguments by name + /// + /// Returns argument converted to selected type. If the argument is not known or can not be + /// converted to selected type, the error flag is set (can be checked with [check]). + /// + /// For example, define: + /// ```C++ + /// String keys = "{N count||}"; + /// ``` + /// + /// + /// Call: + /// ```C++ + /// $ ./my-app -N=20 + /// # or + /// $ ./my-app --count=20 + /// ``` + /// + /// + /// Access: + /// ```C++ + /// int N = parser.get("N"); + /// ``` + /// + /// + /// ## Parameters + /// * name: name of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// + /// Note: You can access positional arguments by their `@`-prefixed name: + /// ```C++ + /// parser.get("@image"); + /// ``` + /// + /// + /// ## Note + /// This alternative version of [CommandLineParserTraitConst::get_bool] function uses the following default values for its arguments: + /// * space_delete: true + #[inline] + fn get_bool_def(&self, name: &str) -> Result { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_bool_const_const_StringR(self.as_raw_CommandLineParser(), name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access arguments by name + /// + /// Returns argument converted to selected type. If the argument is not known or can not be + /// converted to selected type, the error flag is set (can be checked with [check]). + /// + /// For example, define: + /// ```C++ + /// String keys = "{N count||}"; + /// ``` + /// + /// + /// Call: + /// ```C++ + /// $ ./my-app -N=20 + /// # or + /// $ ./my-app --count=20 + /// ``` + /// + /// + /// Access: + /// ```C++ + /// int N = parser.get("N"); + /// ``` + /// + /// + /// ## Parameters + /// * name: name of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// + /// Note: You can access positional arguments by their `@`-prefixed name: + /// ```C++ + /// parser.get("@image"); + /// ``` + /// + /// + /// ## C++ default parameters + /// * space_delete: true + #[inline] + fn get_i32(&self, name: &str, space_delete: bool) -> Result { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_int_const_const_StringR_bool(self.as_raw_CommandLineParser(), name.opencv_as_extern(), space_delete, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access arguments by name + /// + /// Returns argument converted to selected type. If the argument is not known or can not be + /// converted to selected type, the error flag is set (can be checked with [check]). + /// + /// For example, define: + /// ```C++ + /// String keys = "{N count||}"; + /// ``` + /// + /// + /// Call: + /// ```C++ + /// $ ./my-app -N=20 + /// # or + /// $ ./my-app --count=20 + /// ``` + /// + /// + /// Access: + /// ```C++ + /// int N = parser.get("N"); + /// ``` + /// + /// + /// ## Parameters + /// * name: name of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// + /// Note: You can access positional arguments by their `@`-prefixed name: + /// ```C++ + /// parser.get("@image"); + /// ``` + /// + /// + /// ## Note + /// This alternative version of [CommandLineParserTraitConst::get_i32] function uses the following default values for its arguments: + /// * space_delete: true + #[inline] + fn get_i32_def(&self, name: &str) -> Result { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_int_const_const_StringR(self.as_raw_CommandLineParser(), name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access arguments by name + /// + /// Returns argument converted to selected type. If the argument is not known or can not be + /// converted to selected type, the error flag is set (can be checked with [check]). + /// + /// For example, define: + /// ```C++ + /// String keys = "{N count||}"; + /// ``` + /// + /// + /// Call: + /// ```C++ + /// $ ./my-app -N=20 + /// # or + /// $ ./my-app --count=20 + /// ``` + /// + /// + /// Access: + /// ```C++ + /// int N = parser.get("N"); + /// ``` + /// + /// + /// ## Parameters + /// * name: name of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// + /// Note: You can access positional arguments by their `@`-prefixed name: + /// ```C++ + /// parser.get("@image"); + /// ``` + /// + /// + /// ## C++ default parameters + /// * space_delete: true + #[inline] + fn get_f64(&self, name: &str, space_delete: bool) -> Result { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_double_const_const_StringR_bool(self.as_raw_CommandLineParser(), name.opencv_as_extern(), space_delete, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access arguments by name + /// + /// Returns argument converted to selected type. If the argument is not known or can not be + /// converted to selected type, the error flag is set (can be checked with [check]). + /// + /// For example, define: + /// ```C++ + /// String keys = "{N count||}"; + /// ``` + /// + /// + /// Call: + /// ```C++ + /// $ ./my-app -N=20 + /// # or + /// $ ./my-app --count=20 + /// ``` + /// + /// + /// Access: + /// ```C++ + /// int N = parser.get("N"); + /// ``` + /// + /// + /// ## Parameters + /// * name: name of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// + /// Note: You can access positional arguments by their `@`-prefixed name: + /// ```C++ + /// parser.get("@image"); + /// ``` + /// + /// + /// ## Note + /// This alternative version of [CommandLineParserTraitConst::get_f64] function uses the following default values for its arguments: + /// * space_delete: true + #[inline] + fn get_f64_def(&self, name: &str) -> Result { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_double_const_const_StringR(self.as_raw_CommandLineParser(), name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access arguments by name + /// + /// Returns argument converted to selected type. If the argument is not known or can not be + /// converted to selected type, the error flag is set (can be checked with [check]). + /// + /// For example, define: + /// ```C++ + /// String keys = "{N count||}"; + /// ``` + /// + /// + /// Call: + /// ```C++ + /// $ ./my-app -N=20 + /// # or + /// $ ./my-app --count=20 + /// ``` + /// + /// + /// Access: + /// ```C++ + /// int N = parser.get("N"); + /// ``` + /// + /// + /// ## Parameters + /// * name: name of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// + /// Note: You can access positional arguments by their `@`-prefixed name: + /// ```C++ + /// parser.get("@image"); + /// ``` + /// + /// + /// ## C++ default parameters + /// * space_delete: true + #[inline] + fn get_str(&self, name: &str, space_delete: bool) -> Result { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_cv_String_const_const_StringR_bool(self.as_raw_CommandLineParser(), name.opencv_as_extern(), space_delete, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Access arguments by name + /// + /// Returns argument converted to selected type. If the argument is not known or can not be + /// converted to selected type, the error flag is set (can be checked with [check]). + /// + /// For example, define: + /// ```C++ + /// String keys = "{N count||}"; + /// ``` + /// + /// + /// Call: + /// ```C++ + /// $ ./my-app -N=20 + /// # or + /// $ ./my-app --count=20 + /// ``` + /// + /// + /// Access: + /// ```C++ + /// int N = parser.get("N"); + /// ``` + /// + /// + /// ## Parameters + /// * name: name of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// + /// Note: You can access positional arguments by their `@`-prefixed name: + /// ```C++ + /// parser.get("@image"); + /// ``` + /// + /// + /// ## Note + /// This alternative version of [CommandLineParserTraitConst::get_str] function uses the following default values for its arguments: + /// * space_delete: true + #[inline] + fn get_str_def(&self, name: &str) -> Result { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_cv_String_const_const_StringR(self.as_raw_CommandLineParser(), name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Access arguments by name + /// + /// Returns argument converted to selected type. If the argument is not known or can not be + /// converted to selected type, the error flag is set (can be checked with [check]). + /// + /// For example, define: + /// ```C++ + /// String keys = "{N count||}"; + /// ``` + /// + /// + /// Call: + /// ```C++ + /// $ ./my-app -N=20 + /// # or + /// $ ./my-app --count=20 + /// ``` + /// + /// + /// Access: + /// ```C++ + /// int N = parser.get("N"); + /// ``` + /// + /// + /// ## Parameters + /// * name: name of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// + /// Note: You can access positional arguments by their `@`-prefixed name: + /// ```C++ + /// parser.get("@image"); + /// ``` + /// + /// + /// ## C++ default parameters + /// * space_delete: true + #[inline] + fn get_u64(&self, name: &str, space_delete: bool) -> Result { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_uint64_t_const_const_StringR_bool(self.as_raw_CommandLineParser(), name.opencv_as_extern(), space_delete, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access arguments by name + /// + /// Returns argument converted to selected type. If the argument is not known or can not be + /// converted to selected type, the error flag is set (can be checked with [check]). + /// + /// For example, define: + /// ```C++ + /// String keys = "{N count||}"; + /// ``` + /// + /// + /// Call: + /// ```C++ + /// $ ./my-app -N=20 + /// # or + /// $ ./my-app --count=20 + /// ``` + /// + /// + /// Access: + /// ```C++ + /// int N = parser.get("N"); + /// ``` + /// + /// + /// ## Parameters + /// * name: name of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// + /// Note: You can access positional arguments by their `@`-prefixed name: + /// ```C++ + /// parser.get("@image"); + /// ``` + /// + /// + /// ## Note + /// This alternative version of [CommandLineParserTraitConst::get_u64] function uses the following default values for its arguments: + /// * space_delete: true + #[inline] + fn get_u64_def(&self, name: &str) -> Result { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_uint64_t_const_const_StringR(self.as_raw_CommandLineParser(), name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access positional arguments by index + /// + /// Returns argument converted to selected type. Indexes are counted from zero. + /// + /// For example, define: + /// ```C++ + /// String keys = "{@arg1||}{@arg2||}" + /// ``` + /// + /// + /// Call: + /// ```C++ + /// ./my-app abc qwe + /// ``` + /// + /// + /// Access arguments: + /// ```C++ + /// String val_1 = parser.get(0); // returns "abc", arg1 + /// String val_2 = parser.get(1); // returns "qwe", arg2 + /// ``` + /// + /// + /// ## Parameters + /// * index: index of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// ## C++ default parameters + /// * space_delete: true + #[inline] + fn get_bool_idx(&self, index: i32, space_delete: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_bool_const_int_bool(self.as_raw_CommandLineParser(), index, space_delete, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access positional arguments by index + /// + /// Returns argument converted to selected type. Indexes are counted from zero. + /// + /// For example, define: + /// ```C++ + /// String keys = "{@arg1||}{@arg2||}" + /// ``` + /// + /// + /// Call: + /// ```C++ + /// ./my-app abc qwe + /// ``` + /// + /// + /// Access arguments: + /// ```C++ + /// String val_1 = parser.get(0); // returns "abc", arg1 + /// String val_2 = parser.get(1); // returns "qwe", arg2 + /// ``` + /// + /// + /// ## Parameters + /// * index: index of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// ## Note + /// This alternative version of [CommandLineParserTraitConst::get_bool_idx] function uses the following default values for its arguments: + /// * space_delete: true + #[inline] + fn get_bool_idx_def(&self, index: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_bool_const_int(self.as_raw_CommandLineParser(), index, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access positional arguments by index + /// + /// Returns argument converted to selected type. Indexes are counted from zero. + /// + /// For example, define: + /// ```C++ + /// String keys = "{@arg1||}{@arg2||}" + /// ``` + /// + /// + /// Call: + /// ```C++ + /// ./my-app abc qwe + /// ``` + /// + /// + /// Access arguments: + /// ```C++ + /// String val_1 = parser.get(0); // returns "abc", arg1 + /// String val_2 = parser.get(1); // returns "qwe", arg2 + /// ``` + /// + /// + /// ## Parameters + /// * index: index of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// ## C++ default parameters + /// * space_delete: true + #[inline] + fn get_i32_idx(&self, index: i32, space_delete: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_int_const_int_bool(self.as_raw_CommandLineParser(), index, space_delete, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access positional arguments by index + /// + /// Returns argument converted to selected type. Indexes are counted from zero. + /// + /// For example, define: + /// ```C++ + /// String keys = "{@arg1||}{@arg2||}" + /// ``` + /// + /// + /// Call: + /// ```C++ + /// ./my-app abc qwe + /// ``` + /// + /// + /// Access arguments: + /// ```C++ + /// String val_1 = parser.get(0); // returns "abc", arg1 + /// String val_2 = parser.get(1); // returns "qwe", arg2 + /// ``` + /// + /// + /// ## Parameters + /// * index: index of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// ## Note + /// This alternative version of [CommandLineParserTraitConst::get_i32_idx] function uses the following default values for its arguments: + /// * space_delete: true + #[inline] + fn get_i32_idx_def(&self, index: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_int_const_int(self.as_raw_CommandLineParser(), index, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access positional arguments by index + /// + /// Returns argument converted to selected type. Indexes are counted from zero. + /// + /// For example, define: + /// ```C++ + /// String keys = "{@arg1||}{@arg2||}" + /// ``` + /// + /// + /// Call: + /// ```C++ + /// ./my-app abc qwe + /// ``` + /// + /// + /// Access arguments: + /// ```C++ + /// String val_1 = parser.get(0); // returns "abc", arg1 + /// String val_2 = parser.get(1); // returns "qwe", arg2 + /// ``` + /// + /// + /// ## Parameters + /// * index: index of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// ## C++ default parameters + /// * space_delete: true + #[inline] + fn get_f64_idx(&self, index: i32, space_delete: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_double_const_int_bool(self.as_raw_CommandLineParser(), index, space_delete, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access positional arguments by index + /// + /// Returns argument converted to selected type. Indexes are counted from zero. + /// + /// For example, define: + /// ```C++ + /// String keys = "{@arg1||}{@arg2||}" + /// ``` + /// + /// + /// Call: + /// ```C++ + /// ./my-app abc qwe + /// ``` + /// + /// + /// Access arguments: + /// ```C++ + /// String val_1 = parser.get(0); // returns "abc", arg1 + /// String val_2 = parser.get(1); // returns "qwe", arg2 + /// ``` + /// + /// + /// ## Parameters + /// * index: index of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// ## Note + /// This alternative version of [CommandLineParserTraitConst::get_f64_idx] function uses the following default values for its arguments: + /// * space_delete: true + #[inline] + fn get_f64_idx_def(&self, index: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_double_const_int(self.as_raw_CommandLineParser(), index, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access positional arguments by index + /// + /// Returns argument converted to selected type. Indexes are counted from zero. + /// + /// For example, define: + /// ```C++ + /// String keys = "{@arg1||}{@arg2||}" + /// ``` + /// + /// + /// Call: + /// ```C++ + /// ./my-app abc qwe + /// ``` + /// + /// + /// Access arguments: + /// ```C++ + /// String val_1 = parser.get(0); // returns "abc", arg1 + /// String val_2 = parser.get(1); // returns "qwe", arg2 + /// ``` + /// + /// + /// ## Parameters + /// * index: index of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// ## C++ default parameters + /// * space_delete: true + #[inline] + fn get_str_idx(&self, index: i32, space_delete: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_cv_String_const_int_bool(self.as_raw_CommandLineParser(), index, space_delete, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Access positional arguments by index + /// + /// Returns argument converted to selected type. Indexes are counted from zero. + /// + /// For example, define: + /// ```C++ + /// String keys = "{@arg1||}{@arg2||}" + /// ``` + /// + /// + /// Call: + /// ```C++ + /// ./my-app abc qwe + /// ``` + /// + /// + /// Access arguments: + /// ```C++ + /// String val_1 = parser.get(0); // returns "abc", arg1 + /// String val_2 = parser.get(1); // returns "qwe", arg2 + /// ``` + /// + /// + /// ## Parameters + /// * index: index of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// ## Note + /// This alternative version of [CommandLineParserTraitConst::get_str_idx] function uses the following default values for its arguments: + /// * space_delete: true + #[inline] + fn get_str_idx_def(&self, index: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_cv_String_const_int(self.as_raw_CommandLineParser(), index, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Access positional arguments by index + /// + /// Returns argument converted to selected type. Indexes are counted from zero. + /// + /// For example, define: + /// ```C++ + /// String keys = "{@arg1||}{@arg2||}" + /// ``` + /// + /// + /// Call: + /// ```C++ + /// ./my-app abc qwe + /// ``` + /// + /// + /// Access arguments: + /// ```C++ + /// String val_1 = parser.get(0); // returns "abc", arg1 + /// String val_2 = parser.get(1); // returns "qwe", arg2 + /// ``` + /// + /// + /// ## Parameters + /// * index: index of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// ## C++ default parameters + /// * space_delete: true + #[inline] + fn get_u64_idx(&self, index: i32, space_delete: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_uint64_t_const_int_bool(self.as_raw_CommandLineParser(), index, space_delete, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Access positional arguments by index + /// + /// Returns argument converted to selected type. Indexes are counted from zero. + /// + /// For example, define: + /// ```C++ + /// String keys = "{@arg1||}{@arg2||}" + /// ``` + /// + /// + /// Call: + /// ```C++ + /// ./my-app abc qwe + /// ``` + /// + /// + /// Access arguments: + /// ```C++ + /// String val_1 = parser.get(0); // returns "abc", arg1 + /// String val_2 = parser.get(1); // returns "qwe", arg2 + /// ``` + /// + /// + /// ## Parameters + /// * index: index of the argument + /// * space_delete: remove spaces from the left and right of the string + /// @tparam T the argument will be converted to this type if possible + /// + /// ## Note + /// This alternative version of [CommandLineParserTraitConst::get_u64_idx] function uses the following default values for its arguments: + /// * space_delete: true + #[inline] + fn get_u64_idx_def(&self, index: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_get_uint64_t_const_int(self.as_raw_CommandLineParser(), index, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Check if field was provided in the command line + /// + /// ## Parameters + /// * name: argument name to check + #[inline] + fn has(&self, name: &str) -> Result { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_has_const_const_StringR(self.as_raw_CommandLineParser(), name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Check for parsing errors + /// + /// Returns false if error occurred while accessing the parameters (bad conversion, missing arguments, + /// etc.). Call [printErrors] to print error messages list. + #[inline] + fn check(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_check_const(self.as_raw_CommandLineParser(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Print help message + /// + /// This method will print standard help message containing the about message and arguments description. + /// ## See also + /// about + #[inline] + fn print_message(&self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_printMessage_const(self.as_raw_CommandLineParser(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Print list of errors occurred + /// ## See also + /// check + #[inline] + fn print_errors(&self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_printErrors_const(self.as_raw_CommandLineParser(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::CommandLineParser] + pub trait CommandLineParserTrait: core::CommandLineParserTraitConst { + fn as_raw_mut_CommandLineParser(&mut self) -> *mut c_void; + + /// Assignment operator + #[inline] + fn set(&mut self, parser: &impl core::CommandLineParserTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_operatorST_const_CommandLineParserR(self.as_raw_mut_CommandLineParser(), parser.as_raw_CommandLineParser(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Set the about message + /// + /// The about message will be shown when [printMessage] is called, right before arguments table. + #[inline] + fn about(&mut self, message: &str) -> Result<()> { + extern_container_arg!(message); + return_send!(via ocvrs_return); + unsafe { sys::cv_CommandLineParser_about_const_StringR(self.as_raw_mut_CommandLineParser(), message.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for CommandLineParser { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("CommandLineParser") + .finish() + } + } + + impl core::CommandLineParserTraitConst for CommandLineParser { + #[inline] fn as_raw_CommandLineParser(&self) -> *const c_void { self.as_raw() } + } + + impl core::CommandLineParserTrait for CommandLineParser { + #[inline] fn as_raw_mut_CommandLineParser(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CommandLineParser, core::CommandLineParserTraitConst, as_raw_CommandLineParser, core::CommandLineParserTrait, as_raw_mut_CommandLineParser } + + /// This class is used to perform the non-linear non-constrained minimization of a function + /// with known gradient, + /// + /// defined on an *n*-dimensional Euclidean space, using the **Nonlinear Conjugate Gradient method**. + /// The implementation was done based on the beautifully clear explanatory article [An Introduction to + /// the Conjugate Gradient Method Without the Agonizing + /// Pain](http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf) by Jonathan Richard + /// Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for + /// example ) for numerically solving the + /// systems of linear equations. + /// + /// It should be noted, that this method, although deterministic, is rather a heuristic method and + /// therefore may converge to a local minima, not necessary a global one. What is even more disastrous, + /// most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between + /// local minima and maxima. Therefore, if it starts sufficiently near to the local maximum, it may + /// converge to it. Another obvious restriction is that it should be possible to compute the gradient of + /// a function at any point, thus it is preferable to have analytic expression for gradient and + /// computational burden should be born by the user. + /// + /// The latter responsibility is accomplished via the getGradient method of a + /// MinProblemSolver::Function interface (which represents function being optimized). This method takes + /// point a point in *n*-dimensional space (first argument represents the array of coordinates of that + /// point) and compute its gradient (it should be stored in the second argument as an array). + /// + /// + /// Note: class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface. + /// + /// + /// Note: term criteria should meet following condition: + /// ```C++ + /// termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 + /// // or + /// termcrit.type == TermCriteria::MAX_ITER) && termcrit.maxCount > 0 + /// ``` + /// + pub struct ConjGradSolver { + ptr: *mut c_void, + } + + opencv_type_boxed! { ConjGradSolver } + + impl Drop for ConjGradSolver { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ConjGradSolver_delete(self.as_raw_mut_ConjGradSolver()) }; + } + } + + unsafe impl Send for ConjGradSolver {} + + impl ConjGradSolver { + /// This function returns the reference to the ready-to-use ConjGradSolver object. + /// + /// All the parameters are optional, so this procedure can be called even without parameters at + /// all. In this case, the default values will be used. As default value for terminal criteria are + /// the only sensible ones, MinProblemSolver::setFunction() should be called upon the obtained + /// object, if the function was not given to create(). Otherwise, the two ways (submit it to + /// create() or miss it out and call the MinProblemSolver::setFunction()) are absolutely equivalent + /// (and will drop the same errors in the same way, should invalid input be detected). + /// ## Parameters + /// * f: Pointer to the function that will be minimized, similarly to the one you submit via + /// MinProblemSolver::setFunction. + /// * termcrit: Terminal criteria to the algorithm, similarly to the one you submit via + /// MinProblemSolver::setTermCriteria. + /// + /// ## C++ default parameters + /// * f: Ptr() + /// * termcrit: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001) + #[inline] + pub fn create(f: &core::Ptr, termcrit: core::TermCriteria) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ConjGradSolver_create_const_PtrLFunctionGR_TermCriteria(f.as_raw_PtrOfMinProblemSolver_Function(), &termcrit, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// This function returns the reference to the ready-to-use ConjGradSolver object. + /// + /// All the parameters are optional, so this procedure can be called even without parameters at + /// all. In this case, the default values will be used. As default value for terminal criteria are + /// the only sensible ones, MinProblemSolver::setFunction() should be called upon the obtained + /// object, if the function was not given to create(). Otherwise, the two ways (submit it to + /// create() or miss it out and call the MinProblemSolver::setFunction()) are absolutely equivalent + /// (and will drop the same errors in the same way, should invalid input be detected). + /// ## Parameters + /// * f: Pointer to the function that will be minimized, similarly to the one you submit via + /// MinProblemSolver::setFunction. + /// * termcrit: Terminal criteria to the algorithm, similarly to the one you submit via + /// MinProblemSolver::setTermCriteria. + /// + /// ## Note + /// This alternative version of [ConjGradSolver::create] function uses the following default values for its arguments: + /// * f: Ptr() + /// * termcrit: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001) + #[inline] + pub fn create_def() -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ConjGradSolver_create(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::ConjGradSolver] + pub trait ConjGradSolverTraitConst: core::MinProblemSolverTraitConst { + fn as_raw_ConjGradSolver(&self) -> *const c_void; + + } + + /// Mutable methods for [core::ConjGradSolver] + pub trait ConjGradSolverTrait: core::ConjGradSolverTraitConst + core::MinProblemSolverTrait { + fn as_raw_mut_ConjGradSolver(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for ConjGradSolver { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ConjGradSolver") + .finish() + } + } + + boxed_cast_base! { ConjGradSolver, core::Algorithm, cv_ConjGradSolver_to_Algorithm } + + boxed_cast_base! { ConjGradSolver, core::MinProblemSolver, cv_ConjGradSolver_to_MinProblemSolver } + + impl core::AlgorithmTraitConst for ConjGradSolver { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ConjGradSolver { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConjGradSolver, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl core::MinProblemSolverTraitConst for ConjGradSolver { + #[inline] fn as_raw_MinProblemSolver(&self) -> *const c_void { self.as_raw() } + } + + impl core::MinProblemSolverTrait for ConjGradSolver { + #[inline] fn as_raw_mut_MinProblemSolver(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConjGradSolver, core::MinProblemSolverTraitConst, as_raw_MinProblemSolver, core::MinProblemSolverTrait, as_raw_mut_MinProblemSolver } + + impl core::ConjGradSolverTraitConst for ConjGradSolver { + #[inline] fn as_raw_ConjGradSolver(&self) -> *const c_void { self.as_raw() } + } + + impl core::ConjGradSolverTrait for ConjGradSolver { + #[inline] fn as_raw_mut_ConjGradSolver(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConjGradSolver, core::ConjGradSolverTraitConst, as_raw_ConjGradSolver, core::ConjGradSolverTrait, as_raw_mut_ConjGradSolver } + + /// Class for matching keypoint descriptors + /// + /// query descriptor index, train descriptor index, train image index, and distance between + /// descriptors. + #[repr(C)] + #[derive(Copy, Clone, Debug, PartialEq)] + pub struct DMatch { + /// query descriptor index + pub query_idx: i32, + /// train descriptor index + pub train_idx: i32, + /// train image index + pub img_idx: i32, + pub distance: f32, + } + + opencv_type_simple! { core::DMatch } + + impl DMatch { + #[inline] + pub fn less_than(&self, m: core::DMatch) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_DMatch_operatorL_const_const_DMatchR(self.as_raw_DMatch(), &m, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// This class is used to perform the non-linear non-constrained minimization of a function, + /// + /// defined on an `n`-dimensional Euclidean space, using the **Nelder-Mead method**, also known as + /// **downhill simplex method**. The basic idea about the method can be obtained from + /// . + /// + /// It should be noted, that this method, although deterministic, is rather a heuristic and therefore + /// may converge to a local minima, not necessary a global one. It is iterative optimization technique, + /// which at each step uses an information about the values of a function evaluated only at `n+1` + /// points, arranged as a *simplex* in `n`-dimensional space (hence the second name of the method). At + /// each step new point is chosen to evaluate function at, obtained value is compared with previous + /// ones and based on this information simplex changes it's shape , slowly moving to the local minimum. + /// Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear + /// Conjugate Gradient method (which is also implemented in optim). + /// + /// Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the + /// function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so + /// small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first, for some + /// defined by user positive integer termcrit.maxCount and positive non-integer termcrit.epsilon. + /// + /// + /// Note: DownhillSolver is a derivative of the abstract interface + /// cv::MinProblemSolver, which in turn is derived from the Algorithm interface and is used to + /// encapsulate the functionality, common to all non-linear optimization algorithms in the optim + /// module. + /// + /// + /// Note: term criteria should meet following condition: + /// ```C++ + /// termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 + /// ``` + /// + pub struct DownhillSolver { + ptr: *mut c_void, + } + + opencv_type_boxed! { DownhillSolver } + + impl Drop for DownhillSolver { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_DownhillSolver_delete(self.as_raw_mut_DownhillSolver()) }; + } + } + + unsafe impl Send for DownhillSolver {} + + impl DownhillSolver { + /// This function returns the reference to the ready-to-use DownhillSolver object. + /// + /// All the parameters are optional, so this procedure can be called even without parameters at + /// all. In this case, the default values will be used. As default value for terminal criteria are + /// the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep() + /// should be called upon the obtained object, if the respective parameters were not given to + /// create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out + /// and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely + /// equivalent (and will drop the same errors in the same way, should invalid input be detected). + /// ## Parameters + /// * f: Pointer to the function that will be minimized, similarly to the one you submit via + /// MinProblemSolver::setFunction. + /// * initStep: Initial step, that will be used to construct the initial simplex, similarly to the one + /// you submit via MinProblemSolver::setInitStep. + /// * termcrit: Terminal criteria to the algorithm, similarly to the one you submit via + /// MinProblemSolver::setTermCriteria. + /// + /// ## C++ default parameters + /// * f: Ptr() + /// * init_step: Mat_(1,1,0.0) + /// * termcrit: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001) + #[inline] + pub fn create(f: &core::Ptr, init_step: &impl ToInputArray, termcrit: core::TermCriteria) -> Result> { + input_array_arg!(init_step); + return_send!(via ocvrs_return); + unsafe { sys::cv_DownhillSolver_create_const_PtrLFunctionGR_const__InputArrayR_TermCriteria(f.as_raw_PtrOfMinProblemSolver_Function(), init_step.as_raw__InputArray(), &termcrit, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// This function returns the reference to the ready-to-use DownhillSolver object. + /// + /// All the parameters are optional, so this procedure can be called even without parameters at + /// all. In this case, the default values will be used. As default value for terminal criteria are + /// the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep() + /// should be called upon the obtained object, if the respective parameters were not given to + /// create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out + /// and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely + /// equivalent (and will drop the same errors in the same way, should invalid input be detected). + /// ## Parameters + /// * f: Pointer to the function that will be minimized, similarly to the one you submit via + /// MinProblemSolver::setFunction. + /// * initStep: Initial step, that will be used to construct the initial simplex, similarly to the one + /// you submit via MinProblemSolver::setInitStep. + /// * termcrit: Terminal criteria to the algorithm, similarly to the one you submit via + /// MinProblemSolver::setTermCriteria. + /// + /// ## Note + /// This alternative version of [DownhillSolver::create] function uses the following default values for its arguments: + /// * f: Ptr() + /// * init_step: Mat_(1,1,0.0) + /// * termcrit: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001) + #[inline] + pub fn create_def() -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_DownhillSolver_create(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::DownhillSolver] + pub trait DownhillSolverTraitConst: core::MinProblemSolverTraitConst { + fn as_raw_DownhillSolver(&self) -> *const c_void; + + /// Returns the initial step that will be used in downhill simplex algorithm. + /// + /// ## Parameters + /// * step: Initial step that will be used in algorithm. Note, that although corresponding setter + /// accepts column-vectors as well as row-vectors, this method will return a row-vector. + /// ## See also + /// DownhillSolver::setInitStep + #[inline] + fn get_init_step(&self, step: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(step); + return_send!(via ocvrs_return); + unsafe { sys::cv_DownhillSolver_getInitStep_const_const__OutputArrayR(self.as_raw_DownhillSolver(), step.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::DownhillSolver] + pub trait DownhillSolverTrait: core::DownhillSolverTraitConst + core::MinProblemSolverTrait { + fn as_raw_mut_DownhillSolver(&mut self) -> *mut c_void; + + /// Sets the initial step that will be used in downhill simplex algorithm. + /// + /// Step, together with initial point (given in DownhillSolver::minimize) are two `n`-dimensional + /// vectors that are used to determine the shape of initial simplex. Roughly said, initial point + /// determines the position of a simplex (it will become simplex's centroid), while step determines the + /// spread (size in each dimension) of a simplex. To be more precise, if ![inline formula](https://latex.codecogs.com/png.latex?s%2Cx%5F0%5Cin%5Cmathbb%7BR%7D%5En) are + /// the initial step and initial point respectively, the vertices of a simplex will be: + /// ![inline formula](https://latex.codecogs.com/png.latex?v%5F0%3A%3Dx%5F0%2D%5Cfrac%7B1%7D%7B2%7D%20s) and ![inline formula](https://latex.codecogs.com/png.latex?v%5Fi%3A%3Dx%5F0%2Bs%5Fi) for ![inline formula](https://latex.codecogs.com/png.latex?i%3D1%2C2%2C%5Cdots%2Cn) where ![inline formula](https://latex.codecogs.com/png.latex?s%5Fi) denotes + /// projections of the initial step of *n*-th coordinate (the result of projection is treated to be + /// vector given by ![inline formula](https://latex.codecogs.com/png.latex?s%5Fi%3A%3De%5Fi%5Ccdot%5Cleft%3Ce%5Fi%5Ccdot%20s%5Cright%3E), where ![inline formula](https://latex.codecogs.com/png.latex?e%5Fi) form canonical basis) + /// + /// ## Parameters + /// * step: Initial step that will be used in algorithm. Roughly said, it determines the spread + /// (size in each dimension) of an initial simplex. + #[inline] + fn set_init_step(&mut self, step: &impl ToInputArray) -> Result<()> { + input_array_arg!(step); + return_send!(via ocvrs_return); + unsafe { sys::cv_DownhillSolver_setInitStep_const__InputArrayR(self.as_raw_mut_DownhillSolver(), step.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for DownhillSolver { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("DownhillSolver") + .finish() + } + } + + boxed_cast_base! { DownhillSolver, core::Algorithm, cv_DownhillSolver_to_Algorithm } + + boxed_cast_base! { DownhillSolver, core::MinProblemSolver, cv_DownhillSolver_to_MinProblemSolver } + + impl core::AlgorithmTraitConst for DownhillSolver { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for DownhillSolver { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DownhillSolver, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl core::MinProblemSolverTraitConst for DownhillSolver { + #[inline] fn as_raw_MinProblemSolver(&self) -> *const c_void { self.as_raw() } + } + + impl core::MinProblemSolverTrait for DownhillSolver { + #[inline] fn as_raw_mut_MinProblemSolver(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DownhillSolver, core::MinProblemSolverTraitConst, as_raw_MinProblemSolver, core::MinProblemSolverTrait, as_raw_mut_MinProblemSolver } + + impl core::DownhillSolverTraitConst for DownhillSolver { + #[inline] fn as_raw_DownhillSolver(&self) -> *const c_void { self.as_raw() } + } + + impl core::DownhillSolverTrait for DownhillSolver { + #[inline] fn as_raw_mut_DownhillSolver(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DownhillSolver, core::DownhillSolverTraitConst, as_raw_DownhillSolver, core::DownhillSolverTrait, as_raw_mut_DownhillSolver } + + /// ! Class passed to an error. + /// + /// This class encapsulates all or almost all necessary + /// information about the error happened in the program. The exception is + /// usually constructed and thrown implicitly via CV_Error and CV_Error_ macros. + /// ## See also + /// error + pub struct Exception { + ptr: *mut c_void, + } + + opencv_type_boxed! { Exception } + + impl Drop for Exception { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Exception_delete(self.as_raw_mut_Exception()) }; + } + } + + unsafe impl Send for Exception {} + + impl Exception { + /// ! + /// Default constructor + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Exception_Exception(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Exception::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ! + /// Full constructor. Normally the constructor is not called explicitly. + /// Instead, the macros CV_Error(), CV_Error_() and CV_Assert() are used. + #[inline] + pub fn new(_code: i32, _err: &str, _func: &str, _file: &str, _line: i32) -> Result { + extern_container_arg!(_err); + extern_container_arg!(_func); + extern_container_arg!(_file); + return_send!(via ocvrs_return); + unsafe { sys::cv_Exception_Exception_int_const_StringR_const_StringR_const_StringR_int(_code, _err.opencv_as_extern(), _func.opencv_as_extern(), _file.opencv_as_extern(), _line, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Exception::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Exception] + pub trait ExceptionTraitConst { + fn as_raw_Exception(&self) -> *const c_void; + + /// the formatted error message + #[inline] + fn msg(&self) -> String { + let ret = unsafe { sys::cv_Exception_propMsg_const(self.as_raw_Exception()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + /// error code see also: CVStatus + #[inline] + fn code(&self) -> i32 { + let ret = unsafe { sys::cv_Exception_propCode_const(self.as_raw_Exception()) }; + ret + } + + /// error description + #[inline] + fn err(&self) -> String { + let ret = unsafe { sys::cv_Exception_propErr_const(self.as_raw_Exception()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + /// function name. Available only when the compiler supports getting it + #[inline] + fn func(&self) -> String { + let ret = unsafe { sys::cv_Exception_propFunc_const(self.as_raw_Exception()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + /// source file name where the error has occurred + #[inline] + fn file(&self) -> String { + let ret = unsafe { sys::cv_Exception_propFile_const(self.as_raw_Exception()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + /// line number in the source file where the error has occurred + #[inline] + fn line(&self) -> i32 { + let ret = unsafe { sys::cv_Exception_propLine_const(self.as_raw_Exception()) }; + ret + } + + /// ! + /// \return the error description and the context as a text string. + #[inline] + fn what(&self) -> String { + let ret = unsafe { sys::cv_Exception_what_const(self.as_raw_Exception()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + } + + /// Mutable methods for [core::Exception] + pub trait ExceptionTrait: core::ExceptionTraitConst { + fn as_raw_mut_Exception(&mut self) -> *mut c_void; + + /// the formatted error message + #[inline] + fn set_msg(&mut self, val: &str) { + extern_container_arg!(nofail val); + let ret = unsafe { sys::cv_Exception_propMsg_const_String(self.as_raw_mut_Exception(), val.opencv_as_extern()) }; + ret + } + + /// error code see also: CVStatus + #[inline] + fn set_code(&mut self, val: i32) { + let ret = unsafe { sys::cv_Exception_propCode_const_int(self.as_raw_mut_Exception(), val) }; + ret + } + + /// error description + #[inline] + fn set_err(&mut self, val: &str) { + extern_container_arg!(nofail val); + let ret = unsafe { sys::cv_Exception_propErr_const_String(self.as_raw_mut_Exception(), val.opencv_as_extern()) }; + ret + } + + /// function name. Available only when the compiler supports getting it + #[inline] + fn set_func(&mut self, val: &str) { + extern_container_arg!(nofail val); + let ret = unsafe { sys::cv_Exception_propFunc_const_String(self.as_raw_mut_Exception(), val.opencv_as_extern()) }; + ret + } + + /// source file name where the error has occurred + #[inline] + fn set_file(&mut self, val: &str) { + extern_container_arg!(nofail val); + let ret = unsafe { sys::cv_Exception_propFile_const_String(self.as_raw_mut_Exception(), val.opencv_as_extern()) }; + ret + } + + /// line number in the source file where the error has occurred + #[inline] + fn set_line(&mut self, val: i32) { + let ret = unsafe { sys::cv_Exception_propLine_const_int(self.as_raw_mut_Exception(), val) }; + ret + } + + #[inline] + fn format_message(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Exception_formatMessage(self.as_raw_mut_Exception(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for Exception { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Exception") + .field("msg", &core::ExceptionTraitConst::msg(self)) + .field("code", &core::ExceptionTraitConst::code(self)) + .field("err", &core::ExceptionTraitConst::err(self)) + .field("func", &core::ExceptionTraitConst::func(self)) + .field("file", &core::ExceptionTraitConst::file(self)) + .field("line", &core::ExceptionTraitConst::line(self)) + .finish() + } + } + + impl core::ExceptionTraitConst for Exception { + #[inline] fn as_raw_Exception(&self) -> *const c_void { self.as_raw() } + } + + impl core::ExceptionTrait for Exception { + #[inline] fn as_raw_mut_Exception(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Exception, core::ExceptionTraitConst, as_raw_Exception, core::ExceptionTrait, as_raw_mut_Exception } + + /// File Storage Node class. + /// + /// The node is used to store each and every element of the file storage opened for reading. When + /// XML/YAML file is read, it is first parsed and stored in the memory as a hierarchical collection of + /// nodes. Each node can be a "leaf" that is contain a single number or a string, or be a collection of + /// other nodes. There can be named collections (mappings) where each element has a name and it is + /// accessed by a name, and ordered collections (sequences) where elements do not have names but rather + /// accessed by index. Type of the file node can be determined using FileNode::type method. + /// + /// Note that file nodes are only used for navigating file storages opened for reading. When a file + /// storage is opened for writing, no data is stored in memory after it is written. + pub struct FileNode { + ptr: *mut c_void, + } + + opencv_type_boxed! { FileNode } + + impl Drop for FileNode { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_FileNode_delete(self.as_raw_mut_FileNode()) }; + } + } + + unsafe impl Send for FileNode {} + + impl FileNode { + #[inline] + pub fn is_map(flags: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_isMap_int(flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn is_seq(flags: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_isSeq_int(flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn is_collection(flags: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_isCollection_int(flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn is_empty_collection(flags: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_isEmptyCollection_int(flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn is_flow(flags: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_isFlow_int(flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Constant methods for [core::FileNode] + pub trait FileNodeTraitConst { + fn as_raw_FileNode(&self) -> *const c_void; + + #[inline] + fn block_idx(&self) -> size_t { + let ret = unsafe { sys::cv_FileNode_propBlockIdx_const(self.as_raw_FileNode()) }; + ret + } + + #[inline] + fn ofs(&self) -> size_t { + let ret = unsafe { sys::cv_FileNode_propOfs_const(self.as_raw_FileNode()) }; + ret + } + + /// Returns element of a mapping node or a sequence node. + /// ## Parameters + /// * nodename: Name of an element in the mapping node. + /// ## Returns + /// Returns the element with the given identifier. + #[inline] + fn get(&self, nodename: &str) -> Result { + extern_container_arg!(nodename); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_operator___const_const_StringR(self.as_raw_FileNode(), nodename.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::FileNode::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns element of a mapping node or a sequence node. + /// ## Parameters + /// * nodename: Name of an element in the mapping node. + /// ## Returns + /// Returns the element with the given identifier. + /// + /// ## Overloaded parameters + /// + /// * nodename: Name of an element in the mapping node. + #[inline] + fn get_node(&self, nodename: &str) -> Result { + extern_container_arg!(nodename); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_operator___const_const_charX(self.as_raw_FileNode(), nodename.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::FileNode::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns element of a mapping node or a sequence node. + /// ## Parameters + /// * nodename: Name of an element in the mapping node. + /// ## Returns + /// Returns the element with the given identifier. + /// + /// ## Overloaded parameters + /// + /// * i: Index of an element in the sequence node. + #[inline] + fn at(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_operator___const_int(self.as_raw_FileNode(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::FileNode::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns keys of a mapping node. + /// ## Returns + /// Keys of a mapping node. + #[inline] + fn keys(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_keys_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns type of the node. + /// ## Returns + /// Type of the node. See FileNode::Type + #[inline] + fn typ(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_type_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns true if the node is empty + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_empty_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns true if the node is a "none" object + #[inline] + fn is_none(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_isNone_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns true if the node is a sequence + #[inline] + fn is_seq(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_isSeq_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns true if the node is a mapping + #[inline] + fn is_map(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_isMap_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns true if the node is an integer + #[inline] + fn is_int(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_isInt_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns true if the node is a floating-point number + #[inline] + fn is_real(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_isReal_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns true if the node is a text string + #[inline] + fn is_string(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_isString_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns true if the node has a name + #[inline] + fn is_named(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_isNamed_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the node name or an empty string if the node is nameless + #[inline] + fn name(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_name_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// returns the number of elements in the node, if it is a sequence or mapping, or 1 otherwise. + #[inline] + fn size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_size_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns raw size of the FileNode in bytes + #[inline] + fn raw_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_rawSize_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the node content as an integer. If the node stores floating-point number, it is rounded. + #[inline] + fn to_i32(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_operator_int_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the node content as a signed 64bit integer. If the node stores floating-point number, it is rounded. + #[inline] + fn to_i64(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_operator_int64_t_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the node content as float + #[inline] + fn to_f32(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_operator_float_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the node content as double + #[inline] + fn to_f64(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_operator_double_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the node content as text string + #[inline] + fn to_string(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_operator_std_string_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn ptr(&self) -> Result<*const u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_ptr_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Reads node elements to the buffer with the specified format. + /// + /// Usually it is more convenient to use operator `>>` instead of this method. + /// ## Parameters + /// * fmt: Specification of each array element. See [format_spec] "format specification" + /// * vec: Pointer to the destination array. + /// * len: Number of bytes to read (buffer size limit). If it is greater than number of + /// remaining elements then all of them will be read. + #[inline] + fn read_raw(&self, fmt: &str, vec: &mut [u8]) -> Result<()> { + extern_container_arg!(fmt); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_readRaw_const_const_StringR_voidX_size_t(self.as_raw_FileNode(), fmt.opencv_as_extern(), vec.as_mut_ptr().cast(), vec.len(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Simplified reading API to use with bindings. + #[inline] + fn real(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_real_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Simplified reading API to use with bindings. + #[inline] + fn string(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_string_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Simplified reading API to use with bindings. + #[inline] + fn mat(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_mat_const(self.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Mutable methods for [core::FileNode] + pub trait FileNodeTrait: core::FileNodeTraitConst { + fn as_raw_mut_FileNode(&mut self) -> *mut c_void; + + #[inline] + fn set_block_idx(&mut self, val: size_t) { + let ret = unsafe { sys::cv_FileNode_propBlockIdx_const_size_t(self.as_raw_mut_FileNode(), val) }; + ret + } + + #[inline] + fn set_ofs(&mut self, val: size_t) { + let ret = unsafe { sys::cv_FileNode_propOfs_const_size_t(self.as_raw_mut_FileNode(), val) }; + ret + } + + #[inline] + fn set(&mut self, node: &impl core::FileNodeTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_operatorST_const_FileNodeR(self.as_raw_mut_FileNode(), node.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn ptr_1(&mut self) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_ptr(self.as_raw_mut_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Internal method used when reading FileStorage. + /// Sets the type (int, real or string) and value of the previously created node. + #[inline] + fn set_value(&mut self, typ: i32, value: &mut [u8]) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNode_setValue_int_const_voidX_int(self.as_raw_mut_FileNode(), typ, value.as_ptr().cast(), value.len().try_into()?, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl Clone for FileNode { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_FileNode_implicitClone_const(self.as_raw_FileNode())) } + } + } + + impl std::fmt::Debug for FileNode { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("FileNode") + .field("block_idx", &core::FileNodeTraitConst::block_idx(self)) + .field("ofs", &core::FileNodeTraitConst::ofs(self)) + .finish() + } + } + + impl core::FileNodeTraitConst for FileNode { + #[inline] fn as_raw_FileNode(&self) -> *const c_void { self.as_raw() } + } + + impl core::FileNodeTrait for FileNode { + #[inline] fn as_raw_mut_FileNode(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { FileNode, core::FileNodeTraitConst, as_raw_FileNode, core::FileNodeTrait, as_raw_mut_FileNode } + + /// used to iterate through sequences and mappings. + /// + /// A standard STL notation, with node.begin(), node.end() denoting the beginning and the end of a + /// sequence, stored in node. See the data reading sample in the beginning of the section. + pub struct FileNodeIterator { + ptr: *mut c_void, + } + + opencv_type_boxed! { FileNodeIterator } + + impl Drop for FileNodeIterator { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_FileNodeIterator_delete(self.as_raw_mut_FileNodeIterator()) }; + } + } + + unsafe impl Send for FileNodeIterator {} + + /// Constant methods for [core::FileNodeIterator] + pub trait FileNodeIteratorTraitConst { + fn as_raw_FileNodeIterator(&self) -> *const c_void; + + /// returns the currently observed element + #[inline] + fn try_deref(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNodeIterator_operatorX_const(self.as_raw_FileNodeIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::FileNode::opencv_from_extern(ret) }; + Ok(ret) + } + + /// returns the number of remaining (not read yet) elements + #[inline] + fn remaining(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNodeIterator_remaining_const(self.as_raw_FileNodeIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn equal_to(&self, it: &impl core::FileNodeIteratorTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNodeIterator_equalTo_const_const_FileNodeIteratorR(self.as_raw_FileNodeIterator(), it.as_raw_FileNodeIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::FileNodeIterator] + pub trait FileNodeIteratorTrait: core::FileNodeIteratorTraitConst { + fn as_raw_mut_FileNodeIterator(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, it: &impl core::FileNodeIteratorTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNodeIterator_operatorST_const_FileNodeIteratorR(self.as_raw_mut_FileNodeIterator(), it.as_raw_FileNodeIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// moves iterator to the next node + #[inline] + fn incr(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNodeIterator_operatorAA(self.as_raw_mut_FileNodeIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::FileNodeIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads node elements to the buffer with the specified format. + /// + /// Usually it is more convenient to use operator `>>` instead of this method. + /// ## Parameters + /// * fmt: Specification of each array element. See [format_spec] "format specification" + /// * vec: Pointer to the destination array. + /// * len: Number of bytes to read (buffer size limit). If it is greater than number of + /// remaining elements then all of them will be read. + #[inline] + fn read_raw(&mut self, fmt: &str, vec: &mut [u8]) -> Result { + extern_container_arg!(fmt); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileNodeIterator_readRaw_const_StringR_voidX_size_t(self.as_raw_mut_FileNodeIterator(), fmt.opencv_as_extern(), vec.as_mut_ptr().cast(), vec.len(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::FileNodeIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl std::fmt::Debug for FileNodeIterator { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("FileNodeIterator") + .finish() + } + } + + impl core::FileNodeIteratorTraitConst for FileNodeIterator { + #[inline] fn as_raw_FileNodeIterator(&self) -> *const c_void { self.as_raw() } + } + + impl core::FileNodeIteratorTrait for FileNodeIterator { + #[inline] fn as_raw_mut_FileNodeIterator(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { FileNodeIterator, core::FileNodeIteratorTraitConst, as_raw_FileNodeIterator, core::FileNodeIteratorTrait, as_raw_mut_FileNodeIterator } + + /// XML/YAML/JSON file storage class that encapsulates all the information necessary for writing or + /// reading data to/from a file. + pub struct FileStorage { + ptr: *mut c_void, + } + + opencv_type_boxed! { FileStorage } + + impl Drop for FileStorage { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_FileStorage_delete(self.as_raw_mut_FileStorage()) }; + } + } + + unsafe impl Send for FileStorage {} + + impl FileStorage { + /// The constructors. + /// + /// The full constructor opens the file. Alternatively you can use the default constructor and then + /// call FileStorage::open. + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_FileStorage(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::FileStorage::opencv_from_extern(ret) }; + Ok(ret) + } + + /// The constructors. + /// + /// The full constructor opens the file. Alternatively you can use the default constructor and then + /// call FileStorage::open. + /// + /// ## Overloaded parameters + /// + /// @copydoc open() + /// + /// ## C++ default parameters + /// * encoding: String() + #[inline] + pub fn new(filename: &str, flags: i32, encoding: &str) -> Result { + extern_container_arg!(filename); + extern_container_arg!(encoding); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_FileStorage_const_StringR_int_const_StringR(filename.opencv_as_extern(), flags, encoding.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::FileStorage::opencv_from_extern(ret) }; + Ok(ret) + } + + /// The constructors. + /// + /// The full constructor opens the file. Alternatively you can use the default constructor and then + /// call FileStorage::open. + /// + /// ## Overloaded parameters + /// + /// @copydoc open() + /// + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * encoding: String() + #[inline] + pub fn new_def(filename: &str, flags: i32) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_FileStorage_const_StringR_int(filename.opencv_as_extern(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::FileStorage::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns the normalized object name for the specified name of a file. + /// ## Parameters + /// * filename: Name of a file + /// ## Returns + /// The normalized object name. + #[inline] + pub fn get_default_object_name(filename: &str) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_getDefaultObjectName_const_StringR(filename.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::FileStorage] + pub trait FileStorageTraitConst { + fn as_raw_FileStorage(&self) -> *const c_void; + + #[inline] + fn state(&self) -> i32 { + let ret = unsafe { sys::cv_FileStorage_propState_const(self.as_raw_FileStorage()) }; + ret + } + + #[inline] + fn elname(&self) -> String { + let ret = unsafe { sys::cv_FileStorage_propElname_const(self.as_raw_FileStorage()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + /// Checks whether the file is opened. + /// + /// ## Returns + /// true if the object is associated with the current file and false otherwise. It is a + /// good practice to call this method after you tried to open a file. + #[inline] + fn is_opened(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_isOpened_const(self.as_raw_FileStorage(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the current format. + /// ## Returns + /// The current format, see FileStorage::Mode + #[inline] + fn get_format(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_getFormat_const(self.as_raw_FileStorage(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::FileStorage] + pub trait FileStorageTrait: core::FileStorageTraitConst { + fn as_raw_mut_FileStorage(&mut self) -> *mut c_void; + + #[inline] + fn set_state(&mut self, val: i32) { + let ret = unsafe { sys::cv_FileStorage_propState_const_int(self.as_raw_mut_FileStorage(), val) }; + ret + } + + #[inline] + fn set_elname(&mut self, val: &str) { + extern_container_arg!(nofail val); + let ret = unsafe { sys::cv_FileStorage_propElname_const_string(self.as_raw_mut_FileStorage(), val.opencv_as_extern()) }; + ret + } + + /// Opens a file. + /// + /// See description of parameters in FileStorage::FileStorage. The method calls FileStorage::release + /// before opening the file. + /// ## Parameters + /// * filename: Name of the file to open or the text string to read the data from. + /// Extension of the file (.xml, .yml/.yaml or .json) determines its format (XML, YAML or JSON + /// respectively). Also you can append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both + /// FileStorage::WRITE and FileStorage::MEMORY flags are specified, source is used just to specify + /// the output file format (e.g. mydata.xml, .yml etc.). A file name can also contain parameters. + /// You can use this format, "*?base64" (e.g. "file.json?base64" (case sensitive)), as an alternative to + /// FileStorage::BASE64 flag. + /// * flags: Mode of operation. One of FileStorage::Mode + /// * encoding: Encoding of the file. Note that UTF-16 XML encoding is not supported currently and + /// you should use 8-bit encoding instead of it. + /// + /// ## C++ default parameters + /// * encoding: String() + #[inline] + fn open(&mut self, filename: &str, flags: i32, encoding: &str) -> Result { + extern_container_arg!(filename); + extern_container_arg!(encoding); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_open_const_StringR_int_const_StringR(self.as_raw_mut_FileStorage(), filename.opencv_as_extern(), flags, encoding.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Opens a file. + /// + /// See description of parameters in FileStorage::FileStorage. The method calls FileStorage::release + /// before opening the file. + /// ## Parameters + /// * filename: Name of the file to open or the text string to read the data from. + /// Extension of the file (.xml, .yml/.yaml or .json) determines its format (XML, YAML or JSON + /// respectively). Also you can append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both + /// FileStorage::WRITE and FileStorage::MEMORY flags are specified, source is used just to specify + /// the output file format (e.g. mydata.xml, .yml etc.). A file name can also contain parameters. + /// You can use this format, "*?base64" (e.g. "file.json?base64" (case sensitive)), as an alternative to + /// FileStorage::BASE64 flag. + /// * flags: Mode of operation. One of FileStorage::Mode + /// * encoding: Encoding of the file. Note that UTF-16 XML encoding is not supported currently and + /// you should use 8-bit encoding instead of it. + /// + /// ## Note + /// This alternative version of [FileStorageTrait::open] function uses the following default values for its arguments: + /// * encoding: String() + #[inline] + fn open_def(&mut self, filename: &str, flags: i32) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_open_const_StringR_int(self.as_raw_mut_FileStorage(), filename.opencv_as_extern(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Closes the file and releases all the memory buffers. + /// + /// Call this method after all I/O operations with the storage are finished. + #[inline] + fn release(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_release(self.as_raw_mut_FileStorage(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Closes the file and releases all the memory buffers. + /// + /// Call this method after all I/O operations with the storage are finished. If the storage was + /// opened for writing data and FileStorage::WRITE was specified + #[inline] + fn release_and_get_string(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_releaseAndGetString(self.as_raw_mut_FileStorage(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Simplified writing API to use with bindings. + /// ## Parameters + /// * name: Name of the written object. When writing to sequences (a.k.a. "arrays"), pass an empty string. + /// * val: Value of the written object. + #[inline] + fn write_i32(&mut self, name: &str, val: i32) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_write_const_StringR_int(self.as_raw_mut_FileStorage(), name.opencv_as_extern(), val, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Simplified writing API to use with bindings. + /// ## Parameters + /// * name: Name of the written object. When writing to sequences (a.k.a. "arrays"), pass an empty string. + /// * val: Value of the written object. + /// + /// ## Overloaded parameters + #[inline] + fn write(&mut self, name: &str, val: i64) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_write_const_StringR_int64_t(self.as_raw_mut_FileStorage(), name.opencv_as_extern(), val, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Simplified writing API to use with bindings. + /// ## Parameters + /// * name: Name of the written object. When writing to sequences (a.k.a. "arrays"), pass an empty string. + /// * val: Value of the written object. + /// + /// ## Overloaded parameters + #[inline] + fn write_f64(&mut self, name: &str, val: f64) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_write_const_StringR_double(self.as_raw_mut_FileStorage(), name.opencv_as_extern(), val, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Simplified writing API to use with bindings. + /// ## Parameters + /// * name: Name of the written object. When writing to sequences (a.k.a. "arrays"), pass an empty string. + /// * val: Value of the written object. + /// + /// ## Overloaded parameters + #[inline] + fn write_str(&mut self, name: &str, val: &str) -> Result<()> { + extern_container_arg!(name); + extern_container_arg!(val); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_write_const_StringR_const_StringR(self.as_raw_mut_FileStorage(), name.opencv_as_extern(), val.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Simplified writing API to use with bindings. + /// ## Parameters + /// * name: Name of the written object. When writing to sequences (a.k.a. "arrays"), pass an empty string. + /// * val: Value of the written object. + /// + /// ## Overloaded parameters + #[inline] + fn write_mat(&mut self, name: &str, val: &impl core::MatTraitConst) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_write_const_StringR_const_MatR(self.as_raw_mut_FileStorage(), name.opencv_as_extern(), val.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Simplified writing API to use with bindings. + /// ## Parameters + /// * name: Name of the written object. When writing to sequences (a.k.a. "arrays"), pass an empty string. + /// * val: Value of the written object. + /// + /// ## Overloaded parameters + #[inline] + fn write_str_vec(&mut self, name: &str, val: &core::Vector) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_write_const_StringR_const_vectorLStringGR(self.as_raw_mut_FileStorage(), name.opencv_as_extern(), val.as_raw_VectorOfString(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Writes multiple numbers. + /// + /// Writes one or more numbers of the specified format to the currently written structure. Usually it is + /// more convenient to use operator `<<` instead of this method. + /// ## Parameters + /// * fmt: Specification of each array element, see [format_spec] "format specification" + /// * vec: Pointer to the written array. + /// * len: Number of the uchar elements to write. + #[inline] + fn write_raw(&mut self, fmt: &str, vec: &mut [u8]) -> Result<()> { + extern_container_arg!(fmt); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_writeRaw_const_StringR_const_voidX_size_t(self.as_raw_mut_FileStorage(), fmt.opencv_as_extern(), vec.as_ptr().cast(), vec.len(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Writes a comment. + /// + /// The function writes a comment into file storage. The comments are skipped when the storage is read. + /// ## Parameters + /// * comment: The written comment, single-line or multi-line + /// * append: If true, the function tries to put the comment at the end of current line. + /// Else if the comment is multi-line, or if it does not fit at the end of the current + /// line, the comment starts a new line. + /// + /// ## C++ default parameters + /// * append: false + #[inline] + fn write_comment(&mut self, comment: &str, append: bool) -> Result<()> { + extern_container_arg!(comment); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_writeComment_const_StringR_bool(self.as_raw_mut_FileStorage(), comment.opencv_as_extern(), append, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Writes a comment. + /// + /// The function writes a comment into file storage. The comments are skipped when the storage is read. + /// ## Parameters + /// * comment: The written comment, single-line or multi-line + /// * append: If true, the function tries to put the comment at the end of current line. + /// Else if the comment is multi-line, or if it does not fit at the end of the current + /// line, the comment starts a new line. + /// + /// ## Note + /// This alternative version of [FileStorageTrait::write_comment] function uses the following default values for its arguments: + /// * append: false + #[inline] + fn write_comment_def(&mut self, comment: &str) -> Result<()> { + extern_container_arg!(comment); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_writeComment_const_StringR(self.as_raw_mut_FileStorage(), comment.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Starts to write a nested structure (sequence or a mapping). + /// ## Parameters + /// * name: name of the structure. When writing to sequences (a.k.a. "arrays"), pass an empty string. + /// * flags: type of the structure (FileNode::MAP or FileNode::SEQ (both with optional FileNode::FLOW)). + /// * typeName: optional name of the type you store. The effect of setting this depends on the storage format. + /// I.e. if the format has a specification for storing type information, this parameter is used. + /// + /// ## C++ default parameters + /// * type_name: String() + #[inline] + fn start_write_struct(&mut self, name: &str, flags: i32, type_name: &str) -> Result<()> { + extern_container_arg!(name); + extern_container_arg!(type_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_startWriteStruct_const_StringR_int_const_StringR(self.as_raw_mut_FileStorage(), name.opencv_as_extern(), flags, type_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Starts to write a nested structure (sequence or a mapping). + /// ## Parameters + /// * name: name of the structure. When writing to sequences (a.k.a. "arrays"), pass an empty string. + /// * flags: type of the structure (FileNode::MAP or FileNode::SEQ (both with optional FileNode::FLOW)). + /// * typeName: optional name of the type you store. The effect of setting this depends on the storage format. + /// I.e. if the format has a specification for storing type information, this parameter is used. + /// + /// ## Note + /// This alternative version of [FileStorageTrait::start_write_struct] function uses the following default values for its arguments: + /// * type_name: String() + #[inline] + fn start_write_struct_def(&mut self, name: &str, flags: i32) -> Result<()> { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_startWriteStruct_const_StringR_int(self.as_raw_mut_FileStorage(), name.opencv_as_extern(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finishes writing nested structure (should pair startWriteStruct()) + #[inline] + fn end_write_struct(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_FileStorage_endWriteStruct(self.as_raw_mut_FileStorage(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for FileStorage { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("FileStorage") + .field("state", &core::FileStorageTraitConst::state(self)) + .field("elname", &core::FileStorageTraitConst::elname(self)) + .finish() + } + } + + impl core::FileStorageTraitConst for FileStorage { + #[inline] fn as_raw_FileStorage(&self) -> *const c_void { self.as_raw() } + } + + impl core::FileStorageTrait for FileStorage { + #[inline] fn as_raw_mut_FileStorage(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { FileStorage, core::FileStorageTraitConst, as_raw_FileStorage, core::FileStorageTrait, as_raw_mut_FileStorage } + + /// @todo document + pub struct Formatted { + ptr: *mut c_void, + } + + opencv_type_boxed! { Formatted } + + impl Drop for Formatted { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Formatted_delete(self.as_raw_mut_Formatted()) }; + } + } + + unsafe impl Send for Formatted {} + + /// Constant methods for [core::Formatted] + pub trait FormattedTraitConst { + fn as_raw_Formatted(&self) -> *const c_void; + + } + + /// Mutable methods for [core::Formatted] + pub trait FormattedTrait: core::FormattedTraitConst { + fn as_raw_mut_Formatted(&mut self) -> *mut c_void; + + #[inline] + fn next(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Formatted_next(self.as_raw_mut_Formatted(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn reset(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Formatted_reset(self.as_raw_mut_Formatted(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for Formatted { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Formatted") + .finish() + } + } + + impl core::FormattedTraitConst for Formatted { + #[inline] fn as_raw_Formatted(&self) -> *const c_void { self.as_raw() } + } + + impl core::FormattedTrait for Formatted { + #[inline] fn as_raw_mut_Formatted(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Formatted, core::FormattedTraitConst, as_raw_Formatted, core::FormattedTrait, as_raw_mut_Formatted } + + /// @todo document + pub struct Formatter { + ptr: *mut c_void, + } + + opencv_type_boxed! { Formatter } + + impl Drop for Formatter { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Formatter_delete(self.as_raw_mut_Formatter()) }; + } + } + + unsafe impl Send for Formatter {} + + impl Formatter { + /// ## C++ default parameters + /// * fmt: FMT_DEFAULT + #[inline] + pub fn get(fmt: core::Formatter_FormatType) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Formatter_get_FormatType(fmt, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [Formatter::get] function uses the following default values for its arguments: + /// * fmt: FMT_DEFAULT + #[inline] + pub fn get_def() -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Formatter_get(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Formatter] + pub trait FormatterTraitConst { + fn as_raw_Formatter(&self) -> *const c_void; + + #[inline] + fn format(&self, mtx: &impl core::MatTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Formatter_format_const_const_MatR(self.as_raw_Formatter(), mtx.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Mutable methods for [core::Formatter] + pub trait FormatterTrait: core::FormatterTraitConst { + fn as_raw_mut_Formatter(&mut self) -> *mut c_void; + + /// ## C++ default parameters + /// * p: 4 + #[inline] + fn set16f_precision(&mut self, p: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Formatter_set16fPrecision_int(self.as_raw_mut_Formatter(), p, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [FormatterTrait::set16f_precision] function uses the following default values for its arguments: + /// * p: 4 + #[inline] + fn set16f_precision_def(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Formatter_set16fPrecision(self.as_raw_mut_Formatter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * p: 8 + #[inline] + fn set32f_precision(&mut self, p: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Formatter_set32fPrecision_int(self.as_raw_mut_Formatter(), p, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [FormatterTrait::set32f_precision] function uses the following default values for its arguments: + /// * p: 8 + #[inline] + fn set32f_precision_def(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Formatter_set32fPrecision(self.as_raw_mut_Formatter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * p: 16 + #[inline] + fn set64f_precision(&mut self, p: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Formatter_set64fPrecision_int(self.as_raw_mut_Formatter(), p, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [FormatterTrait::set64f_precision] function uses the following default values for its arguments: + /// * p: 16 + #[inline] + fn set64f_precision_def(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Formatter_set64fPrecision(self.as_raw_mut_Formatter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * ml: true + #[inline] + fn set_multiline(&mut self, ml: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Formatter_setMultiline_bool(self.as_raw_mut_Formatter(), ml, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [FormatterTrait::set_multiline] function uses the following default values for its arguments: + /// * ml: true + #[inline] + fn set_multiline_def(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Formatter_setMultiline(self.as_raw_mut_Formatter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for Formatter { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Formatter") + .finish() + } + } + + impl core::FormatterTraitConst for Formatter { + #[inline] fn as_raw_Formatter(&self) -> *const c_void { self.as_raw() } + } + + impl core::FormatterTrait for Formatter { + #[inline] fn as_raw_mut_Formatter(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Formatter, core::FormatterTraitConst, as_raw_Formatter, core::FormatterTrait, as_raw_mut_Formatter } + + pub struct Hamming { + ptr: *mut c_void, + } + + opencv_type_boxed! { Hamming } + + impl Drop for Hamming { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Hamming_delete(self.as_raw_mut_Hamming()) }; + } + } + + unsafe impl Send for Hamming {} + + impl Hamming { + pub const normType: u32 = 6; + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> core::Hamming { + let ret = unsafe { sys::cv_Hamming_defaultNew_const() }; + let ret = unsafe { core::Hamming::opencv_from_extern(ret) }; + ret + } + + } + + /// Constant methods for [core::Hamming] + pub trait HammingTraitConst { + fn as_raw_Hamming(&self) -> *const c_void; + + /// this will count the bits in a ^ b + #[inline] + fn apply(&self, a: &[u8], b: &[u8]) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Hamming_operator___const_const_unsigned_charX_const_unsigned_charX_int(self.as_raw_Hamming(), a.as_ptr(), b.as_ptr(), a.len().min(b.len()).try_into()?, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::Hamming] + pub trait HammingTrait: core::HammingTraitConst { + fn as_raw_mut_Hamming(&mut self) -> *mut c_void; + + } + + impl Default for Hamming { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for Hamming { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Hamming") + .finish() + } + } + + impl core::HammingTraitConst for Hamming { + #[inline] fn as_raw_Hamming(&self) -> *const c_void { self.as_raw() } + } + + impl core::HammingTrait for Hamming { + #[inline] fn as_raw_mut_Hamming(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Hamming, core::HammingTraitConst, as_raw_Hamming, core::HammingTrait, as_raw_mut_Hamming } + + /// Data structure for salient point detectors. + /// + /// The class instance stores a keypoint, i.e. a point feature found by one of many available keypoint + /// detectors, such as Harris corner detector, #FAST, %StarDetector, %SURF, %SIFT etc. + /// + /// The keypoint is characterized by the 2D position, scale (proportional to the diameter of the + /// neighborhood that needs to be taken into account), orientation and some other parameters. The + /// keypoint neighborhood is then analyzed by another algorithm that builds a descriptor (usually + /// represented as a feature vector). The keypoints representing the same object in different images + /// can then be matched using %KDTree or another method. + pub struct KeyPoint { + ptr: *mut c_void, + } + + opencv_type_boxed! { KeyPoint } + + impl Drop for KeyPoint { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_KeyPoint_delete(self.as_raw_mut_KeyPoint()) }; + } + } + + unsafe impl Send for KeyPoint {} + + impl KeyPoint { + /// This method converts vector of keypoints to vector of points or the reverse, where each keypoint is + /// assigned the same size and the same orientation. + /// + /// ## Parameters + /// * keypoints: Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + /// * points2f: Array of (x,y) coordinates of each keypoint + /// * keypointIndexes: Array of indexes of keypoints to be converted to points. (Acts like a mask to + /// convert only specified keypoints) + /// + /// ## C++ default parameters + /// * keypoint_indexes: std::vector() + #[inline] + pub fn convert(keypoints: &core::Vector, points2f: &mut core::Vector, keypoint_indexes: &core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_KeyPoint_convert_const_vectorLKeyPointGR_vectorLPoint2fGR_const_vectorLintGR(keypoints.as_raw_VectorOfKeyPoint(), points2f.as_raw_mut_VectorOfPoint2f(), keypoint_indexes.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This method converts vector of keypoints to vector of points or the reverse, where each keypoint is + /// assigned the same size and the same orientation. + /// + /// ## Parameters + /// * keypoints: Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + /// * points2f: Array of (x,y) coordinates of each keypoint + /// * keypointIndexes: Array of indexes of keypoints to be converted to points. (Acts like a mask to + /// convert only specified keypoints) + /// + /// ## Note + /// This alternative version of [KeyPoint::convert] function uses the following default values for its arguments: + /// * keypoint_indexes: std::vector() + #[inline] + pub fn convert_def(keypoints: &core::Vector, points2f: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_KeyPoint_convert_const_vectorLKeyPointGR_vectorLPoint2fGR(keypoints.as_raw_VectorOfKeyPoint(), points2f.as_raw_mut_VectorOfPoint2f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This method converts vector of keypoints to vector of points or the reverse, where each keypoint is + /// assigned the same size and the same orientation. + /// + /// ## Parameters + /// * keypoints: Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + /// * points2f: Array of (x,y) coordinates of each keypoint + /// * keypointIndexes: Array of indexes of keypoints to be converted to points. (Acts like a mask to + /// convert only specified keypoints) + /// + /// ## Overloaded parameters + /// + /// * points2f: Array of (x,y) coordinates of each keypoint + /// * keypoints: Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + /// * size: keypoint diameter + /// * response: keypoint detector response on the keypoint (that is, strength of the keypoint) + /// * octave: pyramid octave in which the keypoint has been detected + /// * class_id: object id + /// + /// ## C++ default parameters + /// * size: 1 + /// * response: 1 + /// * octave: 0 + /// * class_id: -1 + #[inline] + pub fn convert_to(points2f: &core::Vector, keypoints: &mut core::Vector, size: f32, response: f32, octave: i32, class_id: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_KeyPoint_convert_const_vectorLPoint2fGR_vectorLKeyPointGR_float_float_int_int(points2f.as_raw_VectorOfPoint2f(), keypoints.as_raw_mut_VectorOfKeyPoint(), size, response, octave, class_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This method converts vector of keypoints to vector of points or the reverse, where each keypoint is + /// assigned the same size and the same orientation. + /// + /// ## Parameters + /// * keypoints: Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + /// * points2f: Array of (x,y) coordinates of each keypoint + /// * keypointIndexes: Array of indexes of keypoints to be converted to points. (Acts like a mask to + /// convert only specified keypoints) + /// + /// ## Overloaded parameters + /// + /// * points2f: Array of (x,y) coordinates of each keypoint + /// * keypoints: Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB + /// * size: keypoint diameter + /// * response: keypoint detector response on the keypoint (that is, strength of the keypoint) + /// * octave: pyramid octave in which the keypoint has been detected + /// * class_id: object id + /// + /// ## Note + /// This alternative version of [KeyPoint::convert_to] function uses the following default values for its arguments: + /// * size: 1 + /// * response: 1 + /// * octave: 0 + /// * class_id: -1 + #[inline] + pub fn convert_to_def(points2f: &core::Vector, keypoints: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_KeyPoint_convert_const_vectorLPoint2fGR_vectorLKeyPointGR(points2f.as_raw_VectorOfPoint2f(), keypoints.as_raw_mut_VectorOfKeyPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint + /// regions' intersection and area of keypoint regions' union (considering keypoint region as circle). + /// If they don't overlap, we get zero. If they coincide at same location with same size, we get 1. + /// ## Parameters + /// * kp1: First keypoint + /// * kp2: Second keypoint + #[inline] + pub fn overlap(kp1: &impl core::KeyPointTraitConst, kp2: &impl core::KeyPointTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_KeyPoint_overlap_const_KeyPointR_const_KeyPointR(kp1.as_raw_KeyPoint(), kp2.as_raw_KeyPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Constant methods for [core::KeyPoint] + pub trait KeyPointTraitConst { + fn as_raw_KeyPoint(&self) -> *const c_void; + + /// coordinates of the keypoints + #[inline] + fn pt(&self) -> core::Point2f { + return_send!(via ocvrs_return); + unsafe { sys::cv_KeyPoint_propPt_const(self.as_raw_KeyPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + /// diameter of the meaningful keypoint neighborhood + #[inline] + fn size(&self) -> f32 { + let ret = unsafe { sys::cv_KeyPoint_propSize_const(self.as_raw_KeyPoint()) }; + ret + } + + /// computed orientation of the keypoint (-1 if not applicable); + /// it's in [0,360) degrees and measured relative to + /// image coordinate system, ie in clockwise. + #[inline] + fn angle(&self) -> f32 { + let ret = unsafe { sys::cv_KeyPoint_propAngle_const(self.as_raw_KeyPoint()) }; + ret + } + + /// the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling + #[inline] + fn response(&self) -> f32 { + let ret = unsafe { sys::cv_KeyPoint_propResponse_const(self.as_raw_KeyPoint()) }; + ret + } + + /// octave (pyramid layer) from which the keypoint has been extracted + #[inline] + fn octave(&self) -> i32 { + let ret = unsafe { sys::cv_KeyPoint_propOctave_const(self.as_raw_KeyPoint()) }; + ret + } + + /// object class (if the keypoints need to be clustered by an object they belong to) + #[inline] + fn class_id(&self) -> i32 { + let ret = unsafe { sys::cv_KeyPoint_propClass_id_const(self.as_raw_KeyPoint()) }; + ret + } + + #[inline] + fn hash(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_KeyPoint_hash_const(self.as_raw_KeyPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::KeyPoint] + pub trait KeyPointTrait: core::KeyPointTraitConst { + fn as_raw_mut_KeyPoint(&mut self) -> *mut c_void; + + /// coordinates of the keypoints + #[inline] + fn set_pt(&mut self, val: core::Point2f) { + let ret = unsafe { sys::cv_KeyPoint_propPt_const_Point2f(self.as_raw_mut_KeyPoint(), &val) }; + ret + } + + /// diameter of the meaningful keypoint neighborhood + #[inline] + fn set_size(&mut self, val: f32) { + let ret = unsafe { sys::cv_KeyPoint_propSize_const_float(self.as_raw_mut_KeyPoint(), val) }; + ret + } + + /// computed orientation of the keypoint (-1 if not applicable); + /// it's in [0,360) degrees and measured relative to + /// image coordinate system, ie in clockwise. + #[inline] + fn set_angle(&mut self, val: f32) { + let ret = unsafe { sys::cv_KeyPoint_propAngle_const_float(self.as_raw_mut_KeyPoint(), val) }; + ret + } + + /// the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling + #[inline] + fn set_response(&mut self, val: f32) { + let ret = unsafe { sys::cv_KeyPoint_propResponse_const_float(self.as_raw_mut_KeyPoint(), val) }; + ret + } + + /// octave (pyramid layer) from which the keypoint has been extracted + #[inline] + fn set_octave(&mut self, val: i32) { + let ret = unsafe { sys::cv_KeyPoint_propOctave_const_int(self.as_raw_mut_KeyPoint(), val) }; + ret + } + + /// object class (if the keypoints need to be clustered by an object they belong to) + #[inline] + fn set_class_id(&mut self, val: i32) { + let ret = unsafe { sys::cv_KeyPoint_propClass_id_const_int(self.as_raw_mut_KeyPoint(), val) }; + ret + } + + } + + impl Clone for KeyPoint { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_KeyPoint_implicitClone_const(self.as_raw_KeyPoint())) } + } + } + + impl std::fmt::Debug for KeyPoint { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("KeyPoint") + .field("pt", &core::KeyPointTraitConst::pt(self)) + .field("size", &core::KeyPointTraitConst::size(self)) + .field("angle", &core::KeyPointTraitConst::angle(self)) + .field("response", &core::KeyPointTraitConst::response(self)) + .field("octave", &core::KeyPointTraitConst::octave(self)) + .field("class_id", &core::KeyPointTraitConst::class_id(self)) + .finish() + } + } + + impl core::KeyPointTraitConst for KeyPoint { + #[inline] fn as_raw_KeyPoint(&self) -> *const c_void { self.as_raw() } + } + + impl core::KeyPointTrait for KeyPoint { + #[inline] fn as_raw_mut_KeyPoint(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { KeyPoint, core::KeyPointTraitConst, as_raw_KeyPoint, core::KeyPointTrait, as_raw_mut_KeyPoint } + + /// Linear Discriminant Analysis + /// @todo document this class + pub struct LDA { + ptr: *mut c_void, + } + + opencv_type_boxed! { LDA } + + impl Drop for LDA { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_LDA_delete(self.as_raw_mut_LDA()) }; + } + } + + unsafe impl Send for LDA {} + + impl LDA { + /// constructor + /// Initializes a LDA with num_components (default 0). + /// + /// ## C++ default parameters + /// * num_components: 0 + #[inline] + pub fn new(num_components: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_LDA_int(num_components, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::LDA::opencv_from_extern(ret) }; + Ok(ret) + } + + /// constructor + /// Initializes a LDA with num_components (default 0). + /// + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * num_components: 0 + #[inline] + pub fn new_def() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_LDA(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::LDA::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Initializes and performs a Discriminant Analysis with Fisher's + /// Optimization Criterion on given data in src and corresponding labels + /// in labels. If 0 (or less) number of components are given, they are + /// automatically determined for given data in computation. + /// + /// ## C++ default parameters + /// * num_components: 0 + #[inline] + pub fn new_with_data(src: &impl ToInputArray, labels: &impl ToInputArray, num_components: i32) -> Result { + input_array_arg!(src); + input_array_arg!(labels); + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_LDA_const__InputArrayR_const__InputArrayR_int(src.as_raw__InputArray(), labels.as_raw__InputArray(), num_components, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::LDA::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Initializes and performs a Discriminant Analysis with Fisher's + /// Optimization Criterion on given data in src and corresponding labels + /// in labels. If 0 (or less) number of components are given, they are + /// automatically determined for given data in computation. + /// + /// ## Note + /// This alternative version of [new_with_data] function uses the following default values for its arguments: + /// * num_components: 0 + #[inline] + pub fn new_with_data_def(src: &impl ToInputArray, labels: &impl ToInputArray) -> Result { + input_array_arg!(src); + input_array_arg!(labels); + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_LDA_const__InputArrayR_const__InputArrayR(src.as_raw__InputArray(), labels.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::LDA::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn subspace_project(w: &impl ToInputArray, mean: &impl ToInputArray, src: &impl ToInputArray) -> Result { + input_array_arg!(w); + input_array_arg!(mean); + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_subspaceProject_const__InputArrayR_const__InputArrayR_const__InputArrayR(w.as_raw__InputArray(), mean.as_raw__InputArray(), src.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn subspace_reconstruct(w: &impl ToInputArray, mean: &impl ToInputArray, src: &impl ToInputArray) -> Result { + input_array_arg!(w); + input_array_arg!(mean); + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_subspaceReconstruct_const__InputArrayR_const__InputArrayR_const__InputArrayR(w.as_raw__InputArray(), mean.as_raw__InputArray(), src.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::LDA] + pub trait LDATraitConst { + fn as_raw_LDA(&self) -> *const c_void; + + /// Serializes this object to a given filename. + #[inline] + fn save(&self, filename: &str) -> Result<()> { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_save_const_const_StringR(self.as_raw_LDA(), filename.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Serializes this object to a given cv::FileStorage. + #[inline] + fn save_1(&self, fs: &mut impl core::FileStorageTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_save_const_FileStorageR(self.as_raw_LDA(), fs.as_raw_mut_FileStorage(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the eigenvectors of this LDA. + #[inline] + fn eigenvectors(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_eigenvectors_const(self.as_raw_LDA(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns the eigenvalues of this LDA. + #[inline] + fn eigenvalues(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_eigenvalues_const(self.as_raw_LDA(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Mutable methods for [core::LDA] + pub trait LDATrait: core::LDATraitConst { + fn as_raw_mut_LDA(&mut self) -> *mut c_void; + + /// Deserializes this object from a given filename. + #[inline] + fn load(&mut self, filename: &str) -> Result<()> { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_load_const_StringR(self.as_raw_mut_LDA(), filename.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Deserializes this object from a given cv::FileStorage. + #[inline] + fn load_1(&mut self, node: &impl core::FileStorageTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_load_const_FileStorageR(self.as_raw_mut_LDA(), node.as_raw_FileStorage(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Compute the discriminants for data in src (row aligned) and labels. + #[inline] + fn compute(&mut self, src: &impl ToInputArray, labels: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + input_array_arg!(labels); + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_compute_const__InputArrayR_const__InputArrayR(self.as_raw_mut_LDA(), src.as_raw__InputArray(), labels.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Projects samples into the LDA subspace. + /// src may be one or more row aligned samples. + #[inline] + fn project(&mut self, src: &impl ToInputArray) -> Result { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_project_const__InputArrayR(self.as_raw_mut_LDA(), src.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reconstructs projections from the LDA subspace. + /// src may be one or more row aligned projections. + #[inline] + fn reconstruct(&mut self, src: &impl ToInputArray) -> Result { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_LDA_reconstruct_const__InputArrayR(self.as_raw_mut_LDA(), src.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl std::fmt::Debug for LDA { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("LDA") + .finish() + } + } + + impl core::LDATraitConst for LDA { + #[inline] fn as_raw_LDA(&self) -> *const c_void { self.as_raw() } + } + + impl core::LDATrait for LDA { + #[inline] fn as_raw_mut_LDA(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LDA, core::LDATraitConst, as_raw_LDA, core::LDATrait, as_raw_mut_LDA } + + /// n-dimensional dense array class \anchor CVMat_Details + /// + /// The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It + /// can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel + /// volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms + /// may be better stored in a SparseMat ). The data layout of the array `M` is defined by the array + /// `M.step[]`, so that the address of element ![inline formula](https://latex.codecogs.com/png.latex?%28i%5F0%2C%2E%2E%2E%2Ci%5F%7BM%2Edims%2D1%7D%29), where ![inline formula](https://latex.codecogs.com/png.latex?0%5Cleq%20i%5Fk%3CM%2Esize%5Bk%5D), is + /// computed as: + /// ![block formula](https://latex.codecogs.com/png.latex?addr%28M%5F%7Bi%5F0%2C%2E%2E%2E%2Ci%5F%7BM%2Edims%2D1%7D%7D%29%20%3D%20M%2Edata%20%2B%20M%2Estep%5B0%5D%2Ai%5F0%20%2B%20M%2Estep%5B1%5D%2Ai%5F1%20%2B%20%2E%2E%2E%20%2B%20M%2Estep%5BM%2Edims%2D1%5D%2Ai%5F%7BM%2Edims%2D1%7D) + /// In case of a 2-dimensional array, the above formula is reduced to: + /// ![block formula](https://latex.codecogs.com/png.latex?addr%28M%5F%7Bi%2Cj%7D%29%20%3D%20M%2Edata%20%2B%20M%2Estep%5B0%5D%2Ai%20%2B%20M%2Estep%5B1%5D%2Aj) + /// Note that `M.step[i] >= M.step[i+1]` (in fact, `M.step[i] >= M.step[i+1]*M.size[i+1]` ). This means + /// that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane, + /// and so on. M.step[M.dims-1] is minimal and always equal to the element size M.elemSize() . + /// + /// So, the data layout in Mat is compatible with the majority of dense array types from the standard + /// toolkits and SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others, + /// that is, with any array that uses *steps* (or *strides*) to compute the position of a pixel. + /// Due to this compatibility, it is possible to make a Mat header for user-allocated data and process + /// it in-place using OpenCV functions. + /// + /// There are many different ways to create a Mat object. The most popular options are listed below: + /// + /// - Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue]) + /// constructor. A new array of the specified size and type is allocated. type has the same meaning as + /// in the cvCreateMat method. For example, CV_8UC1 means a 8-bit single-channel array, CV_32FC2 + /// means a 2-channel (complex) floating-point array, and so on. + /// ```C++ + /// // make a 7x7 complex matrix filled with 1+3j. + /// Mat M(7,7,CV_32FC2,Scalar(1,3)); + /// // and now turn M to a 100x60 15-channel 8-bit matrix. + /// // The old content will be deallocated + /// M.create(100,60,CV_8UC(15)); + /// ``` + /// + /// As noted in the introduction to this chapter, create() allocates only a new array when the shape + /// or type of the current array are different from the specified ones. + /// + /// - Create a multi-dimensional array: + /// ```C++ + /// // create a 100x100x100 8-bit array + /// int sz[] = {100, 100, 100}; + /// Mat bigCube(3, sz, CV_8U, Scalar::all(0)); + /// ``` + /// + /// It passes the number of dimensions =1 to the Mat constructor but the created array will be + /// 2-dimensional with the number of columns set to 1. So, Mat::dims is always \>= 2 (can also be 0 + /// when the array is empty). + /// + /// - Use a copy constructor or assignment operator where there can be an array or expression on the + /// right side (see below). As noted in the introduction, the array assignment is an O(1) operation + /// because it only copies the header and increases the reference counter. The Mat::clone() method can + /// be used to get a full (deep) copy of the array when you need it. + /// + /// - Construct a header for a part of another array. It can be a single row, single column, several + /// rows, several columns, rectangular region in the array (called a *minor* in algebra) or a + /// diagonal. Such operations are also O(1) because the new header references the same data. You can + /// actually modify a part of the array using this feature, for example: + /// ```C++ + /// // add the 5-th row, multiplied by 3 to the 3rd row + /// M.row(3) = M.row(3) + M.row(5)*3; + /// // now copy the 7-th column to the 1-st column + /// // M.col(1) = M.col(7); // this will not work + /// Mat M1 = M.col(1); + /// M.col(7).copyTo(M1); + /// // create a new 320x240 image + /// Mat img(Size(320,240),CV_8UC3); + /// // select a ROI + /// Mat roi(img, Rect(10,10,100,100)); + /// // fill the ROI with (0,255,0) (which is green in RGB space); + /// // the original 320x240 image will be modified + /// roi = Scalar(0,255,0); + /// ``` + /// + /// Due to the additional datastart and dataend members, it is possible to compute a relative + /// sub-array position in the main *container* array using locateROI(): + /// ```C++ + /// Mat A = Mat::eye(10, 10, CV_32S); + /// // extracts A columns, 1 (inclusive) to 3 (exclusive). + /// Mat B = A(Range::all(), Range(1, 3)); + /// // extracts B rows, 5 (inclusive) to 9 (exclusive). + /// // that is, C \~ A(Range(5, 9), Range(1, 3)) + /// Mat C = B(Range(5, 9), Range::all()); + /// Size size; Point ofs; + /// C.locateROI(size, ofs); + /// // size will be (width=10,height=10) and the ofs will be (x=1, y=5) + /// ``` + /// + /// As in case of whole matrices, if you need a deep copy, use the `clone()` method of the extracted + /// sub-matrices. + /// + /// - Make a header for user-allocated data. It can be useful to do the following: + /// -# Process "foreign" data using OpenCV (for example, when you implement a DirectShow\* filter or + /// a processing module for gstreamer, and so on). For example: + /// ```C++ + /// Mat process_video_frame(const unsigned char* pixels, + /// int width, int height, int step) + /// { + /// // wrap input buffer + /// Mat img(height, width, CV_8UC3, (unsigned char*)pixels, step); + /// + /// Mat result; + /// GaussianBlur(img, result, Size(7, 7), 1.5, 1.5); + /// + /// return result; + /// } + /// ``` + /// + /// -# Quickly initialize small matrices and/or get a super-fast element access. + /// ```C++ + /// double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}}; + /// Mat M = Mat(3, 3, CV_64F, m).inv(); + /// ``` + /// + /// . + /// + /// - Use MATLAB-style array initializers, zeros(), ones(), eye(), for example: + /// ```C++ + /// // create a double-precision identity matrix and add it to M. + /// M += Mat::eye(M.rows, M.cols, CV_64F); + /// ``` + /// + /// + /// - Use a comma-separated initializer: + /// ```C++ + /// // create a 3x3 double-precision identity matrix + /// Mat M = (Mat_(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1); + /// ``` + /// + /// With this approach, you first call a constructor of the Mat class with the proper parameters, and + /// then you just put `<< operator` followed by comma-separated values that can be constants, + /// variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation + /// errors. + /// + /// Once the array is created, it is automatically managed via a reference-counting mechanism. If the + /// array header is built on top of user-allocated data, you should handle the data by yourself. The + /// array data is deallocated when no one points to it. If you want to release the data pointed by a + /// array header before the array destructor is called, use Mat::release(). + /// + /// The next important thing to learn about the array class is element access. This manual already + /// described how to compute an address of each array element. Normally, you are not required to use the + /// formula directly in the code. If you know the array element type (which can be retrieved using the + /// method Mat::type() ), you can access the element ![inline formula](https://latex.codecogs.com/png.latex?M%5F%7Bij%7D) of a 2-dimensional array as: + /// ```C++ + /// M.at(i,j) += 1.f; + /// ``` + /// + /// assuming that `M` is a double-precision floating-point array. There are several variants of the method + /// at for a different number of dimensions. + /// + /// If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to + /// the row first, and then just use the plain C operator [] : + /// ```C++ + /// // compute sum of positive matrix elements + /// // (assuming that M is a double-precision matrix) + /// double sum=0; + /// for(int i = 0; i < M.rows; i++) + /// { + /// const double* Mi = M.ptr(i); + /// for(int j = 0; j < M.cols; j++) + /// sum += std::max(Mi[j], 0.); + /// } + /// ``` + /// + /// Some operations, like the one above, do not actually depend on the array shape. They just process + /// elements of an array one by one (or elements from multiple arrays that have the same coordinates, + /// for example, array addition). Such operations are called *element-wise*. It makes sense to check + /// whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If + /// yes, process them as a long single row: + /// ```C++ + /// // compute the sum of positive matrix elements, optimized variant + /// double sum=0; + /// int cols = M.cols, rows = M.rows; + /// if(M.isContinuous()) + /// { + /// cols *= rows; + /// rows = 1; + /// } + /// for(int i = 0; i < rows; i++) + /// { + /// const double* Mi = M.ptr(i); + /// for(int j = 0; j < cols; j++) + /// sum += std::max(Mi[j], 0.); + /// } + /// ``` + /// + /// In case of the continuous matrix, the outer loop body is executed just once. So, the overhead is + /// smaller, which is especially noticeable in case of small matrices. + /// + /// Finally, there are STL-style iterators that are smart enough to skip gaps between successive rows: + /// ```C++ + /// // compute sum of positive matrix elements, iterator-based variant + /// double sum=0; + /// MatConstIterator_ it = M.begin(), it_end = M.end(); + /// for(; it != it_end; ++it) + /// sum += std::max(*it, 0.); + /// ``` + /// + /// The matrix iterators are random-access iterators, so they can be passed to any STL algorithm, + /// including std::sort(). + /// + /// + /// Note: Matrix Expressions and arithmetic see MatExpr + pub struct Mat { + ptr: *mut c_void, + } + + opencv_type_boxed! { Mat } + + impl Drop for Mat { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Mat_delete(self.as_raw_mut_Mat()) }; + } + } + + unsafe impl Send for Mat {} + + impl Mat { + /// creates a diagonal matrix + /// + /// The method creates a square diagonal matrix from specified main diagonal. + /// ## Parameters + /// * d: One-dimensional matrix that represents the main diagonal. + #[inline] + #[must_use] + pub fn diag_mat(d: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_diag_const_MatR(d.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Mat] + pub trait MatTraitConst { + fn as_raw_Mat(&self) -> *const c_void; + + /// ! includes several bit-fields: + /// - the magic signature + /// - continuity flag + /// - depth + /// - number of channels + #[inline] + fn flags(&self) -> i32 { + let ret = unsafe { sys::cv_Mat_propFlags_const(self.as_raw_Mat()) }; + ret + } + + /// the matrix dimensionality, >= 2 + #[inline] + fn dims(&self) -> i32 { + let ret = unsafe { sys::cv_Mat_propDims_const(self.as_raw_Mat()) }; + ret + } + + /// the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions + #[inline] + fn rows(&self) -> i32 { + let ret = unsafe { sys::cv_Mat_propRows_const(self.as_raw_Mat()) }; + ret + } + + /// the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions + #[inline] + fn cols(&self) -> i32 { + let ret = unsafe { sys::cv_Mat_propCols_const(self.as_raw_Mat()) }; + ret + } + + /// pointer to the data + #[inline] + fn data(&self) -> *const u8 { + let ret = unsafe { sys::cv_Mat_propData_const(self.as_raw_Mat()) }; + ret + } + + /// helper fields used in locateROI and adjustROI + #[inline] + fn datastart(&self) -> *const u8 { + let ret = unsafe { sys::cv_Mat_propDatastart_const(self.as_raw_Mat()) }; + ret + } + + #[inline] + fn dataend(&self) -> *const u8 { + let ret = unsafe { sys::cv_Mat_propDataend_const(self.as_raw_Mat()) }; + ret + } + + #[inline] + fn datalimit(&self) -> *const u8 { + let ret = unsafe { sys::cv_Mat_propDatalimit_const(self.as_raw_Mat()) }; + ret + } + + #[inline] + fn mat_size(&self) -> core::MatSize<'_> { + let ret = unsafe { sys::cv_Mat_propSize_const(self.as_raw_Mat()) }; + let ret = unsafe { core::MatSize::<'_>::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn mat_step(&self) -> core::MatStep { + let ret = unsafe { sys::cv_Mat_propStep_const(self.as_raw_Mat()) }; + let ret = unsafe { core::MatStep::opencv_from_extern(ret) }; + ret + } + + /// Creates a matrix header for the specified matrix row. + /// + /// The method makes a new header for the specified matrix row and returns it. This is an O(1) + /// operation, regardless of the matrix size. The underlying data of the new matrix is shared with the + /// original matrix. Here is the example of one of the classical basic matrix processing operations, + /// axpy, used by LU and many other algorithms: + /// ```C++ + /// inline void matrix_axpy(Mat& A, int i, int j, double alpha) + /// { + /// A.row(i) += A.row(j)*alpha; + /// } + /// ``` + /// + /// + /// Note: In the current implementation, the following code does not work as expected: + /// ```C++ + /// Mat A; + /// ... + /// A.row(i) = A.row(j); // will not work + /// ``` + /// + /// This happens because A.row(i) forms a temporary header that is further assigned to another header. + /// Remember that each of these operations is O(1), that is, no data is copied. Thus, the above + /// assignment is not true if you may have expected the j-th row to be copied to the i-th row. To + /// achieve that, you should either turn this simple assignment into an expression or use the + /// Mat::copyTo method: + /// ```C++ + /// Mat A; + /// ... + /// // works, but looks a bit obscure. + /// A.row(i) = A.row(j) + 0; + /// // this is a bit longer, but the recommended method. + /// A.row(j).copyTo(A.row(i)); + /// ``` + /// + /// ## Parameters + /// * y: A 0-based row index. + #[inline] + fn row(&self, y: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_row_const_int(self.as_raw_Mat(), y, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a matrix header for the specified matrix column. + /// + /// The method makes a new header for the specified matrix column and returns it. This is an O(1) + /// operation, regardless of the matrix size. The underlying data of the new matrix is shared with the + /// original matrix. See also the Mat::row description. + /// ## Parameters + /// * x: A 0-based column index. + #[inline] + fn col(&self, x: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_col_const_int(self.as_raw_Mat(), x, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a matrix header for the specified row span. + /// + /// The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and + /// Mat::col , this is an O(1) operation. + /// ## Parameters + /// * startrow: An inclusive 0-based start index of the row span. + /// * endrow: An exclusive 0-based ending index of the row span. + #[inline] + fn row_bounds(&self, startrow: i32, endrow: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_rowRange_const_int_int(self.as_raw_Mat(), startrow, endrow, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a matrix header for the specified row span. + /// + /// The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and + /// Mat::col , this is an O(1) operation. + /// ## Parameters + /// * startrow: An inclusive 0-based start index of the row span. + /// * endrow: An exclusive 0-based ending index of the row span. + /// + /// ## Overloaded parameters + /// + /// * r: Range structure containing both the start and the end indices. + #[inline] + fn row_range(&self, r: &impl core::RangeTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_rowRange_const_const_RangeR(self.as_raw_Mat(), r.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a matrix header for the specified column span. + /// + /// The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and + /// Mat::col , this is an O(1) operation. + /// ## Parameters + /// * startcol: An inclusive 0-based start index of the column span. + /// * endcol: An exclusive 0-based ending index of the column span. + #[inline] + fn col_bounds(&self, startcol: i32, endcol: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_colRange_const_int_int(self.as_raw_Mat(), startcol, endcol, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a matrix header for the specified column span. + /// + /// The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and + /// Mat::col , this is an O(1) operation. + /// ## Parameters + /// * startcol: An inclusive 0-based start index of the column span. + /// * endcol: An exclusive 0-based ending index of the column span. + /// + /// ## Overloaded parameters + /// + /// * r: Range structure containing both the start and the end indices. + #[inline] + fn col_range(&self, r: &impl core::RangeTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_colRange_const_const_RangeR(self.as_raw_Mat(), r.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Extracts a diagonal from a matrix + /// + /// The method makes a new header for the specified matrix diagonal. The new matrix is represented as a + /// single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation. + /// ## Parameters + /// * d: index of the diagonal, with the following values: + /// - `d=0` is the main diagonal. + /// - `d<0` is a diagonal from the lower half. For example, d=-1 means the diagonal is set + /// immediately below the main one. + /// - `d>0` is a diagonal from the upper half. For example, d=1 means the diagonal is set + /// immediately above the main one. + /// For example: + /// ```C++ + /// Mat m = (Mat_(3,3) << + /// 1,2,3, + /// 4,5,6, + /// 7,8,9); + /// Mat d0 = m.diag(0); + /// Mat d1 = m.diag(1); + /// Mat d_1 = m.diag(-1); + /// ``` + /// + /// The resulting matrices are + /// ```C++ + /// d0 = + /// [1; + /// 5; + /// 9] + /// d1 = + /// [2; + /// 6] + /// d_1 = + /// [4; + /// 8] + /// ``` + /// + /// + /// ## C++ default parameters + /// * d: 0 + #[inline] + fn diag(&self, d: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_diag_const_int(self.as_raw_Mat(), d, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Extracts a diagonal from a matrix + /// + /// The method makes a new header for the specified matrix diagonal. The new matrix is represented as a + /// single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation. + /// ## Parameters + /// * d: index of the diagonal, with the following values: + /// - `d=0` is the main diagonal. + /// - `d<0` is a diagonal from the lower half. For example, d=-1 means the diagonal is set + /// immediately below the main one. + /// - `d>0` is a diagonal from the upper half. For example, d=1 means the diagonal is set + /// immediately above the main one. + /// For example: + /// ```C++ + /// Mat m = (Mat_(3,3) << + /// 1,2,3, + /// 4,5,6, + /// 7,8,9); + /// Mat d0 = m.diag(0); + /// Mat d1 = m.diag(1); + /// Mat d_1 = m.diag(-1); + /// ``` + /// + /// The resulting matrices are + /// ```C++ + /// d0 = + /// [1; + /// 5; + /// 9] + /// d1 = + /// [2; + /// 6] + /// d_1 = + /// [4; + /// 8] + /// ``` + /// + /// + /// ## Note + /// This alternative version of [MatTraitConst::diag] function uses the following default values for its arguments: + /// * d: 0 + #[inline] + fn diag_def(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_diag_const(self.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a full copy of the array and the underlying data. + /// + /// The method creates a full copy of the array. The original step[] is not taken into account. So, the + /// array copy is a continuous array occupying total()*elemSize() bytes. + #[inline] + #[must_use] + fn try_clone(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_clone_const(self.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Copies the matrix to another one. + /// + /// The method copies the matrix data to another matrix. Before copying the data, the method invokes : + /// ```C++ + /// m.create(this->size(), this->type()); + /// ``` + /// + /// so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the + /// function does not handle the case of a partial overlap between the source and the destination + /// matrices. + /// + /// When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, + /// the newly allocated matrix is initialized with all zeros before copying the data. + /// + /// If (re)allocation of destination memory is not necessary (e.g. updating ROI), use copyAt() . + /// + /// ## Parameters + /// * m: Destination matrix. If it does not have a proper size or type before the operation, it is + /// reallocated. + /// ## See also + /// copyAt + #[inline] + fn copy_to(&self, m: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_copyTo_const_const__OutputArrayR(self.as_raw_Mat(), m.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Copies the matrix to another one. + /// + /// The method copies the matrix data to another matrix. Before copying the data, the method invokes : + /// ```C++ + /// m.create(this->size(), this->type()); + /// ``` + /// + /// so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the + /// function does not handle the case of a partial overlap between the source and the destination + /// matrices. + /// + /// When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, + /// the newly allocated matrix is initialized with all zeros before copying the data. + /// + /// If (re)allocation of destination memory is not necessary (e.g. updating ROI), use copyAt() . + /// + /// ## Parameters + /// * m: Destination matrix. If it does not have a proper size or type before the operation, it is + /// reallocated. + /// ## See also + /// copyAt + /// + /// ## Overloaded parameters + /// + /// * m: Destination matrix. If it does not have a proper size or type before the operation, it is + /// reallocated. + /// * mask: Operation mask of the same size as \*this. Its non-zero elements indicate which matrix + /// elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels. + #[inline] + fn copy_to_masked(&self, m: &mut impl ToOutputArray, mask: &impl ToInputArray) -> Result<()> { + output_array_arg!(m); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_copyTo_const_const__OutputArrayR_const__InputArrayR(self.as_raw_Mat(), m.as_raw__OutputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Overwrites the existing matrix + /// + /// This method writes existing matrix data, just like copyTo(). + /// But if it does not have a proper size or type before the operation, an exception is thrown. + /// This function is helpful to update ROI in an existing matrix. + /// + /// If (re)allocation of destination memory is necessary, use copyTo() . + /// + /// ## Parameters + /// * m: Destination matrix. + /// If it does not have a proper size or type before the operation, an exception is thrown. + /// ## See also + /// copyTo + #[inline] + fn copy_at(&self, m: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_copyAt_const_const__OutputArrayR(self.as_raw_Mat(), m.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Overwrites the existing matrix + /// + /// This method writes existing matrix data, just like copyTo(). + /// But if it does not have a proper size or type before the operation, an exception is thrown. + /// This function is helpful to update ROI in an existing matrix. + /// + /// If (re)allocation of destination memory is necessary, use copyTo() . + /// + /// ## Parameters + /// * m: Destination matrix. + /// If it does not have a proper size or type before the operation, an exception is thrown. + /// ## See also + /// copyTo + /// + /// ## Overloaded parameters + /// + /// * m: Destination matrix. + /// If it does not have a proper size or type before the operation, an exception is thrown. + /// * mask: Operation mask of the same size as \*this. Its non-zero elements indicate which matrix + /// elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels. + #[inline] + fn copy_at_1(&self, m: &mut impl ToOutputArray, mask: &impl ToInputArray) -> Result<()> { + output_array_arg!(m); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_copyAt_const_const__OutputArrayR_const__InputArrayR(self.as_raw_Mat(), m.as_raw__OutputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Converts an array to another data type with optional scaling. + /// + /// The method converts source pixel values to the target data type. saturate_cast\<\> is applied at + /// the end to avoid possible overflows: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?m%28x%2Cy%29%20%3D%20saturate%20%5C%5F%20cast%3CrType%3E%28%20%5Calpha%20%28%2Athis%29%28x%2Cy%29%20%2B%20%20%5Cbeta%20%29) + /// ## Parameters + /// * m: output matrix; if it does not have a proper size or type before the operation, it is + /// reallocated. + /// * rtype: desired output matrix type or, rather, the depth since the number of channels are the + /// same as the input has; if rtype is negative, the output matrix will have the same type as the input. + /// * alpha: optional scale factor. + /// * beta: optional delta added to the scaled values. + /// + /// ## C++ default parameters + /// * alpha: 1 + /// * beta: 0 + #[inline] + fn convert_to(&self, m: &mut impl ToOutputArray, rtype: i32, alpha: f64, beta: f64) -> Result<()> { + output_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_convertTo_const_const__OutputArrayR_int_double_double(self.as_raw_Mat(), m.as_raw__OutputArray(), rtype, alpha, beta, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Converts an array to another data type with optional scaling. + /// + /// The method converts source pixel values to the target data type. saturate_cast\<\> is applied at + /// the end to avoid possible overflows: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?m%28x%2Cy%29%20%3D%20saturate%20%5C%5F%20cast%3CrType%3E%28%20%5Calpha%20%28%2Athis%29%28x%2Cy%29%20%2B%20%20%5Cbeta%20%29) + /// ## Parameters + /// * m: output matrix; if it does not have a proper size or type before the operation, it is + /// reallocated. + /// * rtype: desired output matrix type or, rather, the depth since the number of channels are the + /// same as the input has; if rtype is negative, the output matrix will have the same type as the input. + /// * alpha: optional scale factor. + /// * beta: optional delta added to the scaled values. + /// + /// ## Note + /// This alternative version of [MatTraitConst::convert_to] function uses the following default values for its arguments: + /// * alpha: 1 + /// * beta: 0 + #[inline] + fn convert_to_def(&self, m: &mut impl ToOutputArray, rtype: i32) -> Result<()> { + output_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_convertTo_const_const__OutputArrayR_int(self.as_raw_Mat(), m.as_raw__OutputArray(), rtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Provides a functional form of convertTo. + /// + /// This is an internally used method called by the [MatrixExpressions] engine. + /// ## Parameters + /// * m: Destination array. + /// * type: Desired destination array depth (or -1 if it should be the same as the source type). + /// + /// ## C++ default parameters + /// * typ: -1 + #[inline] + fn assign_to(&self, m: &mut impl core::MatTrait, typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_assignTo_const_MatR_int(self.as_raw_Mat(), m.as_raw_mut_Mat(), typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Provides a functional form of convertTo. + /// + /// This is an internally used method called by the [MatrixExpressions] engine. + /// ## Parameters + /// * m: Destination array. + /// * type: Desired destination array depth (or -1 if it should be the same as the source type). + /// + /// ## Note + /// This alternative version of [MatTraitConst::assign_to] function uses the following default values for its arguments: + /// * typ: -1 + #[inline] + fn assign_to_def(&self, m: &mut impl core::MatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_assignTo_const_MatR(self.as_raw_Mat(), m.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Changes the shape and/or the number of channels of a 2D matrix without copying the data. + /// + /// The method makes a new matrix header for \*this elements. The new matrix may have a different size + /// and/or different number of channels. Any combination is possible if: + /// * No extra elements are included into the new matrix and no elements are excluded. Consequently, + /// the product rows\*cols\*channels() must stay the same after the transformation. + /// * No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of + /// rows, or the operation changes the indices of elements row in some other way, the matrix must be + /// continuous. See Mat::isContinuous . + /// + /// For example, if there is a set of 3D points stored as an STL vector, and you want to represent the + /// points as a 3xN matrix, do the following: + /// ```C++ + /// std::vector vec; + /// ... + /// Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation + /// reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. + /// // Also, an O(1) operation + /// t(); // finally, transpose the Nx3 matrix. + /// // This involves copying all the elements + /// ``` + /// + /// 3-channel 2x2 matrix reshaped to 1-channel 4x3 matrix, each column has values from one of original channels: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// vector new_shape {4, 3}; + /// m = m.reshape(1, new_shape); + /// ``` + /// + /// or: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// const int new_shape[] = {4, 3}; + /// m = m.reshape(1, 2, new_shape); + /// ``` + /// + /// ## Parameters + /// * cn: New number of channels. If the parameter is 0, the number of channels remains the same. + /// * rows: New number of rows. If the parameter is 0, the number of rows remains the same. + /// + /// ## C++ default parameters + /// * rows: 0 + #[inline] + fn reshape(&self, cn: i32, rows: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_reshape_const_int_int(self.as_raw_Mat(), cn, rows, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Changes the shape and/or the number of channels of a 2D matrix without copying the data. + /// + /// The method makes a new matrix header for \*this elements. The new matrix may have a different size + /// and/or different number of channels. Any combination is possible if: + /// * No extra elements are included into the new matrix and no elements are excluded. Consequently, + /// the product rows\*cols\*channels() must stay the same after the transformation. + /// * No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of + /// rows, or the operation changes the indices of elements row in some other way, the matrix must be + /// continuous. See Mat::isContinuous . + /// + /// For example, if there is a set of 3D points stored as an STL vector, and you want to represent the + /// points as a 3xN matrix, do the following: + /// ```C++ + /// std::vector vec; + /// ... + /// Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation + /// reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. + /// // Also, an O(1) operation + /// t(); // finally, transpose the Nx3 matrix. + /// // This involves copying all the elements + /// ``` + /// + /// 3-channel 2x2 matrix reshaped to 1-channel 4x3 matrix, each column has values from one of original channels: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// vector new_shape {4, 3}; + /// m = m.reshape(1, new_shape); + /// ``` + /// + /// or: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// const int new_shape[] = {4, 3}; + /// m = m.reshape(1, 2, new_shape); + /// ``` + /// + /// ## Parameters + /// * cn: New number of channels. If the parameter is 0, the number of channels remains the same. + /// * rows: New number of rows. If the parameter is 0, the number of rows remains the same. + /// + /// ## Note + /// This alternative version of [MatTraitConst::reshape] function uses the following default values for its arguments: + /// * rows: 0 + #[inline] + fn reshape_def(&self, cn: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_reshape_const_int(self.as_raw_Mat(), cn, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Changes the shape and/or the number of channels of a 2D matrix without copying the data. + /// + /// The method makes a new matrix header for \*this elements. The new matrix may have a different size + /// and/or different number of channels. Any combination is possible if: + /// * No extra elements are included into the new matrix and no elements are excluded. Consequently, + /// the product rows\*cols\*channels() must stay the same after the transformation. + /// * No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of + /// rows, or the operation changes the indices of elements row in some other way, the matrix must be + /// continuous. See Mat::isContinuous . + /// + /// For example, if there is a set of 3D points stored as an STL vector, and you want to represent the + /// points as a 3xN matrix, do the following: + /// ```C++ + /// std::vector vec; + /// ... + /// Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation + /// reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. + /// // Also, an O(1) operation + /// t(); // finally, transpose the Nx3 matrix. + /// // This involves copying all the elements + /// ``` + /// + /// 3-channel 2x2 matrix reshaped to 1-channel 4x3 matrix, each column has values from one of original channels: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// vector new_shape {4, 3}; + /// m = m.reshape(1, new_shape); + /// ``` + /// + /// or: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// const int new_shape[] = {4, 3}; + /// m = m.reshape(1, 2, new_shape); + /// ``` + /// + /// ## Parameters + /// * cn: New number of channels. If the parameter is 0, the number of channels remains the same. + /// * rows: New number of rows. If the parameter is 0, the number of rows remains the same. + /// + /// ## Overloaded parameters + /// + /// * cn: New number of channels. If the parameter is 0, the number of channels remains the same. + /// * newndims: New number of dimensions. + /// * newsz: Array with new matrix size by all dimensions. If some sizes are zero, + /// the original sizes in those dimensions are presumed. + #[inline] + fn reshape_nd(&self, cn: i32, newsz: &[i32]) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_reshape_const_int_int_const_intX(self.as_raw_Mat(), cn, newsz.len().try_into()?, newsz.as_ptr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Changes the shape and/or the number of channels of a 2D matrix without copying the data. + /// + /// The method makes a new matrix header for \*this elements. The new matrix may have a different size + /// and/or different number of channels. Any combination is possible if: + /// * No extra elements are included into the new matrix and no elements are excluded. Consequently, + /// the product rows\*cols\*channels() must stay the same after the transformation. + /// * No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of + /// rows, or the operation changes the indices of elements row in some other way, the matrix must be + /// continuous. See Mat::isContinuous . + /// + /// For example, if there is a set of 3D points stored as an STL vector, and you want to represent the + /// points as a 3xN matrix, do the following: + /// ```C++ + /// std::vector vec; + /// ... + /// Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation + /// reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. + /// // Also, an O(1) operation + /// t(); // finally, transpose the Nx3 matrix. + /// // This involves copying all the elements + /// ``` + /// + /// 3-channel 2x2 matrix reshaped to 1-channel 4x3 matrix, each column has values from one of original channels: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// vector new_shape {4, 3}; + /// m = m.reshape(1, new_shape); + /// ``` + /// + /// or: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// const int new_shape[] = {4, 3}; + /// m = m.reshape(1, 2, new_shape); + /// ``` + /// + /// ## Parameters + /// * cn: New number of channels. If the parameter is 0, the number of channels remains the same. + /// * rows: New number of rows. If the parameter is 0, the number of rows remains the same. + /// + /// ## Overloaded parameters + /// + /// * cn: New number of channels. If the parameter is 0, the number of channels remains the same. + /// * newshape: Vector with new matrix size by all dimensions. If some sizes are zero, + /// the original sizes in those dimensions are presumed. + #[inline] + fn reshape_nd_vec(&self, cn: i32, newshape: &core::Vector) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_reshape_const_int_const_vectorLintGR(self.as_raw_Mat(), cn, newshape.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reset the type of matrix. + /// + /// The methods reset the data type of matrix. If the new type and the old type of the matrix + /// have the same element size, the current buffer can be reused. The method needs to consider whether the + /// current mat is a submatrix or has any references. + /// ## Parameters + /// * type: New data type. + #[inline] + fn reinterpret(&self, typ: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_reinterpret_const_int(self.as_raw_Mat(), typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Computes a cross-product of two 3-element vectors. + /// + /// The method computes a cross-product of two 3-element vectors. The vectors must be 3-element + /// floating-point vectors of the same shape and size. The result is another 3-element vector of the + /// same shape and type as operands. + /// ## Parameters + /// * m: Another cross-product operand. + #[inline] + fn cross(&self, m: &impl ToInputArray) -> Result { + input_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_cross_const_const__InputArrayR(self.as_raw_Mat(), m.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Computes a dot-product of two vectors. + /// + /// The method computes a dot-product of two matrices. If the matrices are not single-column or + /// single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D + /// vectors. The vectors must have the same size and type. If the matrices have more than one channel, + /// the dot products from all the channels are summed together. + /// ## Parameters + /// * m: another dot-product operand. + #[inline] + fn dot(&self, m: &impl ToInputArray) -> Result { + input_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_dot_const_const__InputArrayR(self.as_raw_Mat(), m.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Locates the matrix header within a parent matrix. + /// + /// After you extracted a submatrix from a matrix using Mat::row, Mat::col, Mat::rowRange, + /// Mat::colRange, and others, the resultant submatrix points just to the part of the original big + /// matrix. However, each submatrix contains information (represented by datastart and dataend + /// fields) that helps reconstruct the original matrix size and the position of the extracted + /// submatrix within the original matrix. The method locateROI does exactly that. + /// ## Parameters + /// * wholeSize: Output parameter that contains the size of the whole matrix containing *this* + /// as a part. + /// * ofs: Output parameter that contains an offset of *this* inside the whole matrix. + #[inline] + fn locate_roi(&self, whole_size: &mut core::Size, ofs: &mut core::Point) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_locateROI_const_SizeR_PointR(self.as_raw_Mat(), whole_size, ofs, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Extracts a rectangular submatrix. + /// + /// The operators make a new header for the specified sub-array of \*this . They are the most + /// generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example, + /// `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above, + /// the operators are O(1) operations, that is, no matrix data is copied. + /// ## Parameters + /// * rowRange: Start and end row of the extracted submatrix. The upper boundary is not included. To + /// select all the rows, use Range::all(). + /// * colRange: Start and end column of the extracted submatrix. The upper boundary is not included. + /// To select all the columns, use Range::all(). + #[inline] + fn rowscols(&self, mut row_range: impl core::RangeTrait, mut col_range: impl core::RangeTrait) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_operator___const_Range_Range(self.as_raw_Mat(), row_range.as_raw_mut_Range(), col_range.as_raw_mut_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Extracts a rectangular submatrix. + /// + /// The operators make a new header for the specified sub-array of \*this . They are the most + /// generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example, + /// `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above, + /// the operators are O(1) operations, that is, no matrix data is copied. + /// ## Parameters + /// * rowRange: Start and end row of the extracted submatrix. The upper boundary is not included. To + /// select all the rows, use Range::all(). + /// * colRange: Start and end column of the extracted submatrix. The upper boundary is not included. + /// To select all the columns, use Range::all(). + /// + /// ## Overloaded parameters + /// + /// * roi: Extracted submatrix specified as a rectangle. + #[inline] + fn roi(&self, roi: core::Rect) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_operator___const_const_RectR(self.as_raw_Mat(), &roi, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Extracts a rectangular submatrix. + /// + /// The operators make a new header for the specified sub-array of \*this . They are the most + /// generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example, + /// `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above, + /// the operators are O(1) operations, that is, no matrix data is copied. + /// ## Parameters + /// * rowRange: Start and end row of the extracted submatrix. The upper boundary is not included. To + /// select all the rows, use Range::all(). + /// * colRange: Start and end column of the extracted submatrix. The upper boundary is not included. + /// To select all the columns, use Range::all(). + /// + /// ## Overloaded parameters + /// + /// * ranges: Array of selected ranges along each array dimension. + #[inline] + fn ranges(&self, ranges: &core::Vector) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_operator___const_const_vectorLRangeGR(self.as_raw_Mat(), ranges.as_raw_VectorOfRange(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reports whether the matrix is continuous or not. + /// + /// The method returns true if the matrix elements are stored continuously without gaps at the end of + /// each row. Otherwise, it returns false. Obviously, 1x1 or 1xN matrices are always continuous. + /// Matrices created with Mat::create are always continuous. But if you extract a part of the matrix + /// using Mat::col, Mat::diag, and so on, or constructed a matrix header for externally allocated data, + /// such matrices may no longer have this property. + /// + /// The continuity flag is stored as a bit in the Mat::flags field and is computed automatically when + /// you construct a matrix header. Thus, the continuity check is a very fast operation, though + /// theoretically it could be done as follows: + /// ```C++ + /// // alternative implementation of Mat::isContinuous() + /// bool myCheckMatContinuity(const Mat& m) + /// { + /// //return (m.flags & Mat::CONTINUOUS_FLAG) != 0; + /// return m.rows == 1 || m.step == m.cols*m.elemSize(); + /// } + /// ``` + /// + /// The method is used in quite a few of OpenCV functions. The point is that element-wise operations + /// (such as arithmetic and logical operations, math functions, alpha blending, color space + /// transformations, and others) do not depend on the image geometry. Thus, if all the input and output + /// arrays are continuous, the functions can process them as very long single-row vectors. The example + /// below illustrates how an alpha-blending function can be implemented: + /// ```C++ + /// template + /// void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) + /// { + /// const float alpha_scale = (float)std::numeric_limits::max(), + /// inv_scale = 1.f/alpha_scale; + /// + /// CV_Assert( src1.type() == src2.type() && + /// src1.type() == CV_MAKETYPE(traits::Depth::value, 4) && + /// src1.size() == src2.size()); + /// Size size = src1.size(); + /// dst.create(size, src1.type()); + /// + /// // here is the idiom: check the arrays for continuity and, + /// // if this is the case, + /// // treat the arrays as 1D vectors + /// if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() ) + /// { + /// size.width *= size.height; + /// size.height = 1; + /// } + /// size.width *= 4; + /// + /// for( int i = 0; i < size.height; i++ ) + /// { + /// // when the arrays are continuous, + /// // the outer loop is executed only once + /// const T* ptr1 = src1.ptr(i); + /// const T* ptr2 = src2.ptr(i); + /// T* dptr = dst.ptr(i); + /// + /// for( int j = 0; j < size.width; j += 4 ) + /// { + /// float alpha = ptr1[j+3]*inv_scale, beta = ptr2[j+3]*inv_scale; + /// dptr[j] = saturate_cast(ptr1[j]*alpha + ptr2[j]*beta); + /// dptr[j+1] = saturate_cast(ptr1[j+1]*alpha + ptr2[j+1]*beta); + /// dptr[j+2] = saturate_cast(ptr1[j+2]*alpha + ptr2[j+2]*beta); + /// dptr[j+3] = saturate_cast((1 - (1-alpha)*(1-beta))*alpha_scale); + /// } + /// } + /// } + /// ``` + /// + /// This approach, while being very simple, can boost the performance of a simple element-operation by + /// 10-20 percents, especially if the image is rather small and the operation is quite simple. + /// + /// Another OpenCV idiom in this function, a call of Mat::create for the destination array, that + /// allocates the destination array unless it already has the proper size and type. And while the newly + /// allocated arrays are always continuous, you still need to check the destination array because + /// Mat::create does not always allocate a new matrix. + #[inline] + fn is_continuous(&self) -> bool { + let ret = unsafe { sys::cv_Mat_isContinuous_const(self.as_raw_Mat()) }; + ret + } + + /// returns true if the matrix is a submatrix of another matrix + #[inline] + fn is_submatrix(&self) -> bool { + let ret = unsafe { sys::cv_Mat_isSubmatrix_const(self.as_raw_Mat()) }; + ret + } + + /// Returns the matrix element size in bytes. + /// + /// The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 , + /// the method returns 3\*sizeof(short) or 6. + #[inline] + fn elem_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_elemSize_const(self.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the size of each matrix element channel in bytes. + /// + /// The method returns the matrix element channel size in bytes, that is, it ignores the number of + /// channels. For example, if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2. + #[inline] + fn elem_size1(&self) -> size_t { + let ret = unsafe { sys::cv_Mat_elemSize1_const(self.as_raw_Mat()) }; + ret + } + + /// Returns the type of a matrix element. + /// + /// The method returns a matrix element type. This is an identifier compatible with the CvMat type + /// system, like CV_16SC3 or 16-bit signed 3-channel array, and so on. + #[inline] + fn typ(&self) -> i32 { + let ret = unsafe { sys::cv_Mat_type_const(self.as_raw_Mat()) }; + ret + } + + /// Returns the depth of a matrix element. + /// + /// The method returns the identifier of the matrix element depth (the type of each individual channel). + /// For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of + /// matrix types contains the following values: + /// * CV_8U - 8-bit unsigned integers ( 0..255 ) + /// * CV_8S - 8-bit signed integers ( -128..127 ) + /// * CV_16U - 16-bit unsigned integers ( 0..65535 ) + /// * CV_16S - 16-bit signed integers ( -32768..32767 ) + /// * CV_32S - 32-bit signed integers ( -2147483648..2147483647 ) + /// * CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN ) + /// * CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN ) + #[inline] + fn depth(&self) -> i32 { + let ret = unsafe { sys::cv_Mat_depth_const(self.as_raw_Mat()) }; + ret + } + + /// Returns the number of matrix channels. + /// + /// The method returns the number of matrix channels. + #[inline] + fn channels(&self) -> i32 { + let ret = unsafe { sys::cv_Mat_channels_const(self.as_raw_Mat()) }; + ret + } + + /// Returns a normalized step. + /// + /// The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an + /// arbitrary matrix element. + /// + /// ## C++ default parameters + /// * i: 0 + #[inline] + fn step1(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_step1_const_int(self.as_raw_Mat(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a normalized step. + /// + /// The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an + /// arbitrary matrix element. + /// + /// ## Note + /// This alternative version of [MatTraitConst::step1] function uses the following default values for its arguments: + /// * i: 0 + #[inline] + fn step1_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_step1_const(self.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns true if the array has no elements. + /// + /// The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and + /// resize() methods `M.total() == 0` does not imply that `M.data == NULL`. + #[inline] + fn empty(&self) -> bool { + let ret = unsafe { sys::cv_Mat_empty_const(self.as_raw_Mat()) }; + ret + } + + /// Returns the total number of array elements. + /// + /// The method returns the number of array elements (a number of pixels if the array represents an + /// image). + #[inline] + fn total(&self) -> size_t { + let ret = unsafe { sys::cv_Mat_total_const(self.as_raw_Mat()) }; + ret + } + + /// Returns the total number of array elements. + /// + /// The method returns the number of elements within a certain sub-array slice with startDim <= dim < endDim + /// + /// ## C++ default parameters + /// * end_dim: INT_MAX + #[inline] + fn total_slice(&self, start_dim: i32, end_dim: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_total_const_int_int(self.as_raw_Mat(), start_dim, end_dim, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the total number of array elements. + /// + /// The method returns the number of elements within a certain sub-array slice with startDim <= dim < endDim + /// + /// ## Note + /// This alternative version of [MatTraitConst::total_slice] function uses the following default values for its arguments: + /// * end_dim: INT_MAX + #[inline] + fn total_slice_def(&self, start_dim: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_total_const_int(self.as_raw_Mat(), start_dim, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Parameters + /// * elemChannels: Number of channels or number of columns the matrix should have. + /// For a 2-D matrix, when the matrix has only 1 column, then it should have + /// elemChannels channels; When the matrix has only 1 channel, + /// then it should have elemChannels columns. + /// For a 3-D matrix, it should have only one channel. Furthermore, + /// if the number of planes is not one, then the number of rows + /// within every plane has to be 1; if the number of rows within + /// every plane is not 1, then the number of planes has to be 1. + /// * depth: The depth the matrix should have. Set it to -1 when any depth is fine. + /// * requireContinuous: Set it to true to require the matrix to be continuous + /// ## Returns + /// -1 if the requirement is not satisfied. + /// Otherwise, it returns the number of elements in the matrix. Note + /// that an element may have multiple channels. + /// + /// The following code demonstrates its usage for a 2-d matrix: + /// [example-2d](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_mat_checkVector.cpp#L1) + /// + /// The following code demonstrates its usage for a 3-d matrix: + /// [example-3d](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_mat_checkVector.cpp#L1) + /// + /// ## C++ default parameters + /// * depth: -1 + /// * require_continuous: true + #[inline] + fn check_vector(&self, elem_channels: i32, depth: i32, require_continuous: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_checkVector_const_int_int_bool(self.as_raw_Mat(), elem_channels, depth, require_continuous, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Parameters + /// * elemChannels: Number of channels or number of columns the matrix should have. + /// For a 2-D matrix, when the matrix has only 1 column, then it should have + /// elemChannels channels; When the matrix has only 1 channel, + /// then it should have elemChannels columns. + /// For a 3-D matrix, it should have only one channel. Furthermore, + /// if the number of planes is not one, then the number of rows + /// within every plane has to be 1; if the number of rows within + /// every plane is not 1, then the number of planes has to be 1. + /// * depth: The depth the matrix should have. Set it to -1 when any depth is fine. + /// * requireContinuous: Set it to true to require the matrix to be continuous + /// ## Returns + /// -1 if the requirement is not satisfied. + /// Otherwise, it returns the number of elements in the matrix. Note + /// that an element may have multiple channels. + /// + /// The following code demonstrates its usage for a 2-d matrix: + /// [example-2d](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_mat_checkVector.cpp#L1) + /// + /// The following code demonstrates its usage for a 3-d matrix: + /// [example-3d](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_mat_checkVector.cpp#L1) + /// + /// ## Note + /// This alternative version of [MatTraitConst::check_vector] function uses the following default values for its arguments: + /// * depth: -1 + /// * require_continuous: true + #[inline] + fn check_vector_def(&self, elem_channels: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_checkVector_const_int(self.as_raw_Mat(), elem_channels, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a pointer to the specified matrix row. + /// + /// The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + /// Mat::isContinuous to know how to use these methods. + /// ## Parameters + /// * i0: A 0-based row index. + /// + /// ## Overloaded parameters + /// + /// ## C++ default parameters + /// * i0: 0 + #[inline] + fn ptr(&self, i0: i32) -> Result<*const u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_ptr_const_int(self.as_raw_Mat(), i0, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a pointer to the specified matrix row. + /// + /// The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + /// Mat::isContinuous to know how to use these methods. + /// ## Parameters + /// * i0: A 0-based row index. + /// + /// ## Overloaded parameters + /// + /// + /// ## Note + /// This alternative version of [MatTraitConst::ptr] function uses the following default values for its arguments: + /// * i0: 0 + #[inline] + fn ptr_def(&self) -> Result<*const u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_ptr_const(self.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a pointer to the specified matrix row. + /// + /// The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + /// Mat::isContinuous to know how to use these methods. + /// ## Parameters + /// * i0: A 0-based row index. + /// + /// ## Overloaded parameters + /// + /// * row: Index along the dimension 0 + /// * col: Index along the dimension 1 + #[inline] + fn ptr_2d(&self, row: i32, col: i32) -> Result<*const u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_ptr_const_int_int(self.as_raw_Mat(), row, col, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a pointer to the specified matrix row. + /// + /// The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + /// Mat::isContinuous to know how to use these methods. + /// ## Parameters + /// * i0: A 0-based row index. + /// + /// ## Overloaded parameters + #[inline] + fn ptr_3d(&self, i0: i32, i1: i32, i2: i32) -> Result<*const u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_ptr_const_int_int_int(self.as_raw_Mat(), i0, i1, i2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a pointer to the specified matrix row. + /// + /// The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + /// Mat::isContinuous to know how to use these methods. + /// ## Parameters + /// * i0: A 0-based row index. + /// + /// ## Overloaded parameters + #[inline] + fn ptr_nd(&self, idx: &[i32]) -> Result<*const u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_ptr_const_const_intX(self.as_raw_Mat(), idx.as_ptr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a reference to the specified array element. + /// + /// The template methods return a reference to the specified array element. For the sake of higher + /// performance, the index range checks are only performed in the Debug configuration. + /// + /// Note that the variants with a single index (i) can be used to access elements of single-row or + /// single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + /// B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + /// instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + /// + /// The example below initializes a Hilbert matrix: + /// ```C++ + /// Mat H(100, 100, CV_64F); + /// for(int i = 0; i < H.rows; i++) + /// for(int j = 0; j < H.cols; j++) + /// H.at(i,j)=1./(i+j+1); + /// ``` + /// + /// + /// Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + /// on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + /// - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + /// + /// ## Parameters + /// * i0: Index along the dimension 0 + /// + /// ## Overloaded parameters + /// + /// * i0: Index along the dimension 0 + /// + /// ## C++ default parameters + /// * i0: 0 + #[inline] + fn at(&self, i0: i32) -> Result<&T> { + core::mat_forward::at(self, i0) + } + + /// Returns a reference to the specified array element. + /// + /// The template methods return a reference to the specified array element. For the sake of higher + /// performance, the index range checks are only performed in the Debug configuration. + /// + /// Note that the variants with a single index (i) can be used to access elements of single-row or + /// single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + /// B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + /// instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + /// + /// The example below initializes a Hilbert matrix: + /// ```C++ + /// Mat H(100, 100, CV_64F); + /// for(int i = 0; i < H.rows; i++) + /// for(int j = 0; j < H.cols; j++) + /// H.at(i,j)=1./(i+j+1); + /// ``` + /// + /// + /// Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + /// on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + /// - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + /// + /// ## Parameters + /// * i0: Index along the dimension 0 + /// + /// ## Overloaded parameters + /// + /// * i0: Index along the dimension 0 + /// + /// ## Note + /// This alternative version of [MatTraitConst::at] function uses the following default values for its arguments: + /// * i0: 0 + #[inline] + fn at_def(&self) -> Result<&T> { + core::mat_forward::at_def(self, ) + } + + /// Returns a reference to the specified array element. + /// + /// The template methods return a reference to the specified array element. For the sake of higher + /// performance, the index range checks are only performed in the Debug configuration. + /// + /// Note that the variants with a single index (i) can be used to access elements of single-row or + /// single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + /// B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + /// instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + /// + /// The example below initializes a Hilbert matrix: + /// ```C++ + /// Mat H(100, 100, CV_64F); + /// for(int i = 0; i < H.rows; i++) + /// for(int j = 0; j < H.cols; j++) + /// H.at(i,j)=1./(i+j+1); + /// ``` + /// + /// + /// Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + /// on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + /// - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + /// + /// ## Parameters + /// * i0: Index along the dimension 0 + /// + /// ## Overloaded parameters + /// + /// * row: Index along the dimension 0 + /// * col: Index along the dimension 1 + #[inline] + fn at_2d(&self, row: i32, col: i32) -> Result<&T> { + core::mat_forward::at_2d(self, row, col) + } + + /// Returns a reference to the specified array element. + /// + /// The template methods return a reference to the specified array element. For the sake of higher + /// performance, the index range checks are only performed in the Debug configuration. + /// + /// Note that the variants with a single index (i) can be used to access elements of single-row or + /// single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + /// B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + /// instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + /// + /// The example below initializes a Hilbert matrix: + /// ```C++ + /// Mat H(100, 100, CV_64F); + /// for(int i = 0; i < H.rows; i++) + /// for(int j = 0; j < H.cols; j++) + /// H.at(i,j)=1./(i+j+1); + /// ``` + /// + /// + /// Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + /// on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + /// - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + /// + /// ## Parameters + /// * i0: Index along the dimension 0 + /// + /// ## Overloaded parameters + /// + /// * i0: Index along the dimension 0 + /// * i1: Index along the dimension 1 + /// * i2: Index along the dimension 2 + #[inline] + fn at_3d(&self, i0: i32, i1: i32, i2: i32) -> Result<&T> { + core::mat_forward::at_3d(self, i0, i1, i2) + } + + /// Returns a reference to the specified array element. + /// + /// The template methods return a reference to the specified array element. For the sake of higher + /// performance, the index range checks are only performed in the Debug configuration. + /// + /// Note that the variants with a single index (i) can be used to access elements of single-row or + /// single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + /// B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + /// instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + /// + /// The example below initializes a Hilbert matrix: + /// ```C++ + /// Mat H(100, 100, CV_64F); + /// for(int i = 0; i < H.rows; i++) + /// for(int j = 0; j < H.cols; j++) + /// H.at(i,j)=1./(i+j+1); + /// ``` + /// + /// + /// Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + /// on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + /// - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + /// + /// ## Parameters + /// * i0: Index along the dimension 0 + /// + /// ## Overloaded parameters + /// + /// * idx: Array of Mat::dims indices. + #[inline] + fn at_nd(&self, idx: &[i32]) -> Result<&T> { + core::mat_forward::at_nd(self, idx) + } + + /// Returns a reference to the specified array element. + /// + /// The template methods return a reference to the specified array element. For the sake of higher + /// performance, the index range checks are only performed in the Debug configuration. + /// + /// Note that the variants with a single index (i) can be used to access elements of single-row or + /// single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + /// B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + /// instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + /// + /// The example below initializes a Hilbert matrix: + /// ```C++ + /// Mat H(100, 100, CV_64F); + /// for(int i = 0; i < H.rows; i++) + /// for(int j = 0; j < H.cols; j++) + /// H.at(i,j)=1./(i+j+1); + /// ``` + /// + /// + /// Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + /// on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + /// - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + /// + /// ## Parameters + /// * i0: Index along the dimension 0 + /// + /// ## Overloaded parameters + /// + /// special versions for 2D arrays (especially convenient for referencing image pixels) + /// * pt: Element position specified as Point(j,i) . + #[inline] + fn at_pt(&self, pt: core::Point) -> Result<&T> { + core::mat_forward::at_pt(self, pt) + } + + #[inline] + fn size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_size_const(self.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Return the dump of the Mat's data + #[inline] + fn get_data_dump(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_getDataDump_const(self.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Mutable methods for [core::Mat] + pub trait MatTrait: core::MatTraitConst { + fn as_raw_mut_Mat(&mut self) -> *mut c_void; + + /// ! includes several bit-fields: + /// - the magic signature + /// - continuity flag + /// - depth + /// - number of channels + #[inline] + fn set_flags(&mut self, val: i32) { + let ret = unsafe { sys::cv_Mat_propFlags_const_int(self.as_raw_mut_Mat(), val) }; + ret + } + + /// the matrix dimensionality, >= 2 + #[inline] + fn set_dims(&mut self, val: i32) { + let ret = unsafe { sys::cv_Mat_propDims_const_int(self.as_raw_mut_Mat(), val) }; + ret + } + + /// the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions + #[inline] + fn set_rows(&mut self, val: i32) { + let ret = unsafe { sys::cv_Mat_propRows_const_int(self.as_raw_mut_Mat(), val) }; + ret + } + + /// the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions + #[inline] + fn set_cols(&mut self, val: i32) { + let ret = unsafe { sys::cv_Mat_propCols_const_int(self.as_raw_mut_Mat(), val) }; + ret + } + + /// pointer to the data + #[inline] + fn data_mut(&mut self) -> *mut u8 { + let ret = unsafe { sys::cv_Mat_propData(self.as_raw_mut_Mat()) }; + ret + } + + /// pointer to the data + #[inline] + unsafe fn set_data(&mut self, val: *const u8) { + let ret = unsafe { sys::cv_Mat_propData_unsigned_charX(self.as_raw_mut_Mat(), val) }; + ret + } + + /// interaction with UMat + #[inline] + fn u(&mut self) -> core::UMatData { + let ret = unsafe { sys::cv_Mat_propU(self.as_raw_mut_Mat()) }; + let ret = unsafe { core::UMatData::opencv_from_extern(ret) }; + ret + } + + /// interaction with UMat + #[inline] + fn set_u(&mut self, val: &impl core::UMatDataTraitConst) { + let ret = unsafe { sys::cv_Mat_propU_UMatDataX(self.as_raw_mut_Mat(), val.as_raw_UMatData()) }; + ret + } + + #[inline] + fn set_mat_size(&mut self, val: core::MatSize<'_>) { + let ret = unsafe { sys::cv_Mat_propSize_const_MatSize(self.as_raw_mut_Mat(), val.as_raw_MatSize()) }; + ret + } + + /// Creates a matrix header for the specified matrix row. + /// + /// The method makes a new header for the specified matrix row and returns it. This is an O(1) + /// operation, regardless of the matrix size. The underlying data of the new matrix is shared with the + /// original matrix. Here is the example of one of the classical basic matrix processing operations, + /// axpy, used by LU and many other algorithms: + /// ```C++ + /// inline void matrix_axpy(Mat& A, int i, int j, double alpha) + /// { + /// A.row(i) += A.row(j)*alpha; + /// } + /// ``` + /// + /// + /// Note: In the current implementation, the following code does not work as expected: + /// ```C++ + /// Mat A; + /// ... + /// A.row(i) = A.row(j); // will not work + /// ``` + /// + /// This happens because A.row(i) forms a temporary header that is further assigned to another header. + /// Remember that each of these operations is O(1), that is, no data is copied. Thus, the above + /// assignment is not true if you may have expected the j-th row to be copied to the i-th row. To + /// achieve that, you should either turn this simple assignment into an expression or use the + /// Mat::copyTo method: + /// ```C++ + /// Mat A; + /// ... + /// // works, but looks a bit obscure. + /// A.row(i) = A.row(j) + 0; + /// // this is a bit longer, but the recommended method. + /// A.row(j).copyTo(A.row(i)); + /// ``` + /// + /// ## Parameters + /// * y: A 0-based row index. + #[inline] + fn row_mut(&mut self, y: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_row_int(self.as_raw_mut_Mat(), y, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a matrix header for the specified matrix column. + /// + /// The method makes a new header for the specified matrix column and returns it. This is an O(1) + /// operation, regardless of the matrix size. The underlying data of the new matrix is shared with the + /// original matrix. See also the Mat::row description. + /// ## Parameters + /// * x: A 0-based column index. + #[inline] + fn col_mut(&mut self, x: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_col_int(self.as_raw_mut_Mat(), x, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a matrix header for the specified row span. + /// + /// The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and + /// Mat::col , this is an O(1) operation. + /// ## Parameters + /// * startrow: An inclusive 0-based start index of the row span. + /// * endrow: An exclusive 0-based ending index of the row span. + #[inline] + fn row_bounds_mut(&mut self, startrow: i32, endrow: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_rowRange_int_int(self.as_raw_mut_Mat(), startrow, endrow, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a matrix header for the specified row span. + /// + /// The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and + /// Mat::col , this is an O(1) operation. + /// ## Parameters + /// * startrow: An inclusive 0-based start index of the row span. + /// * endrow: An exclusive 0-based ending index of the row span. + /// + /// ## Overloaded parameters + /// + /// * r: Range structure containing both the start and the end indices. + #[inline] + fn row_range_mut(&mut self, r: &impl core::RangeTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_rowRange_const_RangeR(self.as_raw_mut_Mat(), r.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a matrix header for the specified column span. + /// + /// The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and + /// Mat::col , this is an O(1) operation. + /// ## Parameters + /// * startcol: An inclusive 0-based start index of the column span. + /// * endcol: An exclusive 0-based ending index of the column span. + #[inline] + fn col_bounds_mut(&mut self, startcol: i32, endcol: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_colRange_int_int(self.as_raw_mut_Mat(), startcol, endcol, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a matrix header for the specified column span. + /// + /// The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and + /// Mat::col , this is an O(1) operation. + /// ## Parameters + /// * startcol: An inclusive 0-based start index of the column span. + /// * endcol: An exclusive 0-based ending index of the column span. + /// + /// ## Overloaded parameters + /// + /// * r: Range structure containing both the start and the end indices. + #[inline] + fn col_range_mut(&mut self, r: &impl core::RangeTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_colRange_const_RangeR(self.as_raw_mut_Mat(), r.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Extracts a diagonal from a matrix + /// + /// The method makes a new header for the specified matrix diagonal. The new matrix is represented as a + /// single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation. + /// ## Parameters + /// * d: index of the diagonal, with the following values: + /// - `d=0` is the main diagonal. + /// - `d<0` is a diagonal from the lower half. For example, d=-1 means the diagonal is set + /// immediately below the main one. + /// - `d>0` is a diagonal from the upper half. For example, d=1 means the diagonal is set + /// immediately above the main one. + /// For example: + /// ```C++ + /// Mat m = (Mat_(3,3) << + /// 1,2,3, + /// 4,5,6, + /// 7,8,9); + /// Mat d0 = m.diag(0); + /// Mat d1 = m.diag(1); + /// Mat d_1 = m.diag(-1); + /// ``` + /// + /// The resulting matrices are + /// ```C++ + /// d0 = + /// [1; + /// 5; + /// 9] + /// d1 = + /// [2; + /// 6] + /// d_1 = + /// [4; + /// 8] + /// ``` + /// + /// + /// ## Note + /// This alternative version of [MatTraitConst::diag] function uses the following default values for its arguments: + /// * d: 0 + #[inline] + fn diag_def_mut(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_diag(self.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Extracts a diagonal from a matrix + /// + /// The method makes a new header for the specified matrix diagonal. The new matrix is represented as a + /// single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation. + /// ## Parameters + /// * d: index of the diagonal, with the following values: + /// - `d=0` is the main diagonal. + /// - `d<0` is a diagonal from the lower half. For example, d=-1 means the diagonal is set + /// immediately below the main one. + /// - `d>0` is a diagonal from the upper half. For example, d=1 means the diagonal is set + /// immediately above the main one. + /// For example: + /// ```C++ + /// Mat m = (Mat_(3,3) << + /// 1,2,3, + /// 4,5,6, + /// 7,8,9); + /// Mat d0 = m.diag(0); + /// Mat d1 = m.diag(1); + /// Mat d_1 = m.diag(-1); + /// ``` + /// + /// The resulting matrices are + /// ```C++ + /// d0 = + /// [1; + /// 5; + /// 9] + /// d1 = + /// [2; + /// 6] + /// d_1 = + /// [4; + /// 8] + /// ``` + /// + /// + /// ## C++ default parameters + /// * d: 0 + #[inline] + fn diag_mut(&mut self, d: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_diag_int(self.as_raw_mut_Mat(), d, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Sets all or some of the array elements to the specified value. + /// ## Parameters + /// * s: Assigned scalar converted to the actual array type. + #[inline] + fn set_scalar(&mut self, s: core::Scalar) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_operatorST_const_ScalarR(self.as_raw_mut_Mat(), &s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets all or some of the array elements to the specified value. + /// + /// This is an advanced variant of the Mat::operator=(const Scalar& s) operator. + /// ## Parameters + /// * value: Assigned scalar converted to the actual array type. + /// * mask: Operation mask of the same size as \*this. Its non-zero elements indicate which matrix + /// elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels + /// + /// ## C++ default parameters + /// * mask: noArray() + #[inline] + fn set_to(&mut self, value: &impl ToInputArray, mask: &impl ToInputArray) -> Result { + input_array_arg!(value); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_setTo_const__InputArrayR_const__InputArrayR(self.as_raw_mut_Mat(), value.as_raw__InputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Sets all or some of the array elements to the specified value. + /// + /// This is an advanced variant of the Mat::operator=(const Scalar& s) operator. + /// ## Parameters + /// * value: Assigned scalar converted to the actual array type. + /// * mask: Operation mask of the same size as \*this. Its non-zero elements indicate which matrix + /// elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels + /// + /// ## Note + /// This alternative version of [MatTrait::set_to] function uses the following default values for its arguments: + /// * mask: noArray() + #[inline] + fn set_to_def(&mut self, value: &impl ToInputArray) -> Result { + input_array_arg!(value); + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_setTo_const__InputArrayR(self.as_raw_mut_Mat(), value.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Changes the shape and/or the number of channels of a 2D matrix without copying the data. + /// + /// The method makes a new matrix header for \*this elements. The new matrix may have a different size + /// and/or different number of channels. Any combination is possible if: + /// * No extra elements are included into the new matrix and no elements are excluded. Consequently, + /// the product rows\*cols\*channels() must stay the same after the transformation. + /// * No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of + /// rows, or the operation changes the indices of elements row in some other way, the matrix must be + /// continuous. See Mat::isContinuous . + /// + /// For example, if there is a set of 3D points stored as an STL vector, and you want to represent the + /// points as a 3xN matrix, do the following: + /// ```C++ + /// std::vector vec; + /// ... + /// Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation + /// reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. + /// // Also, an O(1) operation + /// t(); // finally, transpose the Nx3 matrix. + /// // This involves copying all the elements + /// ``` + /// + /// 3-channel 2x2 matrix reshaped to 1-channel 4x3 matrix, each column has values from one of original channels: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// vector new_shape {4, 3}; + /// m = m.reshape(1, new_shape); + /// ``` + /// + /// or: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// const int new_shape[] = {4, 3}; + /// m = m.reshape(1, 2, new_shape); + /// ``` + /// + /// ## Parameters + /// * cn: New number of channels. If the parameter is 0, the number of channels remains the same. + /// * rows: New number of rows. If the parameter is 0, the number of rows remains the same. + /// + /// ## Note + /// This alternative version of [MatTraitConst::reshape] function uses the following default values for its arguments: + /// * rows: 0 + #[inline] + fn reshape_def_mut(&mut self, cn: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_reshape_int(self.as_raw_mut_Mat(), cn, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Changes the shape and/or the number of channels of a 2D matrix without copying the data. + /// + /// The method makes a new matrix header for \*this elements. The new matrix may have a different size + /// and/or different number of channels. Any combination is possible if: + /// * No extra elements are included into the new matrix and no elements are excluded. Consequently, + /// the product rows\*cols\*channels() must stay the same after the transformation. + /// * No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of + /// rows, or the operation changes the indices of elements row in some other way, the matrix must be + /// continuous. See Mat::isContinuous . + /// + /// For example, if there is a set of 3D points stored as an STL vector, and you want to represent the + /// points as a 3xN matrix, do the following: + /// ```C++ + /// std::vector vec; + /// ... + /// Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation + /// reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. + /// // Also, an O(1) operation + /// t(); // finally, transpose the Nx3 matrix. + /// // This involves copying all the elements + /// ``` + /// + /// 3-channel 2x2 matrix reshaped to 1-channel 4x3 matrix, each column has values from one of original channels: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// vector new_shape {4, 3}; + /// m = m.reshape(1, new_shape); + /// ``` + /// + /// or: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// const int new_shape[] = {4, 3}; + /// m = m.reshape(1, 2, new_shape); + /// ``` + /// + /// ## Parameters + /// * cn: New number of channels. If the parameter is 0, the number of channels remains the same. + /// * rows: New number of rows. If the parameter is 0, the number of rows remains the same. + /// + /// ## C++ default parameters + /// * rows: 0 + #[inline] + fn reshape_mut(&mut self, cn: i32, rows: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_reshape_int_int(self.as_raw_mut_Mat(), cn, rows, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Changes the shape and/or the number of channels of a 2D matrix without copying the data. + /// + /// The method makes a new matrix header for \*this elements. The new matrix may have a different size + /// and/or different number of channels. Any combination is possible if: + /// * No extra elements are included into the new matrix and no elements are excluded. Consequently, + /// the product rows\*cols\*channels() must stay the same after the transformation. + /// * No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of + /// rows, or the operation changes the indices of elements row in some other way, the matrix must be + /// continuous. See Mat::isContinuous . + /// + /// For example, if there is a set of 3D points stored as an STL vector, and you want to represent the + /// points as a 3xN matrix, do the following: + /// ```C++ + /// std::vector vec; + /// ... + /// Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation + /// reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. + /// // Also, an O(1) operation + /// t(); // finally, transpose the Nx3 matrix. + /// // This involves copying all the elements + /// ``` + /// + /// 3-channel 2x2 matrix reshaped to 1-channel 4x3 matrix, each column has values from one of original channels: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// vector new_shape {4, 3}; + /// m = m.reshape(1, new_shape); + /// ``` + /// + /// or: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// const int new_shape[] = {4, 3}; + /// m = m.reshape(1, 2, new_shape); + /// ``` + /// + /// ## Parameters + /// * cn: New number of channels. If the parameter is 0, the number of channels remains the same. + /// * rows: New number of rows. If the parameter is 0, the number of rows remains the same. + /// + /// ## Overloaded parameters + /// + /// * cn: New number of channels. If the parameter is 0, the number of channels remains the same. + /// * newndims: New number of dimensions. + /// * newsz: Array with new matrix size by all dimensions. If some sizes are zero, + /// the original sizes in those dimensions are presumed. + #[inline] + fn reshape_nd_mut(&mut self, cn: i32, newsz: &[i32]) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_reshape_int_int_const_intX(self.as_raw_mut_Mat(), cn, newsz.len().try_into()?, newsz.as_ptr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Changes the shape and/or the number of channels of a 2D matrix without copying the data. + /// + /// The method makes a new matrix header for \*this elements. The new matrix may have a different size + /// and/or different number of channels. Any combination is possible if: + /// * No extra elements are included into the new matrix and no elements are excluded. Consequently, + /// the product rows\*cols\*channels() must stay the same after the transformation. + /// * No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of + /// rows, or the operation changes the indices of elements row in some other way, the matrix must be + /// continuous. See Mat::isContinuous . + /// + /// For example, if there is a set of 3D points stored as an STL vector, and you want to represent the + /// points as a 3xN matrix, do the following: + /// ```C++ + /// std::vector vec; + /// ... + /// Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation + /// reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. + /// // Also, an O(1) operation + /// t(); // finally, transpose the Nx3 matrix. + /// // This involves copying all the elements + /// ``` + /// + /// 3-channel 2x2 matrix reshaped to 1-channel 4x3 matrix, each column has values from one of original channels: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// vector new_shape {4, 3}; + /// m = m.reshape(1, new_shape); + /// ``` + /// + /// or: + /// ```C++ + /// Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3)); + /// const int new_shape[] = {4, 3}; + /// m = m.reshape(1, 2, new_shape); + /// ``` + /// + /// ## Parameters + /// * cn: New number of channels. If the parameter is 0, the number of channels remains the same. + /// * rows: New number of rows. If the parameter is 0, the number of rows remains the same. + /// + /// ## Overloaded parameters + /// + /// * cn: New number of channels. If the parameter is 0, the number of channels remains the same. + /// * newshape: Vector with new matrix size by all dimensions. If some sizes are zero, + /// the original sizes in those dimensions are presumed. + #[inline] + fn reshape_nd_vec_mut(&mut self, cn: i32, newshape: &core::Vector) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_reshape_int_const_vectorLintGR(self.as_raw_mut_Mat(), cn, newshape.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Allocates new array data if needed. + /// + /// This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays + /// call this method for each output array. The method uses the following algorithm: + /// + /// -# If the current array shape and the type match the new ones, return immediately. Otherwise, + /// de-reference the previous data by calling Mat::release. + /// -# Initialize the new header. + /// -# Allocate the new data of total()\*elemSize() bytes. + /// -# Allocate the new, associated with the data, reference counter and set it to 1. + /// + /// Such a scheme makes the memory management robust and efficient at the same time and helps avoid + /// extra typing for you. This means that usually there is no need to explicitly allocate output arrays. + /// That is, instead of writing: + /// ```C++ + /// Mat color; + /// ... + /// Mat gray(color.rows, color.cols, color.depth()); + /// cvtColor(color, gray, COLOR_BGR2GRAY); + /// ``` + /// + /// you can simply write: + /// ```C++ + /// Mat color; + /// ... + /// Mat gray; + /// cvtColor(color, gray, COLOR_BGR2GRAY); + /// ``` + /// + /// because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array + /// internally. + /// ## Parameters + /// * rows: New number of rows. + /// * cols: New number of columns. + /// * type: New matrix type. + #[inline] + unsafe fn create_rows_cols(&mut self, rows: i32, cols: i32, typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_create_int_int_int(self.as_raw_mut_Mat(), rows, cols, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Allocates new array data if needed. + /// + /// This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays + /// call this method for each output array. The method uses the following algorithm: + /// + /// -# If the current array shape and the type match the new ones, return immediately. Otherwise, + /// de-reference the previous data by calling Mat::release. + /// -# Initialize the new header. + /// -# Allocate the new data of total()\*elemSize() bytes. + /// -# Allocate the new, associated with the data, reference counter and set it to 1. + /// + /// Such a scheme makes the memory management robust and efficient at the same time and helps avoid + /// extra typing for you. This means that usually there is no need to explicitly allocate output arrays. + /// That is, instead of writing: + /// ```C++ + /// Mat color; + /// ... + /// Mat gray(color.rows, color.cols, color.depth()); + /// cvtColor(color, gray, COLOR_BGR2GRAY); + /// ``` + /// + /// you can simply write: + /// ```C++ + /// Mat color; + /// ... + /// Mat gray; + /// cvtColor(color, gray, COLOR_BGR2GRAY); + /// ``` + /// + /// because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array + /// internally. + /// ## Parameters + /// * rows: New number of rows. + /// * cols: New number of columns. + /// * type: New matrix type. + /// + /// ## Overloaded parameters + /// + /// * size: Alternative new matrix size specification: Size(cols, rows) + /// * type: New matrix type. + #[inline] + unsafe fn create_size(&mut self, size: core::Size, typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_create_Size_int(self.as_raw_mut_Mat(), &size, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Allocates new array data if needed. + /// + /// This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays + /// call this method for each output array. The method uses the following algorithm: + /// + /// -# If the current array shape and the type match the new ones, return immediately. Otherwise, + /// de-reference the previous data by calling Mat::release. + /// -# Initialize the new header. + /// -# Allocate the new data of total()\*elemSize() bytes. + /// -# Allocate the new, associated with the data, reference counter and set it to 1. + /// + /// Such a scheme makes the memory management robust and efficient at the same time and helps avoid + /// extra typing for you. This means that usually there is no need to explicitly allocate output arrays. + /// That is, instead of writing: + /// ```C++ + /// Mat color; + /// ... + /// Mat gray(color.rows, color.cols, color.depth()); + /// cvtColor(color, gray, COLOR_BGR2GRAY); + /// ``` + /// + /// you can simply write: + /// ```C++ + /// Mat color; + /// ... + /// Mat gray; + /// cvtColor(color, gray, COLOR_BGR2GRAY); + /// ``` + /// + /// because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array + /// internally. + /// ## Parameters + /// * rows: New number of rows. + /// * cols: New number of columns. + /// * type: New matrix type. + /// + /// ## Overloaded parameters + /// + /// * ndims: New array dimensionality. + /// * sizes: Array of integers specifying a new array shape. + /// * type: New matrix type. + #[inline] + unsafe fn create_nd(&mut self, sizes: &[i32], typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_create_int_const_intX_int(self.as_raw_mut_Mat(), sizes.len().try_into()?, sizes.as_ptr(), typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Allocates new array data if needed. + /// + /// This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays + /// call this method for each output array. The method uses the following algorithm: + /// + /// -# If the current array shape and the type match the new ones, return immediately. Otherwise, + /// de-reference the previous data by calling Mat::release. + /// -# Initialize the new header. + /// -# Allocate the new data of total()\*elemSize() bytes. + /// -# Allocate the new, associated with the data, reference counter and set it to 1. + /// + /// Such a scheme makes the memory management robust and efficient at the same time and helps avoid + /// extra typing for you. This means that usually there is no need to explicitly allocate output arrays. + /// That is, instead of writing: + /// ```C++ + /// Mat color; + /// ... + /// Mat gray(color.rows, color.cols, color.depth()); + /// cvtColor(color, gray, COLOR_BGR2GRAY); + /// ``` + /// + /// you can simply write: + /// ```C++ + /// Mat color; + /// ... + /// Mat gray; + /// cvtColor(color, gray, COLOR_BGR2GRAY); + /// ``` + /// + /// because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array + /// internally. + /// ## Parameters + /// * rows: New number of rows. + /// * cols: New number of columns. + /// * type: New matrix type. + /// + /// ## Overloaded parameters + /// + /// * sizes: Array of integers specifying a new array shape. + /// * type: New matrix type. + #[inline] + unsafe fn create_nd_vec(&mut self, sizes: &core::Vector, typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_create_const_vectorLintGR_int(self.as_raw_mut_Mat(), sizes.as_raw_VectorOfi32(), typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Increments the reference counter. + /// + /// The method increments the reference counter associated with the matrix data. If the matrix header + /// points to an external data set (see Mat::Mat ), the reference counter is NULL, and the method has no + /// effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It + /// is called implicitly by the matrix assignment operator. The reference counter increment is an atomic + /// operation on the platforms that support it. Thus, it is safe to operate on the same matrices + /// asynchronously in different threads. + #[inline] + unsafe fn addref(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_addref(self.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Decrements the reference counter and deallocates the matrix if needed. + /// + /// The method decrements the reference counter associated with the matrix data. When the reference + /// counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers + /// are set to NULL's. If the matrix header points to an external data set (see Mat::Mat ), the + /// reference counter is NULL, and the method has no effect in this case. + /// + /// This method can be called manually to force the matrix data deallocation. But since this method is + /// automatically called in the destructor, or by any other method that changes the data pointer, it is + /// usually not needed. The reference counter decrement and check for 0 is an atomic operation on the + /// platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in + /// different threads. + #[inline] + unsafe fn release(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_release(self.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// internal use function, consider to use 'release' method instead; deallocates the matrix data + #[inline] + fn deallocate(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_deallocate(self.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Reserves space for the certain number of rows. + /// + /// The method reserves space for sz rows. If the matrix already has enough space to store sz rows, + /// nothing happens. If the matrix is reallocated, the first Mat::rows rows are preserved. The method + /// emulates the corresponding method of the STL vector class. + /// ## Parameters + /// * sz: Number of rows. + #[inline] + fn reserve(&mut self, sz: size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_reserve_size_t(self.as_raw_mut_Mat(), sz, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Reserves space for the certain number of bytes. + /// + /// The method reserves space for sz bytes. If the matrix already has enough space to store sz bytes, + /// nothing happens. If matrix has to be reallocated its previous content could be lost. + /// ## Parameters + /// * sz: Number of bytes. + #[inline] + fn reserve_buffer(&mut self, sz: size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_reserveBuffer_size_t(self.as_raw_mut_Mat(), sz, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Changes the number of matrix rows. + /// + /// The methods change the number of matrix rows. If the matrix is reallocated, the first + /// min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL + /// vector class. + /// ## Parameters + /// * sz: New number of rows. + #[inline] + fn resize(&mut self, sz: size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_resize_size_t(self.as_raw_mut_Mat(), sz, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Changes the number of matrix rows. + /// + /// The methods change the number of matrix rows. If the matrix is reallocated, the first + /// min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL + /// vector class. + /// ## Parameters + /// * sz: New number of rows. + /// + /// ## Overloaded parameters + /// + /// * sz: New number of rows. + /// * s: Value assigned to the newly added elements. + #[inline] + fn resize_with_default(&mut self, sz: size_t, s: core::Scalar) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_resize_size_t_const_ScalarR(self.as_raw_mut_Mat(), sz, &s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Adds elements to the bottom of the matrix. + /// + /// The methods add one or more elements to the bottom of the matrix. They emulate the corresponding + /// method of the STL vector class. When elem is Mat , its type and the number of columns must be the + /// same as in the container matrix. + /// ## Parameters + /// * elem: Added element(s). + /// + /// ## Overloaded parameters + /// + /// * m: Added line(s). + #[inline] + fn push_back(&mut self, m: &impl core::MatTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_push_back_const_MatR(self.as_raw_mut_Mat(), m.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Removes elements from the bottom of the matrix. + /// + /// The method removes one or more rows from the bottom of the matrix. + /// ## Parameters + /// * nelems: Number of removed rows. If it is greater than the total number of rows, an exception + /// is thrown. + /// + /// ## C++ default parameters + /// * nelems: 1 + #[inline] + fn pop_back(&mut self, nelems: size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_pop_back_size_t(self.as_raw_mut_Mat(), nelems, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Removes elements from the bottom of the matrix. + /// + /// The method removes one or more rows from the bottom of the matrix. + /// ## Parameters + /// * nelems: Number of removed rows. If it is greater than the total number of rows, an exception + /// is thrown. + /// + /// ## Note + /// This alternative version of [MatTrait::pop_back] function uses the following default values for its arguments: + /// * nelems: 1 + #[inline] + fn pop_back_def(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_pop_back(self.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Adjusts a submatrix size and position within the parent matrix. + /// + /// The method is complimentary to Mat::locateROI . The typical use of these functions is to determine + /// the submatrix position within the parent matrix and then shift the position somehow. Typically, it + /// can be required for filtering operations when pixels outside of the ROI should be taken into + /// account. When all the method parameters are positive, the ROI needs to grow in all directions by the + /// specified amount, for example: + /// ```C++ + /// A.adjustROI(2, 2, 2, 2); + /// ``` + /// + /// In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted + /// by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the + /// filtering with the 5x5 kernel. + /// + /// adjustROI forces the adjusted ROI to be inside of the parent matrix that is boundaries of the + /// adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix A is + /// located in the first row of a parent matrix and you called A.adjustROI(2, 2, 2, 2) then A will not + /// be increased in the upward direction. + /// + /// The function is used internally by the OpenCV filtering functions, like filter2D , morphological + /// operations, and so on. + /// ## Parameters + /// * dtop: Shift of the top submatrix boundary upwards. + /// * dbottom: Shift of the bottom submatrix boundary downwards. + /// * dleft: Shift of the left submatrix boundary to the left. + /// * dright: Shift of the right submatrix boundary to the right. + /// ## See also + /// copyMakeBorder + #[inline] + fn adjust_roi(&mut self, dtop: i32, dbottom: i32, dleft: i32, dright: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_adjustROI_int_int_int_int(self.as_raw_mut_Mat(), dtop, dbottom, dleft, dright, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Extracts a rectangular submatrix. + /// + /// The operators make a new header for the specified sub-array of \*this . They are the most + /// generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example, + /// `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above, + /// the operators are O(1) operations, that is, no matrix data is copied. + /// ## Parameters + /// * rowRange: Start and end row of the extracted submatrix. The upper boundary is not included. To + /// select all the rows, use Range::all(). + /// * colRange: Start and end column of the extracted submatrix. The upper boundary is not included. + /// To select all the columns, use Range::all(). + #[inline] + fn rowscols_mut(&mut self, mut row_range: impl core::RangeTrait, mut col_range: impl core::RangeTrait) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_operator___Range_Range(self.as_raw_mut_Mat(), row_range.as_raw_mut_Range(), col_range.as_raw_mut_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Extracts a rectangular submatrix. + /// + /// The operators make a new header for the specified sub-array of \*this . They are the most + /// generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example, + /// `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above, + /// the operators are O(1) operations, that is, no matrix data is copied. + /// ## Parameters + /// * rowRange: Start and end row of the extracted submatrix. The upper boundary is not included. To + /// select all the rows, use Range::all(). + /// * colRange: Start and end column of the extracted submatrix. The upper boundary is not included. + /// To select all the columns, use Range::all(). + /// + /// ## Overloaded parameters + /// + /// * roi: Extracted submatrix specified as a rectangle. + #[inline] + fn roi_mut(&mut self, roi: core::Rect) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_operator___const_RectR(self.as_raw_mut_Mat(), &roi, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Extracts a rectangular submatrix. + /// + /// The operators make a new header for the specified sub-array of \*this . They are the most + /// generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example, + /// `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above, + /// the operators are O(1) operations, that is, no matrix data is copied. + /// ## Parameters + /// * rowRange: Start and end row of the extracted submatrix. The upper boundary is not included. To + /// select all the rows, use Range::all(). + /// * colRange: Start and end column of the extracted submatrix. The upper boundary is not included. + /// To select all the columns, use Range::all(). + /// + /// ## Overloaded parameters + /// + /// * ranges: Array of selected ranges along each array dimension. + #[inline] + fn ranges_mut(&mut self, ranges: &core::Vector) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_operator___const_vectorLRangeGR(self.as_raw_mut_Mat(), ranges.as_raw_VectorOfRange(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::Mat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns a pointer to the specified matrix row. + /// + /// The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + /// Mat::isContinuous to know how to use these methods. + /// ## Parameters + /// * i0: A 0-based row index. + /// + /// ## C++ default parameters + /// * i0: 0 + #[inline] + fn ptr_mut(&mut self, i0: i32) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_ptr_int(self.as_raw_mut_Mat(), i0, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a pointer to the specified matrix row. + /// + /// The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + /// Mat::isContinuous to know how to use these methods. + /// ## Parameters + /// * i0: A 0-based row index. + /// + /// ## Note + /// This alternative version of [MatTrait::ptr_mut] function uses the following default values for its arguments: + /// * i0: 0 + #[inline] + fn ptr_mut_def(&mut self) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_ptr(self.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a pointer to the specified matrix row. + /// + /// The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + /// Mat::isContinuous to know how to use these methods. + /// ## Parameters + /// * i0: A 0-based row index. + /// + /// ## Overloaded parameters + /// + /// * row: Index along the dimension 0 + /// * col: Index along the dimension 1 + #[inline] + fn ptr_2d_mut(&mut self, row: i32, col: i32) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_ptr_int_int(self.as_raw_mut_Mat(), row, col, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a pointer to the specified matrix row. + /// + /// The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + /// Mat::isContinuous to know how to use these methods. + /// ## Parameters + /// * i0: A 0-based row index. + /// + /// ## Overloaded parameters + #[inline] + fn ptr_3d_mut(&mut self, i0: i32, i1: i32, i2: i32) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_ptr_int_int_int(self.as_raw_mut_Mat(), i0, i1, i2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a pointer to the specified matrix row. + /// + /// The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in + /// Mat::isContinuous to know how to use these methods. + /// ## Parameters + /// * i0: A 0-based row index. + /// + /// ## Overloaded parameters + #[inline] + fn ptr_nd_mut(&mut self, idx: &[i32]) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_ptr_const_intX(self.as_raw_mut_Mat(), idx.as_ptr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a reference to the specified array element. + /// + /// The template methods return a reference to the specified array element. For the sake of higher + /// performance, the index range checks are only performed in the Debug configuration. + /// + /// Note that the variants with a single index (i) can be used to access elements of single-row or + /// single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + /// B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + /// instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + /// + /// The example below initializes a Hilbert matrix: + /// ```C++ + /// Mat H(100, 100, CV_64F); + /// for(int i = 0; i < H.rows; i++) + /// for(int j = 0; j < H.cols; j++) + /// H.at(i,j)=1./(i+j+1); + /// ``` + /// + /// + /// Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + /// on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + /// - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + /// + /// ## Parameters + /// * i0: Index along the dimension 0 + /// + /// ## C++ default parameters + /// * i0: 0 + #[inline] + fn at_mut(&mut self, i0: i32) -> Result<&mut T> { + core::mat_forward::at_mut(self, i0) + } + + /// Returns a reference to the specified array element. + /// + /// The template methods return a reference to the specified array element. For the sake of higher + /// performance, the index range checks are only performed in the Debug configuration. + /// + /// Note that the variants with a single index (i) can be used to access elements of single-row or + /// single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + /// B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + /// instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + /// + /// The example below initializes a Hilbert matrix: + /// ```C++ + /// Mat H(100, 100, CV_64F); + /// for(int i = 0; i < H.rows; i++) + /// for(int j = 0; j < H.cols; j++) + /// H.at(i,j)=1./(i+j+1); + /// ``` + /// + /// + /// Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + /// on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + /// - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + /// + /// ## Parameters + /// * i0: Index along the dimension 0 + /// + /// ## Note + /// This alternative version of [MatTrait::at_mut] function uses the following default values for its arguments: + /// * i0: 0 + #[inline] + fn at_mut_def(&mut self) -> Result<&mut T> { + core::mat_forward::at_mut_def(self, ) + } + + /// Returns a reference to the specified array element. + /// + /// The template methods return a reference to the specified array element. For the sake of higher + /// performance, the index range checks are only performed in the Debug configuration. + /// + /// Note that the variants with a single index (i) can be used to access elements of single-row or + /// single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + /// B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + /// instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + /// + /// The example below initializes a Hilbert matrix: + /// ```C++ + /// Mat H(100, 100, CV_64F); + /// for(int i = 0; i < H.rows; i++) + /// for(int j = 0; j < H.cols; j++) + /// H.at(i,j)=1./(i+j+1); + /// ``` + /// + /// + /// Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + /// on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + /// - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + /// + /// ## Parameters + /// * i0: Index along the dimension 0 + /// + /// ## Overloaded parameters + /// + /// * row: Index along the dimension 0 + /// * col: Index along the dimension 1 + #[inline] + fn at_2d_mut(&mut self, row: i32, col: i32) -> Result<&mut T> { + core::mat_forward::at_2d_mut(self, row, col) + } + + /// Returns a reference to the specified array element. + /// + /// The template methods return a reference to the specified array element. For the sake of higher + /// performance, the index range checks are only performed in the Debug configuration. + /// + /// Note that the variants with a single index (i) can be used to access elements of single-row or + /// single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + /// B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + /// instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + /// + /// The example below initializes a Hilbert matrix: + /// ```C++ + /// Mat H(100, 100, CV_64F); + /// for(int i = 0; i < H.rows; i++) + /// for(int j = 0; j < H.cols; j++) + /// H.at(i,j)=1./(i+j+1); + /// ``` + /// + /// + /// Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + /// on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + /// - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + /// + /// ## Parameters + /// * i0: Index along the dimension 0 + /// + /// ## Overloaded parameters + /// + /// * i0: Index along the dimension 0 + /// * i1: Index along the dimension 1 + /// * i2: Index along the dimension 2 + #[inline] + fn at_3d_mut(&mut self, i0: i32, i1: i32, i2: i32) -> Result<&mut T> { + core::mat_forward::at_3d_mut(self, i0, i1, i2) + } + + /// Returns a reference to the specified array element. + /// + /// The template methods return a reference to the specified array element. For the sake of higher + /// performance, the index range checks are only performed in the Debug configuration. + /// + /// Note that the variants with a single index (i) can be used to access elements of single-row or + /// single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + /// B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + /// instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + /// + /// The example below initializes a Hilbert matrix: + /// ```C++ + /// Mat H(100, 100, CV_64F); + /// for(int i = 0; i < H.rows; i++) + /// for(int j = 0; j < H.cols; j++) + /// H.at(i,j)=1./(i+j+1); + /// ``` + /// + /// + /// Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + /// on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + /// - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + /// + /// ## Parameters + /// * i0: Index along the dimension 0 + /// + /// ## Overloaded parameters + /// + /// * idx: Array of Mat::dims indices. + #[inline] + fn at_nd_mut(&mut self, idx: &[i32]) -> Result<&mut T> { + core::mat_forward::at_nd_mut(self, idx) + } + + /// Returns a reference to the specified array element. + /// + /// The template methods return a reference to the specified array element. For the sake of higher + /// performance, the index range checks are only performed in the Debug configuration. + /// + /// Note that the variants with a single index (i) can be used to access elements of single-row or + /// single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and + /// B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` + /// instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. + /// + /// The example below initializes a Hilbert matrix: + /// ```C++ + /// Mat H(100, 100, CV_64F); + /// for(int i = 0; i < H.rows; i++) + /// for(int j = 0; j < H.cols; j++) + /// H.at(i,j)=1./(i+j+1); + /// ``` + /// + /// + /// Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends + /// on the image from which you are trying to retrieve the data. The table below gives a better insight in this: + /// - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. + /// - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. + /// + /// ## Parameters + /// * i0: Index along the dimension 0 + /// + /// ## Overloaded parameters + /// + /// special versions for 2D arrays (especially convenient for referencing image pixels) + /// * pt: Element position specified as Point(j,i) . + #[inline] + fn at_pt_mut(&mut self, pt: core::Point) -> Result<&mut T> { + core::mat_forward::at_pt_mut(self, pt) + } + + #[inline] + fn set(&mut self, mut m: core::Mat) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_operatorST_MatRR(self.as_raw_mut_Mat(), m.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// internal use method: updates the continuity flag + #[inline] + fn update_continuity_flag(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Mat_updateContinuityFlag(self.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl Clone for Mat { + #[inline] + /// Calls try_clone() and panics if that fails + fn clone(&self) -> Self { + self.try_clone().expect("Cannot clone Mat") + } + } + + impl core::MatTraitConst for Mat { + #[inline] fn as_raw_Mat(&self) -> *const c_void { self.as_raw() } + } + + impl core::MatTrait for Mat { + #[inline] fn as_raw_mut_Mat(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Mat, core::MatTraitConst, as_raw_Mat, core::MatTrait, as_raw_mut_Mat } + + /// /////////////////////////////// MatConstIterator ////////////////////////////////// + pub struct MatConstIterator { + ptr: *mut c_void, + } + + opencv_type_boxed! { MatConstIterator } + + impl Drop for MatConstIterator { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_MatConstIterator_delete(self.as_raw_mut_MatConstIterator()) }; + } + } + + unsafe impl Send for MatConstIterator {} + + /// Constant methods for [core::MatConstIterator] + pub trait MatConstIteratorTraitConst { + fn as_raw_MatConstIterator(&self) -> *const c_void; + + #[inline] + fn m(&self) -> core::Mat { + let ret = unsafe { sys::cv_MatConstIterator_propM_const(self.as_raw_MatConstIterator()) }; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn elem_size(&self) -> size_t { + let ret = unsafe { sys::cv_MatConstIterator_propElemSize_const(self.as_raw_MatConstIterator()) }; + ret + } + + #[inline] + fn ptr(&self) -> *const u8 { + let ret = unsafe { sys::cv_MatConstIterator_propPtr_const(self.as_raw_MatConstIterator()) }; + ret + } + + #[inline] + fn slice_start(&self) -> *const u8 { + let ret = unsafe { sys::cv_MatConstIterator_propSliceStart_const(self.as_raw_MatConstIterator()) }; + ret + } + + #[inline] + fn slice_end(&self) -> *const u8 { + let ret = unsafe { sys::cv_MatConstIterator_propSliceEnd_const(self.as_raw_MatConstIterator()) }; + ret + } + + /// returns the current matrix element + #[inline] + fn try_deref(&self) -> Result<*const u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatConstIterator_operatorX_const(self.as_raw_MatConstIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the i-th matrix element, relative to the current + #[inline] + fn get(&self, i: ptrdiff_t) -> Result<*const u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatConstIterator_operator___const_ptrdiff_t(self.as_raw_MatConstIterator(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the current iterator position + #[inline] + fn pos(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatConstIterator_pos_const(self.as_raw_MatConstIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the current iterator position + #[inline] + unsafe fn pos_to(&self, _idx: *mut i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatConstIterator_pos_const_intX(self.as_raw_MatConstIterator(), _idx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn lpos(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatConstIterator_lpos_const(self.as_raw_MatConstIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn typ(&self) -> i32 { + let ret = unsafe { sys::cv_MatConstIterator_type_const(self.as_raw_MatConstIterator()) }; + ret + } + + } + + /// Mutable methods for [core::MatConstIterator] + pub trait MatConstIteratorTrait: core::MatConstIteratorTraitConst { + fn as_raw_mut_MatConstIterator(&mut self) -> *mut c_void; + + #[inline] + fn set_elem_size(&mut self, val: size_t) { + let ret = unsafe { sys::cv_MatConstIterator_propElemSize_const_size_t(self.as_raw_mut_MatConstIterator(), val) }; + ret + } + + /// copy operator + #[inline] + fn set(&mut self, it: &impl core::MatConstIteratorTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatConstIterator_operatorST_const_MatConstIteratorR(self.as_raw_mut_MatConstIterator(), it.as_raw_MatConstIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// decrements the iterator + #[inline] + fn decr(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatConstIterator_operatorSS(self.as_raw_mut_MatConstIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatConstIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + /// increments the iterator + #[inline] + fn incr(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatConstIterator_operatorAA(self.as_raw_mut_MatConstIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatConstIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * relative: false + #[inline] + fn seek(&mut self, ofs: ptrdiff_t, relative: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatConstIterator_seek_ptrdiff_t_bool(self.as_raw_mut_MatConstIterator(), ofs, relative, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [MatConstIteratorTrait::seek] function uses the following default values for its arguments: + /// * relative: false + #[inline] + fn seek_def(&mut self, ofs: ptrdiff_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatConstIterator_seek_ptrdiff_t(self.as_raw_mut_MatConstIterator(), ofs, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * relative: false + #[inline] + fn seek_idx(&mut self, _idx: &i32, relative: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatConstIterator_seek_const_intX_bool(self.as_raw_mut_MatConstIterator(), _idx, relative, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [MatConstIteratorTrait::seek_idx] function uses the following default values for its arguments: + /// * relative: false + #[inline] + fn seek_idx_def(&mut self, _idx: &i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatConstIterator_seek_const_intX(self.as_raw_mut_MatConstIterator(), _idx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for MatConstIterator { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("MatConstIterator") + .field("m", &core::MatConstIteratorTraitConst::m(self)) + .field("elem_size", &core::MatConstIteratorTraitConst::elem_size(self)) + .field("ptr", &core::MatConstIteratorTraitConst::ptr(self)) + .field("slice_start", &core::MatConstIteratorTraitConst::slice_start(self)) + .field("slice_end", &core::MatConstIteratorTraitConst::slice_end(self)) + .finish() + } + } + + impl core::MatConstIteratorTraitConst for MatConstIterator { + #[inline] fn as_raw_MatConstIterator(&self) -> *const c_void { self.as_raw() } + } + + impl core::MatConstIteratorTrait for MatConstIterator { + #[inline] fn as_raw_mut_MatConstIterator(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MatConstIterator, core::MatConstIteratorTraitConst, as_raw_MatConstIterator, core::MatConstIteratorTrait, as_raw_mut_MatConstIterator } + + /// Matrix expression representation + /// @anchor MatrixExpressions + /// This is a list of implemented matrix operations that can be combined in arbitrary complex + /// expressions (here A, B stand for matrices ( cv::Mat ), s for a cv::Scalar, alpha for a + /// real-valued scalar ( double )): + /// * Addition, subtraction, negation: `A+B`, `A-B`, `A+s`, `A-s`, `s+A`, `s-A`, `-A` + /// * Scaling: `A*alpha` + /// * Per-element multiplication and division: `A.mul(B)`, `A/B`, `alpha/A` + /// * Matrix multiplication: `A*B` + /// * Transposition: `A.t()` (means AT) + /// * Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems: + /// `A.inv([method]) (~ A-1)`, `A.inv([method])*B (~ X: AX=B)` + /// * Comparison: `A cmpop B`, `A cmpop alpha`, `alpha cmpop A`, where *cmpop* is one of + /// `>`, `>=`, `==`, `!=`, `<=`, `<`. The result of comparison is an 8-bit single channel mask whose + /// elements are set to 255 (if the particular element or pair of elements satisfy the condition) or + /// 0. + /// * Bitwise logical operations: `A logicop B`, `A logicop s`, `s logicop A`, `~A`, where *logicop* is one of + /// `&`, `|`, `^`. + /// * Element-wise minimum and maximum: cv::min(A, B), cv::min(A, alpha), cv::max(A, B), cv::max(A, alpha) + /// * Element-wise absolute value: cv::abs(A) + /// * Cross-product, dot-product: `A.cross(B)`, `A.dot(B)` + /// * Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as cv::norm, + /// cv::mean, cv::sum, cv::countNonZero, cv::trace, cv::determinant, cv::repeat, and others. + /// * Matrix initializers ( Mat::eye(), Mat::zeros(), Mat::ones() ), matrix comma-separated + /// initializers, matrix constructors and operators that extract sub-matrices (see cv::Mat description). + /// * Mat_() constructors to cast the result to the proper type. + /// + /// Note: Comma-separated initializers and probably some other operations may require additional + /// explicit Mat() or Mat_() constructor calls to resolve a possible ambiguity. + /// + /// Here are examples of matrix expressions: + /// ```C++ + /// // compute pseudo-inverse of A, equivalent to A.inv(DECOMP_SVD) + /// SVD svd(A); + /// Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t(); + /// + /// // compute the new vector of parameters in the Levenberg-Marquardt algorithm + /// x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err); + /// + /// // sharpen image using "unsharp mask" algorithm + /// Mat blurred; double sigma = 1, threshold = 5, amount = 1; + /// GaussianBlur(img, blurred, Size(), sigma, sigma); + /// Mat lowContrastMask = abs(img - blurred) < threshold; + /// Mat sharpened = img*(1+amount) + blurred*(-amount); + /// img.copyTo(sharpened, lowContrastMask); + /// ``` + /// + pub struct MatExpr { + ptr: *mut c_void, + } + + opencv_type_boxed! { MatExpr } + + impl Drop for MatExpr { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_MatExpr_delete(self.as_raw_mut_MatExpr()) }; + } + } + + unsafe impl Send for MatExpr {} + + /// Constant methods for [core::MatExpr] + pub trait MatExprTraitConst { + fn as_raw_MatExpr(&self) -> *const c_void; + + #[inline] + fn flags(&self) -> i32 { + let ret = unsafe { sys::cv_MatExpr_propFlags_const(self.as_raw_MatExpr()) }; + ret + } + + #[inline] + fn a(&self) -> core::Mat { + let ret = unsafe { sys::cv_MatExpr_propA_const(self.as_raw_MatExpr()) }; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn b(&self) -> core::Mat { + let ret = unsafe { sys::cv_MatExpr_propB_const(self.as_raw_MatExpr()) }; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn c(&self) -> core::Mat { + let ret = unsafe { sys::cv_MatExpr_propC_const(self.as_raw_MatExpr()) }; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn alpha(&self) -> f64 { + let ret = unsafe { sys::cv_MatExpr_propAlpha_const(self.as_raw_MatExpr()) }; + ret + } + + #[inline] + fn beta(&self) -> f64 { + let ret = unsafe { sys::cv_MatExpr_propBeta_const(self.as_raw_MatExpr()) }; + ret + } + + #[inline] + fn s(&self) -> core::Scalar { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_propS_const(self.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn to_mat(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_operator_cv_Mat_const(self.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_size_const(self.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn typ(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_type_const(self.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn row(&self, y: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_row_const_int(self.as_raw_MatExpr(), y, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn col(&self, x: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_col_const_int(self.as_raw_MatExpr(), x, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * d: 0 + #[inline] + fn diag(&self, d: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_diag_const_int(self.as_raw_MatExpr(), d, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [MatExprTraitConst::diag] function uses the following default values for its arguments: + /// * d: 0 + #[inline] + fn diag_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_diag_const(self.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn rowscols(&self, row_range: &impl core::RangeTraitConst, col_range: &impl core::RangeTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_operator___const_const_RangeR_const_RangeR(self.as_raw_MatExpr(), row_range.as_raw_Range(), col_range.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn roi(&self, roi: core::Rect) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_operator___const_const_RectR(self.as_raw_MatExpr(), &roi, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn t(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_t_const(self.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * method: DECOMP_LU + #[inline] + fn inv(&self, method: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_inv_const_int(self.as_raw_MatExpr(), method, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [MatExprTraitConst::inv] function uses the following default values for its arguments: + /// * method: DECOMP_LU + #[inline] + fn inv_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_inv_const(self.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * scale: 1 + #[inline] + fn mul_matexpr(&self, e: &impl core::MatExprTraitConst, scale: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_mul_const_const_MatExprR_double(self.as_raw_MatExpr(), e.as_raw_MatExpr(), scale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [MatExprTraitConst::mul_matexpr] function uses the following default values for its arguments: + /// * scale: 1 + #[inline] + fn mul_matexpr_def(&self, e: &impl core::MatExprTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_mul_const_const_MatExprR(self.as_raw_MatExpr(), e.as_raw_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * scale: 1 + #[inline] + fn mul(&self, m: &impl core::MatTraitConst, scale: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_mul_const_const_MatR_double(self.as_raw_MatExpr(), m.as_raw_Mat(), scale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [MatExprTraitConst::mul] function uses the following default values for its arguments: + /// * scale: 1 + #[inline] + fn mul_def(&self, m: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_mul_const_const_MatR(self.as_raw_MatExpr(), m.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::MatExpr::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn cross(&self, m: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_cross_const_const_MatR(self.as_raw_MatExpr(), m.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn dot(&self, m: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_dot_const_const_MatR(self.as_raw_MatExpr(), m.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::MatExpr] + pub trait MatExprTrait: core::MatExprTraitConst { + fn as_raw_mut_MatExpr(&mut self) -> *mut c_void; + + #[inline] + fn set_flags(&mut self, val: i32) { + let ret = unsafe { sys::cv_MatExpr_propFlags_const_int(self.as_raw_mut_MatExpr(), val) }; + ret + } + + #[inline] + fn set_a(&mut self, val: core::Mat) { + let ret = unsafe { sys::cv_MatExpr_propA_const_Mat(self.as_raw_mut_MatExpr(), val.as_raw_Mat()) }; + ret + } + + #[inline] + fn set_b(&mut self, val: core::Mat) { + let ret = unsafe { sys::cv_MatExpr_propB_const_Mat(self.as_raw_mut_MatExpr(), val.as_raw_Mat()) }; + ret + } + + #[inline] + fn set_c(&mut self, val: core::Mat) { + let ret = unsafe { sys::cv_MatExpr_propC_const_Mat(self.as_raw_mut_MatExpr(), val.as_raw_Mat()) }; + ret + } + + #[inline] + fn set_alpha(&mut self, val: f64) { + let ret = unsafe { sys::cv_MatExpr_propAlpha_const_double(self.as_raw_mut_MatExpr(), val) }; + ret + } + + #[inline] + fn set_beta(&mut self, val: f64) { + let ret = unsafe { sys::cv_MatExpr_propBeta_const_double(self.as_raw_mut_MatExpr(), val) }; + ret + } + + #[inline] + fn set_s(&mut self, val: core::Scalar) { + let ret = unsafe { sys::cv_MatExpr_propS_const_Scalar(self.as_raw_mut_MatExpr(), &val) }; + ret + } + + #[inline] + fn swap(&mut self, b: &mut impl core::MatExprTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatExpr_swap_MatExprR(self.as_raw_mut_MatExpr(), b.as_raw_mut_MatExpr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for MatExpr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("MatExpr") + .field("flags", &core::MatExprTraitConst::flags(self)) + .field("a", &core::MatExprTraitConst::a(self)) + .field("b", &core::MatExprTraitConst::b(self)) + .field("c", &core::MatExprTraitConst::c(self)) + .field("alpha", &core::MatExprTraitConst::alpha(self)) + .field("beta", &core::MatExprTraitConst::beta(self)) + .field("s", &core::MatExprTraitConst::s(self)) + .finish() + } + } + + impl core::MatExprTraitConst for MatExpr { + #[inline] fn as_raw_MatExpr(&self) -> *const c_void { self.as_raw() } + } + + impl core::MatExprTrait for MatExpr { + #[inline] fn as_raw_mut_MatExpr(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MatExpr, core::MatExprTraitConst, as_raw_MatExpr, core::MatExprTrait, as_raw_mut_MatExpr } + + /// ////////////////////////////// Matrix Expressions ///////////////////////////////// + pub struct MatOp { + ptr: *mut c_void, + } + + opencv_type_boxed! { MatOp } + + impl Drop for MatOp { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_MatOp_delete(self.as_raw_mut_MatOp()) }; + } + } + + unsafe impl Send for MatOp {} + + /// Constant methods for [core::MatOp] + pub trait MatOpTraitConst { + fn as_raw_MatOp(&self) -> *const c_void; + + } + + /// Mutable methods for [core::MatOp] + pub trait MatOpTrait: core::MatOpTraitConst { + fn as_raw_mut_MatOp(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for MatOp { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("MatOp") + .finish() + } + } + + impl core::MatOpTraitConst for MatOp { + #[inline] fn as_raw_MatOp(&self) -> *const c_void { self.as_raw() } + } + + impl core::MatOpTrait for MatOp { + #[inline] fn as_raw_mut_MatOp(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MatOp, core::MatOpTraitConst, as_raw_MatOp, core::MatOpTrait, as_raw_mut_MatOp } + + pub struct MatSize<'mat> { + ptr: *mut c_void, + _d: PhantomData<&'mat mut ()>, + } + + opencv_type_boxed! { MatSize<'mat>: for <'mat> } + + impl Drop for MatSize<'_> { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_MatSize_delete(self.as_raw_mut_MatSize()) }; + } + } + + unsafe impl Send for MatSize<'_> {} + + impl<'mat> MatSize<'mat> { + /// ////////////////////////// MatSize //////////////////////////// + #[inline] + pub unsafe fn new(_p: *mut i32) -> core::MatSize<'mat> { + let ret = unsafe { sys::cv_MatSize_MatSize_intX(_p) }; + let ret = unsafe { core::MatSize::<'mat>::opencv_from_extern(ret) }; + ret + } + + } + + /// Constant methods for [core::MatSize] + pub trait MatSizeTraitConst { + fn as_raw_MatSize(&self) -> *const c_void; + + #[inline] + fn p(&self) -> *const i32 { + let ret = unsafe { sys::cv_MatSize_propP_const(self.as_raw_MatSize()) }; + ret + } + + #[inline] + fn dims(&self) -> i32 { + let ret = unsafe { sys::cv_MatSize_dims_const(self.as_raw_MatSize()) }; + ret + } + + #[inline] + fn apply(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatSize_operator___const(self.as_raw_MatSize(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatSize_operator___const_int(self.as_raw_MatSize(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn to_xconst_i32(&self) -> *const i32 { + let ret = unsafe { sys::cv_MatSize_operator_const_intX_const(self.as_raw_MatSize()) }; + ret + } + + #[inline] + fn equals(&self, sz: &impl core::MatSizeTraitConst) -> bool { + let ret = unsafe { sys::cv_MatSize_operatorEQ_const_const_MatSizeR(self.as_raw_MatSize(), sz.as_raw_MatSize()) }; + ret + } + + #[inline] + fn not_equals(&self, sz: &impl core::MatSizeTraitConst) -> bool { + let ret = unsafe { sys::cv_MatSize_operatorNE_const_const_MatSizeR(self.as_raw_MatSize(), sz.as_raw_MatSize()) }; + ret + } + + } + + /// Mutable methods for [core::MatSize] + pub trait MatSizeTrait: core::MatSizeTraitConst { + fn as_raw_mut_MatSize(&mut self) -> *mut c_void; + + #[inline] + fn p_mut(&mut self) -> *mut i32 { + let ret = unsafe { sys::cv_MatSize_propP(self.as_raw_mut_MatSize()) }; + ret + } + + #[inline] + unsafe fn set_p(&mut self, val: *const i32) { + let ret = unsafe { sys::cv_MatSize_propP_intX(self.as_raw_mut_MatSize(), val) }; + ret + } + + #[inline] + fn get_mut(&mut self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatSize_operator___int(self.as_raw_mut_MatSize(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl core::MatSizeTraitConst for MatSize<'_> { + #[inline] fn as_raw_MatSize(&self) -> *const c_void { self.as_raw() } + } + + impl core::MatSizeTrait for MatSize<'_> { + #[inline] fn as_raw_mut_MatSize(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MatSize<'_>, core::MatSizeTraitConst, as_raw_MatSize, core::MatSizeTrait, as_raw_mut_MatSize } + + pub struct MatStep { + ptr: *mut c_void, + } + + opencv_type_boxed! { MatStep } + + impl Drop for MatStep { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_MatStep_delete(self.as_raw_mut_MatStep()) }; + } + } + + unsafe impl Send for MatStep {} + + impl MatStep { + /// ////////////////////////// MatStep //////////////////////////// + #[inline] + pub fn default() -> core::MatStep { + let ret = unsafe { sys::cv_MatStep_MatStep() }; + let ret = unsafe { core::MatStep::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn new(s: size_t) -> core::MatStep { + let ret = unsafe { sys::cv_MatStep_MatStep_size_t(s) }; + let ret = unsafe { core::MatStep::opencv_from_extern(ret) }; + ret + } + + } + + /// Constant methods for [core::MatStep] + pub trait MatStepTraitConst { + fn as_raw_MatStep(&self) -> *const c_void; + + #[inline] + fn p(&self) -> *const size_t { + let ret = unsafe { sys::cv_MatStep_propP_const(self.as_raw_MatStep()) }; + ret + } + + #[inline] + fn buf(&self) -> &[size_t; 2] { + let ret = unsafe { sys::cv_MatStep_propBuf_const(self.as_raw_MatStep()) }; + let ret = unsafe { ret.as_ref() }.expect("Function returned null pointer"); + ret + } + + #[inline] + unsafe fn get(&self, i: i32) -> size_t { + let ret = unsafe { sys::cv_MatStep_operator___const_int(self.as_raw_MatStep(), i) }; + ret + } + + #[inline] + fn to_size_t(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatStep_operator_size_t_const(self.as_raw_MatStep(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::MatStep] + pub trait MatStepTrait: core::MatStepTraitConst { + fn as_raw_mut_MatStep(&mut self) -> *mut c_void; + + #[inline] + fn p_mut(&mut self) -> *mut size_t { + let ret = unsafe { sys::cv_MatStep_propP(self.as_raw_mut_MatStep()) }; + ret + } + + #[inline] + unsafe fn set_p(&mut self, val: *const size_t) { + let ret = unsafe { sys::cv_MatStep_propP_size_tX(self.as_raw_mut_MatStep(), val) }; + ret + } + + #[inline] + fn buf_mut(&mut self) -> &mut [size_t; 2] { + let ret = unsafe { sys::cv_MatStep_propBuf(self.as_raw_mut_MatStep()) }; + let ret = unsafe { ret.as_mut() }.expect("Function returned null pointer"); + ret + } + + #[inline] + unsafe fn get_mut(&mut self, i: i32) -> size_t { + let ret = unsafe { sys::cv_MatStep_operator___int(self.as_raw_mut_MatStep(), i) }; + ret + } + + #[inline] + fn set_size_t(&mut self, s: size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MatStep_operatorST_size_t(self.as_raw_mut_MatStep(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl Default for MatStep { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for MatStep { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("MatStep") + .field("p", &core::MatStepTraitConst::p(self)) + .field("buf", &core::MatStepTraitConst::buf(self)) + .finish() + } + } + + impl core::MatStepTraitConst for MatStep { + #[inline] fn as_raw_MatStep(&self) -> *const c_void { self.as_raw() } + } + + impl core::MatStepTrait for MatStep { + #[inline] fn as_raw_mut_MatStep(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MatStep, core::MatStepTraitConst, as_raw_MatStep, core::MatStepTrait, as_raw_mut_MatStep } + + /// @cond IGNORED + pub struct Matx_AddOp { + ptr: *mut c_void, + } + + opencv_type_boxed! { Matx_AddOp } + + impl Drop for Matx_AddOp { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Matx_AddOp_delete(self.as_raw_mut_Matx_AddOp()) }; + } + } + + unsafe impl Send for Matx_AddOp {} + + impl Matx_AddOp { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_AddOp_Matx_AddOp(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_AddOp::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn copy(unnamed: &impl core::Matx_AddOpTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_AddOp_Matx_AddOp_const_Matx_AddOpR(unnamed.as_raw_Matx_AddOp(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_AddOp::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Matx_AddOp] + pub trait Matx_AddOpTraitConst { + fn as_raw_Matx_AddOp(&self) -> *const c_void; + + } + + /// Mutable methods for [core::Matx_AddOp] + pub trait Matx_AddOpTrait: core::Matx_AddOpTraitConst { + fn as_raw_mut_Matx_AddOp(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for Matx_AddOp { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Matx_AddOp") + .finish() + } + } + + impl core::Matx_AddOpTraitConst for Matx_AddOp { + #[inline] fn as_raw_Matx_AddOp(&self) -> *const c_void { self.as_raw() } + } + + impl core::Matx_AddOpTrait for Matx_AddOp { + #[inline] fn as_raw_mut_Matx_AddOp(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Matx_AddOp, core::Matx_AddOpTraitConst, as_raw_Matx_AddOp, core::Matx_AddOpTrait, as_raw_mut_Matx_AddOp } + + pub struct Matx_DivOp { + ptr: *mut c_void, + } + + opencv_type_boxed! { Matx_DivOp } + + impl Drop for Matx_DivOp { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Matx_DivOp_delete(self.as_raw_mut_Matx_DivOp()) }; + } + } + + unsafe impl Send for Matx_DivOp {} + + impl Matx_DivOp { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_DivOp_Matx_DivOp(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_DivOp::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn copy(unnamed: &impl core::Matx_DivOpTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_DivOp_Matx_DivOp_const_Matx_DivOpR(unnamed.as_raw_Matx_DivOp(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_DivOp::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Matx_DivOp] + pub trait Matx_DivOpTraitConst { + fn as_raw_Matx_DivOp(&self) -> *const c_void; + + } + + /// Mutable methods for [core::Matx_DivOp] + pub trait Matx_DivOpTrait: core::Matx_DivOpTraitConst { + fn as_raw_mut_Matx_DivOp(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for Matx_DivOp { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Matx_DivOp") + .finish() + } + } + + impl core::Matx_DivOpTraitConst for Matx_DivOp { + #[inline] fn as_raw_Matx_DivOp(&self) -> *const c_void { self.as_raw() } + } + + impl core::Matx_DivOpTrait for Matx_DivOp { + #[inline] fn as_raw_mut_Matx_DivOp(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Matx_DivOp, core::Matx_DivOpTraitConst, as_raw_Matx_DivOp, core::Matx_DivOpTrait, as_raw_mut_Matx_DivOp } + + pub struct Matx_MatMulOp { + ptr: *mut c_void, + } + + opencv_type_boxed! { Matx_MatMulOp } + + impl Drop for Matx_MatMulOp { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Matx_MatMulOp_delete(self.as_raw_mut_Matx_MatMulOp()) }; + } + } + + unsafe impl Send for Matx_MatMulOp {} + + impl Matx_MatMulOp { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_MatMulOp_Matx_MatMulOp(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_MatMulOp::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn copy(unnamed: &impl core::Matx_MatMulOpTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_MatMulOp_Matx_MatMulOp_const_Matx_MatMulOpR(unnamed.as_raw_Matx_MatMulOp(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_MatMulOp::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Matx_MatMulOp] + pub trait Matx_MatMulOpTraitConst { + fn as_raw_Matx_MatMulOp(&self) -> *const c_void; + + } + + /// Mutable methods for [core::Matx_MatMulOp] + pub trait Matx_MatMulOpTrait: core::Matx_MatMulOpTraitConst { + fn as_raw_mut_Matx_MatMulOp(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for Matx_MatMulOp { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Matx_MatMulOp") + .finish() + } + } + + impl core::Matx_MatMulOpTraitConst for Matx_MatMulOp { + #[inline] fn as_raw_Matx_MatMulOp(&self) -> *const c_void { self.as_raw() } + } + + impl core::Matx_MatMulOpTrait for Matx_MatMulOp { + #[inline] fn as_raw_mut_Matx_MatMulOp(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Matx_MatMulOp, core::Matx_MatMulOpTraitConst, as_raw_Matx_MatMulOp, core::Matx_MatMulOpTrait, as_raw_mut_Matx_MatMulOp } + + pub struct Matx_MulOp { + ptr: *mut c_void, + } + + opencv_type_boxed! { Matx_MulOp } + + impl Drop for Matx_MulOp { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Matx_MulOp_delete(self.as_raw_mut_Matx_MulOp()) }; + } + } + + unsafe impl Send for Matx_MulOp {} + + impl Matx_MulOp { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_MulOp_Matx_MulOp(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_MulOp::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn copy(unnamed: &impl core::Matx_MulOpTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_MulOp_Matx_MulOp_const_Matx_MulOpR(unnamed.as_raw_Matx_MulOp(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_MulOp::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Matx_MulOp] + pub trait Matx_MulOpTraitConst { + fn as_raw_Matx_MulOp(&self) -> *const c_void; + + } + + /// Mutable methods for [core::Matx_MulOp] + pub trait Matx_MulOpTrait: core::Matx_MulOpTraitConst { + fn as_raw_mut_Matx_MulOp(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for Matx_MulOp { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Matx_MulOp") + .finish() + } + } + + impl core::Matx_MulOpTraitConst for Matx_MulOp { + #[inline] fn as_raw_Matx_MulOp(&self) -> *const c_void { self.as_raw() } + } + + impl core::Matx_MulOpTrait for Matx_MulOp { + #[inline] fn as_raw_mut_Matx_MulOp(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Matx_MulOp, core::Matx_MulOpTraitConst, as_raw_Matx_MulOp, core::Matx_MulOpTrait, as_raw_mut_Matx_MulOp } + + pub struct Matx_ScaleOp { + ptr: *mut c_void, + } + + opencv_type_boxed! { Matx_ScaleOp } + + impl Drop for Matx_ScaleOp { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Matx_ScaleOp_delete(self.as_raw_mut_Matx_ScaleOp()) }; + } + } + + unsafe impl Send for Matx_ScaleOp {} + + impl Matx_ScaleOp { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_ScaleOp_Matx_ScaleOp(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_ScaleOp::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn copy(unnamed: &impl core::Matx_ScaleOpTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_ScaleOp_Matx_ScaleOp_const_Matx_ScaleOpR(unnamed.as_raw_Matx_ScaleOp(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_ScaleOp::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Matx_ScaleOp] + pub trait Matx_ScaleOpTraitConst { + fn as_raw_Matx_ScaleOp(&self) -> *const c_void; + + } + + /// Mutable methods for [core::Matx_ScaleOp] + pub trait Matx_ScaleOpTrait: core::Matx_ScaleOpTraitConst { + fn as_raw_mut_Matx_ScaleOp(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for Matx_ScaleOp { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Matx_ScaleOp") + .finish() + } + } + + impl core::Matx_ScaleOpTraitConst for Matx_ScaleOp { + #[inline] fn as_raw_Matx_ScaleOp(&self) -> *const c_void { self.as_raw() } + } + + impl core::Matx_ScaleOpTrait for Matx_ScaleOp { + #[inline] fn as_raw_mut_Matx_ScaleOp(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Matx_ScaleOp, core::Matx_ScaleOpTraitConst, as_raw_Matx_ScaleOp, core::Matx_ScaleOpTrait, as_raw_mut_Matx_ScaleOp } + + pub struct Matx_SubOp { + ptr: *mut c_void, + } + + opencv_type_boxed! { Matx_SubOp } + + impl Drop for Matx_SubOp { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Matx_SubOp_delete(self.as_raw_mut_Matx_SubOp()) }; + } + } + + unsafe impl Send for Matx_SubOp {} + + impl Matx_SubOp { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_SubOp_Matx_SubOp(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_SubOp::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn copy(unnamed: &impl core::Matx_SubOpTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_SubOp_Matx_SubOp_const_Matx_SubOpR(unnamed.as_raw_Matx_SubOp(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_SubOp::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Matx_SubOp] + pub trait Matx_SubOpTraitConst { + fn as_raw_Matx_SubOp(&self) -> *const c_void; + + } + + /// Mutable methods for [core::Matx_SubOp] + pub trait Matx_SubOpTrait: core::Matx_SubOpTraitConst { + fn as_raw_mut_Matx_SubOp(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for Matx_SubOp { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Matx_SubOp") + .finish() + } + } + + impl core::Matx_SubOpTraitConst for Matx_SubOp { + #[inline] fn as_raw_Matx_SubOp(&self) -> *const c_void { self.as_raw() } + } + + impl core::Matx_SubOpTrait for Matx_SubOp { + #[inline] fn as_raw_mut_Matx_SubOp(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Matx_SubOp, core::Matx_SubOpTraitConst, as_raw_Matx_SubOp, core::Matx_SubOpTrait, as_raw_mut_Matx_SubOp } + + pub struct Matx_TOp { + ptr: *mut c_void, + } + + opencv_type_boxed! { Matx_TOp } + + impl Drop for Matx_TOp { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Matx_TOp_delete(self.as_raw_mut_Matx_TOp()) }; + } + } + + unsafe impl Send for Matx_TOp {} + + impl Matx_TOp { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_TOp_Matx_TOp(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_TOp::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn copy(unnamed: &impl core::Matx_TOpTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Matx_TOp_Matx_TOp_const_Matx_TOpR(unnamed.as_raw_Matx_TOp(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Matx_TOp::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Matx_TOp] + pub trait Matx_TOpTraitConst { + fn as_raw_Matx_TOp(&self) -> *const c_void; + + } + + /// Mutable methods for [core::Matx_TOp] + pub trait Matx_TOpTrait: core::Matx_TOpTraitConst { + fn as_raw_mut_Matx_TOp(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for Matx_TOp { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Matx_TOp") + .finish() + } + } + + impl core::Matx_TOpTraitConst for Matx_TOp { + #[inline] fn as_raw_Matx_TOp(&self) -> *const c_void { self.as_raw() } + } + + impl core::Matx_TOpTrait for Matx_TOp { + #[inline] fn as_raw_mut_Matx_TOp(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Matx_TOp, core::Matx_TOpTraitConst, as_raw_Matx_TOp, core::Matx_TOpTrait, as_raw_mut_Matx_TOp } + + /// Basic interface for all solvers + pub struct MinProblemSolver { + ptr: *mut c_void, + } + + opencv_type_boxed! { MinProblemSolver } + + impl Drop for MinProblemSolver { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_MinProblemSolver_delete(self.as_raw_mut_MinProblemSolver()) }; + } + } + + unsafe impl Send for MinProblemSolver {} + + /// Constant methods for [core::MinProblemSolver] + pub trait MinProblemSolverTraitConst: core::AlgorithmTraitConst { + fn as_raw_MinProblemSolver(&self) -> *const c_void; + + /// Getter for the optimized function. + /// + /// The optimized function is represented by Function interface, which requires derivatives to + /// implement the calc(double*) and getDim() methods to evaluate the function. + /// + /// ## Returns + /// Smart-pointer to an object that implements Function interface - it represents the + /// function that is being optimized. It can be empty, if no function was given so far. + #[inline] + fn get_function(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MinProblemSolver_getFunction_const(self.as_raw_MinProblemSolver(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Getter for the previously set terminal criteria for this algorithm. + /// + /// ## Returns + /// Deep copy of the terminal criteria used at the moment. + #[inline] + fn get_term_criteria(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MinProblemSolver_getTermCriteria_const(self.as_raw_MinProblemSolver(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::MinProblemSolver] + pub trait MinProblemSolverTrait: core::AlgorithmTrait + core::MinProblemSolverTraitConst { + fn as_raw_mut_MinProblemSolver(&mut self) -> *mut c_void; + + /// Setter for the optimized function. + /// + /// *It should be called at least once before the call to* minimize(), as default value is not usable. + /// + /// ## Parameters + /// * f: The new function to optimize. + #[inline] + fn set_function(&mut self, f: &core::Ptr) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MinProblemSolver_setFunction_const_PtrLFunctionGR(self.as_raw_mut_MinProblemSolver(), f.as_raw_PtrOfMinProblemSolver_Function(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Set terminal criteria for solver. + /// + /// This method *is not necessary* to be called before the first call to minimize(), as the default + /// value is sensible. + /// + /// Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when + /// the function values at the vertices of simplex are within termcrit.epsilon range or simplex + /// becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes + /// first. + /// ## Parameters + /// * termcrit: Terminal criteria to be used, represented as cv::TermCriteria structure. + #[inline] + fn set_term_criteria(&mut self, termcrit: core::TermCriteria) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MinProblemSolver_setTermCriteria_const_TermCriteriaR(self.as_raw_mut_MinProblemSolver(), &termcrit, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// actually runs the algorithm and performs the minimization. + /// + /// The sole input parameter determines the centroid of the starting simplex (roughly, it tells + /// where to start), all the others (terminal criteria, initial step, function to be minimized) are + /// supposed to be set via the setters before the call to this method or the default values (not + /// always sensible) will be used. + /// + /// ## Parameters + /// * x: The initial point, that will become a centroid of an initial simplex. After the algorithm + /// will terminate, it will be set to the point where the algorithm stops, the point of possible + /// minimum. + /// ## Returns + /// The value of a function at the point found. + #[inline] + fn minimize(&mut self, x: &mut impl ToInputOutputArray) -> Result { + input_output_array_arg!(x); + return_send!(via ocvrs_return); + unsafe { sys::cv_MinProblemSolver_minimize_const__InputOutputArrayR(self.as_raw_mut_MinProblemSolver(), x.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for MinProblemSolver { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("MinProblemSolver") + .finish() + } + } + + boxed_cast_base! { MinProblemSolver, core::Algorithm, cv_MinProblemSolver_to_Algorithm } + + boxed_cast_descendant! { MinProblemSolver, core::ConjGradSolver, cv_MinProblemSolver_to_ConjGradSolver } + + boxed_cast_descendant! { MinProblemSolver, core::DownhillSolver, cv_MinProblemSolver_to_DownhillSolver } + + impl core::AlgorithmTraitConst for MinProblemSolver { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for MinProblemSolver { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MinProblemSolver, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl core::MinProblemSolverTraitConst for MinProblemSolver { + #[inline] fn as_raw_MinProblemSolver(&self) -> *const c_void { self.as_raw() } + } + + impl core::MinProblemSolverTrait for MinProblemSolver { + #[inline] fn as_raw_mut_MinProblemSolver(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MinProblemSolver, core::MinProblemSolverTraitConst, as_raw_MinProblemSolver, core::MinProblemSolverTrait, as_raw_mut_MinProblemSolver } + + /// Represents function being optimized + pub struct MinProblemSolver_Function { + ptr: *mut c_void, + } + + opencv_type_boxed! { MinProblemSolver_Function } + + impl Drop for MinProblemSolver_Function { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_MinProblemSolver_Function_delete(self.as_raw_mut_MinProblemSolver_Function()) }; + } + } + + unsafe impl Send for MinProblemSolver_Function {} + + /// Constant methods for [core::MinProblemSolver_Function] + pub trait MinProblemSolver_FunctionTraitConst { + fn as_raw_MinProblemSolver_Function(&self) -> *const c_void; + + #[inline] + fn get_dims(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MinProblemSolver_Function_getDims_const(self.as_raw_MinProblemSolver_Function(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_gradient_eps(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MinProblemSolver_Function_getGradientEps_const(self.as_raw_MinProblemSolver_Function(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn calc(&self, x: &f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_MinProblemSolver_Function_calc_const_const_doubleX(self.as_raw_MinProblemSolver_Function(), x, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::MinProblemSolver_Function] + pub trait MinProblemSolver_FunctionTrait: core::MinProblemSolver_FunctionTraitConst { + fn as_raw_mut_MinProblemSolver_Function(&mut self) -> *mut c_void; + + #[inline] + fn get_gradient(&mut self, x: &f64, grad: &mut f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_MinProblemSolver_Function_getGradient_const_doubleX_doubleX(self.as_raw_mut_MinProblemSolver_Function(), x, grad, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for MinProblemSolver_Function { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("MinProblemSolver_Function") + .finish() + } + } + + impl core::MinProblemSolver_FunctionTraitConst for MinProblemSolver_Function { + #[inline] fn as_raw_MinProblemSolver_Function(&self) -> *const c_void { self.as_raw() } + } + + impl core::MinProblemSolver_FunctionTrait for MinProblemSolver_Function { + #[inline] fn as_raw_mut_MinProblemSolver_Function(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MinProblemSolver_Function, core::MinProblemSolver_FunctionTraitConst, as_raw_MinProblemSolver_Function, core::MinProblemSolver_FunctionTrait, as_raw_mut_MinProblemSolver_Function } + + /// struct returned by cv::moments + /// + /// The spatial moments ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BMoments%3A%3Am%7D%5F%7Bji%7D) are computed as: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bm%7D%20%5F%7Bji%7D%3D%20%5Csum%20%5F%7Bx%2Cy%7D%20%20%5Cleft%20%28%20%5Ctexttt%7Barray%7D%20%28x%2Cy%29%20%20%5Ccdot%20x%5Ej%20%20%5Ccdot%20y%5Ei%20%5Cright%20%29) + /// + /// The central moments ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BMoments%3A%3Amu%7D%5F%7Bji%7D) are computed as: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bmu%7D%20%5F%7Bji%7D%3D%20%5Csum%20%5F%7Bx%2Cy%7D%20%20%5Cleft%20%28%20%5Ctexttt%7Barray%7D%20%28x%2Cy%29%20%20%5Ccdot%20%28x%20%2D%20%20%5Cbar%7Bx%7D%20%29%5Ej%20%20%5Ccdot%20%28y%20%2D%20%20%5Cbar%7By%7D%20%29%5Ei%20%5Cright%20%29) + /// + /// where ![inline formula](https://latex.codecogs.com/png.latex?%28%5Cbar%7Bx%7D%2C%20%5Cbar%7By%7D%29) is the mass center: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbar%7Bx%7D%20%3D%20%5Cfrac%7B%5Ctexttt%7Bm%7D%5F%7B10%7D%7D%7B%5Ctexttt%7Bm%7D%5F%7B00%7D%7D%20%2C%20%5C%3B%20%5Cbar%7By%7D%20%3D%20%5Cfrac%7B%5Ctexttt%7Bm%7D%5F%7B01%7D%7D%7B%5Ctexttt%7Bm%7D%5F%7B00%7D%7D) + /// + /// The normalized central moments ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BMoments%3A%3Anu%7D%5F%7Bij%7D) are computed as: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bnu%7D%20%5F%7Bji%7D%3D%20%5Cfrac%7B%5Ctexttt%7Bmu%7D%5F%7Bji%7D%7D%7B%5Ctexttt%7Bm%7D%5F%7B00%7D%5E%7B%28i%2Bj%29%2F2%2B1%7D%7D%20%2E) + /// + /// + /// Note: + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bmu%7D%5F%7B00%7D%3D%5Ctexttt%7Bm%7D%5F%7B00%7D), ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bnu%7D%5F%7B00%7D%3D1) + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bnu%7D%5F%7B10%7D%3D%5Ctexttt%7Bmu%7D%5F%7B10%7D%3D%5Ctexttt%7Bmu%7D%5F%7B01%7D%3D%5Ctexttt%7Bmu%7D%5F%7B10%7D%3D0) , hence the values are not + /// stored. + /// + /// The moments of a contour are defined in the same way but computed using the Green's formula (see + /// ). So, due to a limited raster resolution, the moments + /// computed for a contour are slightly different from the moments computed for the same rasterized + /// contour. + /// + /// + /// Note: + /// Since the contour moments are computed using Green formula, you may get seemingly odd results for + /// contours with self-intersections, e.g. a zero area (m00) for butterfly-shaped contours. + #[repr(C)] + #[derive(Copy, Clone, Debug, PartialEq)] + pub struct Moments { + /// spatial moments + pub m00: f64, + /// spatial moments + pub m10: f64, + /// spatial moments + pub m01: f64, + /// spatial moments + pub m20: f64, + /// spatial moments + pub m11: f64, + /// spatial moments + pub m02: f64, + /// spatial moments + pub m30: f64, + /// spatial moments + pub m21: f64, + /// spatial moments + pub m12: f64, + /// spatial moments + pub m03: f64, + /// central moments + pub mu20: f64, + /// central moments + pub mu11: f64, + /// central moments + pub mu02: f64, + /// central moments + pub mu30: f64, + /// central moments + pub mu21: f64, + /// central moments + pub mu12: f64, + /// central moments + pub mu03: f64, + /// central normalized moments + pub nu20: f64, + /// central normalized moments + pub nu11: f64, + /// central normalized moments + pub nu02: f64, + /// central normalized moments + pub nu30: f64, + /// central normalized moments + pub nu21: f64, + /// central normalized moments + pub nu12: f64, + /// central normalized moments + pub nu03: f64, + } + + opencv_type_simple! { core::Moments } + + impl Moments { + /// the default constructor + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Moments_Moments(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// the full constructor + #[inline] + pub fn new(m00: f64, m10: f64, m01: f64, m20: f64, m11: f64, m02: f64, m30: f64, m21: f64, m12: f64, m03: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Moments_Moments_double_double_double_double_double_double_double_double_double_double(m00, m10, m01, m20, m11, m02, m30, m21, m12, m03, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Principal Component Analysis + /// + /// The class is used to calculate a special basis for a set of vectors. The + /// basis will consist of eigenvectors of the covariance matrix calculated + /// from the input set of vectors. The class %PCA can also transform + /// vectors to/from the new coordinate space defined by the basis. Usually, + /// in this new coordinate system, each vector from the original set (and + /// any linear combination of such vectors) can be quite accurately + /// approximated by taking its first few components, corresponding to the + /// eigenvectors of the largest eigenvalues of the covariance matrix. + /// Geometrically it means that you calculate a projection of the vector to + /// a subspace formed by a few eigenvectors corresponding to the dominant + /// eigenvalues of the covariance matrix. And usually such a projection is + /// very close to the original vector. So, you can represent the original + /// vector from a high-dimensional space with a much shorter vector + /// consisting of the projected vector's coordinates in the subspace. Such a + /// transformation is also known as Karhunen-Loeve Transform, or KLT. + /// See + /// + /// The sample below is the function that takes two matrices. The first + /// function stores a set of vectors (a row per vector) that is used to + /// calculate PCA. The second function stores another "test" set of vectors + /// (a row per vector). First, these vectors are compressed with PCA, then + /// reconstructed back, and then the reconstruction error norm is computed + /// and printed for each vector. : + /// + /// ```C++ + /// using namespace cv; + /// + /// PCA compressPCA(const Mat& pcaset, int maxComponents, + /// const Mat& testset, Mat& compressed) + /// { + /// PCA pca(pcaset, // pass the data + /// Mat(), // we do not have a pre-computed mean vector, + /// // so let the PCA engine to compute it + /// PCA::DATA_AS_ROW, // indicate that the vectors + /// // are stored as matrix rows + /// // (use PCA::DATA_AS_COL if the vectors are + /// // the matrix columns) + /// maxComponents // specify, how many principal components to retain + /// ); + /// // if there is no test data, just return the computed basis, ready-to-use + /// if( !testset.data ) + /// return pca; + /// CV_Assert( testset.cols == pcaset.cols ); + /// + /// compressed.create(testset.rows, maxComponents, testset.type()); + /// + /// Mat reconstructed; + /// for( int i = 0; i < testset.rows; i++ ) + /// { + /// Mat vec = testset.row(i), coeffs = compressed.row(i), reconstructed; + /// // compress the vector, the result will be stored + /// // in the i-th row of the output matrix + /// pca.project(vec, coeffs); + /// // and then reconstruct it + /// pca.backProject(coeffs, reconstructed); + /// // and measure the error + /// printf("%d. diff = %g\n", i, norm(vec, reconstructed, NORM_L2)); + /// } + /// return pca; + /// } + /// ``` + /// ## See also + /// calcCovarMatrix, mulTransposed, SVD, dft, dct + pub struct PCA { + ptr: *mut c_void, + } + + opencv_type_boxed! { PCA } + + impl Drop for PCA { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_PCA_delete(self.as_raw_mut_PCA()) }; + } + } + + unsafe impl Send for PCA {} + + impl PCA { + /// default constructor + /// + /// The default constructor initializes an empty %PCA structure. The other + /// constructors initialize the structure and call PCA::operator()(). + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_PCA_PCA(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::PCA::opencv_from_extern(ret) }; + Ok(ret) + } + + /// default constructor + /// + /// The default constructor initializes an empty %PCA structure. The other + /// constructors initialize the structure and call PCA::operator()(). + /// + /// ## Overloaded parameters + /// + /// ## Parameters + /// * data: input samples stored as matrix rows or matrix columns. + /// * mean: optional mean value; if the matrix is empty (@c noArray()), + /// the mean is computed from the data. + /// * flags: operation flags; currently the parameter is only used to + /// specify the data layout (PCA::Flags) + /// * maxComponents: maximum number of components that %PCA should + /// retain; by default, all the components are retained. + /// + /// ## C++ default parameters + /// * max_components: 0 + #[inline] + pub fn new(data: &impl ToInputArray, mean: &impl ToInputArray, flags: i32, max_components: i32) -> Result { + input_array_arg!(data); + input_array_arg!(mean); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCA_PCA_const__InputArrayR_const__InputArrayR_int_int(data.as_raw__InputArray(), mean.as_raw__InputArray(), flags, max_components, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::PCA::opencv_from_extern(ret) }; + Ok(ret) + } + + /// default constructor + /// + /// The default constructor initializes an empty %PCA structure. The other + /// constructors initialize the structure and call PCA::operator()(). + /// + /// ## Overloaded parameters + /// + /// ## Parameters + /// * data: input samples stored as matrix rows or matrix columns. + /// * mean: optional mean value; if the matrix is empty (@c noArray()), + /// the mean is computed from the data. + /// * flags: operation flags; currently the parameter is only used to + /// specify the data layout (PCA::Flags) + /// * maxComponents: maximum number of components that %PCA should + /// retain; by default, all the components are retained. + /// + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * max_components: 0 + #[inline] + pub fn new_def(data: &impl ToInputArray, mean: &impl ToInputArray, flags: i32) -> Result { + input_array_arg!(data); + input_array_arg!(mean); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCA_PCA_const__InputArrayR_const__InputArrayR_int(data.as_raw__InputArray(), mean.as_raw__InputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::PCA::opencv_from_extern(ret) }; + Ok(ret) + } + + /// default constructor + /// + /// The default constructor initializes an empty %PCA structure. The other + /// constructors initialize the structure and call PCA::operator()(). + /// + /// ## Overloaded parameters + /// + /// ## Parameters + /// * data: input samples stored as matrix rows or matrix columns. + /// * mean: optional mean value; if the matrix is empty (noArray()), + /// the mean is computed from the data. + /// * flags: operation flags; currently the parameter is only used to + /// specify the data layout (PCA::Flags) + /// * retainedVariance: Percentage of variance that PCA should retain. + /// Using this parameter will let the PCA decided how many components to + /// retain but it will always keep at least 2. + #[inline] + pub fn new_with_variance(data: &impl ToInputArray, mean: &impl ToInputArray, flags: i32, retained_variance: f64) -> Result { + input_array_arg!(data); + input_array_arg!(mean); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCA_PCA_const__InputArrayR_const__InputArrayR_int_double(data.as_raw__InputArray(), mean.as_raw__InputArray(), flags, retained_variance, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::PCA::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::PCA] + pub trait PCATraitConst { + fn as_raw_PCA(&self) -> *const c_void; + + /// eigenvectors of the covariation matrix + #[inline] + fn eigenvectors(&self) -> core::Mat { + let ret = unsafe { sys::cv_PCA_propEigenvectors_const(self.as_raw_PCA()) }; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + ret + } + + /// eigenvalues of the covariation matrix + #[inline] + fn eigenvalues(&self) -> core::Mat { + let ret = unsafe { sys::cv_PCA_propEigenvalues_const(self.as_raw_PCA()) }; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + ret + } + + /// mean value subtracted before the projection and added after the back projection + #[inline] + fn mean(&self) -> core::Mat { + let ret = unsafe { sys::cv_PCA_propMean_const(self.as_raw_PCA()) }; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + ret + } + + /// Projects vector(s) to the principal component subspace. + /// + /// The methods project one or more vectors to the principal component + /// subspace, where each vector projection is represented by coefficients in + /// the principal component basis. The first form of the method returns the + /// matrix that the second form writes to the result. So the first form can + /// be used as a part of expression while the second form can be more + /// efficient in a processing loop. + /// ## Parameters + /// * vec: input vector(s); must have the same dimensionality and the + /// same layout as the input data used at %PCA phase, that is, if + /// DATA_AS_ROW are specified, then `vec.cols==data.cols` + /// (vector dimensionality) and `vec.rows` is the number of vectors to + /// project, and the same is true for the PCA::DATA_AS_COL case. + #[inline] + fn project(&self, vec: &impl ToInputArray) -> Result { + input_array_arg!(vec); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCA_project_const_const__InputArrayR(self.as_raw_PCA(), vec.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Projects vector(s) to the principal component subspace. + /// + /// The methods project one or more vectors to the principal component + /// subspace, where each vector projection is represented by coefficients in + /// the principal component basis. The first form of the method returns the + /// matrix that the second form writes to the result. So the first form can + /// be used as a part of expression while the second form can be more + /// efficient in a processing loop. + /// ## Parameters + /// * vec: input vector(s); must have the same dimensionality and the + /// same layout as the input data used at %PCA phase, that is, if + /// DATA_AS_ROW are specified, then `vec.cols==data.cols` + /// (vector dimensionality) and `vec.rows` is the number of vectors to + /// project, and the same is true for the PCA::DATA_AS_COL case. + /// + /// ## Overloaded parameters + /// + /// * vec: input vector(s); must have the same dimensionality and the + /// same layout as the input data used at PCA phase, that is, if + /// DATA_AS_ROW are specified, then `vec.cols==data.cols` + /// (vector dimensionality) and `vec.rows` is the number of vectors to + /// project, and the same is true for the PCA::DATA_AS_COL case. + /// * result: output vectors; in case of PCA::DATA_AS_COL, the + /// output matrix has as many columns as the number of input vectors, this + /// means that `result.cols==vec.cols` and the number of rows match the + /// number of principal components (for example, `maxComponents` parameter + /// passed to the constructor). + #[inline] + fn project_to(&self, vec: &impl ToInputArray, result: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(vec); + output_array_arg!(result); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCA_project_const_const__InputArrayR_const__OutputArrayR(self.as_raw_PCA(), vec.as_raw__InputArray(), result.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Reconstructs vectors from their PC projections. + /// + /// The methods are inverse operations to PCA::project. They take PC + /// coordinates of projected vectors and reconstruct the original vectors. + /// Unless all the principal components have been retained, the + /// reconstructed vectors are different from the originals. But typically, + /// the difference is small if the number of components is large enough (but + /// still much smaller than the original vector dimensionality). As a + /// result, PCA is used. + /// ## Parameters + /// * vec: coordinates of the vectors in the principal component + /// subspace, the layout and size are the same as of PCA::project output + /// vectors. + #[inline] + fn back_project(&self, vec: &impl ToInputArray) -> Result { + input_array_arg!(vec); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCA_backProject_const_const__InputArrayR(self.as_raw_PCA(), vec.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reconstructs vectors from their PC projections. + /// + /// The methods are inverse operations to PCA::project. They take PC + /// coordinates of projected vectors and reconstruct the original vectors. + /// Unless all the principal components have been retained, the + /// reconstructed vectors are different from the originals. But typically, + /// the difference is small if the number of components is large enough (but + /// still much smaller than the original vector dimensionality). As a + /// result, PCA is used. + /// ## Parameters + /// * vec: coordinates of the vectors in the principal component + /// subspace, the layout and size are the same as of PCA::project output + /// vectors. + /// + /// ## Overloaded parameters + /// + /// * vec: coordinates of the vectors in the principal component + /// subspace, the layout and size are the same as of PCA::project output + /// vectors. + /// * result: reconstructed vectors; the layout and size are the same as + /// of PCA::project input vectors. + #[inline] + fn back_project_to(&self, vec: &impl ToInputArray, result: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(vec); + output_array_arg!(result); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCA_backProject_const_const__InputArrayR_const__OutputArrayR(self.as_raw_PCA(), vec.as_raw__InputArray(), result.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// write PCA objects + /// + /// Writes [eigenvalues] [eigenvectors] and [mean] to specified FileStorage + #[inline] + fn write(&self, fs: &mut impl core::FileStorageTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_PCA_write_const_FileStorageR(self.as_raw_PCA(), fs.as_raw_mut_FileStorage(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::PCA] + pub trait PCATrait: core::PCATraitConst { + fn as_raw_mut_PCA(&mut self) -> *mut c_void; + + /// eigenvectors of the covariation matrix + #[inline] + fn set_eigenvectors(&mut self, val: core::Mat) { + let ret = unsafe { sys::cv_PCA_propEigenvectors_const_Mat(self.as_raw_mut_PCA(), val.as_raw_Mat()) }; + ret + } + + /// eigenvalues of the covariation matrix + #[inline] + fn set_eigenvalues(&mut self, val: core::Mat) { + let ret = unsafe { sys::cv_PCA_propEigenvalues_const_Mat(self.as_raw_mut_PCA(), val.as_raw_Mat()) }; + ret + } + + /// mean value subtracted before the projection and added after the back projection + #[inline] + fn set_mean(&mut self, val: core::Mat) { + let ret = unsafe { sys::cv_PCA_propMean_const_Mat(self.as_raw_mut_PCA(), val.as_raw_Mat()) }; + ret + } + + /// performs %PCA + /// + /// The operator performs %PCA of the supplied dataset. It is safe to reuse + /// the same PCA structure for multiple datasets. That is, if the structure + /// has been previously used with another dataset, the existing internal + /// data is reclaimed and the new [eigenvalues], [eigenvectors] and [mean] are allocated and computed. + /// + /// The computed [eigenvalues] are sorted from the largest to the smallest and + /// the corresponding [eigenvectors] are stored as eigenvectors rows. + /// + /// ## Parameters + /// * data: input samples stored as the matrix rows or as the matrix + /// columns. + /// * mean: optional mean value; if the matrix is empty (noArray()), + /// the mean is computed from the data. + /// * flags: operation flags; currently the parameter is only used to + /// specify the data layout. (Flags) + /// * maxComponents: maximum number of components that PCA should + /// retain; by default, all the components are retained. + /// + /// ## C++ default parameters + /// * max_components: 0 + #[inline] + fn apply(&mut self, data: &impl ToInputArray, mean: &impl ToInputArray, flags: i32, max_components: i32) -> Result { + input_array_arg!(data); + input_array_arg!(mean); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCA_operator___const__InputArrayR_const__InputArrayR_int_int(self.as_raw_mut_PCA(), data.as_raw__InputArray(), mean.as_raw__InputArray(), flags, max_components, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::PCA::opencv_from_extern(ret) }; + Ok(ret) + } + + /// performs %PCA + /// + /// The operator performs %PCA of the supplied dataset. It is safe to reuse + /// the same PCA structure for multiple datasets. That is, if the structure + /// has been previously used with another dataset, the existing internal + /// data is reclaimed and the new [eigenvalues], [eigenvectors] and [mean] are allocated and computed. + /// + /// The computed [eigenvalues] are sorted from the largest to the smallest and + /// the corresponding [eigenvectors] are stored as eigenvectors rows. + /// + /// ## Parameters + /// * data: input samples stored as the matrix rows or as the matrix + /// columns. + /// * mean: optional mean value; if the matrix is empty (noArray()), + /// the mean is computed from the data. + /// * flags: operation flags; currently the parameter is only used to + /// specify the data layout. (Flags) + /// * maxComponents: maximum number of components that PCA should + /// retain; by default, all the components are retained. + /// + /// ## Note + /// This alternative version of [PCATrait::apply] function uses the following default values for its arguments: + /// * max_components: 0 + #[inline] + fn apply_def(&mut self, data: &impl ToInputArray, mean: &impl ToInputArray, flags: i32) -> Result { + input_array_arg!(data); + input_array_arg!(mean); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCA_operator___const__InputArrayR_const__InputArrayR_int(self.as_raw_mut_PCA(), data.as_raw__InputArray(), mean.as_raw__InputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::PCA::opencv_from_extern(ret) }; + Ok(ret) + } + + /// performs %PCA + /// + /// The operator performs %PCA of the supplied dataset. It is safe to reuse + /// the same PCA structure for multiple datasets. That is, if the structure + /// has been previously used with another dataset, the existing internal + /// data is reclaimed and the new [eigenvalues], [eigenvectors] and [mean] are allocated and computed. + /// + /// The computed [eigenvalues] are sorted from the largest to the smallest and + /// the corresponding [eigenvectors] are stored as eigenvectors rows. + /// + /// ## Parameters + /// * data: input samples stored as the matrix rows or as the matrix + /// columns. + /// * mean: optional mean value; if the matrix is empty (noArray()), + /// the mean is computed from the data. + /// * flags: operation flags; currently the parameter is only used to + /// specify the data layout. (Flags) + /// * maxComponents: maximum number of components that PCA should + /// retain; by default, all the components are retained. + /// + /// ## Overloaded parameters + /// + /// * data: input samples stored as the matrix rows or as the matrix + /// columns. + /// * mean: optional mean value; if the matrix is empty (noArray()), + /// the mean is computed from the data. + /// * flags: operation flags; currently the parameter is only used to + /// specify the data layout. (PCA::Flags) + /// * retainedVariance: Percentage of variance that %PCA should retain. + /// Using this parameter will let the %PCA decided how many components to + /// retain but it will always keep at least 2. + #[inline] + fn apply_1(&mut self, data: &impl ToInputArray, mean: &impl ToInputArray, flags: i32, retained_variance: f64) -> Result { + input_array_arg!(data); + input_array_arg!(mean); + return_send!(via ocvrs_return); + unsafe { sys::cv_PCA_operator___const__InputArrayR_const__InputArrayR_int_double(self.as_raw_mut_PCA(), data.as_raw__InputArray(), mean.as_raw__InputArray(), flags, retained_variance, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::PCA::opencv_from_extern(ret) }; + Ok(ret) + } + + /// load PCA objects + /// + /// Loads [eigenvalues] [eigenvectors] and [mean] from specified FileNode + #[inline] + fn read(&mut self, fn_: &impl core::FileNodeTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_PCA_read_const_FileNodeR(self.as_raw_mut_PCA(), fn_.as_raw_FileNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for PCA { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PCA") + .field("eigenvectors", &core::PCATraitConst::eigenvectors(self)) + .field("eigenvalues", &core::PCATraitConst::eigenvalues(self)) + .field("mean", &core::PCATraitConst::mean(self)) + .finish() + } + } + + impl core::PCATraitConst for PCA { + #[inline] fn as_raw_PCA(&self) -> *const c_void { self.as_raw() } + } + + impl core::PCATrait for PCA { + #[inline] fn as_raw_mut_PCA(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PCA, core::PCATraitConst, as_raw_PCA, core::PCATrait, as_raw_mut_PCA } + + /// Base class for parallel data processors + /// + /// @ingroup core_parallel + pub struct ParallelLoopBody { + ptr: *mut c_void, + } + + opencv_type_boxed! { ParallelLoopBody } + + impl Drop for ParallelLoopBody { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ParallelLoopBody_delete(self.as_raw_mut_ParallelLoopBody()) }; + } + } + + unsafe impl Send for ParallelLoopBody {} + + /// Constant methods for [core::ParallelLoopBody] + pub trait ParallelLoopBodyTraitConst { + fn as_raw_ParallelLoopBody(&self) -> *const c_void; + + #[inline] + fn apply(&self, range: &impl core::RangeTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ParallelLoopBody_operator___const_const_RangeR(self.as_raw_ParallelLoopBody(), range.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::ParallelLoopBody] + pub trait ParallelLoopBodyTrait: core::ParallelLoopBodyTraitConst { + fn as_raw_mut_ParallelLoopBody(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for ParallelLoopBody { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ParallelLoopBody") + .finish() + } + } + + impl core::ParallelLoopBodyTraitConst for ParallelLoopBody { + #[inline] fn as_raw_ParallelLoopBody(&self) -> *const c_void { self.as_raw() } + } + + impl core::ParallelLoopBodyTrait for ParallelLoopBody { + #[inline] fn as_raw_mut_ParallelLoopBody(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ParallelLoopBody, core::ParallelLoopBodyTraitConst, as_raw_ParallelLoopBody, core::ParallelLoopBodyTrait, as_raw_mut_ParallelLoopBody } + + /// Random Number Generator + /// + /// Random number generator. It encapsulates the state (currently, a 64-bit + /// integer) and has methods to return scalar random values and to fill + /// arrays with random values. Currently it supports uniform and Gaussian + /// (normal) distributions. The generator uses Multiply-With-Carry + /// algorithm, introduced by G. Marsaglia ( + /// ). + /// Gaussian-distribution random numbers are generated using the Ziggurat + /// algorithm ( ), + /// introduced by G. Marsaglia and W. W. Tsang. + pub struct RNG { + ptr: *mut c_void, + } + + opencv_type_boxed! { RNG } + + impl Drop for RNG { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_RNG_delete(self.as_raw_mut_RNG()) }; + } + } + + unsafe impl Send for RNG {} + + /// Constant methods for [core::RNG] + pub trait RNGTraitConst { + fn as_raw_RNG(&self) -> *const c_void; + + #[inline] + fn state(&self) -> u64 { + let ret = unsafe { sys::cv_RNG_propState_const(self.as_raw_RNG()) }; + ret + } + + #[inline] + fn equals(&self, other: &impl core::RNGTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_operatorEQ_const_const_RNGR(self.as_raw_RNG(), other.as_raw_RNG(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::RNG] + pub trait RNGTrait: core::RNGTraitConst { + fn as_raw_mut_RNG(&mut self) -> *mut c_void; + + #[inline] + fn set_state(&mut self, val: u64) { + let ret = unsafe { sys::cv_RNG_propState_const_uint64_t(self.as_raw_mut_RNG(), val) }; + ret + } + + /// The method updates the state using the MWC algorithm and returns the + /// next 32-bit random number. + #[inline] + fn next(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_next(self.as_raw_mut_RNG(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Each of the methods updates the state using the MWC algorithm and + /// returns the next random number of the specified type. In case of integer + /// types, the returned number is from the available value range for the + /// specified type. In case of floating-point types, the returned value is + /// from [0,1) range. + #[inline] + fn to_u8(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_operator_unsigned_char(self.as_raw_mut_RNG(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + #[inline] + fn to_i8(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_operator_signed_char(self.as_raw_mut_RNG(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + #[inline] + fn to_u16(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_operator_unsigned_short(self.as_raw_mut_RNG(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + #[inline] + fn to_i16(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_operator_short(self.as_raw_mut_RNG(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + #[inline] + fn to_u32(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_operator_unsigned_int(self.as_raw_mut_RNG(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + #[inline] + fn to_i32(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_operator_int(self.as_raw_mut_RNG(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + #[inline] + fn to_f32(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_operator_float(self.as_raw_mut_RNG(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + #[inline] + fn to_f64(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_operator_double(self.as_raw_mut_RNG(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns a random integer sampled uniformly from [0, N). + /// + /// The methods transform the state using the MWC algorithm and return the + /// next random number. The first form is equivalent to RNG::next . The + /// second form returns the random number modulo N, which means that the + /// result is in the range [0, N) . + #[inline] + fn apply(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_operator__(self.as_raw_mut_RNG(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns a random integer sampled uniformly from [0, N). + /// + /// The methods transform the state using the MWC algorithm and return the + /// next random number. The first form is equivalent to RNG::next . The + /// second form returns the random number modulo N, which means that the + /// result is in the range [0, N) . + /// + /// ## Overloaded parameters + /// + /// ## Parameters + /// * N: upper non-inclusive boundary of the returned random number. + #[inline] + fn apply_range(&mut self, n: u32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_operator___unsigned_int(self.as_raw_mut_RNG(), n, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns uniformly distributed integer random number from [a,b) range + /// + /// The methods transform the state using the MWC algorithm and return the + /// next uniformly-distributed random number of the specified type, deduced + /// from the input parameter type, from the range [a, b) . There is a nuance + /// illustrated by the following sample: + /// + /// ```C++ + /// RNG rng; + /// + /// // always produces 0 + /// double a = rng.uniform(0, 1); + /// + /// // produces double from [0, 1) + /// double a1 = rng.uniform((double)0, (double)1); + /// + /// // produces float from [0, 1) + /// float b = rng.uniform(0.f, 1.f); + /// + /// // produces double from [0, 1) + /// double c = rng.uniform(0., 1.); + /// + /// // may cause compiler error because of ambiguity: + /// // RNG::uniform(0, (int)0.999999)? or RNG::uniform((double)0, 0.99999)? + /// double d = rng.uniform(0, 0.999999); + /// ``` + /// + /// + /// The compiler does not take into account the type of the variable to + /// which you assign the result of RNG::uniform . The only thing that + /// matters to the compiler is the type of a and b parameters. So, if you + /// want a floating-point random number, but the range boundaries are + /// integer numbers, either put dots in the end, if they are constants, or + /// use explicit type cast operators, as in the a1 initialization above. + /// ## Parameters + /// * a: lower inclusive boundary of the returned random number. + /// * b: upper non-inclusive boundary of the returned random number. + #[inline] + fn uniform(&mut self, a: i32, b: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_uniform_int_int(self.as_raw_mut_RNG(), a, b, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns uniformly distributed integer random number from [a,b) range + /// + /// The methods transform the state using the MWC algorithm and return the + /// next uniformly-distributed random number of the specified type, deduced + /// from the input parameter type, from the range [a, b) . There is a nuance + /// illustrated by the following sample: + /// + /// ```C++ + /// RNG rng; + /// + /// // always produces 0 + /// double a = rng.uniform(0, 1); + /// + /// // produces double from [0, 1) + /// double a1 = rng.uniform((double)0, (double)1); + /// + /// // produces float from [0, 1) + /// float b = rng.uniform(0.f, 1.f); + /// + /// // produces double from [0, 1) + /// double c = rng.uniform(0., 1.); + /// + /// // may cause compiler error because of ambiguity: + /// // RNG::uniform(0, (int)0.999999)? or RNG::uniform((double)0, 0.99999)? + /// double d = rng.uniform(0, 0.999999); + /// ``` + /// + /// + /// The compiler does not take into account the type of the variable to + /// which you assign the result of RNG::uniform . The only thing that + /// matters to the compiler is the type of a and b parameters. So, if you + /// want a floating-point random number, but the range boundaries are + /// integer numbers, either put dots in the end, if they are constants, or + /// use explicit type cast operators, as in the a1 initialization above. + /// ## Parameters + /// * a: lower inclusive boundary of the returned random number. + /// * b: upper non-inclusive boundary of the returned random number. + /// + /// ## Overloaded parameters + #[inline] + fn uniform_f32(&mut self, a: f32, b: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_uniform_float_float(self.as_raw_mut_RNG(), a, b, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns uniformly distributed integer random number from [a,b) range + /// + /// The methods transform the state using the MWC algorithm and return the + /// next uniformly-distributed random number of the specified type, deduced + /// from the input parameter type, from the range [a, b) . There is a nuance + /// illustrated by the following sample: + /// + /// ```C++ + /// RNG rng; + /// + /// // always produces 0 + /// double a = rng.uniform(0, 1); + /// + /// // produces double from [0, 1) + /// double a1 = rng.uniform((double)0, (double)1); + /// + /// // produces float from [0, 1) + /// float b = rng.uniform(0.f, 1.f); + /// + /// // produces double from [0, 1) + /// double c = rng.uniform(0., 1.); + /// + /// // may cause compiler error because of ambiguity: + /// // RNG::uniform(0, (int)0.999999)? or RNG::uniform((double)0, 0.99999)? + /// double d = rng.uniform(0, 0.999999); + /// ``` + /// + /// + /// The compiler does not take into account the type of the variable to + /// which you assign the result of RNG::uniform . The only thing that + /// matters to the compiler is the type of a and b parameters. So, if you + /// want a floating-point random number, but the range boundaries are + /// integer numbers, either put dots in the end, if they are constants, or + /// use explicit type cast operators, as in the a1 initialization above. + /// ## Parameters + /// * a: lower inclusive boundary of the returned random number. + /// * b: upper non-inclusive boundary of the returned random number. + /// + /// ## Overloaded parameters + #[inline] + fn uniform_f64(&mut self, a: f64, b: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_uniform_double_double(self.as_raw_mut_RNG(), a, b, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fills arrays with random numbers. + /// + /// ## Parameters + /// * mat: 2D or N-dimensional matrix; currently matrices with more than + /// 4 channels are not supported by the methods, use Mat::reshape as a + /// possible workaround. + /// * distType: distribution type, RNG::UNIFORM or RNG::NORMAL. + /// * a: first distribution parameter; in case of the uniform + /// distribution, this is an inclusive lower boundary, in case of the normal + /// distribution, this is a mean value. + /// * b: second distribution parameter; in case of the uniform + /// distribution, this is a non-inclusive upper boundary, in case of the + /// normal distribution, this is a standard deviation (diagonal of the + /// standard deviation matrix or the full standard deviation matrix). + /// * saturateRange: pre-saturation flag; for uniform distribution only; + /// if true, the method will first convert a and b to the acceptable value + /// range (according to the mat datatype) and then will generate uniformly + /// distributed random numbers within the range [saturate(a), saturate(b)), + /// if saturateRange=false, the method will generate uniformly distributed + /// random numbers in the original range [a, b) and then will saturate them, + /// it means, for example, that + /// theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX) will likely + /// produce array mostly filled with 0's and 255's, since the range (0, 255) + /// is significantly smaller than [-DBL_MAX, DBL_MAX). + /// + /// Each of the methods fills the matrix with the random values from the + /// specified distribution. As the new numbers are generated, the RNG state + /// is updated accordingly. In case of multiple-channel images, every + /// channel is filled independently, which means that RNG cannot generate + /// samples from the multi-dimensional Gaussian distribution with + /// non-diagonal covariance matrix directly. To do that, the method + /// generates samples from multi-dimensional standard Gaussian distribution + /// with zero mean and identity covariation matrix, and then transforms them + /// using transform to get samples from the specified Gaussian distribution. + /// + /// ## C++ default parameters + /// * saturate_range: false + #[inline] + fn fill(&mut self, mat: &mut impl ToInputOutputArray, dist_type: i32, a: &impl ToInputArray, b: &impl ToInputArray, saturate_range: bool) -> Result<()> { + input_output_array_arg!(mat); + input_array_arg!(a); + input_array_arg!(b); + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_fill_const__InputOutputArrayR_int_const__InputArrayR_const__InputArrayR_bool(self.as_raw_mut_RNG(), mat.as_raw__InputOutputArray(), dist_type, a.as_raw__InputArray(), b.as_raw__InputArray(), saturate_range, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fills arrays with random numbers. + /// + /// ## Parameters + /// * mat: 2D or N-dimensional matrix; currently matrices with more than + /// 4 channels are not supported by the methods, use Mat::reshape as a + /// possible workaround. + /// * distType: distribution type, RNG::UNIFORM or RNG::NORMAL. + /// * a: first distribution parameter; in case of the uniform + /// distribution, this is an inclusive lower boundary, in case of the normal + /// distribution, this is a mean value. + /// * b: second distribution parameter; in case of the uniform + /// distribution, this is a non-inclusive upper boundary, in case of the + /// normal distribution, this is a standard deviation (diagonal of the + /// standard deviation matrix or the full standard deviation matrix). + /// * saturateRange: pre-saturation flag; for uniform distribution only; + /// if true, the method will first convert a and b to the acceptable value + /// range (according to the mat datatype) and then will generate uniformly + /// distributed random numbers within the range [saturate(a), saturate(b)), + /// if saturateRange=false, the method will generate uniformly distributed + /// random numbers in the original range [a, b) and then will saturate them, + /// it means, for example, that + /// theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX) will likely + /// produce array mostly filled with 0's and 255's, since the range (0, 255) + /// is significantly smaller than [-DBL_MAX, DBL_MAX). + /// + /// Each of the methods fills the matrix with the random values from the + /// specified distribution. As the new numbers are generated, the RNG state + /// is updated accordingly. In case of multiple-channel images, every + /// channel is filled independently, which means that RNG cannot generate + /// samples from the multi-dimensional Gaussian distribution with + /// non-diagonal covariance matrix directly. To do that, the method + /// generates samples from multi-dimensional standard Gaussian distribution + /// with zero mean and identity covariation matrix, and then transforms them + /// using transform to get samples from the specified Gaussian distribution. + /// + /// ## Note + /// This alternative version of [RNGTrait::fill] function uses the following default values for its arguments: + /// * saturate_range: false + #[inline] + fn fill_def(&mut self, mat: &mut impl ToInputOutputArray, dist_type: i32, a: &impl ToInputArray, b: &impl ToInputArray) -> Result<()> { + input_output_array_arg!(mat); + input_array_arg!(a); + input_array_arg!(b); + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_fill_const__InputOutputArrayR_int_const__InputArrayR_const__InputArrayR(self.as_raw_mut_RNG(), mat.as_raw__InputOutputArray(), dist_type, a.as_raw__InputArray(), b.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the next random number sampled from the Gaussian distribution + /// ## Parameters + /// * sigma: standard deviation of the distribution. + /// + /// The method transforms the state using the MWC algorithm and returns the + /// next random number from the Gaussian distribution N(0,sigma) . That is, + /// the mean value of the returned random numbers is zero and the standard + /// deviation is the specified sigma . + #[inline] + fn gaussian(&mut self, sigma: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_gaussian_double(self.as_raw_mut_RNG(), sigma, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for RNG { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("RNG") + .field("state", &core::RNGTraitConst::state(self)) + .finish() + } + } + + impl core::RNGTraitConst for RNG { + #[inline] fn as_raw_RNG(&self) -> *const c_void { self.as_raw() } + } + + impl core::RNGTrait for RNG { + #[inline] fn as_raw_mut_RNG(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RNG, core::RNGTraitConst, as_raw_RNG, core::RNGTrait, as_raw_mut_RNG } + + /// Mersenne Twister random number generator + /// + /// Inspired by + /// @todo document + pub struct RNG_MT19937 { + ptr: *mut c_void, + } + + opencv_type_boxed! { RNG_MT19937 } + + impl Drop for RNG_MT19937 { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_RNG_MT19937_delete(self.as_raw_mut_RNG_MT19937()) }; + } + } + + unsafe impl Send for RNG_MT19937 {} + + impl RNG_MT19937 { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_MT19937_RNG_MT19937(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::RNG_MT19937::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn new(s: u32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_MT19937_RNG_MT19937_unsigned_int(s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::RNG_MT19937::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::RNG_MT19937] + pub trait RNG_MT19937TraitConst { + fn as_raw_RNG_MT19937(&self) -> *const c_void; + + } + + /// Mutable methods for [core::RNG_MT19937] + pub trait RNG_MT19937Trait: core::RNG_MT19937TraitConst { + fn as_raw_mut_RNG_MT19937(&mut self) -> *mut c_void; + + #[inline] + fn seed(&mut self, s: u32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_MT19937_seed_unsigned_int(self.as_raw_mut_RNG_MT19937(), s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn next(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_MT19937_next(self.as_raw_mut_RNG_MT19937(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn to_i32(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_MT19937_operator_int(self.as_raw_mut_RNG_MT19937(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn to_u32(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_MT19937_operator_unsigned_int(self.as_raw_mut_RNG_MT19937(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn to_f32(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_MT19937_operator_float(self.as_raw_mut_RNG_MT19937(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn to_f64(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_MT19937_operator_double(self.as_raw_mut_RNG_MT19937(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn to_u32_with_max(&mut self, n: u32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_MT19937_operator___unsigned_int(self.as_raw_mut_RNG_MT19937(), n, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns uniformly distributed integer random number from [a,b) range + #[inline] + fn uniform(&mut self, a: i32, b: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_MT19937_uniform_int_int(self.as_raw_mut_RNG_MT19937(), a, b, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns uniformly distributed floating-point random number from [a,b) range + #[inline] + fn uniform_1(&mut self, a: f32, b: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_MT19937_uniform_float_float(self.as_raw_mut_RNG_MT19937(), a, b, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns uniformly distributed double-precision floating-point random number from [a,b) range + #[inline] + fn uniform_2(&mut self, a: f64, b: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RNG_MT19937_uniform_double_double(self.as_raw_mut_RNG_MT19937(), a, b, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for RNG_MT19937 { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("RNG_MT19937") + .finish() + } + } + + impl core::RNG_MT19937TraitConst for RNG_MT19937 { + #[inline] fn as_raw_RNG_MT19937(&self) -> *const c_void { self.as_raw() } + } + + impl core::RNG_MT19937Trait for RNG_MT19937 { + #[inline] fn as_raw_mut_RNG_MT19937(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RNG_MT19937, core::RNG_MT19937TraitConst, as_raw_RNG_MT19937, core::RNG_MT19937Trait, as_raw_mut_RNG_MT19937 } + + /// Template class specifying a continuous subsequence (slice) of a sequence. + /// + /// The class is used to specify a row or a column span in a matrix ( Mat ) and for many other purposes. + /// Range(a,b) is basically the same as a:b in Matlab or a..b in Python. As in Python, start is an + /// inclusive left boundary of the range and end is an exclusive right boundary of the range. Such a + /// half-opened interval is usually denoted as ![inline formula](https://latex.codecogs.com/png.latex?%5Bstart%2Cend%29) . + /// + /// The static method Range::all() returns a special variable that means "the whole sequence" or "the + /// whole range", just like " : " in Matlab or " ... " in Python. All the methods and functions in + /// OpenCV that take Range support this special Range::all() value. But, of course, in case of your own + /// custom processing, you will probably have to check and handle it explicitly: + /// ```C++ + /// void my_function(..., const Range& r, ....) + /// { + /// if(r == Range::all()) { + /// // process all the data + /// } + /// else { + /// // process [r.start, r.end) + /// } + /// } + /// ``` + /// + pub struct Range { + ptr: *mut c_void, + } + + opencv_type_boxed! { Range } + + impl Drop for Range { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Range_delete(self.as_raw_mut_Range()) }; + } + } + + unsafe impl Send for Range {} + + impl Range { + #[inline] + pub fn all() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Range_all(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Range::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Range] + pub trait RangeTraitConst { + fn as_raw_Range(&self) -> *const c_void; + + #[inline] + fn start(&self) -> i32 { + let ret = unsafe { sys::cv_Range_propStart_const(self.as_raw_Range()) }; + ret + } + + #[inline] + fn end(&self) -> i32 { + let ret = unsafe { sys::cv_Range_propEnd_const(self.as_raw_Range()) }; + ret + } + + #[inline] + fn size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Range_size_const(self.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Range_empty_const(self.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::Range] + pub trait RangeTrait: core::RangeTraitConst { + fn as_raw_mut_Range(&mut self) -> *mut c_void; + + #[inline] + fn set_start(&mut self, val: i32) { + let ret = unsafe { sys::cv_Range_propStart_const_int(self.as_raw_mut_Range(), val) }; + ret + } + + #[inline] + fn set_end(&mut self, val: i32) { + let ret = unsafe { sys::cv_Range_propEnd_const_int(self.as_raw_mut_Range(), val) }; + ret + } + + } + + impl std::fmt::Debug for Range { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Range") + .field("start", &core::RangeTraitConst::start(self)) + .field("end", &core::RangeTraitConst::end(self)) + .finish() + } + } + + impl core::RangeTraitConst for Range { + #[inline] fn as_raw_Range(&self) -> *const c_void { self.as_raw() } + } + + impl core::RangeTrait for Range { + #[inline] fn as_raw_mut_Range(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Range, core::RangeTraitConst, as_raw_Range, core::RangeTrait, as_raw_mut_Range } + + /// The class represents rotated (i.e. not up-right) rectangles on a plane. + /// + /// Each rectangle is specified by the center point (mass center), length of each side (represented by + /// [size2f] structure) and the rotation angle in degrees. + /// + /// The sample below demonstrates how to use RotatedRect: + /// [RotatedRect_demo](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_various.cpp#L1) + /// ![image](https://docs.opencv.org/4.13.0/rotatedrect.png) + /// ## See also + /// CamShift, fitEllipse, minAreaRect, CvBox2D + #[repr(C)] + #[derive(Copy, Clone, Debug, PartialEq)] + pub struct RotatedRect { + /// returns the rectangle mass center + pub center: core::Point2f, + /// returns width and height of the rectangle + pub size: core::Size2f, + /// returns the rotation angle. When the angle is 0, 90, 180, 270 etc., the rectangle becomes an up-right rectangle. + pub angle: f32, + } + + opencv_type_simple! { core::RotatedRect } + + impl RotatedRect { + /// returns 4 vertices of the rotated rectangle + /// ## Parameters + /// * pts: The points array for storing rectangle vertices. The order is _bottomLeft_, _topLeft_, topRight, bottomRight. + /// + /// Note: _Bottom_, _Top_, _Left_ and _Right_ sides refer to the original rectangle (angle is 0), + /// so after 180 degree rotation _bottomLeft_ point will be located at the top right corner of the + /// rectangle. + #[inline] + pub fn points(&self, pts: &mut [core::Point2f; 4]) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_RotatedRect_points_const_Point2fXX(self.as_raw_RotatedRect(), pts, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn points_vec(&self, pts: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_RotatedRect_points_const_vectorLPoint2fGR(self.as_raw_RotatedRect(), pts.as_raw_mut_VectorOfPoint2f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the minimal up-right integer rectangle containing the rotated rectangle + #[inline] + pub fn bounding_rect(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RotatedRect_boundingRect_const(self.as_raw_RotatedRect(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the minimal (exact) floating point rectangle containing the rotated rectangle, not intended for use with images + #[inline] + pub fn bounding_rect2f(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_RotatedRect_boundingRect2f_const(self.as_raw_RotatedRect(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Singular Value Decomposition + /// + /// Class for computing Singular Value Decomposition of a floating-point + /// matrix. The Singular Value Decomposition is used to solve least-square + /// problems, under-determined linear systems, invert matrices, compute + /// condition numbers, and so on. + /// + /// If you want to compute a condition number of a matrix or an absolute value of + /// its determinant, you do not need `u` and `vt`. You can pass + /// flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that full-size u + /// and vt must be computed, which is not necessary most of the time. + /// ## See also + /// invert, solve, eigen, determinant + pub struct SVD { + ptr: *mut c_void, + } + + opencv_type_boxed! { SVD } + + impl Drop for SVD { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_SVD_delete(self.as_raw_mut_SVD()) }; + } + } + + unsafe impl Send for SVD {} + + impl SVD { + /// the default constructor + /// + /// initializes an empty SVD structure + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SVD_SVD(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::SVD::opencv_from_extern(ret) }; + Ok(ret) + } + + /// the default constructor + /// + /// initializes an empty SVD structure + /// + /// ## Overloaded parameters + /// + /// initializes an empty SVD structure and then calls SVD::operator() + /// ## Parameters + /// * src: decomposed matrix. The depth has to be CV_32F or CV_64F. + /// * flags: operation flags (SVD::Flags) + /// + /// ## C++ default parameters + /// * flags: 0 + #[inline] + pub fn new(src: &impl ToInputArray, flags: i32) -> Result { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVD_SVD_const__InputArrayR_int(src.as_raw__InputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::SVD::opencv_from_extern(ret) }; + Ok(ret) + } + + /// the default constructor + /// + /// initializes an empty SVD structure + /// + /// ## Overloaded parameters + /// + /// initializes an empty SVD structure and then calls SVD::operator() + /// ## Parameters + /// * src: decomposed matrix. The depth has to be CV_32F or CV_64F. + /// * flags: operation flags (SVD::Flags) + /// + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * flags: 0 + #[inline] + pub fn new_def(src: &impl ToInputArray) -> Result { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVD_SVD_const__InputArrayR(src.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::SVD::opencv_from_extern(ret) }; + Ok(ret) + } + + /// decomposes matrix and stores the results to user-provided matrices + /// + /// The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor + /// and SVD::operator(), they store the results to the user-provided + /// matrices: + /// + /// ```C++ + /// Mat A, w, u, vt; + /// SVD::compute(A, w, u, vt); + /// ``` + /// + /// + /// ## Parameters + /// * src: decomposed matrix. The depth has to be CV_32F or CV_64F. + /// * w: calculated singular values + /// * u: calculated left singular vectors + /// * vt: transposed matrix of right singular vectors + /// * flags: operation flags - see SVD::Flags. + /// + /// ## C++ default parameters + /// * flags: 0 + #[inline] + pub fn compute_ext(src: &impl ToInputArray, w: &mut impl ToOutputArray, u: &mut impl ToOutputArray, vt: &mut impl ToOutputArray, flags: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(w); + output_array_arg!(u); + output_array_arg!(vt); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVD_compute_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), w.as_raw__OutputArray(), u.as_raw__OutputArray(), vt.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// decomposes matrix and stores the results to user-provided matrices + /// + /// The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor + /// and SVD::operator(), they store the results to the user-provided + /// matrices: + /// + /// ```C++ + /// Mat A, w, u, vt; + /// SVD::compute(A, w, u, vt); + /// ``` + /// + /// + /// ## Parameters + /// * src: decomposed matrix. The depth has to be CV_32F or CV_64F. + /// * w: calculated singular values + /// * u: calculated left singular vectors + /// * vt: transposed matrix of right singular vectors + /// * flags: operation flags - see SVD::Flags. + /// + /// ## Note + /// This alternative version of [SVD::compute_ext] function uses the following default values for its arguments: + /// * flags: 0 + #[inline] + pub fn compute_ext_def(src: &impl ToInputArray, w: &mut impl ToOutputArray, u: &mut impl ToOutputArray, vt: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(w); + output_array_arg!(u); + output_array_arg!(vt); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVD_compute_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(src.as_raw__InputArray(), w.as_raw__OutputArray(), u.as_raw__OutputArray(), vt.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// decomposes matrix and stores the results to user-provided matrices + /// + /// The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor + /// and SVD::operator(), they store the results to the user-provided + /// matrices: + /// + /// ```C++ + /// Mat A, w, u, vt; + /// SVD::compute(A, w, u, vt); + /// ``` + /// + /// + /// ## Parameters + /// * src: decomposed matrix. The depth has to be CV_32F or CV_64F. + /// * w: calculated singular values + /// * u: calculated left singular vectors + /// * vt: transposed matrix of right singular vectors + /// * flags: operation flags - see SVD::Flags. + /// + /// ## Overloaded parameters + /// + /// computes singular values of a matrix + /// * src: decomposed matrix. The depth has to be CV_32F or CV_64F. + /// * w: calculated singular values + /// * flags: operation flags - see SVD::Flags. + /// + /// ## C++ default parameters + /// * flags: 0 + #[inline] + pub fn compute(src: &impl ToInputArray, w: &mut impl ToOutputArray, flags: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(w); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVD_compute_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), w.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// decomposes matrix and stores the results to user-provided matrices + /// + /// The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor + /// and SVD::operator(), they store the results to the user-provided + /// matrices: + /// + /// ```C++ + /// Mat A, w, u, vt; + /// SVD::compute(A, w, u, vt); + /// ``` + /// + /// + /// ## Parameters + /// * src: decomposed matrix. The depth has to be CV_32F or CV_64F. + /// * w: calculated singular values + /// * u: calculated left singular vectors + /// * vt: transposed matrix of right singular vectors + /// * flags: operation flags - see SVD::Flags. + /// + /// ## Overloaded parameters + /// + /// computes singular values of a matrix + /// * src: decomposed matrix. The depth has to be CV_32F or CV_64F. + /// * w: calculated singular values + /// * flags: operation flags - see SVD::Flags. + /// + /// ## Note + /// This alternative version of [SVD::compute] function uses the following default values for its arguments: + /// * flags: 0 + #[inline] + pub fn compute_def(src: &impl ToInputArray, w: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(w); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVD_compute_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), w.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// performs back substitution + #[inline] + pub fn back_subst_multi(w: &impl ToInputArray, u: &impl ToInputArray, vt: &impl ToInputArray, rhs: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(w); + input_array_arg!(u); + input_array_arg!(vt); + input_array_arg!(rhs); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVD_backSubst_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(w.as_raw__InputArray(), u.as_raw__InputArray(), vt.as_raw__InputArray(), rhs.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// solves an under-determined singular linear system + /// + /// The method finds a unit-length solution x of a singular linear system + /// A\*x = 0. Depending on the rank of A, there can be no solutions, a + /// single solution or an infinite number of solutions. In general, the + /// algorithm solves the following problem: + /// ![block formula](https://latex.codecogs.com/png.latex?dst%20%3D%20%20%5Carg%20%5Cmin%20%5F%7Bx%3A%20%20%5C%7C%20x%20%5C%7C%20%3D1%7D%20%20%5C%7C%20src%20%20%5Ccdot%20x%20%20%5C%7C) + /// ## Parameters + /// * src: left-hand-side matrix. + /// * dst: found solution. + #[inline] + pub fn solve_z(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVD_solveZ_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Constant methods for [core::SVD] + pub trait SVDTraitConst { + fn as_raw_SVD(&self) -> *const c_void; + + #[inline] + fn u(&self) -> core::Mat { + let ret = unsafe { sys::cv_SVD_propU_const(self.as_raw_SVD()) }; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn w(&self) -> core::Mat { + let ret = unsafe { sys::cv_SVD_propW_const(self.as_raw_SVD()) }; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn vt(&self) -> core::Mat { + let ret = unsafe { sys::cv_SVD_propVt_const(self.as_raw_SVD()) }; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + ret + } + + /// performs a singular value back substitution. + /// + /// The method calculates a back substitution for the specified right-hand + /// side: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bx%7D%20%3D%20%20%5Ctexttt%7Bvt%7D%20%5ET%20%20%5Ccdot%20diag%28%20%5Ctexttt%7Bw%7D%20%29%5E%7B%2D1%7D%20%20%5Ccdot%20%5Ctexttt%7Bu%7D%20%5ET%20%20%5Ccdot%20%5Ctexttt%7Brhs%7D%20%5Csim%20%5Ctexttt%7BA%7D%20%5E%7B%2D1%7D%20%20%5Ccdot%20%5Ctexttt%7Brhs%7D) + /// + /// Using this technique you can either get a very accurate solution of the + /// convenient linear system, or the best (in the least-squares terms) + /// pseudo-solution of an overdetermined linear system. + /// + /// ## Parameters + /// * rhs: right-hand side of a linear system (u\*w\*v')\*dst = rhs to + /// be solved, where A has been previously decomposed. + /// + /// * dst: found solution of the system. + /// + /// + /// Note: Explicit SVD with the further back substitution only makes sense + /// if you need to solve many linear systems with the same left-hand side + /// (for example, src ). If all you need is to solve a single system + /// (possibly with multiple rhs immediately available), simply call solve + /// add pass [DECOMP_SVD] there. It does absolutely the same thing. + #[inline] + fn back_subst(&self, rhs: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(rhs); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVD_backSubst_const_const__InputArrayR_const__OutputArrayR(self.as_raw_SVD(), rhs.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::SVD] + pub trait SVDTrait: core::SVDTraitConst { + fn as_raw_mut_SVD(&mut self) -> *mut c_void; + + #[inline] + fn set_u(&mut self, val: core::Mat) { + let ret = unsafe { sys::cv_SVD_propU_const_Mat(self.as_raw_mut_SVD(), val.as_raw_Mat()) }; + ret + } + + #[inline] + fn set_w(&mut self, val: core::Mat) { + let ret = unsafe { sys::cv_SVD_propW_const_Mat(self.as_raw_mut_SVD(), val.as_raw_Mat()) }; + ret + } + + #[inline] + fn set_vt(&mut self, val: core::Mat) { + let ret = unsafe { sys::cv_SVD_propVt_const_Mat(self.as_raw_mut_SVD(), val.as_raw_Mat()) }; + ret + } + + /// the operator that performs SVD. The previously allocated u, w and vt are released. + /// + /// The operator performs the singular value decomposition of the supplied + /// matrix. The u,`vt` , and the vector of singular values w are stored in + /// the structure. The same SVD structure can be reused many times with + /// different matrices. Each time, if needed, the previous u,`vt` , and w + /// are reclaimed and the new matrices are created, which is all handled by + /// Mat::create. + /// ## Parameters + /// * src: decomposed matrix. The depth has to be CV_32F or CV_64F. + /// * flags: operation flags (SVD::Flags) + /// + /// ## C++ default parameters + /// * flags: 0 + #[inline] + fn apply(&mut self, src: &impl ToInputArray, flags: i32) -> Result { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVD_operator___const__InputArrayR_int(self.as_raw_mut_SVD(), src.as_raw__InputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::SVD::opencv_from_extern(ret) }; + Ok(ret) + } + + /// the operator that performs SVD. The previously allocated u, w and vt are released. + /// + /// The operator performs the singular value decomposition of the supplied + /// matrix. The u,`vt` , and the vector of singular values w are stored in + /// the structure. The same SVD structure can be reused many times with + /// different matrices. Each time, if needed, the previous u,`vt` , and w + /// are reclaimed and the new matrices are created, which is all handled by + /// Mat::create. + /// ## Parameters + /// * src: decomposed matrix. The depth has to be CV_32F or CV_64F. + /// * flags: operation flags (SVD::Flags) + /// + /// ## Note + /// This alternative version of [SVDTrait::apply] function uses the following default values for its arguments: + /// * flags: 0 + #[inline] + fn apply_def(&mut self, src: &impl ToInputArray) -> Result { + input_array_arg!(src); + return_send!(via ocvrs_return); + unsafe { sys::cv_SVD_operator___const__InputArrayR(self.as_raw_mut_SVD(), src.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::SVD::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl std::fmt::Debug for SVD { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SVD") + .field("u", &core::SVDTraitConst::u(self)) + .field("w", &core::SVDTraitConst::w(self)) + .field("vt", &core::SVDTraitConst::vt(self)) + .finish() + } + } + + impl core::SVDTraitConst for SVD { + #[inline] fn as_raw_SVD(&self) -> *const c_void { self.as_raw() } + } + + impl core::SVDTrait for SVD { + #[inline] fn as_raw_mut_SVD(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SVD, core::SVDTraitConst, as_raw_SVD, core::SVDTrait, as_raw_mut_SVD } + + /// The class SparseMat represents multi-dimensional sparse numerical arrays. + /// + /// Such a sparse array can store elements of any type that Mat can store. *Sparse* means that only + /// non-zero elements are stored (though, as a result of operations on a sparse matrix, some of its + /// stored elements can actually become 0. It is up to you to detect such elements and delete them + /// using SparseMat::erase ). The non-zero elements are stored in a hash table that grows when it is + /// filled so that the search time is O(1) in average (regardless of whether element is there or not). + /// Elements can be accessed using the following methods: + /// * Query operations (SparseMat::ptr and the higher-level SparseMat::ref, SparseMat::value and + /// SparseMat::find), for example: + /// ```C++ + /// const int dims = 5; + /// int size[5] = {10, 10, 10, 10, 10}; + /// SparseMat sparse_mat(dims, size, CV_32F); + /// for(int i = 0; i < 1000; i++) + /// { + /// int idx[dims]; + /// for(int k = 0; k < dims; k++) + /// idx[k] = rand() % size[k]; + /// sparse_mat.ref(idx) += 1.f; + /// } + /// cout << "nnz = " << sparse_mat.nzcount() << endl; + /// ``` + /// + /// * Sparse matrix iterators. They are similar to MatIterator but different from NAryMatIterator. + /// That is, the iteration loop is familiar to STL users: + /// ```C++ + /// // prints elements of a sparse floating-point matrix + /// // and the sum of elements. + /// SparseMatConstIterator_ + /// it = sparse_mat.begin(), + /// it_end = sparse_mat.end(); + /// double s = 0; + /// int dims = sparse_mat.dims(); + /// for(; it != it_end; ++it) + /// { + /// // print element indices and the element value + /// const SparseMat::Node* n = it.node(); + /// printf("("); + /// for(int i = 0; i < dims; i++) + /// printf("%d%s", n->idx[i], i < dims-1 ? ", " : ")"); + /// printf(": %g\n", it.value()); + /// s += *it; + /// } + /// printf("Element sum is %g\n", s); + /// ``` + /// + /// If you run this loop, you will notice that elements are not enumerated in a logical order + /// (lexicographical, and so on). They come in the same order as they are stored in the hash table + /// (semi-randomly). You may collect pointers to the nodes and sort them to get the proper ordering. + /// Note, however, that pointers to the nodes may become invalid when you add more elements to the + /// matrix. This may happen due to possible buffer reallocation. + /// * Combination of the above 2 methods when you need to process 2 or more sparse matrices + /// simultaneously. For example, this is how you can compute unnormalized cross-correlation of the 2 + /// floating-point sparse matrices: + /// ```C++ + /// double cross_corr(const SparseMat& a, const SparseMat& b) + /// { + /// const SparseMat *_a = &a, *_b = &b; + /// // if b contains less elements than a, + /// // it is faster to iterate through b + /// if(_a->nzcount() > _b->nzcount()) + /// std::swap(_a, _b); + /// SparseMatConstIterator_ it = _a->begin(), + /// it_end = _a->end(); + /// double ccorr = 0; + /// for(; it != it_end; ++it) + /// { + /// // take the next element from the first matrix + /// float avalue = *it; + /// const Node* anode = it.node(); + /// // and try to find an element with the same index in the second matrix. + /// // since the hash value depends only on the element index, + /// // reuse the hash value stored in the node + /// float bvalue = _b->value(anode->idx,&anode->hashval); + /// ccorr += avalue*bvalue; + /// } + /// return ccorr; + /// } + /// ``` + /// + pub struct SparseMat { + ptr: *mut c_void, + } + + opencv_type_boxed! { SparseMat } + + impl Drop for SparseMat { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_SparseMat_delete(self.as_raw_mut_SparseMat()) }; + } + } + + unsafe impl Send for SparseMat {} + + /// Constant methods for [core::SparseMat] + pub trait SparseMatTraitConst { + fn as_raw_SparseMat(&self) -> *const c_void; + + #[inline] + fn flags(&self) -> i32 { + let ret = unsafe { sys::cv_SparseMat_propFlags_const(self.as_raw_SparseMat()) }; + ret + } + + /// creates full copy of the matrix + #[inline] + #[must_use] + fn try_clone(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_clone_const(self.as_raw_SparseMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::SparseMat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// copies all the data to the destination matrix. All the previous content of m is erased + #[inline] + fn copy_to(&self, m: &mut impl core::SparseMatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_copyTo_const_SparseMatR(self.as_raw_SparseMat(), m.as_raw_mut_SparseMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// converts sparse matrix to dense matrix. + #[inline] + fn copy_to_mat(&self, m: &mut impl core::MatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_copyTo_const_MatR(self.as_raw_SparseMat(), m.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// multiplies all the matrix elements by the specified scale factor alpha and converts the results to the specified data type + /// + /// ## C++ default parameters + /// * alpha: 1 + #[inline] + fn convert_to(&self, m: &mut impl core::SparseMatTrait, rtype: i32, alpha: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_convertTo_const_SparseMatR_int_double(self.as_raw_SparseMat(), m.as_raw_mut_SparseMat(), rtype, alpha, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// multiplies all the matrix elements by the specified scale factor alpha and converts the results to the specified data type + /// + /// ## Note + /// This alternative version of [SparseMatTraitConst::convert_to] function uses the following default values for its arguments: + /// * alpha: 1 + #[inline] + fn convert_to_def(&self, m: &mut impl core::SparseMatTrait, rtype: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_convertTo_const_SparseMatR_int(self.as_raw_SparseMat(), m.as_raw_mut_SparseMat(), rtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// converts sparse matrix to dense n-dim matrix with optional type conversion and scaling. + /// + /// ## Parameters + /// * m:[out] - output matrix; if it does not have a proper size or type before the operation, + /// it is reallocated + /// * rtype: - desired output matrix type or, rather, the depth since the number of channels + /// are the same as the input has; if rtype is negative, the output matrix will have the + /// same type as the input. + /// * alpha: - optional scale factor + /// * beta: - optional delta added to the scaled values + /// + /// ## C++ default parameters + /// * alpha: 1 + /// * beta: 0 + #[inline] + fn convert_to_1(&self, m: &mut impl core::MatTrait, rtype: i32, alpha: f64, beta: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_convertTo_const_MatR_int_double_double(self.as_raw_SparseMat(), m.as_raw_mut_Mat(), rtype, alpha, beta, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// converts sparse matrix to dense n-dim matrix with optional type conversion and scaling. + /// + /// ## Parameters + /// * m:[out] - output matrix; if it does not have a proper size or type before the operation, + /// it is reallocated + /// * rtype: - desired output matrix type or, rather, the depth since the number of channels + /// are the same as the input has; if rtype is negative, the output matrix will have the + /// same type as the input. + /// * alpha: - optional scale factor + /// * beta: - optional delta added to the scaled values + /// + /// ## Note + /// This alternative version of [SparseMatTraitConst::convert_to] function uses the following default values for its arguments: + /// * alpha: 1 + /// * beta: 0 + #[inline] + fn convert_to_def_1(&self, m: &mut impl core::MatTrait, rtype: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_convertTo_const_MatR_int(self.as_raw_SparseMat(), m.as_raw_mut_Mat(), rtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * typ: -1 + #[inline] + fn assign_to(&self, m: &mut impl core::SparseMatTrait, typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_assignTo_const_SparseMatR_int(self.as_raw_SparseMat(), m.as_raw_mut_SparseMat(), typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [SparseMatTraitConst::assign_to] function uses the following default values for its arguments: + /// * typ: -1 + #[inline] + fn assign_to_def(&self, m: &mut impl core::SparseMatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_assignTo_const_SparseMatR(self.as_raw_SparseMat(), m.as_raw_mut_SparseMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the size of each element in bytes (not including the overhead - the space occupied by SparseMat::Node elements) + #[inline] + fn elem_size(&self) -> size_t { + let ret = unsafe { sys::cv_SparseMat_elemSize_const(self.as_raw_SparseMat()) }; + ret + } + + /// returns elemSize()/channels() + #[inline] + fn elem_size1(&self) -> size_t { + let ret = unsafe { sys::cv_SparseMat_elemSize1_const(self.as_raw_SparseMat()) }; + ret + } + + /// returns type of sparse matrix elements + #[inline] + fn typ(&self) -> i32 { + let ret = unsafe { sys::cv_SparseMat_type_const(self.as_raw_SparseMat()) }; + ret + } + + /// returns the depth of sparse matrix elements + #[inline] + fn depth(&self) -> i32 { + let ret = unsafe { sys::cv_SparseMat_depth_const(self.as_raw_SparseMat()) }; + ret + } + + /// returns the number of channels + #[inline] + fn channels(&self) -> i32 { + let ret = unsafe { sys::cv_SparseMat_channels_const(self.as_raw_SparseMat()) }; + ret + } + + /// returns the array of sizes, or NULL if the matrix is not allocated + #[inline] + fn size(&self) -> Result<*const i32> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_size_const(self.as_raw_SparseMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the size of i-th matrix dimension (or 0) + #[inline] + fn size_1(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_size_const_int(self.as_raw_SparseMat(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the matrix dimensionality + #[inline] + fn dims(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_dims_const(self.as_raw_SparseMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the number of non-zero elements (=the number of hash table nodes) + #[inline] + fn nzcount(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_nzcount_const(self.as_raw_SparseMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// computes the element hash value (1D case) + #[inline] + fn hash(&self, i0: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_hash_const_int(self.as_raw_SparseMat(), i0, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// computes the element hash value (2D case) + #[inline] + fn hash_1(&self, i0: i32, i1: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_hash_const_int_int(self.as_raw_SparseMat(), i0, i1, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// computes the element hash value (3D case) + #[inline] + fn hash_2(&self, i0: i32, i1: i32, i2: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_hash_const_int_int_int(self.as_raw_SparseMat(), i0, i1, i2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// computes the element hash value (nD case) + #[inline] + fn hash_3(&self, idx: &i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_hash_const_const_intX(self.as_raw_SparseMat(), idx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn node(&self, nidx: size_t) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_node_const_size_t(self.as_raw_SparseMat(), nidx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::SparseMat_Node::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Mutable methods for [core::SparseMat] + pub trait SparseMatTrait: core::SparseMatTraitConst { + fn as_raw_mut_SparseMat(&mut self) -> *mut c_void; + + #[inline] + fn set_flags(&mut self, val: i32) { + let ret = unsafe { sys::cv_SparseMat_propFlags_const_int(self.as_raw_mut_SparseMat(), val) }; + ret + } + + #[inline] + fn hdr(&mut self) -> core::SparseMat_Hdr { + let ret = unsafe { sys::cv_SparseMat_propHdr(self.as_raw_mut_SparseMat()) }; + let ret = unsafe { core::SparseMat_Hdr::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn set_hdr(&mut self, val: &impl core::SparseMat_HdrTraitConst) { + let ret = unsafe { sys::cv_SparseMat_propHdr_HdrX(self.as_raw_mut_SparseMat(), val.as_raw_SparseMat_Hdr()) }; + ret + } + + /// assignment operator. This is O(1) operation, i.e. no data is copied + #[inline] + fn set(&mut self, m: &impl core::SparseMatTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_operatorST_const_SparseMatR(self.as_raw_mut_SparseMat(), m.as_raw_SparseMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// equivalent to the corresponding constructor + #[inline] + fn set_mat(&mut self, m: &impl core::MatTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_operatorST_const_MatR(self.as_raw_mut_SparseMat(), m.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// reallocates sparse matrix. + /// + /// If the matrix already had the proper size and type, + /// it is simply cleared with clear(), otherwise, + /// the old matrix is released (using release()) and the new one is allocated. + #[inline] + fn create(&mut self, _sizes: &[i32], _type: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_create_int_const_intX_int(self.as_raw_mut_SparseMat(), _sizes.len().try_into()?, _sizes.as_ptr(), _type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// sets all the sparse matrix elements to 0, which means clearing the hash table. + #[inline] + fn clear(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_clear(self.as_raw_mut_SparseMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// manually increments the reference counter to the header. + #[inline] + unsafe fn addref(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_addref(self.as_raw_mut_SparseMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + unsafe fn release(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_release(self.as_raw_mut_SparseMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// specialized variants for 1D, 2D, 3D cases and the generic_type one for n-D case. + /// return pointer to the matrix element. + /// - if the element is there (it's non-zero), the pointer to it is returned + /// - if it's not there and createMissing=false, NULL pointer is returned + /// - if it's not there and createMissing=true, then the new element + /// is created and initialized with 0. Pointer to it is returned + /// - if the optional hashval pointer is not NULL, the element hash value is + /// not computed, but *hashval is taken instead. + /// + /// returns pointer to the specified element (1D case) + /// + /// ## C++ default parameters + /// * hashval: 0 + #[inline] + fn ptr(&mut self, i0: i32, create_missing: bool, hashval: &mut size_t) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_ptr_int_bool_size_tX(self.as_raw_mut_SparseMat(), i0, create_missing, hashval, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// specialized variants for 1D, 2D, 3D cases and the generic_type one for n-D case. + /// return pointer to the matrix element. + /// - if the element is there (it's non-zero), the pointer to it is returned + /// - if it's not there and createMissing=false, NULL pointer is returned + /// - if it's not there and createMissing=true, then the new element + /// is created and initialized with 0. Pointer to it is returned + /// - if the optional hashval pointer is not NULL, the element hash value is + /// not computed, but *hashval is taken instead. + /// + /// returns pointer to the specified element (1D case) + /// + /// ## Note + /// This alternative version of [SparseMatTrait::ptr] function uses the following default values for its arguments: + /// * hashval: 0 + #[inline] + fn ptr_def(&mut self, i0: i32, create_missing: bool) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_ptr_int_bool(self.as_raw_mut_SparseMat(), i0, create_missing, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns pointer to the specified element (2D case) + /// + /// ## C++ default parameters + /// * hashval: 0 + #[inline] + fn ptr_1(&mut self, i0: i32, i1: i32, create_missing: bool, hashval: &mut size_t) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_ptr_int_int_bool_size_tX(self.as_raw_mut_SparseMat(), i0, i1, create_missing, hashval, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns pointer to the specified element (2D case) + /// + /// ## Note + /// This alternative version of [SparseMatTrait::ptr] function uses the following default values for its arguments: + /// * hashval: 0 + #[inline] + fn ptr_def_1(&mut self, i0: i32, i1: i32, create_missing: bool) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_ptr_int_int_bool(self.as_raw_mut_SparseMat(), i0, i1, create_missing, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns pointer to the specified element (3D case) + /// + /// ## C++ default parameters + /// * hashval: 0 + #[inline] + fn ptr_2(&mut self, i0: i32, i1: i32, i2: i32, create_missing: bool, hashval: &mut size_t) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_ptr_int_int_int_bool_size_tX(self.as_raw_mut_SparseMat(), i0, i1, i2, create_missing, hashval, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns pointer to the specified element (3D case) + /// + /// ## Note + /// This alternative version of [SparseMatTrait::ptr] function uses the following default values for its arguments: + /// * hashval: 0 + #[inline] + fn ptr_def_2(&mut self, i0: i32, i1: i32, i2: i32, create_missing: bool) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_ptr_int_int_int_bool(self.as_raw_mut_SparseMat(), i0, i1, i2, create_missing, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns pointer to the specified element (nD case) + /// + /// ## C++ default parameters + /// * hashval: 0 + #[inline] + fn ptr_3(&mut self, idx: &i32, create_missing: bool, hashval: &mut size_t) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_ptr_const_intX_bool_size_tX(self.as_raw_mut_SparseMat(), idx, create_missing, hashval, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns pointer to the specified element (nD case) + /// + /// ## Note + /// This alternative version of [SparseMatTrait::ptr] function uses the following default values for its arguments: + /// * hashval: 0 + #[inline] + fn ptr_def_3(&mut self, idx: &i32, create_missing: bool) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_ptr_const_intX_bool(self.as_raw_mut_SparseMat(), idx, create_missing, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// erases the specified element (2D case) + /// + /// ## C++ default parameters + /// * hashval: 0 + #[inline] + fn erase(&mut self, i0: i32, i1: i32, hashval: &mut size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_erase_int_int_size_tX(self.as_raw_mut_SparseMat(), i0, i1, hashval, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// erases the specified element (2D case) + /// + /// ## Note + /// This alternative version of [SparseMatTrait::erase] function uses the following default values for its arguments: + /// * hashval: 0 + #[inline] + fn erase_def(&mut self, i0: i32, i1: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_erase_int_int(self.as_raw_mut_SparseMat(), i0, i1, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// erases the specified element (3D case) + /// + /// ## C++ default parameters + /// * hashval: 0 + #[inline] + fn erase_1(&mut self, i0: i32, i1: i32, i2: i32, hashval: &mut size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_erase_int_int_int_size_tX(self.as_raw_mut_SparseMat(), i0, i1, i2, hashval, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// erases the specified element (3D case) + /// + /// ## Note + /// This alternative version of [SparseMatTrait::erase] function uses the following default values for its arguments: + /// * hashval: 0 + #[inline] + fn erase_def_1(&mut self, i0: i32, i1: i32, i2: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_erase_int_int_int(self.as_raw_mut_SparseMat(), i0, i1, i2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// erases the specified element (nD case) + /// + /// ## C++ default parameters + /// * hashval: 0 + #[inline] + fn erase_2(&mut self, idx: &i32, hashval: &mut size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_erase_const_intX_size_tX(self.as_raw_mut_SparseMat(), idx, hashval, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// erases the specified element (nD case) + /// + /// ## Note + /// This alternative version of [SparseMatTrait::erase] function uses the following default values for its arguments: + /// * hashval: 0 + #[inline] + fn erase_def_2(&mut self, idx: &i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_erase_const_intX(self.as_raw_mut_SparseMat(), idx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// /////////// some internal-use methods /////////////// + #[inline] + fn node_1(&mut self, nidx: size_t) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_node_size_t(self.as_raw_mut_SparseMat(), nidx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::SparseMat_Node::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn new_node(&mut self, idx: &i32, hashval: size_t) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_newNode_const_intX_size_t(self.as_raw_mut_SparseMat(), idx, hashval, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn remove_node(&mut self, hidx: size_t, nidx: size_t, previdx: size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_removeNode_size_t_size_t_size_t(self.as_raw_mut_SparseMat(), hidx, nidx, previdx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn resize_hash_tab(&mut self, newsize: size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_resizeHashTab_size_t(self.as_raw_mut_SparseMat(), newsize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl Clone for SparseMat { + #[inline] + /// Calls try_clone() and panics if that fails + fn clone(&self) -> Self { + self.try_clone().expect("Cannot clone SparseMat") + } + } + + impl std::fmt::Debug for SparseMat { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SparseMat") + .field("flags", &core::SparseMatTraitConst::flags(self)) + .finish() + } + } + + impl core::SparseMatTraitConst for SparseMat { + #[inline] fn as_raw_SparseMat(&self) -> *const c_void { self.as_raw() } + } + + impl core::SparseMatTrait for SparseMat { + #[inline] fn as_raw_mut_SparseMat(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SparseMat, core::SparseMatTraitConst, as_raw_SparseMat, core::SparseMatTrait, as_raw_mut_SparseMat } + + /// the sparse matrix header + pub struct SparseMat_Hdr { + ptr: *mut c_void, + } + + opencv_type_boxed! { SparseMat_Hdr } + + impl Drop for SparseMat_Hdr { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_SparseMat_Hdr_delete(self.as_raw_mut_SparseMat_Hdr()) }; + } + } + + unsafe impl Send for SparseMat_Hdr {} + + impl SparseMat_Hdr { + #[inline] + pub fn new(_sizes: &[i32], _type: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_Hdr_Hdr_int_const_intX_int(_sizes.len().try_into()?, _sizes.as_ptr(), _type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::SparseMat_Hdr::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::SparseMat_Hdr] + pub trait SparseMat_HdrTraitConst { + fn as_raw_SparseMat_Hdr(&self) -> *const c_void; + + #[inline] + fn refcount(&self) -> i32 { + let ret = unsafe { sys::cv_SparseMat_Hdr_propRefcount_const(self.as_raw_SparseMat_Hdr()) }; + ret + } + + #[inline] + fn dims(&self) -> i32 { + let ret = unsafe { sys::cv_SparseMat_Hdr_propDims_const(self.as_raw_SparseMat_Hdr()) }; + ret + } + + #[inline] + fn value_offset(&self) -> i32 { + let ret = unsafe { sys::cv_SparseMat_Hdr_propValueOffset_const(self.as_raw_SparseMat_Hdr()) }; + ret + } + + #[inline] + fn node_size(&self) -> size_t { + let ret = unsafe { sys::cv_SparseMat_Hdr_propNodeSize_const(self.as_raw_SparseMat_Hdr()) }; + ret + } + + #[inline] + fn node_count(&self) -> size_t { + let ret = unsafe { sys::cv_SparseMat_Hdr_propNodeCount_const(self.as_raw_SparseMat_Hdr()) }; + ret + } + + #[inline] + fn free_list(&self) -> size_t { + let ret = unsafe { sys::cv_SparseMat_Hdr_propFreeList_const(self.as_raw_SparseMat_Hdr()) }; + ret + } + + #[inline] + fn pool(&self) -> core::Vector { + let ret = unsafe { sys::cv_SparseMat_Hdr_propPool_const(self.as_raw_SparseMat_Hdr()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn hashtab(&self) -> core::Vector { + let ret = unsafe { sys::cv_SparseMat_Hdr_propHashtab_const(self.as_raw_SparseMat_Hdr()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn size(&self) -> &[i32; 32] { + let ret = unsafe { sys::cv_SparseMat_Hdr_propSize_const(self.as_raw_SparseMat_Hdr()) }; + let ret = unsafe { ret.as_ref() }.expect("Function returned null pointer"); + ret + } + + } + + /// Mutable methods for [core::SparseMat_Hdr] + pub trait SparseMat_HdrTrait: core::SparseMat_HdrTraitConst { + fn as_raw_mut_SparseMat_Hdr(&mut self) -> *mut c_void; + + #[inline] + fn set_refcount(&mut self, val: i32) { + let ret = unsafe { sys::cv_SparseMat_Hdr_propRefcount_const_int(self.as_raw_mut_SparseMat_Hdr(), val) }; + ret + } + + #[inline] + fn set_dims(&mut self, val: i32) { + let ret = unsafe { sys::cv_SparseMat_Hdr_propDims_const_int(self.as_raw_mut_SparseMat_Hdr(), val) }; + ret + } + + #[inline] + fn set_value_offset(&mut self, val: i32) { + let ret = unsafe { sys::cv_SparseMat_Hdr_propValueOffset_const_int(self.as_raw_mut_SparseMat_Hdr(), val) }; + ret + } + + #[inline] + fn set_node_size(&mut self, val: size_t) { + let ret = unsafe { sys::cv_SparseMat_Hdr_propNodeSize_const_size_t(self.as_raw_mut_SparseMat_Hdr(), val) }; + ret + } + + #[inline] + fn set_node_count(&mut self, val: size_t) { + let ret = unsafe { sys::cv_SparseMat_Hdr_propNodeCount_const_size_t(self.as_raw_mut_SparseMat_Hdr(), val) }; + ret + } + + #[inline] + fn set_free_list(&mut self, val: size_t) { + let ret = unsafe { sys::cv_SparseMat_Hdr_propFreeList_const_size_t(self.as_raw_mut_SparseMat_Hdr(), val) }; + ret + } + + #[inline] + fn set_pool(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_SparseMat_Hdr_propPool_const_vectorLunsigned_charG(self.as_raw_mut_SparseMat_Hdr(), val.as_raw_VectorOfu8()) }; + ret + } + + #[inline] + fn set_hashtab(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_SparseMat_Hdr_propHashtab_const_vectorLsize_tG(self.as_raw_mut_SparseMat_Hdr(), val.as_raw_VectorOfsize_t()) }; + ret + } + + #[inline] + fn size_mut(&mut self) -> &mut [i32; 32] { + let ret = unsafe { sys::cv_SparseMat_Hdr_propSize(self.as_raw_mut_SparseMat_Hdr()) }; + let ret = unsafe { ret.as_mut() }.expect("Function returned null pointer"); + ret + } + + #[inline] + fn clear(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMat_Hdr_clear(self.as_raw_mut_SparseMat_Hdr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for SparseMat_Hdr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SparseMat_Hdr") + .field("refcount", &core::SparseMat_HdrTraitConst::refcount(self)) + .field("dims", &core::SparseMat_HdrTraitConst::dims(self)) + .field("value_offset", &core::SparseMat_HdrTraitConst::value_offset(self)) + .field("node_size", &core::SparseMat_HdrTraitConst::node_size(self)) + .field("node_count", &core::SparseMat_HdrTraitConst::node_count(self)) + .field("free_list", &core::SparseMat_HdrTraitConst::free_list(self)) + .field("pool", &core::SparseMat_HdrTraitConst::pool(self)) + .field("hashtab", &core::SparseMat_HdrTraitConst::hashtab(self)) + .field("size", &core::SparseMat_HdrTraitConst::size(self)) + .finish() + } + } + + impl core::SparseMat_HdrTraitConst for SparseMat_Hdr { + #[inline] fn as_raw_SparseMat_Hdr(&self) -> *const c_void { self.as_raw() } + } + + impl core::SparseMat_HdrTrait for SparseMat_Hdr { + #[inline] fn as_raw_mut_SparseMat_Hdr(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SparseMat_Hdr, core::SparseMat_HdrTraitConst, as_raw_SparseMat_Hdr, core::SparseMat_HdrTrait, as_raw_mut_SparseMat_Hdr } + + /// sparse matrix node - element of a hash table + pub struct SparseMat_Node { + ptr: *mut c_void, + } + + opencv_type_boxed! { SparseMat_Node } + + impl Drop for SparseMat_Node { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_SparseMat_Node_delete(self.as_raw_mut_SparseMat_Node()) }; + } + } + + unsafe impl Send for SparseMat_Node {} + + impl SparseMat_Node { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> core::SparseMat_Node { + let ret = unsafe { sys::cv_SparseMat_Node_defaultNew_const() }; + let ret = unsafe { core::SparseMat_Node::opencv_from_extern(ret) }; + ret + } + + } + + /// Constant methods for [core::SparseMat_Node] + pub trait SparseMat_NodeTraitConst { + fn as_raw_SparseMat_Node(&self) -> *const c_void; + + /// hash value + #[inline] + fn hashval(&self) -> size_t { + let ret = unsafe { sys::cv_SparseMat_Node_propHashval_const(self.as_raw_SparseMat_Node()) }; + ret + } + + /// index of the next node in the same hash table entry + #[inline] + fn next(&self) -> size_t { + let ret = unsafe { sys::cv_SparseMat_Node_propNext_const(self.as_raw_SparseMat_Node()) }; + ret + } + + /// index of the matrix element + #[inline] + fn idx(&self) -> &[i32; 32] { + let ret = unsafe { sys::cv_SparseMat_Node_propIdx_const(self.as_raw_SparseMat_Node()) }; + let ret = unsafe { ret.as_ref() }.expect("Function returned null pointer"); + ret + } + + } + + /// Mutable methods for [core::SparseMat_Node] + pub trait SparseMat_NodeTrait: core::SparseMat_NodeTraitConst { + fn as_raw_mut_SparseMat_Node(&mut self) -> *mut c_void; + + /// hash value + #[inline] + fn set_hashval(&mut self, val: size_t) { + let ret = unsafe { sys::cv_SparseMat_Node_propHashval_const_size_t(self.as_raw_mut_SparseMat_Node(), val) }; + ret + } + + /// index of the next node in the same hash table entry + #[inline] + fn set_next(&mut self, val: size_t) { + let ret = unsafe { sys::cv_SparseMat_Node_propNext_const_size_t(self.as_raw_mut_SparseMat_Node(), val) }; + ret + } + + /// index of the matrix element + #[inline] + fn idx_mut(&mut self) -> &mut [i32; 32] { + let ret = unsafe { sys::cv_SparseMat_Node_propIdx(self.as_raw_mut_SparseMat_Node()) }; + let ret = unsafe { ret.as_mut() }.expect("Function returned null pointer"); + ret + } + + } + + impl Default for SparseMat_Node { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SparseMat_Node { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SparseMat_Node") + .field("hashval", &core::SparseMat_NodeTraitConst::hashval(self)) + .field("next", &core::SparseMat_NodeTraitConst::next(self)) + .field("idx", &core::SparseMat_NodeTraitConst::idx(self)) + .finish() + } + } + + impl core::SparseMat_NodeTraitConst for SparseMat_Node { + #[inline] fn as_raw_SparseMat_Node(&self) -> *const c_void { self.as_raw() } + } + + impl core::SparseMat_NodeTrait for SparseMat_Node { + #[inline] fn as_raw_mut_SparseMat_Node(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SparseMat_Node, core::SparseMat_NodeTraitConst, as_raw_SparseMat_Node, core::SparseMat_NodeTrait, as_raw_mut_SparseMat_Node } + + /// Read-Only Sparse Matrix Iterator. + /// + /// Here is how to use the iterator to compute the sum of floating-point sparse matrix elements: + /// + /// \code + /// SparseMatConstIterator it = m.begin(), it_end = m.end(); + /// double s = 0; + /// CV_Assert( m.type() == CV_32F ); + /// for( ; it != it_end; ++it ) + /// s += it.value(); + /// \endcode + pub struct SparseMatConstIterator { + ptr: *mut c_void, + } + + opencv_type_boxed! { SparseMatConstIterator } + + impl Drop for SparseMatConstIterator { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_SparseMatConstIterator_delete(self.as_raw_mut_SparseMatConstIterator()) }; + } + } + + unsafe impl Send for SparseMatConstIterator {} + + /// Constant methods for [core::SparseMatConstIterator] + pub trait SparseMatConstIteratorTraitConst { + fn as_raw_SparseMatConstIterator(&self) -> *const c_void; + + #[inline] + fn m(&self) -> core::SparseMat { + let ret = unsafe { sys::cv_SparseMatConstIterator_propM_const(self.as_raw_SparseMatConstIterator()) }; + let ret = unsafe { core::SparseMat::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn hashidx(&self) -> size_t { + let ret = unsafe { sys::cv_SparseMatConstIterator_propHashidx_const(self.as_raw_SparseMatConstIterator()) }; + ret + } + + #[inline] + fn ptr(&self) -> *const u8 { + let ret = unsafe { sys::cv_SparseMatConstIterator_propPtr_const(self.as_raw_SparseMatConstIterator()) }; + ret + } + + /// returns the current node of the sparse matrix. it.node->idx is the current element index + #[inline] + fn node(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMatConstIterator_node_const(self.as_raw_SparseMatConstIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::SparseMat_Node::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Mutable methods for [core::SparseMatConstIterator] + pub trait SparseMatConstIteratorTrait: core::SparseMatConstIteratorTraitConst { + fn as_raw_mut_SparseMatConstIterator(&mut self) -> *mut c_void; + + #[inline] + fn set_hashidx(&mut self, val: size_t) { + let ret = unsafe { sys::cv_SparseMatConstIterator_propHashidx_const_size_t(self.as_raw_mut_SparseMatConstIterator(), val) }; + ret + } + + #[inline] + fn ptr_mut(&mut self) -> *mut u8 { + let ret = unsafe { sys::cv_SparseMatConstIterator_propPtr(self.as_raw_mut_SparseMatConstIterator()) }; + ret + } + + #[inline] + unsafe fn set_ptr(&mut self, val: *const u8) { + let ret = unsafe { sys::cv_SparseMatConstIterator_propPtr_unsigned_charX(self.as_raw_mut_SparseMatConstIterator(), val) }; + ret + } + + /// the assignment operator + #[inline] + fn set(&mut self, it: &impl core::SparseMatConstIteratorTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMatConstIterator_operatorST_const_SparseMatConstIteratorR(self.as_raw_mut_SparseMatConstIterator(), it.as_raw_SparseMatConstIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// moves iterator to the next element + #[inline] + fn incr(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMatConstIterator_operatorAA(self.as_raw_mut_SparseMatConstIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::SparseMatConstIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + /// moves iterator to the element after the last element + #[inline] + fn seek_end(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMatConstIterator_seekEnd(self.as_raw_mut_SparseMatConstIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for SparseMatConstIterator { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SparseMatConstIterator") + .field("m", &core::SparseMatConstIteratorTraitConst::m(self)) + .field("hashidx", &core::SparseMatConstIteratorTraitConst::hashidx(self)) + .field("ptr", &core::SparseMatConstIteratorTraitConst::ptr(self)) + .finish() + } + } + + impl core::SparseMatConstIteratorTraitConst for SparseMatConstIterator { + #[inline] fn as_raw_SparseMatConstIterator(&self) -> *const c_void { self.as_raw() } + } + + impl core::SparseMatConstIteratorTrait for SparseMatConstIterator { + #[inline] fn as_raw_mut_SparseMatConstIterator(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SparseMatConstIterator, core::SparseMatConstIteratorTraitConst, as_raw_SparseMatConstIterator, core::SparseMatConstIteratorTrait, as_raw_mut_SparseMatConstIterator } + + /// Read-write Sparse Matrix Iterator + /// + /// The class is similar to cv::SparseMatConstIterator, + /// but can be used for in-place modification of the matrix elements. + pub struct SparseMatIterator { + ptr: *mut c_void, + } + + opencv_type_boxed! { SparseMatIterator } + + impl Drop for SparseMatIterator { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_SparseMatIterator_delete(self.as_raw_mut_SparseMatIterator()) }; + } + } + + unsafe impl Send for SparseMatIterator {} + + /// Constant methods for [core::SparseMatIterator] + pub trait SparseMatIteratorTraitConst: core::SparseMatConstIteratorTraitConst { + fn as_raw_SparseMatIterator(&self) -> *const c_void; + + /// returns pointer to the current sparse matrix node. it.node->idx is the index of the current element (do not modify it!) + #[inline] + fn node(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMatIterator_node_const(self.as_raw_SparseMatIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::SparseMat_Node::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Mutable methods for [core::SparseMatIterator] + pub trait SparseMatIteratorTrait: core::SparseMatConstIteratorTrait + core::SparseMatIteratorTraitConst { + fn as_raw_mut_SparseMatIterator(&mut self) -> *mut c_void; + + /// the assignment operator + #[inline] + fn set(&mut self, it: &impl core::SparseMatIteratorTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMatIterator_operatorST_const_SparseMatIteratorR(self.as_raw_mut_SparseMatIterator(), it.as_raw_SparseMatIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// moves iterator to the next element + #[inline] + fn incr(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_SparseMatIterator_operatorAA(self.as_raw_mut_SparseMatIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::SparseMatIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl std::fmt::Debug for SparseMatIterator { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SparseMatIterator") + .field("m", &core::SparseMatConstIteratorTraitConst::m(self)) + .field("hashidx", &core::SparseMatConstIteratorTraitConst::hashidx(self)) + .field("ptr", &core::SparseMatConstIteratorTraitConst::ptr(self)) + .finish() + } + } + + boxed_cast_base! { SparseMatIterator, core::SparseMatConstIterator, cv_SparseMatIterator_to_SparseMatConstIterator } + + impl core::SparseMatConstIteratorTraitConst for SparseMatIterator { + #[inline] fn as_raw_SparseMatConstIterator(&self) -> *const c_void { self.as_raw() } + } + + impl core::SparseMatConstIteratorTrait for SparseMatIterator { + #[inline] fn as_raw_mut_SparseMatConstIterator(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SparseMatIterator, core::SparseMatConstIteratorTraitConst, as_raw_SparseMatConstIterator, core::SparseMatConstIteratorTrait, as_raw_mut_SparseMatConstIterator } + + impl core::SparseMatIteratorTraitConst for SparseMatIterator { + #[inline] fn as_raw_SparseMatIterator(&self) -> *const c_void { self.as_raw() } + } + + impl core::SparseMatIteratorTrait for SparseMatIterator { + #[inline] fn as_raw_mut_SparseMatIterator(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SparseMatIterator, core::SparseMatIteratorTraitConst, as_raw_SparseMatIterator, core::SparseMatIteratorTrait, as_raw_mut_SparseMatIterator } + + /// The class defining termination criteria for iterative algorithms. + /// + /// You can initialize it by default constructor and then override any parameters, or the structure may + /// be fully initialized using the advanced variant of the constructor. + #[repr(C)] + #[derive(Copy, Clone, Debug, PartialEq)] + pub struct TermCriteria { + /// the type of termination criteria: COUNT, EPS or COUNT + EPS + pub typ: i32, + /// the maximum number of iterations/elements + pub max_count: i32, + /// the desired accuracy + pub epsilon: f64, + } + + opencv_type_simple! { core::TermCriteria } + + impl TermCriteria { + #[inline] + pub fn is_valid(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TermCriteria_isValid_const(self.as_raw_TermCriteria(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// a Class to measure passing time. + /// + /// The class computes passing time by counting the number of ticks per second. That is, the following code computes the + /// execution time in seconds: + /// [TickMeter_total](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_various.cpp#L1) + /// + /// It is also possible to compute the average time over multiple runs: + /// [TickMeter_average](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/core_various.cpp#L1) + /// ## See also + /// getTickCount, getTickFrequency + pub struct TickMeter { + ptr: *mut c_void, + } + + opencv_type_boxed! { TickMeter } + + impl Drop for TickMeter { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_TickMeter_delete(self.as_raw_mut_TickMeter()) }; + } + } + + unsafe impl Send for TickMeter {} + + impl TickMeter { + /// the default constructor + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_TickMeter(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::TickMeter::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::TickMeter] + pub trait TickMeterTraitConst { + fn as_raw_TickMeter(&self) -> *const c_void; + + /// returns counted ticks. + #[inline] + fn get_time_ticks(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_getTimeTicks_const(self.as_raw_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns passed time in microseconds. + #[inline] + fn get_time_micro(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_getTimeMicro_const(self.as_raw_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns passed time in milliseconds. + #[inline] + fn get_time_milli(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_getTimeMilli_const(self.as_raw_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns passed time in seconds. + #[inline] + fn get_time_sec(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_getTimeSec_const(self.as_raw_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns counted ticks of the last iteration. + #[inline] + fn get_last_time_ticks(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_getLastTimeTicks_const(self.as_raw_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns passed time of the last iteration in microseconds. + #[inline] + fn get_last_time_micro(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_getLastTimeMicro_const(self.as_raw_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns passed time of the last iteration in milliseconds. + #[inline] + fn get_last_time_milli(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_getLastTimeMilli_const(self.as_raw_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns passed time of the last iteration in seconds. + #[inline] + fn get_last_time_sec(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_getLastTimeSec_const(self.as_raw_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns internal counter value. + #[inline] + fn get_counter(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_getCounter_const(self.as_raw_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns average FPS (frames per second) value. + #[inline] + fn get_fps(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_getFPS_const(self.as_raw_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns average time in seconds + #[inline] + fn get_avg_time_sec(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_getAvgTimeSec_const(self.as_raw_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns average time in milliseconds + #[inline] + fn get_avg_time_milli(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_getAvgTimeMilli_const(self.as_raw_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::TickMeter] + pub trait TickMeterTrait: core::TickMeterTraitConst { + fn as_raw_mut_TickMeter(&mut self) -> *mut c_void; + + /// starts counting ticks. + #[inline] + fn start(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_start(self.as_raw_mut_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// stops counting ticks. + #[inline] + fn stop(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_stop(self.as_raw_mut_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// resets internal values. + #[inline] + fn reset(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_TickMeter_reset(self.as_raw_mut_TickMeter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for TickMeter { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("TickMeter") + .finish() + } + } + + impl core::TickMeterTraitConst for TickMeter { + #[inline] fn as_raw_TickMeter(&self) -> *const c_void { self.as_raw() } + } + + impl core::TickMeterTrait for TickMeter { + #[inline] fn as_raw_mut_TickMeter(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TickMeter, core::TickMeterTraitConst, as_raw_TickMeter, core::TickMeterTrait, as_raw_mut_TickMeter } + + /// @todo document + pub struct UMat { + ptr: *mut c_void, + } + + opencv_type_boxed! { UMat } + + impl Drop for UMat { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_UMat_delete(self.as_raw_mut_UMat()) }; + } + } + + unsafe impl Send for UMat {} + + impl UMat { + /// constructs a square diagonal matrix which main diagonal is vector "d" + #[inline] + #[must_use] + pub fn diag_flags(d: &impl core::UMatTraitConst, usage_flags: core::UMatUsageFlags) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_diag_const_UMatR_UMatUsageFlags(d.as_raw_UMat(), usage_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn diag(d: &impl core::UMatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_diag_const_UMatR(d.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Matlab-style matrix initialization + #[inline] + #[must_use] + pub fn zeros_flags(rows: i32, cols: i32, typ: i32, usage_flags: core::UMatUsageFlags) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_zeros_int_int_int_UMatUsageFlags(rows, cols, typ, usage_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn zeros_size_flags(size: core::Size, typ: i32, usage_flags: core::UMatUsageFlags) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_zeros_Size_int_UMatUsageFlags(&size, typ, usage_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn zeros_nd_flags(sz: &[i32], typ: i32, usage_flags: core::UMatUsageFlags) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_zeros_int_const_intX_int_UMatUsageFlags(sz.len().try_into()?, sz.as_ptr(), typ, usage_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn zeros(rows: i32, cols: i32, typ: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_zeros_int_int_int(rows, cols, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn zeros_size(size: core::Size, typ: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_zeros_Size_int(&size, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn zeros_nd(sz: &[i32], typ: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_zeros_int_const_intX_int(sz.len().try_into()?, sz.as_ptr(), typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn ones_flags(rows: i32, cols: i32, typ: i32, usage_flags: core::UMatUsageFlags) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_ones_int_int_int_UMatUsageFlags(rows, cols, typ, usage_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn ones_size_flags(size: core::Size, typ: i32, usage_flags: core::UMatUsageFlags) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_ones_Size_int_UMatUsageFlags(&size, typ, usage_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn ones_nd_flags(sz: &[i32], typ: i32, usage_flags: core::UMatUsageFlags) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_ones_int_const_intX_int_UMatUsageFlags(sz.len().try_into()?, sz.as_ptr(), typ, usage_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn ones(rows: i32, cols: i32, typ: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_ones_int_int_int(rows, cols, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn ones_size(size: core::Size, typ: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_ones_Size_int(&size, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn ones_nd(sz: &[i32], typ: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_ones_int_const_intX_int(sz.len().try_into()?, sz.as_ptr(), typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn eye_flags(rows: i32, cols: i32, typ: i32, usage_flags: core::UMatUsageFlags) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_eye_int_int_int_UMatUsageFlags(rows, cols, typ, usage_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn eye_size_flags(size: core::Size, typ: i32, usage_flags: core::UMatUsageFlags) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_eye_Size_int_UMatUsageFlags(&size, typ, usage_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn eye(rows: i32, cols: i32, typ: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_eye_int_int_int(rows, cols, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + #[must_use] + pub fn eye_size(size: core::Size, typ: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_eye_Size_int(&size, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::UMat] + pub trait UMatTraitConst { + fn as_raw_UMat(&self) -> *const c_void; + + /// ! includes several bit-fields: + /// - the magic signature + /// - continuity flag + /// - depth + /// - number of channels + #[inline] + fn flags(&self) -> i32 { + let ret = unsafe { sys::cv_UMat_propFlags_const(self.as_raw_UMat()) }; + ret + } + + /// the matrix dimensionality, >= 2 + #[inline] + fn dims(&self) -> i32 { + let ret = unsafe { sys::cv_UMat_propDims_const(self.as_raw_UMat()) }; + ret + } + + /// number of rows in the matrix; -1 when the matrix has more than 2 dimensions + #[inline] + fn rows(&self) -> i32 { + let ret = unsafe { sys::cv_UMat_propRows_const(self.as_raw_UMat()) }; + ret + } + + /// number of columns in the matrix; -1 when the matrix has more than 2 dimensions + #[inline] + fn cols(&self) -> i32 { + let ret = unsafe { sys::cv_UMat_propCols_const(self.as_raw_UMat()) }; + ret + } + + /// usage flags for allocator; recommend do not set directly, instead set during construct/create/getUMat + #[inline] + fn usage_flags(&self) -> core::UMatUsageFlags { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_propUsageFlags_const(self.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + /// offset of the submatrix (or 0) + #[inline] + fn offset(&self) -> size_t { + let ret = unsafe { sys::cv_UMat_propOffset_const(self.as_raw_UMat()) }; + ret + } + + /// dimensional size of the matrix; accessible in various formats + #[inline] + fn mat_size(&self) -> core::MatSize<'_> { + let ret = unsafe { sys::cv_UMat_propSize_const(self.as_raw_UMat()) }; + let ret = unsafe { core::MatSize::<'_>::opencv_from_extern(ret) }; + ret + } + + /// number of bytes each matrix element/row/plane/dimension occupies + #[inline] + fn mat_step(&self) -> core::MatStep { + let ret = unsafe { sys::cv_UMat_propStep_const(self.as_raw_UMat()) }; + let ret = unsafe { core::MatStep::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn get_mat(&self, flags: core::AccessFlag) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_getMat_const_AccessFlag(self.as_raw_UMat(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// returns a new matrix header for the specified row + #[inline] + fn row(&self, y: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_row_const_int(self.as_raw_UMat(), y, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// returns a new matrix header for the specified column + #[inline] + fn col(&self, x: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_col_const_int(self.as_raw_UMat(), x, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ... for the specified row span + #[inline] + fn row_bounds(&self, startrow: i32, endrow: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_rowRange_const_int_int(self.as_raw_UMat(), startrow, endrow, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn row_range(&self, r: &impl core::RangeTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_rowRange_const_const_RangeR(self.as_raw_UMat(), r.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ... for the specified column span + #[inline] + fn col_bounds(&self, startcol: i32, endcol: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_colRange_const_int_int(self.as_raw_UMat(), startcol, endcol, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn col_range(&self, r: &impl core::RangeTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_colRange_const_const_RangeR(self.as_raw_UMat(), r.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ... for the specified diagonal + /// (d=0 - the main diagonal, + /// >0 - a diagonal from the upper half, + /// <0 - a diagonal from the lower half) + /// + /// ## C++ default parameters + /// * d: 0 + #[inline] + fn diag(&self, d: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_diag_const_int(self.as_raw_UMat(), d, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ... for the specified diagonal + /// (d=0 - the main diagonal, + /// >0 - a diagonal from the upper half, + /// <0 - a diagonal from the lower half) + /// + /// ## Note + /// This alternative version of [UMatTraitConst::diag] function uses the following default values for its arguments: + /// * d: 0 + #[inline] + fn diag_def(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_diag_const(self.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// returns deep copy of the matrix, i.e. the data is copied + #[inline] + #[must_use] + fn try_clone(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_clone_const(self.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// copies the matrix content to "m". + #[inline] + fn copy_to(&self, m: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_copyTo_const_const__OutputArrayR(self.as_raw_UMat(), m.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// copies those matrix elements to "m" that are marked with non-zero mask elements. + #[inline] + fn copy_to_masked(&self, m: &mut impl ToOutputArray, mask: &impl ToInputArray) -> Result<()> { + output_array_arg!(m); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_copyTo_const_const__OutputArrayR_const__InputArrayR(self.as_raw_UMat(), m.as_raw__OutputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// converts matrix to another datatype with optional scaling. See cvConvertScale. + /// + /// ## C++ default parameters + /// * alpha: 1 + /// * beta: 0 + #[inline] + fn convert_to(&self, m: &mut impl ToOutputArray, rtype: i32, alpha: f64, beta: f64) -> Result<()> { + output_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_convertTo_const_const__OutputArrayR_int_double_double(self.as_raw_UMat(), m.as_raw__OutputArray(), rtype, alpha, beta, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// converts matrix to another datatype with optional scaling. See cvConvertScale. + /// + /// ## Note + /// This alternative version of [UMatTraitConst::convert_to] function uses the following default values for its arguments: + /// * alpha: 1 + /// * beta: 0 + #[inline] + fn convert_to_def(&self, m: &mut impl ToOutputArray, rtype: i32) -> Result<()> { + output_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_convertTo_const_const__OutputArrayR_int(self.as_raw_UMat(), m.as_raw__OutputArray(), rtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * typ: -1 + #[inline] + fn assign_to(&self, m: &mut impl core::UMatTrait, typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_assignTo_const_UMatR_int(self.as_raw_UMat(), m.as_raw_mut_UMat(), typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [UMatTraitConst::assign_to] function uses the following default values for its arguments: + /// * typ: -1 + #[inline] + fn assign_to_def(&self, m: &mut impl core::UMatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_assignTo_const_UMatR(self.as_raw_UMat(), m.as_raw_mut_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// creates alternative matrix header for the same data, with different + /// + /// ## C++ default parameters + /// * rows: 0 + #[inline] + fn reshape(&self, cn: i32, rows: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_reshape_const_int_int(self.as_raw_UMat(), cn, rows, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// creates alternative matrix header for the same data, with different + /// + /// ## Note + /// This alternative version of [UMatTraitConst::reshape] function uses the following default values for its arguments: + /// * rows: 0 + #[inline] + fn reshape_def(&self, cn: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_reshape_const_int(self.as_raw_UMat(), cn, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn reshape_nd(&self, cn: i32, newsz: &[i32]) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_reshape_const_int_int_const_intX(self.as_raw_UMat(), cn, newsz.len().try_into()?, newsz.as_ptr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// matrix transposition by means of matrix expressions + #[inline] + fn t(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_t_const(self.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// matrix inversion by means of matrix expressions + /// + /// ## C++ default parameters + /// * method: DECOMP_LU + #[inline] + fn inv(&self, method: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_inv_const_int(self.as_raw_UMat(), method, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// matrix inversion by means of matrix expressions + /// + /// ## Note + /// This alternative version of [UMatTraitConst::inv] function uses the following default values for its arguments: + /// * method: DECOMP_LU + #[inline] + fn inv_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_inv_const(self.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// per-element matrix multiplication by means of matrix expressions + /// + /// ## C++ default parameters + /// * scale: 1 + #[inline] + fn mul(&self, m: &impl ToInputArray, scale: f64) -> Result { + input_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_mul_const_const__InputArrayR_double(self.as_raw_UMat(), m.as_raw__InputArray(), scale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// per-element matrix multiplication by means of matrix expressions + /// + /// ## Note + /// This alternative version of [UMatTraitConst::mul] function uses the following default values for its arguments: + /// * scale: 1 + #[inline] + fn mul_def(&self, m: &impl ToInputArray) -> Result { + input_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_mul_const_const__InputArrayR(self.as_raw_UMat(), m.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// computes dot-product + #[inline] + fn dot(&self, m: &impl ToInputArray) -> Result { + input_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_dot_const_const__InputArrayR(self.as_raw_UMat(), m.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// locates matrix header within a parent matrix. See below + #[inline] + fn locate_roi(&self, whole_size: &mut core::Size, ofs: &mut core::Point) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_locateROI_const_SizeR_PointR(self.as_raw_UMat(), whole_size, ofs, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// extracts a rectangular sub-matrix + #[inline] + fn rowscols(&self, mut row_range: impl core::RangeTrait, mut col_range: impl core::RangeTrait) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_operator___const_Range_Range(self.as_raw_UMat(), row_range.as_raw_mut_Range(), col_range.as_raw_mut_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn roi(&self, roi: core::Rect) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_operator___const_const_RectR(self.as_raw_UMat(), &roi, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn ranges(&self, ranges: &core::Vector) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_operator___const_const_vectorLRangeGR(self.as_raw_UMat(), ranges.as_raw_VectorOfRange(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// returns true iff the matrix data is continuous + #[inline] + fn is_continuous(&self) -> bool { + let ret = unsafe { sys::cv_UMat_isContinuous_const(self.as_raw_UMat()) }; + ret + } + + /// returns true if the matrix is a submatrix of another matrix + #[inline] + fn is_submatrix(&self) -> bool { + let ret = unsafe { sys::cv_UMat_isSubmatrix_const(self.as_raw_UMat()) }; + ret + } + + /// returns element size in bytes, + #[inline] + fn elem_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_elemSize_const(self.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns the size of element channel in bytes. + #[inline] + fn elem_size1(&self) -> size_t { + let ret = unsafe { sys::cv_UMat_elemSize1_const(self.as_raw_UMat()) }; + ret + } + + /// returns element type, similar to CV_MAT_TYPE(cvmat->type) + #[inline] + fn typ(&self) -> i32 { + let ret = unsafe { sys::cv_UMat_type_const(self.as_raw_UMat()) }; + ret + } + + /// returns element type, similar to CV_MAT_DEPTH(cvmat->type) + #[inline] + fn depth(&self) -> i32 { + let ret = unsafe { sys::cv_UMat_depth_const(self.as_raw_UMat()) }; + ret + } + + /// returns element type, similar to CV_MAT_CN(cvmat->type) + #[inline] + fn channels(&self) -> i32 { + let ret = unsafe { sys::cv_UMat_channels_const(self.as_raw_UMat()) }; + ret + } + + /// returns step/elemSize1() + /// + /// ## C++ default parameters + /// * i: 0 + #[inline] + fn step1(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_step1_const_int(self.as_raw_UMat(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns step/elemSize1() + /// + /// ## Note + /// This alternative version of [UMatTraitConst::step1] function uses the following default values for its arguments: + /// * i: 0 + #[inline] + fn step1_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_step1_const(self.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns true if matrix data is NULL + #[inline] + fn empty(&self) -> bool { + let ret = unsafe { sys::cv_UMat_empty_const(self.as_raw_UMat()) }; + ret + } + + /// returns the total number of matrix elements + #[inline] + fn total(&self) -> size_t { + let ret = unsafe { sys::cv_UMat_total_const(self.as_raw_UMat()) }; + ret + } + + /// returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise + /// + /// ## C++ default parameters + /// * depth: -1 + /// * require_continuous: true + #[inline] + fn check_vector(&self, elem_channels: i32, depth: i32, require_continuous: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_checkVector_const_int_int_bool(self.as_raw_UMat(), elem_channels, depth, require_continuous, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise + /// + /// ## Note + /// This alternative version of [UMatTraitConst::check_vector] function uses the following default values for its arguments: + /// * depth: -1 + /// * require_continuous: true + #[inline] + fn check_vector_def(&self, elem_channels: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_checkVector_const_int(self.as_raw_UMat(), elem_channels, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ! Returns the OpenCL buffer handle on which UMat operates on. + /// The UMat instance should be kept alive during the use of the handle to prevent the buffer to be + /// returned to the OpenCV buffer pool. + #[inline] + fn handle(&self, access_flags: core::AccessFlag) -> Result<*mut c_void> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_handle_const_AccessFlag(self.as_raw_UMat(), access_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn ndoffset(&self, ofs: &mut size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_ndoffset_const_size_tX(self.as_raw_UMat(), ofs, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_size_const(self.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::UMat] + pub trait UMatTrait: core::UMatTraitConst { + fn as_raw_mut_UMat(&mut self) -> *mut c_void; + + /// ! includes several bit-fields: + /// - the magic signature + /// - continuity flag + /// - depth + /// - number of channels + #[inline] + fn set_flags(&mut self, val: i32) { + let ret = unsafe { sys::cv_UMat_propFlags_const_int(self.as_raw_mut_UMat(), val) }; + ret + } + + /// the matrix dimensionality, >= 2 + #[inline] + fn set_dims(&mut self, val: i32) { + let ret = unsafe { sys::cv_UMat_propDims_const_int(self.as_raw_mut_UMat(), val) }; + ret + } + + /// number of rows in the matrix; -1 when the matrix has more than 2 dimensions + #[inline] + fn set_rows(&mut self, val: i32) { + let ret = unsafe { sys::cv_UMat_propRows_const_int(self.as_raw_mut_UMat(), val) }; + ret + } + + /// number of columns in the matrix; -1 when the matrix has more than 2 dimensions + #[inline] + fn set_cols(&mut self, val: i32) { + let ret = unsafe { sys::cv_UMat_propCols_const_int(self.as_raw_mut_UMat(), val) }; + ret + } + + /// usage flags for allocator; recommend do not set directly, instead set during construct/create/getUMat + #[inline] + fn set_usage_flags(&mut self, val: core::UMatUsageFlags) { + let ret = unsafe { sys::cv_UMat_propUsageFlags_const_UMatUsageFlags(self.as_raw_mut_UMat(), val) }; + ret + } + + /// black-box container of UMat data + #[inline] + fn u(&mut self) -> core::UMatData { + let ret = unsafe { sys::cv_UMat_propU(self.as_raw_mut_UMat()) }; + let ret = unsafe { core::UMatData::opencv_from_extern(ret) }; + ret + } + + /// black-box container of UMat data + #[inline] + fn set_u(&mut self, val: &impl core::UMatDataTraitConst) { + let ret = unsafe { sys::cv_UMat_propU_UMatDataX(self.as_raw_mut_UMat(), val.as_raw_UMatData()) }; + ret + } + + /// offset of the submatrix (or 0) + #[inline] + fn set_offset(&mut self, val: size_t) { + let ret = unsafe { sys::cv_UMat_propOffset_const_size_t(self.as_raw_mut_UMat(), val) }; + ret + } + + /// dimensional size of the matrix; accessible in various formats + #[inline] + fn set_mat_size(&mut self, val: core::MatSize<'_>) { + let ret = unsafe { sys::cv_UMat_propSize_const_MatSize(self.as_raw_mut_UMat(), val.as_raw_MatSize()) }; + ret + } + + /// assignment operators + #[inline] + fn set(&mut self, m: &impl core::UMatTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_operatorST_const_UMatR(self.as_raw_mut_UMat(), m.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns a new matrix header for the specified row + #[inline] + fn row_mut(&mut self, y: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_row_int(self.as_raw_mut_UMat(), y, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// returns a new matrix header for the specified column + #[inline] + fn col_mut(&mut self, x: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_col_int(self.as_raw_mut_UMat(), x, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ... for the specified row span + #[inline] + fn row_bounds_mut(&mut self, startrow: i32, endrow: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_rowRange_int_int(self.as_raw_mut_UMat(), startrow, endrow, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn row_range_mut(&mut self, r: &impl core::RangeTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_rowRange_const_RangeR(self.as_raw_mut_UMat(), r.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ... for the specified column span + #[inline] + fn col_bounds_mut(&mut self, startcol: i32, endcol: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_colRange_int_int(self.as_raw_mut_UMat(), startcol, endcol, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn col_range_mut(&mut self, r: &impl core::RangeTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_colRange_const_RangeR(self.as_raw_mut_UMat(), r.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ... for the specified diagonal + /// (d=0 - the main diagonal, + /// >0 - a diagonal from the upper half, + /// <0 - a diagonal from the lower half) + /// + /// ## Note + /// This alternative version of [UMatTraitConst::diag] function uses the following default values for its arguments: + /// * d: 0 + #[inline] + fn diag_def_mut(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_diag(self.as_raw_mut_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ... for the specified diagonal + /// (d=0 - the main diagonal, + /// >0 - a diagonal from the upper half, + /// <0 - a diagonal from the lower half) + /// + /// ## C++ default parameters + /// * d: 0 + #[inline] + fn diag_mut(&mut self, d: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_diag_int(self.as_raw_mut_UMat(), d, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// sets every matrix element to s + #[inline] + fn set_scalar(&mut self, s: core::Scalar) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_operatorST_const_ScalarR(self.as_raw_mut_UMat(), &s, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// sets some of the matrix elements to s, according to the mask + /// + /// ## C++ default parameters + /// * mask: noArray() + #[inline] + fn set_to(&mut self, value: &impl ToInputArray, mask: &impl ToInputArray) -> Result { + input_array_arg!(value); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_setTo_const__InputArrayR_const__InputArrayR(self.as_raw_mut_UMat(), value.as_raw__InputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// sets some of the matrix elements to s, according to the mask + /// + /// ## Note + /// This alternative version of [UMatTrait::set_to] function uses the following default values for its arguments: + /// * mask: noArray() + #[inline] + fn set_to_def(&mut self, value: &impl ToInputArray) -> Result { + input_array_arg!(value); + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_setTo_const__InputArrayR(self.as_raw_mut_UMat(), value.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// creates alternative matrix header for the same data, with different + /// + /// ## Note + /// This alternative version of [UMatTraitConst::reshape] function uses the following default values for its arguments: + /// * rows: 0 + #[inline] + fn reshape_def_mut(&mut self, cn: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_reshape_int(self.as_raw_mut_UMat(), cn, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// creates alternative matrix header for the same data, with different + /// + /// ## C++ default parameters + /// * rows: 0 + #[inline] + fn reshape_mut(&mut self, cn: i32, rows: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_reshape_int_int(self.as_raw_mut_UMat(), cn, rows, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn reshape_nd_mut(&mut self, cn: i32, newsz: &[i32]) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_reshape_int_int_const_intX(self.as_raw_mut_UMat(), cn, newsz.len().try_into()?, newsz.as_ptr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// allocates new matrix data unless the matrix already has specified size and type. + /// + /// ## C++ default parameters + /// * usage_flags: USAGE_DEFAULT + #[inline] + unsafe fn create_rows_cols(&mut self, rows: i32, cols: i32, typ: i32, usage_flags: core::UMatUsageFlags) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_create_int_int_int_UMatUsageFlags(self.as_raw_mut_UMat(), rows, cols, typ, usage_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// allocates new matrix data unless the matrix already has specified size and type. + /// + /// ## Note + /// This alternative version of [UMatTrait::create_rows_cols] function uses the following default values for its arguments: + /// * usage_flags: USAGE_DEFAULT + #[inline] + fn create_rows_cols_def(&mut self, rows: i32, cols: i32, typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_create_int_int_int(self.as_raw_mut_UMat(), rows, cols, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * usage_flags: USAGE_DEFAULT + #[inline] + unsafe fn create_size(&mut self, size: core::Size, typ: i32, usage_flags: core::UMatUsageFlags) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_create_Size_int_UMatUsageFlags(self.as_raw_mut_UMat(), &size, typ, usage_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [UMatTrait::create_size] function uses the following default values for its arguments: + /// * usage_flags: USAGE_DEFAULT + #[inline] + fn create_size_def(&mut self, size: core::Size, typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_create_Size_int(self.as_raw_mut_UMat(), &size, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * usage_flags: USAGE_DEFAULT + #[inline] + unsafe fn create_nd(&mut self, sizes: &[i32], typ: i32, usage_flags: core::UMatUsageFlags) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_create_int_const_intX_int_UMatUsageFlags(self.as_raw_mut_UMat(), sizes.len().try_into()?, sizes.as_ptr(), typ, usage_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [UMatTrait::create_nd] function uses the following default values for its arguments: + /// * usage_flags: USAGE_DEFAULT + #[inline] + fn create_nd_def(&mut self, sizes: &[i32], typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_create_int_const_intX_int(self.as_raw_mut_UMat(), sizes.len().try_into()?, sizes.as_ptr(), typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * usage_flags: USAGE_DEFAULT + #[inline] + unsafe fn create_nd_vec(&mut self, sizes: &core::Vector, typ: i32, usage_flags: core::UMatUsageFlags) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_create_const_vectorLintGR_int_UMatUsageFlags(self.as_raw_mut_UMat(), sizes.as_raw_VectorOfi32(), typ, usage_flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [UMatTrait::create_nd_vec] function uses the following default values for its arguments: + /// * usage_flags: USAGE_DEFAULT + #[inline] + fn create_nd_vec_def(&mut self, sizes: &core::Vector, typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_create_const_vectorLintGR_int(self.as_raw_mut_UMat(), sizes.as_raw_VectorOfi32(), typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// increases the reference counter; use with care to avoid memleaks + #[inline] + unsafe fn addref(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_addref(self.as_raw_mut_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// decreases reference counter; + #[inline] + unsafe fn release(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_release(self.as_raw_mut_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// deallocates the matrix data + #[inline] + fn deallocate(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_deallocate(self.as_raw_mut_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// moves/resizes the current matrix ROI inside the parent matrix. + #[inline] + fn adjust_roi(&mut self, dtop: i32, dbottom: i32, dleft: i32, dright: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_adjustROI_int_int_int_int(self.as_raw_mut_UMat(), dtop, dbottom, dleft, dright, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// extracts a rectangular sub-matrix + #[inline] + fn rowscols_mut(&mut self, mut row_range: impl core::RangeTrait, mut col_range: impl core::RangeTrait) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_operator___Range_Range(self.as_raw_mut_UMat(), row_range.as_raw_mut_Range(), col_range.as_raw_mut_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn roi_mut(&mut self, roi: core::Rect) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_operator___const_RectR(self.as_raw_mut_UMat(), &roi, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn ranges_mut(&mut self, ranges: &core::Vector) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_operator___const_vectorLRangeGR(self.as_raw_mut_UMat(), ranges.as_raw_VectorOfRange(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::UMat>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn set_1(&mut self, mut m: core::UMat) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_operatorST_UMatRR(self.as_raw_mut_UMat(), m.as_raw_mut_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// internal use method: updates the continuity flag + #[inline] + fn update_continuity_flag(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMat_updateContinuityFlag(self.as_raw_mut_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl Clone for UMat { + #[inline] + /// Calls try_clone() and panics if that fails + fn clone(&self) -> Self { + self.try_clone().expect("Cannot clone UMat") + } + } + + impl std::fmt::Debug for UMat { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("UMat") + .field("flags", &core::UMatTraitConst::flags(self)) + .field("dims", &core::UMatTraitConst::dims(self)) + .field("rows", &core::UMatTraitConst::rows(self)) + .field("cols", &core::UMatTraitConst::cols(self)) + .field("usage_flags", &core::UMatTraitConst::usage_flags(self)) + .field("offset", &core::UMatTraitConst::offset(self)) + .field("mat_size", &core::UMatTraitConst::mat_size(self)) + .field("mat_step", &core::UMatTraitConst::mat_step(self)) + .finish() + } + } + + impl core::UMatTraitConst for UMat { + #[inline] fn as_raw_UMat(&self) -> *const c_void { self.as_raw() } + } + + impl core::UMatTrait for UMat { + #[inline] fn as_raw_mut_UMat(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { UMat, core::UMatTraitConst, as_raw_UMat, core::UMatTrait, as_raw_mut_UMat } + + pub struct UMatData { + ptr: *mut c_void, + } + + opencv_type_boxed! { UMatData } + + impl Drop for UMatData { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_UMatData_delete(self.as_raw_mut_UMatData()) }; + } + } + + unsafe impl Send for UMatData {} + + /// Constant methods for [core::UMatData] + pub trait UMatDataTraitConst { + fn as_raw_UMatData(&self) -> *const c_void; + + #[inline] + fn urefcount(&self) -> i32 { + let ret = unsafe { sys::cv_UMatData_propUrefcount_const(self.as_raw_UMatData()) }; + ret + } + + #[inline] + fn refcount(&self) -> i32 { + let ret = unsafe { sys::cv_UMatData_propRefcount_const(self.as_raw_UMatData()) }; + ret + } + + #[inline] + fn data(&self) -> *const u8 { + let ret = unsafe { sys::cv_UMatData_propData_const(self.as_raw_UMatData()) }; + ret + } + + #[inline] + fn origdata(&self) -> *const u8 { + let ret = unsafe { sys::cv_UMatData_propOrigdata_const(self.as_raw_UMatData()) }; + ret + } + + #[inline] + fn size(&self) -> size_t { + let ret = unsafe { sys::cv_UMatData_propSize_const(self.as_raw_UMatData()) }; + ret + } + + #[inline] + fn flags(&self) -> core::UMatData_MemoryFlag { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMatData_propFlags_const(self.as_raw_UMatData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn allocator_flags_(&self) -> i32 { + let ret = unsafe { sys::cv_UMatData_propAllocatorFlags__const(self.as_raw_UMatData()) }; + ret + } + + #[inline] + fn mapcount(&self) -> i32 { + let ret = unsafe { sys::cv_UMatData_propMapcount_const(self.as_raw_UMatData()) }; + ret + } + + #[inline] + fn host_copy_obsolete(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMatData_hostCopyObsolete_const(self.as_raw_UMatData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn device_copy_obsolete(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMatData_deviceCopyObsolete_const(self.as_raw_UMatData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn device_mem_mapped(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMatData_deviceMemMapped_const(self.as_raw_UMatData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn copy_on_map(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMatData_copyOnMap_const(self.as_raw_UMatData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn temp_umat(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMatData_tempUMat_const(self.as_raw_UMatData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn temp_copied_umat(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMatData_tempCopiedUMat_const(self.as_raw_UMatData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::UMatData] + pub trait UMatDataTrait: core::UMatDataTraitConst { + fn as_raw_mut_UMatData(&mut self) -> *mut c_void; + + #[inline] + fn set_urefcount(&mut self, val: i32) { + let ret = unsafe { sys::cv_UMatData_propUrefcount_const_int(self.as_raw_mut_UMatData(), val) }; + ret + } + + #[inline] + fn set_refcount(&mut self, val: i32) { + let ret = unsafe { sys::cv_UMatData_propRefcount_const_int(self.as_raw_mut_UMatData(), val) }; + ret + } + + #[inline] + fn data_mut(&mut self) -> *mut u8 { + let ret = unsafe { sys::cv_UMatData_propData(self.as_raw_mut_UMatData()) }; + ret + } + + #[inline] + unsafe fn set_data(&mut self, val: *const u8) { + let ret = unsafe { sys::cv_UMatData_propData_unsigned_charX(self.as_raw_mut_UMatData(), val) }; + ret + } + + #[inline] + fn origdata_mut(&mut self) -> *mut u8 { + let ret = unsafe { sys::cv_UMatData_propOrigdata(self.as_raw_mut_UMatData()) }; + ret + } + + #[inline] + unsafe fn set_origdata(&mut self, val: *const u8) { + let ret = unsafe { sys::cv_UMatData_propOrigdata_unsigned_charX(self.as_raw_mut_UMatData(), val) }; + ret + } + + #[inline] + fn set_size(&mut self, val: size_t) { + let ret = unsafe { sys::cv_UMatData_propSize_const_size_t(self.as_raw_mut_UMatData(), val) }; + ret + } + + #[inline] + fn set_flags(&mut self, val: core::UMatData_MemoryFlag) { + let ret = unsafe { sys::cv_UMatData_propFlags_const_MemoryFlag(self.as_raw_mut_UMatData(), val) }; + ret + } + + #[inline] + fn handle(&mut self) -> *mut c_void { + let ret = unsafe { sys::cv_UMatData_propHandle(self.as_raw_mut_UMatData()) }; + ret + } + + #[inline] + unsafe fn set_handle(&mut self, val: *const c_void) { + let ret = unsafe { sys::cv_UMatData_propHandle_voidX(self.as_raw_mut_UMatData(), val) }; + ret + } + + #[inline] + fn userdata(&mut self) -> *mut c_void { + let ret = unsafe { sys::cv_UMatData_propUserdata(self.as_raw_mut_UMatData()) }; + ret + } + + #[inline] + unsafe fn set_userdata(&mut self, val: *const c_void) { + let ret = unsafe { sys::cv_UMatData_propUserdata_voidX(self.as_raw_mut_UMatData(), val) }; + ret + } + + #[inline] + fn set_allocator_flags_(&mut self, val: i32) { + let ret = unsafe { sys::cv_UMatData_propAllocatorFlags__const_int(self.as_raw_mut_UMatData(), val) }; + ret + } + + #[inline] + fn set_mapcount(&mut self, val: i32) { + let ret = unsafe { sys::cv_UMatData_propMapcount_const_int(self.as_raw_mut_UMatData(), val) }; + ret + } + + #[inline] + fn original_umat_data(&mut self) -> core::UMatData { + let ret = unsafe { sys::cv_UMatData_propOriginalUMatData(self.as_raw_mut_UMatData()) }; + let ret = unsafe { core::UMatData::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn set_original_umat_data(&mut self, val: &impl core::UMatDataTraitConst) { + let ret = unsafe { sys::cv_UMatData_propOriginalUMatData_UMatDataX(self.as_raw_mut_UMatData(), val.as_raw_UMatData()) }; + ret + } + + #[inline] + fn lock(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMatData_lock(self.as_raw_mut_UMatData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn unlock(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMatData_unlock(self.as_raw_mut_UMatData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn mark_host_copy_obsolete(&mut self, flag: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMatData_markHostCopyObsolete_bool(self.as_raw_mut_UMatData(), flag, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn mark_device_copy_obsolete(&mut self, flag: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMatData_markDeviceCopyObsolete_bool(self.as_raw_mut_UMatData(), flag, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn mark_device_mem_mapped(&mut self, flag: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_UMatData_markDeviceMemMapped_bool(self.as_raw_mut_UMatData(), flag, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for UMatData { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("UMatData") + .field("urefcount", &core::UMatDataTraitConst::urefcount(self)) + .field("refcount", &core::UMatDataTraitConst::refcount(self)) + .field("data", &core::UMatDataTraitConst::data(self)) + .field("origdata", &core::UMatDataTraitConst::origdata(self)) + .field("size", &core::UMatDataTraitConst::size(self)) + .field("flags", &core::UMatDataTraitConst::flags(self)) + .field("allocator_flags_", &core::UMatDataTraitConst::allocator_flags_(self)) + .field("mapcount", &core::UMatDataTraitConst::mapcount(self)) + .finish() + } + } + + impl core::UMatDataTraitConst for UMatData { + #[inline] fn as_raw_UMatData(&self) -> *const c_void { self.as_raw() } + } + + impl core::UMatDataTrait for UMatData { + #[inline] fn as_raw_mut_UMatData(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { UMatData, core::UMatDataTraitConst, as_raw_UMatData, core::UMatDataTrait, as_raw_mut_UMatData } + + /// This is the proxy class for passing read-only input arrays into OpenCV functions. + /// + /// It is defined as: + /// ```C++ + /// typedef const _InputArray& InputArray; + /// ``` + /// + /// where \ref cv::_InputArray is a class that can be constructed from \ref cv::Mat, \ref cv::Mat_, + /// \ref cv::Matx, std::vector, std::vector>, std::vector, + /// std::vector>, \ref cv::UMat, std::vector or `double`. It can also be constructed from + /// a matrix expression. + /// + /// Since this is mostly implementation-level class, and its interface may change in future versions, we + /// do not describe it in details. There are a few key things, though, that should be kept in mind: + /// + /// * When you see in the reference manual or in OpenCV source code a function that takes + /// InputArray, it means that you can actually pass `Mat`, `Matx`, `vector` etc. (see above the + /// complete list). + /// * Optional input arguments: If some of the input arrays may be empty, pass cv::noArray() (or + /// simply cv::Mat() as you probably did before). + /// * The class is designed solely for passing parameters. That is, normally you *should not* + /// declare class members, local and global variables of this type. + /// * If you want to design your own function or a class method that can operate of arrays of + /// multiple types, you can use InputArray (or OutputArray) for the respective parameters. Inside + /// a function you should use _InputArray::getMat() method to construct a matrix header for the + /// array (without copying data). _InputArray::kind() can be used to distinguish Mat from + /// `vector<>` etc., but normally it is not needed. + /// + /// Here is how you can use a function that takes InputArray : + /// ```C++ + /// std::vector vec; + /// // points or a circle + /// for( int i = 0; i < 30; i++ ) + /// vec.push_back(Point2f((float)(100 + 30*cos(i*CV_PI*2/5)), + /// (float)(100 - 30*sin(i*CV_PI*2/5)))); + /// cv::transform(vec, vec, cv::Matx23f(0.707, -0.707, 10, 0.707, 0.707, 20)); + /// ``` + /// + /// That is, we form an STL vector containing points, and apply in-place affine transformation to the + /// vector using the 2x3 matrix created inline as `Matx` instance. + /// + /// Here is how such a function can be implemented (for simplicity, we implement a very specific case of + /// it, according to the assertion statement inside) : + /// ```C++ + /// void myAffineTransform(InputArray _src, OutputArray _dst, InputArray _m) + /// { + /// // get Mat headers for input arrays. This is O(1) operation, + /// // unless _src and/or _m are matrix expressions. + /// Mat src = _src.getMat(), m = _m.getMat(); + /// CV_Assert( src.type() == CV_32FC2 && m.type() == CV_32F && m.size() == Size(3, 2) ); + /// + /// // [re]create the output array so that it has the proper size and type. + /// // In case of Mat it calls Mat::create, in case of STL vector it calls vector::resize. + /// _dst.create(src.size(), src.type()); + /// Mat dst = _dst.getMat(); + /// + /// for( int i = 0; i < src.rows; i++ ) + /// for( int j = 0; j < src.cols; j++ ) + /// { + /// Point2f pt = src.at(i, j); + /// dst.at(i, j) = Point2f(m.at(0, 0)*pt.x + + /// m.at(0, 1)*pt.y + + /// m.at(0, 2), + /// m.at(1, 0)*pt.x + + /// m.at(1, 1)*pt.y + + /// m.at(1, 2)); + /// } + /// } + /// ``` + /// + /// There is another related type, InputArrayOfArrays, which is currently defined as a synonym for + /// InputArray: + /// ```C++ + /// typedef InputArray InputArrayOfArrays; + /// ``` + /// + /// It denotes function arguments that are either vectors of vectors or vectors of matrices. A separate + /// synonym is needed to generate Python/Java etc. wrappers properly. At the function implementation + /// level their use is similar, but _InputArray::getMat(idx) should be used to get header for the + /// idx-th component of the outer vector and _InputArray::size().area() should be used to find the + /// number of components (vectors/matrices) of the outer vector. + /// + /// In general, type support is limited to cv::Mat types. Other types are forbidden. + /// But in some cases we need to support passing of custom non-general Mat types, like arrays of cv::KeyPoint, cv::DMatch, etc. + /// This data is not intended to be interpreted as an image data, or processed somehow like regular cv::Mat. + /// To pass such custom type use rawIn() / rawOut() / rawInOut() wrappers. + /// Custom type is wrapped as Mat-compatible `CV_8UC` values (N = sizeof(T), N <= CV_CN_MAX). + pub struct _InputArray { + ptr: *mut c_void, + } + + opencv_type_boxed! { _InputArray } + + impl Drop for _InputArray { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv__InputArray_delete(self.as_raw_mut__InputArray()) }; + } + } + + unsafe impl Send for _InputArray {} + + impl _InputArray { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray__InputArray(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::_InputArray::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray__InputArray_int_voidX(_flags, _obj, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::_InputArray::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn from_bool_vec(vec: &core::Vector) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray__InputArray_const_vectorLboolGR(vec.as_raw_VectorOfbool(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn from_f64(val: &f64) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray__InputArray_const_doubleR(val, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn from_byte_slice(vec: &[u8]) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray__InputArray_const_unsigned_charX_int(vec.as_ptr(), vec.len().try_into()?, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::_InputArray] + pub trait _InputArrayTraitConst { + fn as_raw__InputArray(&self) -> *const c_void; + + #[inline] + fn get_flags(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_getFlags_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_obj(&self) -> Result<*mut c_void> { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_getObj_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_sz(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_getSz_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn kind(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_kind_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + #[inline] + fn dims(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_dims_const_int(self.as_raw__InputArray(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_InputArrayTraitConst::dims] function uses the following default values for its arguments: + /// * i: -1 + #[inline] + fn dims_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_dims_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + #[inline] + fn cols(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_cols_const_int(self.as_raw__InputArray(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_InputArrayTraitConst::cols] function uses the following default values for its arguments: + /// * i: -1 + #[inline] + fn cols_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_cols_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + #[inline] + fn rows(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_rows_const_int(self.as_raw__InputArray(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_InputArrayTraitConst::rows] function uses the following default values for its arguments: + /// * i: -1 + #[inline] + fn rows_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_rows_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + #[inline] + fn size(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_size_const_int(self.as_raw__InputArray(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_InputArrayTraitConst::size] function uses the following default values for its arguments: + /// * i: -1 + #[inline] + fn size_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_size_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + #[inline] + fn sizend(&self, sz: &mut i32, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_sizend_const_intX_int(self.as_raw__InputArray(), sz, i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_InputArrayTraitConst::sizend] function uses the following default values for its arguments: + /// * i: -1 + #[inline] + fn sizend_def(&self, sz: &mut i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_sizend_const_intX(self.as_raw__InputArray(), sz, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn same_size(&self, arr: &impl ToInputArray) -> Result { + input_array_arg!(arr); + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_sameSize_const_const__InputArrayR(self.as_raw__InputArray(), arr.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + #[inline] + fn total(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_total_const_int(self.as_raw__InputArray(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_InputArrayTraitConst::total] function uses the following default values for its arguments: + /// * i: -1 + #[inline] + fn total_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_total_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + #[inline] + fn typ(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_type_const_int(self.as_raw__InputArray(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_InputArrayTraitConst::typ] function uses the following default values for its arguments: + /// * i: -1 + #[inline] + fn typ_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_type_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + #[inline] + fn depth(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_depth_const_int(self.as_raw__InputArray(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_InputArrayTraitConst::depth] function uses the following default values for its arguments: + /// * i: -1 + #[inline] + fn depth_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_depth_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + #[inline] + fn channels(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_channels_const_int(self.as_raw__InputArray(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_InputArrayTraitConst::channels] function uses the following default values for its arguments: + /// * i: -1 + #[inline] + fn channels_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_channels_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + #[inline] + fn is_continuous(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_isContinuous_const_int(self.as_raw__InputArray(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_InputArrayTraitConst::is_continuous] function uses the following default values for its arguments: + /// * i: -1 + #[inline] + fn is_continuous_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_isContinuous_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + #[inline] + fn is_submatrix(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_isSubmatrix_const_int(self.as_raw__InputArray(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_InputArrayTraitConst::is_submatrix] function uses the following default values for its arguments: + /// * i: -1 + #[inline] + fn is_submatrix_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_isSubmatrix_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_empty_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn empty_1(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_empty_const_int(self.as_raw__InputArray(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + #[inline] + fn offset(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_offset_const_int(self.as_raw__InputArray(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_InputArrayTraitConst::offset] function uses the following default values for its arguments: + /// * i: -1 + #[inline] + fn offset_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_offset_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + #[inline] + fn step(&self, i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_step_const_int(self.as_raw__InputArray(), i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_InputArrayTraitConst::step] function uses the following default values for its arguments: + /// * i: -1 + #[inline] + fn step_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_step_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_mat(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_isMat_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_umat(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_isUMat_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_mat_vector(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_isMatVector_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_umat_vector(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_isUMatVector_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_matx(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_isMatx_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_vector(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_isVector_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_gpu_mat(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_isGpuMat_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_gpu_mat_vector(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_isGpuMatVector_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_gpu_mat_nd(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputArray_isGpuMatND_const(self.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::_InputArray] + pub trait _InputArrayTrait: core::_InputArrayTraitConst { + fn as_raw_mut__InputArray(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for _InputArray { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("_InputArray") + .finish() + } + } + + impl core::_InputArrayTraitConst for _InputArray { + #[inline] fn as_raw__InputArray(&self) -> *const c_void { self.as_raw() } + } + + impl core::_InputArrayTrait for _InputArray { + #[inline] fn as_raw_mut__InputArray(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { _InputArray, core::_InputArrayTraitConst, as_raw__InputArray, core::_InputArrayTrait, as_raw_mut__InputArray } + + pub struct _InputOutputArray { + ptr: *mut c_void, + } + + opencv_type_boxed! { _InputOutputArray } + + impl Drop for _InputOutputArray { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv__InputOutputArray_delete(self.as_raw_mut__InputOutputArray()) }; + } + } + + unsafe impl Send for _InputOutputArray {} + + impl _InputOutputArray { + /// //////////////////////////////////////////////////////////////////////////////////////// + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputOutputArray__InputOutputArray(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::_InputOutputArray::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub unsafe fn new(_flags: i32, _obj: *mut c_void) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__InputOutputArray__InputOutputArray_int_voidX(_flags, _obj, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::_InputOutputArray::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::_InputOutputArray] + pub trait _InputOutputArrayTraitConst: core::_OutputArrayTraitConst { + fn as_raw__InputOutputArray(&self) -> *const c_void; + + } + + /// Mutable methods for [core::_InputOutputArray] + pub trait _InputOutputArrayTrait: core::_InputOutputArrayTraitConst + core::_OutputArrayTrait { + fn as_raw_mut__InputOutputArray(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for _InputOutputArray { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("_InputOutputArray") + .finish() + } + } + + boxed_cast_base! { _InputOutputArray, core::_InputArray, cv__InputOutputArray_to__InputArray } + + boxed_cast_base! { _InputOutputArray, core::_OutputArray, cv__InputOutputArray_to__OutputArray } + + impl core::_InputArrayTraitConst for _InputOutputArray { + #[inline] fn as_raw__InputArray(&self) -> *const c_void { self.as_raw() } + } + + impl core::_InputArrayTrait for _InputOutputArray { + #[inline] fn as_raw_mut__InputArray(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { _InputOutputArray, core::_InputArrayTraitConst, as_raw__InputArray, core::_InputArrayTrait, as_raw_mut__InputArray } + + impl core::_OutputArrayTraitConst for _InputOutputArray { + #[inline] fn as_raw__OutputArray(&self) -> *const c_void { self.as_raw() } + } + + impl core::_OutputArrayTrait for _InputOutputArray { + #[inline] fn as_raw_mut__OutputArray(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { _InputOutputArray, core::_OutputArrayTraitConst, as_raw__OutputArray, core::_OutputArrayTrait, as_raw_mut__OutputArray } + + impl core::_InputOutputArrayTraitConst for _InputOutputArray { + #[inline] fn as_raw__InputOutputArray(&self) -> *const c_void { self.as_raw() } + } + + impl core::_InputOutputArrayTrait for _InputOutputArray { + #[inline] fn as_raw_mut__InputOutputArray(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { _InputOutputArray, core::_InputOutputArrayTraitConst, as_raw__InputOutputArray, core::_InputOutputArrayTrait, as_raw_mut__InputOutputArray } + + /// This type is very similar to InputArray except that it is used for input/output and output function + /// parameters. + /// + /// Just like with InputArray, OpenCV users should not care about OutputArray, they just pass `Mat`, + /// `vector` etc. to the functions. The same limitation as for `InputArray`: *Do not explicitly + /// create OutputArray instances* applies here too. + /// + /// If you want to make your function polymorphic (i.e. accept different arrays as output parameters), + /// it is also not very difficult. Take the sample above as the reference. Note that + /// _OutputArray::create() needs to be called before _OutputArray::getMat(). This way you guarantee + /// that the output array is properly allocated. + /// + /// Optional output parameters. If you do not need certain output array to be computed and returned to + /// you, pass cv::noArray(), just like you would in the case of optional input array. At the + /// implementation level, use _OutputArray::needed() to check if certain output array needs to be + /// computed or not. + /// + /// There are several synonyms for OutputArray that are used to assist automatic Python/Java/... wrapper + /// generators: + /// ```C++ + /// typedef OutputArray OutputArrayOfArrays; + /// typedef OutputArray InputOutputArray; + /// typedef OutputArray InputOutputArrayOfArrays; + /// ``` + /// + pub struct _OutputArray { + ptr: *mut c_void, + } + + opencv_type_boxed! { _OutputArray } + + impl Drop for _OutputArray { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv__OutputArray_delete(self.as_raw_mut__OutputArray()) }; + } + } + + unsafe impl Send for _OutputArray {} + + /// Constant methods for [core::_OutputArray] + pub trait _OutputArrayTraitConst: core::_InputArrayTraitConst { + fn as_raw__OutputArray(&self) -> *const c_void; + + #[inline] + fn fixed_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_fixedSize_const(self.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn fixed_type(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_fixedType_const(self.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn needed(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_needed_const(self.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + /// * allow_transposed: false + /// * fixed_depth_mask: static_cast<_OutputArray::DepthMask>(0) + #[inline] + fn create_size(&self, sz: core::Size, typ: i32, i: i32, allow_transposed: bool, fixed_depth_mask: core::_OutputArray_DepthMask) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_create_const_Size_int_int_bool_DepthMask(self.as_raw__OutputArray(), &sz, typ, i, allow_transposed, fixed_depth_mask, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_OutputArrayTraitConst::create_size] function uses the following default values for its arguments: + /// * i: -1 + /// * allow_transposed: false + /// * fixed_depth_mask: static_cast<_OutputArray::DepthMask>(0) + #[inline] + fn create_size_def(&self, sz: core::Size, typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_create_const_Size_int(self.as_raw__OutputArray(), &sz, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + /// * allow_transposed: false + /// * fixed_depth_mask: static_cast<_OutputArray::DepthMask>(0) + #[inline] + fn create(&self, rows: i32, cols: i32, typ: i32, i: i32, allow_transposed: bool, fixed_depth_mask: core::_OutputArray_DepthMask) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_create_const_int_int_int_int_bool_DepthMask(self.as_raw__OutputArray(), rows, cols, typ, i, allow_transposed, fixed_depth_mask, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_OutputArrayTraitConst::create] function uses the following default values for its arguments: + /// * i: -1 + /// * allow_transposed: false + /// * fixed_depth_mask: static_cast<_OutputArray::DepthMask>(0) + #[inline] + fn create_def(&self, rows: i32, cols: i32, typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_create_const_int_int_int(self.as_raw__OutputArray(), rows, cols, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: -1 + /// * allow_transposed: false + /// * fixed_depth_mask: static_cast<_OutputArray::DepthMask>(0) + #[inline] + fn create_nd(&self, size: &[i32], typ: i32, i: i32, allow_transposed: bool, fixed_depth_mask: core::_OutputArray_DepthMask) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_create_const_int_const_intX_int_int_bool_DepthMask(self.as_raw__OutputArray(), size.len().try_into()?, size.as_ptr(), typ, i, allow_transposed, fixed_depth_mask, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_OutputArrayTraitConst::create_nd] function uses the following default values for its arguments: + /// * i: -1 + /// * allow_transposed: false + /// * fixed_depth_mask: static_cast<_OutputArray::DepthMask>(0) + #[inline] + fn create_nd_def(&self, size: &[i32], typ: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_create_const_int_const_intX_int(self.as_raw__OutputArray(), size.len().try_into()?, size.as_ptr(), typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + unsafe fn create_same_size(&self, arr: &impl ToInputArray, mtype: i32) -> Result<()> { + input_array_arg!(arr); + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_createSameSize_const_const__InputArrayR_int(self.as_raw__OutputArray(), arr.as_raw__InputArray(), mtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn release(&self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_release_const(self.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn clear(&self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_clear_const(self.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * mask: _InputArray() + #[inline] + fn set_to(&self, value: &impl ToInputArray, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(value); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_setTo_const_const__InputArrayR_const__InputArrayR(self.as_raw__OutputArray(), value.as_raw__InputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [_OutputArrayTraitConst::set_to] function uses the following default values for its arguments: + /// * mask: _InputArray() + #[inline] + fn set_to_def(&self, value: &impl ToInputArray) -> Result<()> { + input_array_arg!(value); + return_send!(via ocvrs_return); + unsafe { sys::cv__OutputArray_setTo_const_const__InputArrayR(self.as_raw__OutputArray(), value.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::_OutputArray] + pub trait _OutputArrayTrait: core::_InputArrayTrait + core::_OutputArrayTraitConst { + fn as_raw_mut__OutputArray(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for _OutputArray { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("_OutputArray") + .finish() + } + } + + boxed_cast_base! { _OutputArray, core::_InputArray, cv__OutputArray_to__InputArray } + + impl core::_InputArrayTraitConst for _OutputArray { + #[inline] fn as_raw__InputArray(&self) -> *const c_void { self.as_raw() } + } + + impl core::_InputArrayTrait for _OutputArray { + #[inline] fn as_raw_mut__InputArray(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { _OutputArray, core::_InputArrayTraitConst, as_raw__InputArray, core::_InputArrayTrait, as_raw_mut__InputArray } + + impl core::_OutputArrayTraitConst for _OutputArray { + #[inline] fn as_raw__OutputArray(&self) -> *const c_void { self.as_raw() } + } + + impl core::_OutputArrayTrait for _OutputArray { + #[inline] fn as_raw_mut__OutputArray(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { _OutputArray, core::_OutputArrayTraitConst, as_raw__OutputArray, core::_OutputArrayTrait, as_raw_mut__OutputArray } + + pub struct Detail_CheckContext { + ptr: *mut c_void, + } + + opencv_type_boxed! { Detail_CheckContext } + + impl Drop for Detail_CheckContext { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_detail_CheckContext_delete(self.as_raw_mut_Detail_CheckContext()) }; + } + } + + unsafe impl Send for Detail_CheckContext {} + + impl Detail_CheckContext { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> core::Detail_CheckContext { + let ret = unsafe { sys::cv_detail_CheckContext_defaultNew_const() }; + let ret = unsafe { core::Detail_CheckContext::opencv_from_extern(ret) }; + ret + } + + } + + /// Constant methods for [core::Detail_CheckContext] + pub trait Detail_CheckContextTraitConst { + fn as_raw_Detail_CheckContext(&self) -> *const c_void; + + #[inline] + fn func(&self) -> String { + let ret = unsafe { sys::cv_detail_CheckContext_propFunc_const(self.as_raw_Detail_CheckContext()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn file(&self) -> String { + let ret = unsafe { sys::cv_detail_CheckContext_propFile_const(self.as_raw_Detail_CheckContext()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn line(&self) -> i32 { + let ret = unsafe { sys::cv_detail_CheckContext_propLine_const(self.as_raw_Detail_CheckContext()) }; + ret + } + + #[inline] + fn test_op(&self) -> core::Detail_TestOp { + return_send!(via ocvrs_return); + unsafe { sys::cv_detail_CheckContext_propTestOp_const(self.as_raw_Detail_CheckContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn message(&self) -> String { + let ret = unsafe { sys::cv_detail_CheckContext_propMessage_const(self.as_raw_Detail_CheckContext()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn p1_str(&self) -> String { + let ret = unsafe { sys::cv_detail_CheckContext_propP1_str_const(self.as_raw_Detail_CheckContext()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn p2_str(&self) -> String { + let ret = unsafe { sys::cv_detail_CheckContext_propP2_str_const(self.as_raw_Detail_CheckContext()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + } + + /// Mutable methods for [core::Detail_CheckContext] + pub trait Detail_CheckContextTrait: core::Detail_CheckContextTraitConst { + fn as_raw_mut_Detail_CheckContext(&mut self) -> *mut c_void; + + #[inline] + fn set_line(&mut self, val: i32) { + let ret = unsafe { sys::cv_detail_CheckContext_propLine_const_int(self.as_raw_mut_Detail_CheckContext(), val) }; + ret + } + + #[inline] + fn set_test_op(&mut self, val: core::Detail_TestOp) { + let ret = unsafe { sys::cv_detail_CheckContext_propTestOp_const_TestOp(self.as_raw_mut_Detail_CheckContext(), val) }; + ret + } + + } + + impl Default for Detail_CheckContext { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl Clone for Detail_CheckContext { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_detail_CheckContext_implicitClone_const(self.as_raw_Detail_CheckContext())) } + } + } + + impl std::fmt::Debug for Detail_CheckContext { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Detail_CheckContext") + .field("func", &core::Detail_CheckContextTraitConst::func(self)) + .field("file", &core::Detail_CheckContextTraitConst::file(self)) + .field("line", &core::Detail_CheckContextTraitConst::line(self)) + .field("test_op", &core::Detail_CheckContextTraitConst::test_op(self)) + .field("message", &core::Detail_CheckContextTraitConst::message(self)) + .field("p1_str", &core::Detail_CheckContextTraitConst::p1_str(self)) + .field("p2_str", &core::Detail_CheckContextTraitConst::p2_str(self)) + .finish() + } + } + + impl core::Detail_CheckContextTraitConst for Detail_CheckContext { + #[inline] fn as_raw_Detail_CheckContext(&self) -> *const c_void { self.as_raw() } + } + + impl core::Detail_CheckContextTrait for Detail_CheckContext { + #[inline] fn as_raw_mut_Detail_CheckContext(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Detail_CheckContext, core::Detail_CheckContextTraitConst, as_raw_Detail_CheckContext, core::Detail_CheckContextTrait, as_raw_mut_Detail_CheckContext } + + #[repr(C)] + #[derive(Copy, Clone, Debug, PartialEq)] + pub struct hfloat { + h: u16, + } + + opencv_type_simple! { core::hfloat } + + impl hfloat { + #[inline] + pub fn to_f32(self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_hfloat_operator_float_const(&self, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_hfloat_hfloat(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn new(x: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_hfloat_hfloat_float(x, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + pub struct NodeData { + ptr: *mut c_void, + } + + opencv_type_boxed! { NodeData } + + impl Drop for NodeData { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_instr_NodeData_delete(self.as_raw_mut_NodeData()) }; + } + } + + unsafe impl Send for NodeData {} + + impl NodeData { + /// ## C++ default parameters + /// * fun_name: 0 + /// * file_name: NULL + /// * line_num: 0 + /// * ret_address: NULL + /// * always_expand: false + /// * instr_type: TYPE_GENERAL + /// * impl_type: IMPL_PLAIN + #[inline] + pub unsafe fn new(fun_name: &str, file_name: &str, line_num: i32, ret_address: *mut c_void, always_expand: bool, instr_type: core::TYPE, impl_type: core::IMPL) -> Result { + extern_container_arg!(fun_name); + extern_container_arg!(file_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_instr_NodeData_NodeData_const_charX_const_charX_int_voidX_bool_TYPE_IMPL(fun_name.opencv_as_extern(), file_name.opencv_as_extern(), line_num, ret_address, always_expand, instr_type, impl_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::NodeData::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * fun_name: 0 + /// * file_name: NULL + /// * line_num: 0 + /// * ret_address: NULL + /// * always_expand: false + /// * instr_type: TYPE_GENERAL + /// * impl_type: IMPL_PLAIN + #[inline] + pub fn new_def() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_instr_NodeData_NodeData(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::NodeData::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn copy_mut(ref_: &mut impl core::NodeDataTrait) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_instr_NodeData_NodeData_NodeDataR(ref_.as_raw_mut_NodeData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::NodeData::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::NodeData] + pub trait NodeDataTraitConst { + fn as_raw_NodeData(&self) -> *const c_void; + + #[inline] + fn m_fun_name(&self) -> String { + let ret = unsafe { sys::cv_instr_NodeData_propM_funName_const(self.as_raw_NodeData()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn m_instr_type(&self) -> core::TYPE { + return_send!(via ocvrs_return); + unsafe { sys::cv_instr_NodeData_propM_instrType_const(self.as_raw_NodeData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn m_impl_type(&self) -> core::IMPL { + return_send!(via ocvrs_return); + unsafe { sys::cv_instr_NodeData_propM_implType_const(self.as_raw_NodeData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn m_file_name(&self) -> String { + let ret = unsafe { sys::cv_instr_NodeData_propM_fileName_const(self.as_raw_NodeData()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn m_line_num(&self) -> i32 { + let ret = unsafe { sys::cv_instr_NodeData_propM_lineNum_const(self.as_raw_NodeData()) }; + ret + } + + #[inline] + fn m_always_expand(&self) -> bool { + let ret = unsafe { sys::cv_instr_NodeData_propM_alwaysExpand_const(self.as_raw_NodeData()) }; + ret + } + + #[inline] + fn m_fun_error(&self) -> bool { + let ret = unsafe { sys::cv_instr_NodeData_propM_funError_const(self.as_raw_NodeData()) }; + ret + } + + #[inline] + fn m_counter(&self) -> i32 { + let ret = unsafe { sys::cv_instr_NodeData_propM_counter_const(self.as_raw_NodeData()) }; + ret + } + + #[inline] + fn m_ticks_total(&self) -> u64 { + let ret = unsafe { sys::cv_instr_NodeData_propM_ticksTotal_const(self.as_raw_NodeData()) }; + ret + } + + #[inline] + fn m_threads(&self) -> i32 { + let ret = unsafe { sys::cv_instr_NodeData_propM_threads_const(self.as_raw_NodeData()) }; + ret + } + + #[inline] + fn get_total_ms(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_instr_NodeData_getTotalMs_const(self.as_raw_NodeData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_mean_ms(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_instr_NodeData_getMeanMs_const(self.as_raw_NodeData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::NodeData] + pub trait NodeDataTrait: core::NodeDataTraitConst { + fn as_raw_mut_NodeData(&mut self) -> *mut c_void; + + #[inline] + fn set_m_fun_name(&mut self, val: &str) { + extern_container_arg!(nofail val); + let ret = unsafe { sys::cv_instr_NodeData_propM_funName_const_String(self.as_raw_mut_NodeData(), val.opencv_as_extern()) }; + ret + } + + #[inline] + fn set_m_instr_type(&mut self, val: core::TYPE) { + let ret = unsafe { sys::cv_instr_NodeData_propM_instrType_const_TYPE(self.as_raw_mut_NodeData(), val) }; + ret + } + + #[inline] + fn set_m_impl_type(&mut self, val: core::IMPL) { + let ret = unsafe { sys::cv_instr_NodeData_propM_implType_const_IMPL(self.as_raw_mut_NodeData(), val) }; + ret + } + + #[inline] + fn set_m_line_num(&mut self, val: i32) { + let ret = unsafe { sys::cv_instr_NodeData_propM_lineNum_const_int(self.as_raw_mut_NodeData(), val) }; + ret + } + + #[inline] + fn m_ret_address(&mut self) -> *mut c_void { + let ret = unsafe { sys::cv_instr_NodeData_propM_retAddress(self.as_raw_mut_NodeData()) }; + ret + } + + #[inline] + unsafe fn set_m_ret_address(&mut self, val: *const c_void) { + let ret = unsafe { sys::cv_instr_NodeData_propM_retAddress_voidX(self.as_raw_mut_NodeData(), val) }; + ret + } + + #[inline] + fn set_m_always_expand(&mut self, val: bool) { + let ret = unsafe { sys::cv_instr_NodeData_propM_alwaysExpand_const_bool(self.as_raw_mut_NodeData(), val) }; + ret + } + + #[inline] + fn set_m_fun_error(&mut self, val: bool) { + let ret = unsafe { sys::cv_instr_NodeData_propM_funError_const_bool(self.as_raw_mut_NodeData(), val) }; + ret + } + + #[inline] + fn set_m_counter(&mut self, val: i32) { + let ret = unsafe { sys::cv_instr_NodeData_propM_counter_const_int(self.as_raw_mut_NodeData(), val) }; + ret + } + + #[inline] + fn set_m_ticks_total(&mut self, val: u64) { + let ret = unsafe { sys::cv_instr_NodeData_propM_ticksTotal_const_uint64_t(self.as_raw_mut_NodeData(), val) }; + ret + } + + #[inline] + fn set_m_threads(&mut self, val: i32) { + let ret = unsafe { sys::cv_instr_NodeData_propM_threads_const_int(self.as_raw_mut_NodeData(), val) }; + ret + } + + #[inline] + fn set(&mut self, unnamed: &impl core::NodeDataTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_instr_NodeData_operatorST_const_NodeDataR(self.as_raw_mut_NodeData(), unnamed.as_raw_NodeData(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for NodeData { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("NodeData") + .field("m_fun_name", &core::NodeDataTraitConst::m_fun_name(self)) + .field("m_instr_type", &core::NodeDataTraitConst::m_instr_type(self)) + .field("m_impl_type", &core::NodeDataTraitConst::m_impl_type(self)) + .field("m_file_name", &core::NodeDataTraitConst::m_file_name(self)) + .field("m_line_num", &core::NodeDataTraitConst::m_line_num(self)) + .field("m_always_expand", &core::NodeDataTraitConst::m_always_expand(self)) + .field("m_fun_error", &core::NodeDataTraitConst::m_fun_error(self)) + .field("m_counter", &core::NodeDataTraitConst::m_counter(self)) + .field("m_ticks_total", &core::NodeDataTraitConst::m_ticks_total(self)) + .field("m_threads", &core::NodeDataTraitConst::m_threads(self)) + .finish() + } + } + + impl core::NodeDataTraitConst for NodeData { + #[inline] fn as_raw_NodeData(&self) -> *const c_void { self.as_raw() } + } + + impl core::NodeDataTrait for NodeData { + #[inline] fn as_raw_mut_NodeData(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { NodeData, core::NodeDataTraitConst, as_raw_NodeData, core::NodeDataTrait, as_raw_mut_NodeData } + + pub struct WriteStructContext { + ptr: *mut c_void, + } + + opencv_type_boxed! { WriteStructContext } + + impl Drop for WriteStructContext { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_internal_WriteStructContext_delete(self.as_raw_mut_WriteStructContext()) }; + } + } + + unsafe impl Send for WriteStructContext {} + + impl WriteStructContext { + /// ## C++ default parameters + /// * type_name: String() + #[inline] + pub fn new(_fs: &mut impl core::FileStorageTrait, name: &str, flags: i32, type_name: &str) -> Result { + extern_container_arg!(name); + extern_container_arg!(type_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_internal_WriteStructContext_WriteStructContext_FileStorageR_const_StringR_int_const_StringR(_fs.as_raw_mut_FileStorage(), name.opencv_as_extern(), flags, type_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::WriteStructContext::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * type_name: String() + #[inline] + pub fn new_def(_fs: &mut impl core::FileStorageTrait, name: &str, flags: i32) -> Result { + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_internal_WriteStructContext_WriteStructContext_FileStorageR_const_StringR_int(_fs.as_raw_mut_FileStorage(), name.opencv_as_extern(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::WriteStructContext::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::WriteStructContext] + pub trait WriteStructContextTraitConst { + fn as_raw_WriteStructContext(&self) -> *const c_void; + + } + + /// Mutable methods for [core::WriteStructContext] + pub trait WriteStructContextTrait: core::WriteStructContextTraitConst { + fn as_raw_mut_WriteStructContext(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for WriteStructContext { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("WriteStructContext") + .finish() + } + } + + impl core::WriteStructContextTraitConst for WriteStructContext { + #[inline] fn as_raw_WriteStructContext(&self) -> *const c_void { self.as_raw() } + } + + impl core::WriteStructContextTrait for WriteStructContext { + #[inline] fn as_raw_mut_WriteStructContext(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { WriteStructContext, core::WriteStructContextTraitConst, as_raw_WriteStructContext, core::WriteStructContextTrait, as_raw_mut_WriteStructContext } + + pub struct Context { + ptr: *mut c_void, + } + + opencv_type_boxed! { Context } + + impl Drop for Context { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ocl_Context_delete(self.as_raw_mut_Context()) }; + } + } + + unsafe impl Send for Context {} + + impl Context { + /// ## C++ default parameters + /// * initialize: true + #[inline] + pub fn get_default(initialize: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_getDefault_bool(initialize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Context::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [Context::get_default] function uses the following default values for its arguments: + /// * initialize: true + #[inline] + pub fn get_default_def() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_getDefault(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Context::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Parameters + /// * context: OpenCL handle (cl_context). clRetainContext() is called on success + #[inline] + pub unsafe fn from_handle(context: *mut c_void) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_fromHandle_voidX(context, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Context::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn from_device(device: &impl core::DeviceTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_fromDevice_const_DeviceR(device.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Context::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn create(configuration: &str) -> Result { + extern_container_arg!(configuration); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_create_const_stringR(configuration.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Context::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Context] + pub trait ContextTraitConst { + fn as_raw_Context(&self) -> *const c_void; + + #[inline] + fn ndevices(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_ndevices_const(self.as_raw_Context(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn device(&self, idx: size_t) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_device_const_size_t(self.as_raw_Context(), idx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Device::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Returns + /// cl_context value + #[inline] + fn ptr(&self) -> Result<*mut c_void> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_ptr_const(self.as_raw_Context(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Get OpenCL context property specified on context creation + /// ## Parameters + /// * propertyId: Property id (CL_CONTEXT_* as defined in cl_context_properties type) + /// ## Returns + /// Property value if property was specified on clCreateContext, or NULL if context created without the property + #[inline] + fn get_opencl_context_property(&self, property_id: i32) -> Result<*mut c_void> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_getOpenCLContextProperty_const_int(self.as_raw_Context(), property_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn use_svm(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_useSVM_const(self.as_raw_Context(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_empty_const(self.as_raw_Context(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::Context] + pub trait ContextTrait: core::ContextTraitConst { + fn as_raw_mut_Context(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, c: &impl core::ContextTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_operatorST_const_ContextR(self.as_raw_mut_Context(), c.as_raw_Context(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_1(&mut self, mut c: core::Context) { + let ret = unsafe { sys::cv_ocl_Context_operatorST_ContextRR(self.as_raw_mut_Context(), c.as_raw_mut_Context()) }; + ret + } + + /// @deprecated + #[inline] + fn create(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_create(self.as_raw_mut_Context(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// @deprecated + #[inline] + fn create_with_type(&mut self, dtype: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_create_int(self.as_raw_mut_Context(), dtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_use_svm(&mut self, enabled: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_setUseSVM_bool(self.as_raw_mut_Context(), enabled, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn release(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Context_release(self.as_raw_mut_Context(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for Context { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Context") + .finish() + } + } + + impl core::ContextTraitConst for Context { + #[inline] fn as_raw_Context(&self) -> *const c_void { self.as_raw() } + } + + impl core::ContextTrait for Context { + #[inline] fn as_raw_mut_Context(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Context, core::ContextTraitConst, as_raw_Context, core::ContextTrait, as_raw_mut_Context } + + pub struct Context_UserContext { + ptr: *mut c_void, + } + + opencv_type_boxed! { Context_UserContext } + + impl Drop for Context_UserContext { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ocl_Context_UserContext_delete(self.as_raw_mut_Context_UserContext()) }; + } + } + + unsafe impl Send for Context_UserContext {} + + impl Context_UserContext { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> core::Context_UserContext { + let ret = unsafe { sys::cv_ocl_Context_UserContext_defaultNew_const() }; + let ret = unsafe { core::Context_UserContext::opencv_from_extern(ret) }; + ret + } + + } + + /// Constant methods for [core::Context_UserContext] + pub trait Context_UserContextTraitConst { + fn as_raw_Context_UserContext(&self) -> *const c_void; + + } + + /// Mutable methods for [core::Context_UserContext] + pub trait Context_UserContextTrait: core::Context_UserContextTraitConst { + fn as_raw_mut_Context_UserContext(&mut self) -> *mut c_void; + + } + + impl Default for Context_UserContext { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for Context_UserContext { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Context_UserContext") + .finish() + } + } + + impl core::Context_UserContextTraitConst for Context_UserContext { + #[inline] fn as_raw_Context_UserContext(&self) -> *const c_void { self.as_raw() } + } + + impl core::Context_UserContextTrait for Context_UserContext { + #[inline] fn as_raw_mut_Context_UserContext(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Context_UserContext, core::Context_UserContextTraitConst, as_raw_Context_UserContext, core::Context_UserContextTrait, as_raw_mut_Context_UserContext } + + pub struct Device { + ptr: *mut c_void, + } + + opencv_type_boxed! { Device } + + impl Drop for Device { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ocl_Device_delete(self.as_raw_mut_Device()) }; + } + } + + unsafe impl Send for Device {} + + impl Device { + #[inline] + pub fn get_default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_getDefault(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Device::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Parameters + /// * d: OpenCL handle (cl_device_id). clRetainDevice() is called on success. + /// + /// + /// Note: Ownership of the passed device is passed to OpenCV on success. + /// The caller should additionally call `clRetainDevice` on it if it intends + /// to continue using the device. + #[inline] + pub unsafe fn from_handle(d: *mut c_void) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_fromHandle_voidX(d, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Device::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Device] + pub trait DeviceTraitConst { + fn as_raw_Device(&self) -> *const c_void; + + #[inline] + fn name(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_name_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn extensions(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_extensions_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn is_extension_supported(&self, extension_name: &str) -> Result { + extern_container_arg!(extension_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_isExtensionSupported_const_const_StringR(self.as_raw_Device(), extension_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn version(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_version_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn vendor_name(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_vendorName_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn opencl_c_version(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_OpenCL_C_Version_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn opencl_version(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_OpenCLVersion_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn device_version_major(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_deviceVersionMajor_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn device_version_minor(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_deviceVersionMinor_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn driver_version(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_driverVersion_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn ptr(&self) -> Result<*mut c_void> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_ptr_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn typ(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_type_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn address_bits(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_addressBits_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn available(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_available_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn compiler_available(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_compilerAvailable_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn linker_available(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_linkerAvailable_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn double_fp_config(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_doubleFPConfig_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn single_fp_config(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_singleFPConfig_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn half_fp_config(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_halfFPConfig_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// true if 'cl_khr_fp64' extension is available + #[inline] + fn has_fp64(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_hasFP64_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// true if 'cl_khr_fp16' extension is available + #[inline] + fn has_fp16(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_hasFP16_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn endian_little(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_endianLittle_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn error_correction_support(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_errorCorrectionSupport_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn execution_capabilities(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_executionCapabilities_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn global_mem_cache_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_globalMemCacheSize_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn global_mem_cache_type(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_globalMemCacheType_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn global_mem_cache_line_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_globalMemCacheLineSize_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn global_mem_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_globalMemSize_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn local_mem_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_localMemSize_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn local_mem_type(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_localMemType_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn host_unified_memory(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_hostUnifiedMemory_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn image_support(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_imageSupport_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn image_from_buffer_support(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_imageFromBufferSupport_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn image_pitch_alignment(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_imagePitchAlignment_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn image_base_address_alignment(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_imageBaseAddressAlignment_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// deprecated, use isExtensionSupported() method (probably with "cl_khr_subgroups" value) + #[inline] + fn intel_subgroups_support(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_intelSubgroupsSupport_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn image_2d_max_width(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_image2DMaxWidth_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn image_2d_max_height(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_image2DMaxHeight_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn image_3d_max_width(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_image3DMaxWidth_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn image_3d_max_height(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_image3DMaxHeight_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn image_3d_max_depth(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_image3DMaxDepth_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn image_max_buffer_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_imageMaxBufferSize_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn image_max_array_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_imageMaxArraySize_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn vendor_id(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_vendorID_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_amd(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_isAMD_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_intel(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_isIntel_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_n_vidia(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_isNVidia_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn max_clock_frequency(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_maxClockFrequency_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn max_compute_units(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_maxComputeUnits_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn max_constant_args(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_maxConstantArgs_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn max_constant_buffer_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_maxConstantBufferSize_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn max_mem_alloc_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_maxMemAllocSize_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn max_parameter_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_maxParameterSize_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn max_read_image_args(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_maxReadImageArgs_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn max_write_image_args(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_maxWriteImageArgs_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn max_samplers(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_maxSamplers_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn max_work_group_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_maxWorkGroupSize_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn max_work_item_dims(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_maxWorkItemDims_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn max_work_item_sizes(&self, unnamed: &mut size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_maxWorkItemSizes_const_size_tX(self.as_raw_Device(), unnamed, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn mem_base_addr_align(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_memBaseAddrAlign_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn native_vector_width_char(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_nativeVectorWidthChar_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn native_vector_width_short(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_nativeVectorWidthShort_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn native_vector_width_int(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_nativeVectorWidthInt_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn native_vector_width_long(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_nativeVectorWidthLong_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn native_vector_width_float(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_nativeVectorWidthFloat_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn native_vector_width_double(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_nativeVectorWidthDouble_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn native_vector_width_half(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_nativeVectorWidthHalf_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn preferred_vector_width_char(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_preferredVectorWidthChar_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn preferred_vector_width_short(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_preferredVectorWidthShort_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn preferred_vector_width_int(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_preferredVectorWidthInt_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn preferred_vector_width_long(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_preferredVectorWidthLong_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn preferred_vector_width_float(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_preferredVectorWidthFloat_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn preferred_vector_width_double(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_preferredVectorWidthDouble_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn preferred_vector_width_half(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_preferredVectorWidthHalf_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn printf_buffer_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_printfBufferSize_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn profiling_timer_resolution(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_profilingTimerResolution_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_empty_const(self.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::Device] + pub trait DeviceTrait: core::DeviceTraitConst { + fn as_raw_mut_Device(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, d: &impl core::DeviceTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_operatorST_const_DeviceR(self.as_raw_mut_Device(), d.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_1(&mut self, mut d: core::Device) { + let ret = unsafe { sys::cv_ocl_Device_operatorST_DeviceRR(self.as_raw_mut_Device(), d.as_raw_mut_Device()) }; + ret + } + + #[inline] + unsafe fn set_2(&mut self, d: *mut c_void) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Device_set_voidX(self.as_raw_mut_Device(), d, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl Clone for Device { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_ocl_Device_implicitClone_const(self.as_raw_Device())) } + } + } + + impl std::fmt::Debug for Device { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Device") + .finish() + } + } + + impl core::DeviceTraitConst for Device { + #[inline] fn as_raw_Device(&self) -> *const c_void { self.as_raw() } + } + + impl core::DeviceTrait for Device { + #[inline] fn as_raw_mut_Device(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Device, core::DeviceTraitConst, as_raw_Device, core::DeviceTrait, as_raw_mut_Device } + + pub struct Image2D { + ptr: *mut c_void, + } + + opencv_type_boxed! { Image2D } + + impl Drop for Image2D { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ocl_Image2D_delete(self.as_raw_mut_Image2D()) }; + } + } + + unsafe impl Send for Image2D {} + + impl Image2D { + /// Indicates if creating an aliased image should succeed. + /// Depends on the underlying platform and the dimensions of the UMat. + #[inline] + pub fn can_create_alias(u: &impl core::UMatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Image2D_canCreateAlias_const_UMatR(u.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Indicates if the image format is supported. + #[inline] + pub fn is_format_supported(depth: i32, cn: i32, norm: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Image2D_isFormatSupported_int_int_bool(depth, cn, norm, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Constant methods for [core::Image2D] + pub trait Image2DTraitConst { + fn as_raw_Image2D(&self) -> *const c_void; + + #[inline] + fn ptr(&self) -> Result<*mut c_void> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Image2D_ptr_const(self.as_raw_Image2D(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::Image2D] + pub trait Image2DTrait: core::Image2DTraitConst { + fn as_raw_mut_Image2D(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, i: &impl core::Image2DTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Image2D_operatorST_const_Image2DR(self.as_raw_mut_Image2D(), i.as_raw_Image2D(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_1(&mut self, mut unnamed: core::Image2D) { + let ret = unsafe { sys::cv_ocl_Image2D_operatorST_Image2DRR(self.as_raw_mut_Image2D(), unnamed.as_raw_mut_Image2D()) }; + ret + } + + } + + impl std::fmt::Debug for Image2D { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Image2D") + .finish() + } + } + + impl core::Image2DTraitConst for Image2D { + #[inline] fn as_raw_Image2D(&self) -> *const c_void { self.as_raw() } + } + + impl core::Image2DTrait for Image2D { + #[inline] fn as_raw_mut_Image2D(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Image2D, core::Image2DTraitConst, as_raw_Image2D, core::Image2DTrait, as_raw_mut_Image2D } + + pub struct Kernel { + ptr: *mut c_void, + } + + opencv_type_boxed! { Kernel } + + impl Drop for Kernel { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ocl_Kernel_delete(self.as_raw_mut_Kernel()) }; + } + } + + unsafe impl Send for Kernel {} + + /// Constant methods for [core::Kernel] + pub trait KernelTraitConst { + fn as_raw_Kernel(&self) -> *const c_void; + + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_empty_const(self.as_raw_Kernel(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn work_group_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_workGroupSize_const(self.as_raw_Kernel(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn prefered_work_group_size_multiple(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_preferedWorkGroupSizeMultiple_const(self.as_raw_Kernel(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn compile_work_group_size(&self, wsz: &mut [size_t]) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_compileWorkGroupSize_const_size_tX(self.as_raw_Kernel(), wsz.as_mut_ptr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn local_mem_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_localMemSize_const(self.as_raw_Kernel(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn ptr(&self) -> Result<*mut c_void> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_ptr_const(self.as_raw_Kernel(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::Kernel] + pub trait KernelTrait: core::KernelTraitConst { + fn as_raw_mut_Kernel(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, k: &impl core::KernelTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_operatorST_const_KernelR(self.as_raw_mut_Kernel(), k.as_raw_Kernel(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_1(&mut self, mut k: core::Kernel) { + let ret = unsafe { sys::cv_ocl_Kernel_operatorST_KernelRR(self.as_raw_mut_Kernel(), k.as_raw_mut_Kernel()) }; + ret + } + + #[inline] + unsafe fn set_2(&mut self, i: i32, value: *const c_void, sz: size_t) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_set_int_const_voidX_size_t(self.as_raw_mut_Kernel(), i, value, sz, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_umat(&mut self, i: i32, m: &impl core::UMatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_set_int_const_UMatR(self.as_raw_mut_Kernel(), i, m.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_kernel_arg(&mut self, i: i32, arg: &impl core::KernelArgTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_set_int_const_KernelArgR(self.as_raw_mut_Kernel(), i, arg.as_raw_KernelArg(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Run the OpenCL kernel (globalsize value may be adjusted) + /// + /// ## Parameters + /// * dims: the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3. + /// * globalsize: work items for each dimension. It is not the final globalsize passed to + /// OpenCL. Each dimension will be adjusted to the nearest integer divisible by the corresponding + /// value in localsize. If localsize is NULL, it will still be adjusted depending on dims. The + /// adjusted values are greater than or equal to the original values. + /// * localsize: work-group size for each dimension. + /// * sync: specify whether to wait for OpenCL computation to finish before return. + /// * q: command queue + /// + /// + /// Note: Use run_() if your kernel code doesn't support adjusted globalsize. + /// + /// ## C++ default parameters + /// * q: Queue() + #[inline] + fn run(&mut self, globalsize: &mut [size_t], localsize: &mut [size_t], sync: bool, q: &impl core::QueueTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_run_int_size_tX_size_tX_bool_const_QueueR(self.as_raw_mut_Kernel(), globalsize.len().min(localsize.len()).try_into()?, globalsize.as_mut_ptr(), localsize.as_mut_ptr(), sync, q.as_raw_Queue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Run the OpenCL kernel (globalsize value may be adjusted) + /// + /// ## Parameters + /// * dims: the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3. + /// * globalsize: work items for each dimension. It is not the final globalsize passed to + /// OpenCL. Each dimension will be adjusted to the nearest integer divisible by the corresponding + /// value in localsize. If localsize is NULL, it will still be adjusted depending on dims. The + /// adjusted values are greater than or equal to the original values. + /// * localsize: work-group size for each dimension. + /// * sync: specify whether to wait for OpenCL computation to finish before return. + /// * q: command queue + /// + /// + /// Note: Use run_() if your kernel code doesn't support adjusted globalsize. + /// + /// ## Note + /// This alternative version of [KernelTrait::run] function uses the following default values for its arguments: + /// * q: Queue() + #[inline] + fn run_def(&mut self, globalsize: &mut [size_t], localsize: &mut [size_t], sync: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_run_int_size_tX_size_tX_bool(self.as_raw_mut_Kernel(), globalsize.len().min(localsize.len()).try_into()?, globalsize.as_mut_ptr(), localsize.as_mut_ptr(), sync, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Run the OpenCL kernel + /// + /// ## Parameters + /// * dims: the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3. + /// * globalsize: work items for each dimension. This value is passed to OpenCL without changes. + /// * localsize: work-group size for each dimension. + /// * sync: specify whether to wait for OpenCL computation to finish before return. + /// * q: command queue + /// + /// ## C++ default parameters + /// * q: Queue() + #[inline] + fn run_(&mut self, globalsize: &mut [size_t], localsize: &mut [size_t], sync: bool, q: &impl core::QueueTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_run__int_size_tX_size_tX_bool_const_QueueR(self.as_raw_mut_Kernel(), globalsize.len().min(localsize.len()).try_into()?, globalsize.as_mut_ptr(), localsize.as_mut_ptr(), sync, q.as_raw_Queue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Run the OpenCL kernel + /// + /// ## Parameters + /// * dims: the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3. + /// * globalsize: work items for each dimension. This value is passed to OpenCL without changes. + /// * localsize: work-group size for each dimension. + /// * sync: specify whether to wait for OpenCL computation to finish before return. + /// * q: command queue + /// + /// ## Note + /// This alternative version of [KernelTrait::run_] function uses the following default values for its arguments: + /// * q: Queue() + #[inline] + fn run__def(&mut self, globalsize: &mut [size_t], localsize: &mut [size_t], sync: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_run__int_size_tX_size_tX_bool(self.as_raw_mut_Kernel(), globalsize.len().min(localsize.len()).try_into()?, globalsize.as_mut_ptr(), localsize.as_mut_ptr(), sync, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * q: Queue() + #[inline] + fn run_task(&mut self, sync: bool, q: &impl core::QueueTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_runTask_bool_const_QueueR(self.as_raw_mut_Kernel(), sync, q.as_raw_Queue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [KernelTrait::run_task] function uses the following default values for its arguments: + /// * q: Queue() + #[inline] + fn run_task_def(&mut self, sync: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_runTask_bool(self.as_raw_mut_Kernel(), sync, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Similar to synchronized run_() call with returning of kernel execution time + /// + /// Separate OpenCL command queue may be used (with CL_QUEUE_PROFILING_ENABLE) + /// ## Returns + /// Execution time in nanoseconds or negative number on error + /// + /// ## C++ default parameters + /// * q: Queue() + #[inline] + fn run_profiling(&mut self, globalsize: &mut [size_t], localsize: &mut [size_t], q: &impl core::QueueTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_runProfiling_int_size_tX_size_tX_const_QueueR(self.as_raw_mut_Kernel(), globalsize.len().min(localsize.len()).try_into()?, globalsize.as_mut_ptr(), localsize.as_mut_ptr(), q.as_raw_Queue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Similar to synchronized run_() call with returning of kernel execution time + /// + /// Separate OpenCL command queue may be used (with CL_QUEUE_PROFILING_ENABLE) + /// ## Returns + /// Execution time in nanoseconds or negative number on error + /// + /// ## Note + /// This alternative version of [KernelTrait::run_profiling] function uses the following default values for its arguments: + /// * q: Queue() + #[inline] + fn run_profiling_def(&mut self, globalsize: &mut [size_t], localsize: &mut [size_t]) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Kernel_runProfiling_int_size_tX_size_tX(self.as_raw_mut_Kernel(), globalsize.len().min(localsize.len()).try_into()?, globalsize.as_mut_ptr(), localsize.as_mut_ptr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for Kernel { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Kernel") + .finish() + } + } + + impl core::KernelTraitConst for Kernel { + #[inline] fn as_raw_Kernel(&self) -> *const c_void { self.as_raw() } + } + + impl core::KernelTrait for Kernel { + #[inline] fn as_raw_mut_Kernel(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Kernel, core::KernelTraitConst, as_raw_Kernel, core::KernelTrait, as_raw_mut_Kernel } + + pub struct KernelArg { + ptr: *mut c_void, + } + + opencv_type_boxed! { KernelArg } + + impl Drop for KernelArg { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ocl_KernelArg_delete(self.as_raw_mut_KernelArg()) }; + } + } + + unsafe impl Send for KernelArg {} + + impl KernelArg { + /// ## C++ default parameters + /// * wscale: 1 + /// * iwscale: 1 + /// * _obj: 0 + /// * _sz: 0 + #[inline] + pub unsafe fn new(_flags: i32, _m: &mut impl core::UMatTrait, wscale: i32, iwscale: i32, _obj: *const c_void, _sz: size_t) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_KernelArg_int_UMatX_int_int_const_voidX_size_t(_flags, _m.as_raw_mut_UMat(), wscale, iwscale, _obj, _sz, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * wscale: 1 + /// * iwscale: 1 + /// * _obj: 0 + /// * _sz: 0 + #[inline] + pub fn new_def(_flags: i32, _m: &mut impl core::UMatTrait) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_KernelArg_int_UMatX(_flags, _m.as_raw_mut_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn default() -> core::KernelArg { + let ret = unsafe { sys::cv_ocl_KernelArg_KernelArg() }; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn local(local_mem_size: size_t) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_Local_size_t(local_mem_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn ptr_write_only(m: &impl core::UMatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_PtrWriteOnly_const_UMatR(m.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn ptr_read_only(m: &impl core::UMatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_PtrReadOnly_const_UMatR(m.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn ptr_read_write(m: &impl core::UMatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_PtrReadWrite_const_UMatR(m.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * wscale: 1 + /// * iwscale: 1 + #[inline] + pub fn read_write(m: &impl core::UMatTraitConst, wscale: i32, iwscale: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_ReadWrite_const_UMatR_int_int(m.as_raw_UMat(), wscale, iwscale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [KernelArg::read_write] function uses the following default values for its arguments: + /// * wscale: 1 + /// * iwscale: 1 + #[inline] + pub fn read_write_def(m: &impl core::UMatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_ReadWrite_const_UMatR(m.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * wscale: 1 + /// * iwscale: 1 + #[inline] + pub fn read_write_no_size(m: &impl core::UMatTraitConst, wscale: i32, iwscale: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_ReadWriteNoSize_const_UMatR_int_int(m.as_raw_UMat(), wscale, iwscale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [KernelArg::read_write_no_size] function uses the following default values for its arguments: + /// * wscale: 1 + /// * iwscale: 1 + #[inline] + pub fn read_write_no_size_def(m: &impl core::UMatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_ReadWriteNoSize_const_UMatR(m.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * wscale: 1 + /// * iwscale: 1 + #[inline] + pub fn read_only(m: &impl core::UMatTraitConst, wscale: i32, iwscale: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_ReadOnly_const_UMatR_int_int(m.as_raw_UMat(), wscale, iwscale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [KernelArg::read_only] function uses the following default values for its arguments: + /// * wscale: 1 + /// * iwscale: 1 + #[inline] + pub fn read_only_def(m: &impl core::UMatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_ReadOnly_const_UMatR(m.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * wscale: 1 + /// * iwscale: 1 + #[inline] + pub fn write_only(m: &impl core::UMatTraitConst, wscale: i32, iwscale: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_WriteOnly_const_UMatR_int_int(m.as_raw_UMat(), wscale, iwscale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [KernelArg::write_only] function uses the following default values for its arguments: + /// * wscale: 1 + /// * iwscale: 1 + #[inline] + pub fn write_only_def(m: &impl core::UMatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_WriteOnly_const_UMatR(m.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * wscale: 1 + /// * iwscale: 1 + #[inline] + pub fn read_only_no_size(m: &impl core::UMatTraitConst, wscale: i32, iwscale: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_ReadOnlyNoSize_const_UMatR_int_int(m.as_raw_UMat(), wscale, iwscale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [KernelArg::read_only_no_size] function uses the following default values for its arguments: + /// * wscale: 1 + /// * iwscale: 1 + #[inline] + pub fn read_only_no_size_def(m: &impl core::UMatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_ReadOnlyNoSize_const_UMatR(m.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * wscale: 1 + /// * iwscale: 1 + #[inline] + pub fn write_only_no_size(m: &impl core::UMatTraitConst, wscale: i32, iwscale: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_WriteOnlyNoSize_const_UMatR_int_int(m.as_raw_UMat(), wscale, iwscale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [KernelArg::write_only_no_size] function uses the following default values for its arguments: + /// * wscale: 1 + /// * iwscale: 1 + #[inline] + pub fn write_only_no_size_def(m: &impl core::UMatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_WriteOnlyNoSize_const_UMatR(m.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn constant(m: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_KernelArg_Constant_const_MatR(m.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::KernelArg::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::KernelArg] + pub trait KernelArgTraitConst { + fn as_raw_KernelArg(&self) -> *const c_void; + + #[inline] + fn flags(&self) -> i32 { + let ret = unsafe { sys::cv_ocl_KernelArg_propFlags_const(self.as_raw_KernelArg()) }; + ret + } + + #[inline] + fn obj(&self) -> *const c_void { + let ret = unsafe { sys::cv_ocl_KernelArg_propObj_const(self.as_raw_KernelArg()) }; + ret + } + + #[inline] + fn sz(&self) -> size_t { + let ret = unsafe { sys::cv_ocl_KernelArg_propSz_const(self.as_raw_KernelArg()) }; + ret + } + + #[inline] + fn wscale(&self) -> i32 { + let ret = unsafe { sys::cv_ocl_KernelArg_propWscale_const(self.as_raw_KernelArg()) }; + ret + } + + #[inline] + fn iwscale(&self) -> i32 { + let ret = unsafe { sys::cv_ocl_KernelArg_propIwscale_const(self.as_raw_KernelArg()) }; + ret + } + + } + + /// Mutable methods for [core::KernelArg] + pub trait KernelArgTrait: core::KernelArgTraitConst { + fn as_raw_mut_KernelArg(&mut self) -> *mut c_void; + + #[inline] + fn set_flags(&mut self, val: i32) { + let ret = unsafe { sys::cv_ocl_KernelArg_propFlags_const_int(self.as_raw_mut_KernelArg(), val) }; + ret + } + + #[inline] + fn m(&mut self) -> core::UMat { + let ret = unsafe { sys::cv_ocl_KernelArg_propM(self.as_raw_mut_KernelArg()) }; + let ret = unsafe { core::UMat::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn set_m(&mut self, val: &impl core::UMatTraitConst) { + let ret = unsafe { sys::cv_ocl_KernelArg_propM_UMatX(self.as_raw_mut_KernelArg(), val.as_raw_UMat()) }; + ret + } + + #[inline] + fn set_sz(&mut self, val: size_t) { + let ret = unsafe { sys::cv_ocl_KernelArg_propSz_const_size_t(self.as_raw_mut_KernelArg(), val) }; + ret + } + + #[inline] + fn set_wscale(&mut self, val: i32) { + let ret = unsafe { sys::cv_ocl_KernelArg_propWscale_const_int(self.as_raw_mut_KernelArg(), val) }; + ret + } + + #[inline] + fn set_iwscale(&mut self, val: i32) { + let ret = unsafe { sys::cv_ocl_KernelArg_propIwscale_const_int(self.as_raw_mut_KernelArg(), val) }; + ret + } + + } + + impl Default for KernelArg { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for KernelArg { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("KernelArg") + .field("flags", &core::KernelArgTraitConst::flags(self)) + .field("obj", &core::KernelArgTraitConst::obj(self)) + .field("sz", &core::KernelArgTraitConst::sz(self)) + .field("wscale", &core::KernelArgTraitConst::wscale(self)) + .field("iwscale", &core::KernelArgTraitConst::iwscale(self)) + .finish() + } + } + + impl core::KernelArgTraitConst for KernelArg { + #[inline] fn as_raw_KernelArg(&self) -> *const c_void { self.as_raw() } + } + + impl core::KernelArgTrait for KernelArg { + #[inline] fn as_raw_mut_KernelArg(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { KernelArg, core::KernelArgTraitConst, as_raw_KernelArg, core::KernelArgTrait, as_raw_mut_KernelArg } + + pub struct OpenCLExecutionContext { + ptr: *mut c_void, + } + + opencv_type_boxed! { OpenCLExecutionContext } + + impl Drop for OpenCLExecutionContext { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ocl_OpenCLExecutionContext_delete(self.as_raw_mut_OpenCLExecutionContext()) }; + } + } + + unsafe impl Send for OpenCLExecutionContext {} + + impl OpenCLExecutionContext { + #[inline] + pub fn default() -> core::OpenCLExecutionContext { + let ret = unsafe { sys::cv_ocl_OpenCLExecutionContext_OpenCLExecutionContext() }; + let ret = unsafe { core::OpenCLExecutionContext::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn copy(unnamed: &impl core::OpenCLExecutionContextTraitConst) -> core::OpenCLExecutionContext { + let ret = unsafe { sys::cv_ocl_OpenCLExecutionContext_OpenCLExecutionContext_const_OpenCLExecutionContextR(unnamed.as_raw_OpenCLExecutionContext()) }; + let ret = unsafe { core::OpenCLExecutionContext::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn copy_mut(mut unnamed: core::OpenCLExecutionContext) -> core::OpenCLExecutionContext { + let ret = unsafe { sys::cv_ocl_OpenCLExecutionContext_OpenCLExecutionContext_OpenCLExecutionContextRR(unnamed.as_raw_mut_OpenCLExecutionContext()) }; + let ret = unsafe { core::OpenCLExecutionContext::opencv_from_extern(ret) }; + ret + } + + /// Get OpenCL execution context of current thread. + /// + /// Initialize OpenCL execution context if it is empty + /// - create new + /// - reuse context of the main thread (threadID = 0) + #[inline] + pub fn get_current() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_getCurrent(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::OpenCLExecutionContext::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Get OpenCL execution context of current thread (can be empty) + #[inline] + pub fn get_current_ref() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_getCurrentRef(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::OpenCLExecutionContext::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates OpenCL execution context + /// OpenCV will check if available OpenCL platform has platformName name, + /// then assign context to OpenCV. + /// The deviceID device will be used as target device and a new command queue will be created. + /// + /// + /// Note: On success, ownership of one reference of the context and device is taken. + /// The caller should additionally call `clRetainContext` and/or `clRetainDevice` + /// to increase the reference count if it wishes to continue using them. + /// + /// ## Parameters + /// * platformName: name of OpenCL platform to attach, this string is used to check if platform is available to OpenCV at runtime + /// * platformID: ID of platform attached context was created for (cl_platform_id) + /// * context: OpenCL context to be attached to OpenCV (cl_context) + /// * deviceID: OpenCL device (cl_device_id) + #[inline] + pub unsafe fn create(platform_name: &str, platform_id: *mut c_void, context: *mut c_void, device_id: *mut c_void) -> Result { + extern_container_arg!(platform_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_create_const_stringR_voidX_voidX_voidX(platform_name.opencv_as_extern(), platform_id, context, device_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::OpenCLExecutionContext::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates OpenCL execution context + /// + /// ## Parameters + /// * context: non-empty OpenCL context + /// * device: non-empty OpenCL device (must be a part of context) + /// * queue: non-empty OpenCL queue for provided context and device + #[inline] + pub fn create_1(context: &impl core::ContextTraitConst, device: &impl core::DeviceTraitConst, queue: &impl core::QueueTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_create_const_ContextR_const_DeviceR_const_QueueR(context.as_raw_Context(), device.as_raw_Device(), queue.as_raw_Queue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::OpenCLExecutionContext::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates OpenCL execution context + /// + /// ## Parameters + /// * context: non-empty OpenCL context + /// * device: non-empty OpenCL device (must be a part of context) + /// * queue: non-empty OpenCL queue for provided context and device + /// + /// ## Overloaded parameters + #[inline] + pub fn create_2(context: &impl core::ContextTraitConst, device: &impl core::DeviceTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_create_const_ContextR_const_DeviceR(context.as_raw_Context(), device.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::OpenCLExecutionContext::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::OpenCLExecutionContext] + pub trait OpenCLExecutionContextTraitConst { + fn as_raw_OpenCLExecutionContext(&self) -> *const c_void; + + /// Get associated ocl::Context + #[inline] + fn get_context(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_getContext_const(self.as_raw_OpenCLExecutionContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Context::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Get the single default associated ocl::Device + #[inline] + fn get_device(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_getDevice_const(self.as_raw_OpenCLExecutionContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Device::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Get the single ocl::Queue that is associated with the ocl::Context and + /// the single default ocl::Device + #[inline] + fn get_queue(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_getQueue_const(self.as_raw_OpenCLExecutionContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Queue::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn use_opencl(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_useOpenCL_const(self.as_raw_OpenCLExecutionContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Bind this OpenCL execution context to current thread. + /// + /// Context can't be empty. + /// + /// + /// Note: clFinish is not called for queue of previous execution context + #[inline] + fn bind(&self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_bind_const(self.as_raw_OpenCLExecutionContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates new execution context with same OpenCV context and device + /// + /// ## Parameters + /// * q: OpenCL queue + #[inline] + fn clone_with_new_queue(&self, q: &impl core::QueueTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_cloneWithNewQueue_const_const_QueueR(self.as_raw_OpenCLExecutionContext(), q.as_raw_Queue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::OpenCLExecutionContext::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates new execution context with same OpenCV context and device + /// + /// ## Parameters + /// * q: OpenCL queue + /// + /// ## Overloaded parameters + #[inline] + fn clone_with_new_queue_1(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_cloneWithNewQueue_const(self.as_raw_OpenCLExecutionContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::OpenCLExecutionContext::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_empty_const(self.as_raw_OpenCLExecutionContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::OpenCLExecutionContext] + pub trait OpenCLExecutionContextTrait: core::OpenCLExecutionContextTraitConst { + fn as_raw_mut_OpenCLExecutionContext(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, unnamed: &impl core::OpenCLExecutionContextTraitConst) { + let ret = unsafe { sys::cv_ocl_OpenCLExecutionContext_operatorST_const_OpenCLExecutionContextR(self.as_raw_mut_OpenCLExecutionContext(), unnamed.as_raw_OpenCLExecutionContext()) }; + ret + } + + #[inline] + fn set_1(&mut self, mut unnamed: core::OpenCLExecutionContext) { + let ret = unsafe { sys::cv_ocl_OpenCLExecutionContext_operatorST_OpenCLExecutionContextRR(self.as_raw_mut_OpenCLExecutionContext(), unnamed.as_raw_mut_OpenCLExecutionContext()) }; + ret + } + + #[inline] + fn set_use_opencl(&mut self, flag: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_setUseOpenCL_bool(self.as_raw_mut_OpenCLExecutionContext(), flag, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn release(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_OpenCLExecutionContext_release(self.as_raw_mut_OpenCLExecutionContext(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl Default for OpenCLExecutionContext { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for OpenCLExecutionContext { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("OpenCLExecutionContext") + .finish() + } + } + + impl core::OpenCLExecutionContextTraitConst for OpenCLExecutionContext { + #[inline] fn as_raw_OpenCLExecutionContext(&self) -> *const c_void { self.as_raw() } + } + + impl core::OpenCLExecutionContextTrait for OpenCLExecutionContext { + #[inline] fn as_raw_mut_OpenCLExecutionContext(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { OpenCLExecutionContext, core::OpenCLExecutionContextTraitConst, as_raw_OpenCLExecutionContext, core::OpenCLExecutionContextTrait, as_raw_mut_OpenCLExecutionContext } + + /// @deprecated + pub struct Platform { + ptr: *mut c_void, + } + + opencv_type_boxed! { Platform } + + impl Drop for Platform { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ocl_Platform_delete(self.as_raw_mut_Platform()) }; + } + } + + unsafe impl Send for Platform {} + + impl Platform { + #[inline] + pub fn default() -> core::Platform { + let ret = unsafe { sys::cv_ocl_Platform_Platform() }; + let ret = unsafe { core::Platform::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn copy(p: &impl core::PlatformTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Platform_Platform_const_PlatformR(p.as_raw_Platform(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Platform::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn copy_mut(mut p: core::Platform) -> core::Platform { + let ret = unsafe { sys::cv_ocl_Platform_Platform_PlatformRR(p.as_raw_mut_Platform()) }; + let ret = unsafe { core::Platform::opencv_from_extern(ret) }; + ret + } + + /// @deprecated + #[inline] + pub fn get_default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Platform_getDefault(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Platform::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Platform] + pub trait PlatformTraitConst { + fn as_raw_Platform(&self) -> *const c_void; + + #[inline] + fn ptr(&self) -> Result<*mut c_void> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Platform_ptr_const(self.as_raw_Platform(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Platform_empty_const(self.as_raw_Platform(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::Platform] + pub trait PlatformTrait: core::PlatformTraitConst { + fn as_raw_mut_Platform(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, p: &impl core::PlatformTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Platform_operatorST_const_PlatformR(self.as_raw_mut_Platform(), p.as_raw_Platform(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_1(&mut self, mut p: core::Platform) { + let ret = unsafe { sys::cv_ocl_Platform_operatorST_PlatformRR(self.as_raw_mut_Platform(), p.as_raw_mut_Platform()) }; + ret + } + + } + + impl Default for Platform { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for Platform { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Platform") + .finish() + } + } + + impl core::PlatformTraitConst for Platform { + #[inline] fn as_raw_Platform(&self) -> *const c_void { self.as_raw() } + } + + impl core::PlatformTrait for Platform { + #[inline] fn as_raw_mut_Platform(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Platform, core::PlatformTraitConst, as_raw_Platform, core::PlatformTrait, as_raw_mut_Platform } + + pub struct PlatformInfo { + ptr: *mut c_void, + } + + opencv_type_boxed! { PlatformInfo } + + impl Drop for PlatformInfo { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ocl_PlatformInfo_delete(self.as_raw_mut_PlatformInfo()) }; + } + } + + unsafe impl Send for PlatformInfo {} + + /// Constant methods for [core::PlatformInfo] + pub trait PlatformInfoTraitConst { + fn as_raw_PlatformInfo(&self) -> *const c_void; + + #[inline] + fn name(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_PlatformInfo_name_const(self.as_raw_PlatformInfo(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn vendor(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_PlatformInfo_vendor_const(self.as_raw_PlatformInfo(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// See CL_PLATFORM_VERSION + #[inline] + fn version(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_PlatformInfo_version_const(self.as_raw_PlatformInfo(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn version_major(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_PlatformInfo_versionMajor_const(self.as_raw_PlatformInfo(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn version_minor(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_PlatformInfo_versionMinor_const(self.as_raw_PlatformInfo(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn device_number(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_PlatformInfo_deviceNumber_const(self.as_raw_PlatformInfo(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_device(&self, device: &mut impl core::DeviceTrait, d: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_PlatformInfo_getDevice_const_DeviceR_int(self.as_raw_PlatformInfo(), device.as_raw_mut_Device(), d, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_PlatformInfo_empty_const(self.as_raw_PlatformInfo(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::PlatformInfo] + pub trait PlatformInfoTrait: core::PlatformInfoTraitConst { + fn as_raw_mut_PlatformInfo(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, i: &impl core::PlatformInfoTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_PlatformInfo_operatorST_const_PlatformInfoR(self.as_raw_mut_PlatformInfo(), i.as_raw_PlatformInfo(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_1(&mut self, mut i: core::PlatformInfo) { + let ret = unsafe { sys::cv_ocl_PlatformInfo_operatorST_PlatformInfoRR(self.as_raw_mut_PlatformInfo(), i.as_raw_mut_PlatformInfo()) }; + ret + } + + } + + impl std::fmt::Debug for PlatformInfo { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PlatformInfo") + .finish() + } + } + + impl core::PlatformInfoTraitConst for PlatformInfo { + #[inline] fn as_raw_PlatformInfo(&self) -> *const c_void { self.as_raw() } + } + + impl core::PlatformInfoTrait for PlatformInfo { + #[inline] fn as_raw_mut_PlatformInfo(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PlatformInfo, core::PlatformInfoTraitConst, as_raw_PlatformInfo, core::PlatformInfoTrait, as_raw_mut_PlatformInfo } + + pub struct Program { + ptr: *mut c_void, + } + + opencv_type_boxed! { Program } + + impl Drop for Program { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ocl_Program_delete(self.as_raw_mut_Program()) }; + } + } + + unsafe impl Send for Program {} + + impl Program { + #[inline] + pub fn get_prefix_build_flags(buildflags: &str) -> Result { + extern_container_arg!(buildflags); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Program_getPrefix_const_StringR(buildflags.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Program] + pub trait ProgramTraitConst { + fn as_raw_Program(&self) -> *const c_void; + + #[inline] + fn ptr(&self) -> Result<*mut c_void> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Program_ptr_const(self.as_raw_Program(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Query device-specific program binary. + /// + /// Returns RAW OpenCL executable binary without additional attachments. + /// ## See also + /// ProgramSource::fromBinary + /// + /// ## Parameters + /// * binary:[out] output buffer + #[inline] + fn get_binary(&self, binary: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Program_getBinary_const_vectorLcharGR(self.as_raw_Program(), binary.as_raw_mut_VectorOfc_char(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Program_empty_const(self.as_raw_Program(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn write(&self, buf: &mut String) -> Result { + string_arg_output_send!(via buf_via); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Program_write_const_StringR(self.as_raw_Program(), &mut buf_via, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + string_arg_output_receive!(buf_via => buf); + Ok(ret) + } + + #[inline] + fn get_prefix(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Program_getPrefix_const(self.as_raw_Program(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Mutable methods for [core::Program] + pub trait ProgramTrait: core::ProgramTraitConst { + fn as_raw_mut_Program(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, prog: &impl core::ProgramTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Program_operatorST_const_ProgramR(self.as_raw_mut_Program(), prog.as_raw_Program(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_1(&mut self, mut prog: core::Program) { + let ret = unsafe { sys::cv_ocl_Program_operatorST_ProgramRR(self.as_raw_mut_Program(), prog.as_raw_mut_Program()) }; + ret + } + + #[inline] + fn read(&mut self, buf: &str, buildflags: &str) -> Result { + extern_container_arg!(buf); + extern_container_arg!(buildflags); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Program_read_const_StringR_const_StringR(self.as_raw_mut_Program(), buf.opencv_as_extern(), buildflags.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for Program { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Program") + .finish() + } + } + + impl core::ProgramTraitConst for Program { + #[inline] fn as_raw_Program(&self) -> *const c_void { self.as_raw() } + } + + impl core::ProgramTrait for Program { + #[inline] fn as_raw_mut_Program(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Program, core::ProgramTraitConst, as_raw_Program, core::ProgramTrait, as_raw_mut_Program } + + pub struct ProgramSource { + ptr: *mut c_void, + } + + opencv_type_boxed! { ProgramSource } + + impl Drop for ProgramSource { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ocl_ProgramSource_delete(self.as_raw_mut_ProgramSource()) }; + } + } + + unsafe impl Send for ProgramSource {} + + impl ProgramSource { + /// Describe OpenCL program binary. + /// Do not call clCreateProgramWithBinary() and/or clBuildProgram(). + /// + /// Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies). + /// + /// This kind of binary is not portable between platforms in general - it is specific to OpenCL vendor / device / driver version. + /// + /// ## Parameters + /// * module: name of program owner module + /// * name: unique name of program (module+name is used as key for OpenCL program caching) + /// * binary: buffer address. See buffer lifetime requirement in description. + /// * size: buffer size + /// * buildOptions: additional program-related build options passed to clBuildProgram() + /// ## Returns + /// created ProgramSource object + /// + /// ## C++ default parameters + /// * build_options: cv::String() + #[inline] + pub fn from_binary(module: &str, name: &str, binary: &[u8], build_options: &str) -> Result { + extern_container_arg!(module); + extern_container_arg!(name); + extern_container_arg!(build_options); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_ProgramSource_fromBinary_const_StringR_const_StringR_const_unsigned_charX_const_size_t_const_StringR(module.opencv_as_extern(), name.opencv_as_extern(), binary.as_ptr(), binary.len(), build_options.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::ProgramSource::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Describe OpenCL program binary. + /// Do not call clCreateProgramWithBinary() and/or clBuildProgram(). + /// + /// Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies). + /// + /// This kind of binary is not portable between platforms in general - it is specific to OpenCL vendor / device / driver version. + /// + /// ## Parameters + /// * module: name of program owner module + /// * name: unique name of program (module+name is used as key for OpenCL program caching) + /// * binary: buffer address. See buffer lifetime requirement in description. + /// * size: buffer size + /// * buildOptions: additional program-related build options passed to clBuildProgram() + /// ## Returns + /// created ProgramSource object + /// + /// ## Note + /// This alternative version of [ProgramSource::from_binary] function uses the following default values for its arguments: + /// * build_options: cv::String() + #[inline] + pub fn from_binary_def(module: &str, name: &str, binary: &[u8]) -> Result { + extern_container_arg!(module); + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_ProgramSource_fromBinary_const_StringR_const_StringR_const_unsigned_charX_const_size_t(module.opencv_as_extern(), name.opencv_as_extern(), binary.as_ptr(), binary.len(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::ProgramSource::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Describe OpenCL program in SPIR format. + /// Do not call clCreateProgramWithBinary() and/or clBuildProgram(). + /// + /// Supports SPIR 1.2 by default (pass '-spir-std=X.Y' in buildOptions to override this behavior) + /// + /// Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies). + /// + /// Programs in this format are portable between OpenCL implementations with 'khr_spir' extension: + /// + /// (but they are not portable between different platforms: 32-bit / 64-bit) + /// + /// Note: these programs can't support vendor specific extensions, like 'cl_intel_subgroups'. + /// + /// ## Parameters + /// * module: name of program owner module + /// * name: unique name of program (module+name is used as key for OpenCL program caching) + /// * binary: buffer address. See buffer lifetime requirement in description. + /// * size: buffer size + /// * buildOptions: additional program-related build options passed to clBuildProgram() + /// (these options are added automatically: '-x spir' and '-spir-std=1.2') + /// ## Returns + /// created ProgramSource object. + /// + /// ## C++ default parameters + /// * build_options: cv::String() + #[inline] + pub fn from_spir(module: &str, name: &str, binary: &[u8], build_options: &str) -> Result { + extern_container_arg!(module); + extern_container_arg!(name); + extern_container_arg!(build_options); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_ProgramSource_fromSPIR_const_StringR_const_StringR_const_unsigned_charX_const_size_t_const_StringR(module.opencv_as_extern(), name.opencv_as_extern(), binary.as_ptr(), binary.len(), build_options.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::ProgramSource::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Describe OpenCL program in SPIR format. + /// Do not call clCreateProgramWithBinary() and/or clBuildProgram(). + /// + /// Supports SPIR 1.2 by default (pass '-spir-std=X.Y' in buildOptions to override this behavior) + /// + /// Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies). + /// + /// Programs in this format are portable between OpenCL implementations with 'khr_spir' extension: + /// + /// (but they are not portable between different platforms: 32-bit / 64-bit) + /// + /// Note: these programs can't support vendor specific extensions, like 'cl_intel_subgroups'. + /// + /// ## Parameters + /// * module: name of program owner module + /// * name: unique name of program (module+name is used as key for OpenCL program caching) + /// * binary: buffer address. See buffer lifetime requirement in description. + /// * size: buffer size + /// * buildOptions: additional program-related build options passed to clBuildProgram() + /// (these options are added automatically: '-x spir' and '-spir-std=1.2') + /// ## Returns + /// created ProgramSource object. + /// + /// ## Note + /// This alternative version of [ProgramSource::from_spir] function uses the following default values for its arguments: + /// * build_options: cv::String() + #[inline] + pub fn from_spir_def(module: &str, name: &str, binary: &[u8]) -> Result { + extern_container_arg!(module); + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_ProgramSource_fromSPIR_const_StringR_const_StringR_const_unsigned_charX_const_size_t(module.opencv_as_extern(), name.opencv_as_extern(), binary.as_ptr(), binary.len(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::ProgramSource::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::ProgramSource] + pub trait ProgramSourceTraitConst { + fn as_raw_ProgramSource(&self) -> *const c_void; + + #[inline] + fn source(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_ProgramSource_source_const(self.as_raw_ProgramSource(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn hash(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_ProgramSource_hash_const(self.as_raw_ProgramSource(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_ProgramSource_empty_const(self.as_raw_ProgramSource(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::ProgramSource] + pub trait ProgramSourceTrait: core::ProgramSourceTraitConst { + fn as_raw_mut_ProgramSource(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, prog: &impl core::ProgramSourceTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_ProgramSource_operatorST_const_ProgramSourceR(self.as_raw_mut_ProgramSource(), prog.as_raw_ProgramSource(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_1(&mut self, mut prog: core::ProgramSource) { + let ret = unsafe { sys::cv_ocl_ProgramSource_operatorST_ProgramSourceRR(self.as_raw_mut_ProgramSource(), prog.as_raw_mut_ProgramSource()) }; + ret + } + + } + + impl std::fmt::Debug for ProgramSource { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ProgramSource") + .finish() + } + } + + impl core::ProgramSourceTraitConst for ProgramSource { + #[inline] fn as_raw_ProgramSource(&self) -> *const c_void { self.as_raw() } + } + + impl core::ProgramSourceTrait for ProgramSource { + #[inline] fn as_raw_mut_ProgramSource(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ProgramSource, core::ProgramSourceTraitConst, as_raw_ProgramSource, core::ProgramSourceTrait, as_raw_mut_ProgramSource } + + pub struct Queue { + ptr: *mut c_void, + } + + opencv_type_boxed! { Queue } + + impl Drop for Queue { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ocl_Queue_delete(self.as_raw_mut_Queue()) }; + } + } + + unsafe impl Send for Queue {} + + impl Queue { + #[inline] + pub fn get_default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Queue_getDefault(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Queue::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Queue] + pub trait QueueTraitConst { + fn as_raw_Queue(&self) -> *const c_void; + + #[inline] + fn ptr(&self) -> Result<*mut c_void> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Queue_ptr_const(self.as_raw_Queue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns OpenCL command queue with enable profiling mode support + #[inline] + fn get_profiling_queue(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Queue_getProfilingQueue_const(self.as_raw_Queue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Queue::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Queue_empty_const(self.as_raw_Queue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::Queue] + pub trait QueueTrait: core::QueueTraitConst { + fn as_raw_mut_Queue(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, q: &impl core::QueueTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Queue_operatorST_const_QueueR(self.as_raw_mut_Queue(), q.as_raw_Queue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_1(&mut self, mut q: core::Queue) { + let ret = unsafe { sys::cv_ocl_Queue_operatorST_QueueRR(self.as_raw_mut_Queue(), q.as_raw_mut_Queue()) }; + ret + } + + /// ## C++ default parameters + /// * c: Context() + /// * d: Device() + #[inline] + fn create(&mut self, c: &impl core::ContextTraitConst, d: &impl core::DeviceTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Queue_create_const_ContextR_const_DeviceR(self.as_raw_mut_Queue(), c.as_raw_Context(), d.as_raw_Device(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [QueueTrait::create] function uses the following default values for its arguments: + /// * c: Context() + /// * d: Device() + #[inline] + fn create_def(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Queue_create(self.as_raw_mut_Queue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn finish(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Queue_finish(self.as_raw_mut_Queue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for Queue { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Queue") + .finish() + } + } + + impl core::QueueTraitConst for Queue { + #[inline] fn as_raw_Queue(&self) -> *const c_void { self.as_raw() } + } + + impl core::QueueTrait for Queue { + #[inline] fn as_raw_mut_Queue(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Queue, core::QueueTraitConst, as_raw_Queue, core::QueueTrait, as_raw_mut_Queue } + + pub struct Timer { + ptr: *mut c_void, + } + + opencv_type_boxed! { Timer } + + impl Drop for Timer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_ocl_Timer_delete(self.as_raw_mut_Timer()) }; + } + } + + unsafe impl Send for Timer {} + + impl Timer { + #[inline] + pub fn new(q: &impl core::QueueTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Timer_Timer_const_QueueR(q.as_raw_Queue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Timer::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::Timer] + pub trait TimerTraitConst { + fn as_raw_Timer(&self) -> *const c_void; + + #[inline] + fn duration_ns(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Timer_durationNS_const(self.as_raw_Timer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::Timer] + pub trait TimerTrait: core::TimerTraitConst { + fn as_raw_mut_Timer(&mut self) -> *mut c_void; + + #[inline] + fn start(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Timer_start(self.as_raw_mut_Timer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn stop(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ocl_Timer_stop(self.as_raw_mut_Timer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for Timer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Timer") + .finish() + } + } + + impl core::TimerTraitConst for Timer { + #[inline] fn as_raw_Timer(&self) -> *const c_void { self.as_raw() } + } + + impl core::TimerTrait for Timer { + #[inline] fn as_raw_mut_Timer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Timer, core::TimerTraitConst, as_raw_Timer, core::TimerTrait, as_raw_mut_Timer } + + #[repr(C)] + #[derive(Copy, Clone, Debug, PartialEq)] + pub struct ClassWithKeywordProperties { + pub lambda: i32, + pub except: i32, + } + + opencv_type_simple! { core::ClassWithKeywordProperties } + + impl ClassWithKeywordProperties { + /// ## C++ default parameters + /// * lambda_arg: 24 + /// * except_arg: 42 + #[inline] + pub fn new(lambda_arg: i32, except_arg: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_ClassWithKeywordProperties_ClassWithKeywordProperties_int_int(lambda_arg, except_arg, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * lambda_arg: 24 + /// * except_arg: 42 + #[inline] + pub fn new_def() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_ClassWithKeywordProperties_ClassWithKeywordProperties(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + #[repr(C)] + #[derive(Copy, Clone, Debug, PartialEq)] + pub struct FunctionParams { + pub lambda: i32, + pub sigma: f32, + } + + opencv_type_simple! { core::FunctionParams } + + impl FunctionParams { + #[inline] + pub fn set_lambda(self, value: i32) -> core::FunctionParams { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_FunctionParams_setLambda_int(&self, value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + pub fn set_sigma(self, value: f32) -> core::FunctionParams { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_FunctionParams_setSigma_float(&self, value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + } + + pub struct LogTag { + ptr: *mut c_void, + } + + opencv_type_boxed! { LogTag } + + impl Drop for LogTag { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_utils_logging_LogTag_delete(self.as_raw_mut_LogTag()) }; + } + } + + unsafe impl Send for LogTag {} + + impl LogTag { + #[inline] + pub fn new(_name: &str, _level: core::LogLevel) -> Result { + extern_container_arg!(_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_logging_LogTag_LogTag_const_charX_LogLevel(_name.opencv_as_extern(), _level, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::LogTag::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::LogTag] + pub trait LogTagTraitConst { + fn as_raw_LogTag(&self) -> *const c_void; + + #[inline] + fn name(&self) -> String { + let ret = unsafe { sys::cv_utils_logging_LogTag_propName_const(self.as_raw_LogTag()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn level(&self) -> core::LogLevel { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_logging_LogTag_propLevel_const(self.as_raw_LogTag(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + } + + /// Mutable methods for [core::LogTag] + pub trait LogTagTrait: core::LogTagTraitConst { + fn as_raw_mut_LogTag(&mut self) -> *mut c_void; + + #[inline] + fn set_level(&mut self, val: core::LogLevel) { + let ret = unsafe { sys::cv_utils_logging_LogTag_propLevel_const_LogLevel(self.as_raw_mut_LogTag(), val) }; + ret + } + + } + + impl std::fmt::Debug for LogTag { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("LogTag") + .field("name", &core::LogTagTraitConst::name(self)) + .field("level", &core::LogTagTraitConst::level(self)) + .finish() + } + } + + impl core::LogTagTraitConst for LogTag { + #[inline] fn as_raw_LogTag(&self) -> *const c_void { self.as_raw() } + } + + impl core::LogTagTrait for LogTag { + #[inline] fn as_raw_mut_LogTag(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LogTag, core::LogTagTraitConst, as_raw_LogTag, core::LogTagTrait, as_raw_mut_LogTag } + + pub struct OriginalClassName { + ptr: *mut c_void, + } + + opencv_type_boxed! { OriginalClassName } + + impl Drop for OriginalClassName { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_utils_nested_OriginalClassName_delete(self.as_raw_mut_OriginalClassName()) }; + } + } + + unsafe impl Send for OriginalClassName {} + + impl OriginalClassName { + /// ## C++ default parameters + /// * params: OriginalClassName::Params() + #[inline] + pub fn new(params: core::OriginalClassName_Params) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_nested_OriginalClassName_OriginalClassName_const_ParamsR(¶ms, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::OriginalClassName::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * params: OriginalClassName::Params() + #[inline] + pub fn new_def() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_nested_OriginalClassName_OriginalClassName(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::OriginalClassName::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn original_name() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_nested_OriginalClassName_originalName(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * params: OriginalClassName::Params() + #[inline] + pub fn create(params: core::OriginalClassName_Params) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_nested_OriginalClassName_create_const_ParamsR(¶ms, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [OriginalClassName::create] function uses the following default values for its arguments: + /// * params: OriginalClassName::Params() + #[inline] + pub fn create_def() -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_nested_OriginalClassName_create(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [core::OriginalClassName] + pub trait OriginalClassNameTraitConst { + fn as_raw_OriginalClassName(&self) -> *const c_void; + + #[inline] + fn get_int_param(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_nested_OriginalClassName_getIntParam_const(self.as_raw_OriginalClassName(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_float_param(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_nested_OriginalClassName_getFloatParam_const(self.as_raw_OriginalClassName(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [core::OriginalClassName] + pub trait OriginalClassNameTrait: core::OriginalClassNameTraitConst { + fn as_raw_mut_OriginalClassName(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for OriginalClassName { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("OriginalClassName") + .finish() + } + } + + impl core::OriginalClassNameTraitConst for OriginalClassName { + #[inline] fn as_raw_OriginalClassName(&self) -> *const c_void { self.as_raw() } + } + + impl core::OriginalClassNameTrait for OriginalClassName { + #[inline] fn as_raw_mut_OriginalClassName(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { OriginalClassName, core::OriginalClassNameTraitConst, as_raw_OriginalClassName, core::OriginalClassNameTrait, as_raw_mut_OriginalClassName } + + #[repr(C)] + #[derive(Copy, Clone, Debug, PartialEq)] + pub struct OriginalClassName_Params { + pub int_value: i32, + pub float_value: f32, + } + + opencv_type_simple! { core::OriginalClassName_Params } + + impl OriginalClassName_Params { + /// ## C++ default parameters + /// * int_param: 123 + /// * float_param: 3.5f + #[inline] + pub fn new(int_param: i32, float_param: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_nested_OriginalClassName_Params_Params_int_float(int_param, float_param, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * int_param: 123 + /// * float_param: 3.5f + #[inline] + pub fn new_def() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_utils_nested_OriginalClassName_Params_Params(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + +pub use crate::manual::core::*; +} diff --git a/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs b/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs new file mode 100644 index 0000000..0b5f792 --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/opencv/dnn.rs @@ -0,0 +1,21773 @@ +pub mod dnn { + //! # Deep Neural Network module + //! This module contains: + //! - API for new layers creation, layers are building bricks of neural networks; + //! - set of built-in most-useful Layers; + //! - API to construct and modify comprehensive neural networks from layers; + //! - functionality for loading serialized networks models from different frameworks. + //! + //! Functionality of this module is designed only for forward pass computations (i.e. network testing). + //! A network training is in principle not supported. + use crate::mod_prelude::*; + use crate::{core, sys, types}; + pub mod prelude { + pub use super::{AbsLayerTrait, AbsLayerTraitConst, AccumLayerTrait, AccumLayerTraitConst, AcosLayerTrait, AcosLayerTraitConst, AcoshLayerTrait, AcoshLayerTraitConst, ActivationLayerInt8Trait, ActivationLayerInt8TraitConst, ActivationLayerTrait, ActivationLayerTraitConst, ArgLayerTrait, ArgLayerTraitConst, AsinLayerTrait, AsinLayerTraitConst, AsinhLayerTrait, AsinhLayerTraitConst, AtanLayerTrait, AtanLayerTraitConst, AtanhLayerTrait, AtanhLayerTraitConst, AttentionLayerTrait, AttentionLayerTraitConst, BNLLLayerTrait, BNLLLayerTraitConst, BackendNodeTrait, BackendNodeTraitConst, BackendWrapperTrait, BackendWrapperTraitConst, BaseConvolutionLayerTrait, BaseConvolutionLayerTraitConst, BatchNormLayerInt8Trait, BatchNormLayerInt8TraitConst, BatchNormLayerTrait, BatchNormLayerTraitConst, BlankLayerTrait, BlankLayerTraitConst, CeilLayerTrait, CeilLayerTraitConst, CeluLayerTrait, CeluLayerTraitConst, ChannelsPReLULayerTrait, ChannelsPReLULayerTraitConst, ClassificationModelTrait, ClassificationModelTraitConst, CompareLayerTrait, CompareLayerTraitConst, ConcatLayerTrait, ConcatLayerTraitConst, ConstLayerTrait, ConstLayerTraitConst, ConvolutionLayerInt8Trait, ConvolutionLayerInt8TraitConst, ConvolutionLayerTrait, ConvolutionLayerTraitConst, CorrelationLayerTrait, CorrelationLayerTraitConst, CosLayerTrait, CosLayerTraitConst, CoshLayerTrait, CoshLayerTraitConst, CropAndResizeLayerTrait, CropAndResizeLayerTraitConst, CropLayerTrait, CropLayerTraitConst, CumSumLayerTrait, CumSumLayerTraitConst, DataAugmentationLayerTrait, DataAugmentationLayerTraitConst, DeconvolutionLayerTrait, DeconvolutionLayerTraitConst, DepthToSpaceLayerTrait, DepthToSpaceLayerTraitConst, DequantizeLayerTrait, DequantizeLayerTraitConst, DetectionModelTrait, DetectionModelTraitConst, DetectionOutputLayerTrait, DetectionOutputLayerTraitConst, DictTrait, DictTraitConst, DictValueTrait, DictValueTraitConst, ELULayerTrait, ELULayerTraitConst, EinsumLayerTrait, EinsumLayerTraitConst, EltwiseLayerInt8Trait, EltwiseLayerInt8TraitConst, EltwiseLayerTrait, EltwiseLayerTraitConst, ErfLayerTrait, ErfLayerTraitConst, ExpLayerTrait, ExpLayerTraitConst, ExpandLayerTrait, ExpandLayerTraitConst, FlattenLayerTrait, FlattenLayerTraitConst, FloorLayerTrait, FloorLayerTraitConst, FlowWarpLayerTrait, FlowWarpLayerTraitConst, GRULayerTrait, GRULayerTraitConst, GatherElementsLayerTrait, GatherElementsLayerTraitConst, GatherLayerTrait, GatherLayerTraitConst, GeluApproximationLayerTrait, GeluApproximationLayerTraitConst, GeluLayerTrait, GeluLayerTraitConst, GemmLayerTrait, GemmLayerTraitConst, GroupNormLayerTrait, GroupNormLayerTraitConst, HardSigmoidLayerTrait, HardSigmoidLayerTraitConst, HardSwishLayerTrait, HardSwishLayerTraitConst, InnerProductLayerInt8Trait, InnerProductLayerInt8TraitConst, InnerProductLayerTrait, InnerProductLayerTraitConst, InstanceNormLayerTrait, InstanceNormLayerTraitConst, InterpLayerTrait, InterpLayerTraitConst, KeypointsModelTrait, KeypointsModelTraitConst, LRNLayerTrait, LRNLayerTraitConst, LSTMLayerTrait, LSTMLayerTraitConst, LayerFactoryTrait, LayerFactoryTraitConst, LayerNormLayerTrait, LayerNormLayerTraitConst, LayerParamsTrait, LayerParamsTraitConst, LayerTrait, LayerTraitConst, LogLayerTrait, LogLayerTraitConst, MVNLayerTrait, MVNLayerTraitConst, MatMulLayerTrait, MatMulLayerTraitConst, MaxUnpoolLayerTrait, MaxUnpoolLayerTraitConst, MishLayerTrait, MishLayerTraitConst, ModelTrait, ModelTraitConst, NaryEltwiseLayerTrait, NaryEltwiseLayerTraitConst, NetTrait, NetTraitConst, NormalizeBBoxLayerTrait, NormalizeBBoxLayerTraitConst, NotLayerTrait, NotLayerTraitConst, PaddingLayerTrait, PaddingLayerTraitConst, PermuteLayerTrait, PermuteLayerTraitConst, PoolingLayerInt8Trait, PoolingLayerInt8TraitConst, PoolingLayerTrait, PoolingLayerTraitConst, PowerLayerTrait, PowerLayerTraitConst, PriorBoxLayerTrait, PriorBoxLayerTraitConst, ProposalLayerTrait, ProposalLayerTraitConst, QuantizeLayerTrait, QuantizeLayerTraitConst, RNNLayerTrait, RNNLayerTraitConst, ReLU6LayerTrait, ReLU6LayerTraitConst, ReLULayerTrait, ReLULayerTraitConst, ReciprocalLayerTrait, ReciprocalLayerTraitConst, ReduceLayerTrait, ReduceLayerTraitConst, RegionLayerTrait, RegionLayerTraitConst, ReorgLayerTrait, ReorgLayerTraitConst, RequantizeLayerTrait, RequantizeLayerTraitConst, ReshapeLayerTrait, ReshapeLayerTraitConst, ResizeLayerTrait, ResizeLayerTraitConst, RoundLayerTrait, RoundLayerTraitConst, ScaleLayerInt8Trait, ScaleLayerInt8TraitConst, ScaleLayerTrait, ScaleLayerTraitConst, ScatterLayerTrait, ScatterLayerTraitConst, ScatterNDLayerTrait, ScatterNDLayerTraitConst, SegmentationModelTrait, SegmentationModelTraitConst, SeluLayerTrait, SeluLayerTraitConst, ShiftLayerInt8Trait, ShiftLayerInt8TraitConst, ShiftLayerTrait, ShiftLayerTraitConst, ShrinkLayerTrait, ShrinkLayerTraitConst, ShuffleChannelLayerTrait, ShuffleChannelLayerTraitConst, SigmoidLayerTrait, SigmoidLayerTraitConst, SignLayerTrait, SignLayerTraitConst, SinLayerTrait, SinLayerTraitConst, SinhLayerTrait, SinhLayerTraitConst, SliceLayerTrait, SliceLayerTraitConst, SoftmaxLayerInt8Trait, SoftmaxLayerInt8TraitConst, SoftmaxLayerTrait, SoftmaxLayerTraitConst, SoftplusLayerTrait, SoftplusLayerTraitConst, SoftsignLayerTrait, SoftsignLayerTraitConst, SpaceToDepthLayerTrait, SpaceToDepthLayerTraitConst, SplitLayerTrait, SplitLayerTraitConst, SqrtLayerTrait, SqrtLayerTraitConst, SwishLayerTrait, SwishLayerTraitConst, TanHLayerTrait, TanHLayerTraitConst, TanLayerTrait, TanLayerTraitConst, TextDetectionModelTrait, TextDetectionModelTraitConst, TextDetectionModel_DBTrait, TextDetectionModel_DBTraitConst, TextDetectionModel_EASTTrait, TextDetectionModel_EASTTraitConst, TextRecognitionModelTrait, TextRecognitionModelTraitConst, ThresholdedReluLayerTrait, ThresholdedReluLayerTraitConst, TileLayerTrait, TileLayerTraitConst, TopKLayerTrait, TopKLayerTraitConst, _RangeTrait, _RangeTraitConst}; + } + + pub const CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH: &str = "NGRAPH"; + pub const CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API: &str = "NN_BUILDER"; + pub const CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE: &str = "ARM_COMPUTE"; + pub const CV_DNN_INFERENCE_ENGINE_CPU_TYPE_X86: &str = "X86"; + pub const CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2: &str = "Myriad2"; + pub const CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X: &str = "MyriadX"; + pub const CV_DNN_INFERENCE_ENGINE_VPU_TYPE_UNSPECIFIED: &str = ""; + pub const DNN_BACKEND_CANN: i32 = 8; + pub const DNN_BACKEND_CUDA: i32 = 5; + /// DNN_BACKEND_DEFAULT equals to OPENCV_DNN_BACKEND_DEFAULT, which can be defined using CMake or a configuration parameter + pub const DNN_BACKEND_DEFAULT: i32 = 0; + /// DNN_BACKEND_DEFAULT equals to OPENCV_DNN_BACKEND_DEFAULT, which can be defined using CMake or a configuration parameter + pub const DNN_BACKEND_HALIDE: i32 = 1; + /// Intel OpenVINO computational backend, supported targets: CPU, OPENCL, OPENCL_FP16, MYRIAD, HDDL, NPU + /// + /// Note: Tutorial how to build OpenCV with OpenVINO: [tutorial_dnn_openvino] + pub const DNN_BACKEND_INFERENCE_ENGINE: i32 = 2; + pub const DNN_BACKEND_OPENCV: i32 = 3; + pub const DNN_BACKEND_TIMVX: i32 = 7; + pub const DNN_BACKEND_VKCOM: i32 = 4; + pub const DNN_BACKEND_WEBNN: i32 = 6; + /// OpenCV data layout for 5D data. + pub const DNN_LAYOUT_NCDHW: i32 = 3; + /// OpenCV data layout for 4D data. + pub const DNN_LAYOUT_NCHW: i32 = 2; + /// OpenCV data layout for 2D data. + pub const DNN_LAYOUT_ND: i32 = 1; + /// Tensorflow-like data layout for 5D data. + pub const DNN_LAYOUT_NDHWC: i32 = 5; + /// Tensorflow-like data layout for 4D data. + pub const DNN_LAYOUT_NHWC: i32 = 4; + /// Tensorflow-like data layout, it should only be used at tf or tflite model parsing. + pub const DNN_LAYOUT_PLANAR: i32 = 6; + pub const DNN_LAYOUT_UNKNOWN: i32 = 0; + pub const DNN_PMODE_CROP_CENTER: i32 = 1; + pub const DNN_PMODE_LETTERBOX: i32 = 2; + pub const DNN_PMODE_NULL: i32 = 0; + pub const DNN_TARGET_CPU: i32 = 0; + pub const DNN_TARGET_CPU_FP16: i32 = 10; + pub const DNN_TARGET_CUDA: i32 = 6; + pub const DNN_TARGET_CUDA_FP16: i32 = 7; + /// FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin. + pub const DNN_TARGET_FPGA: i32 = 5; + pub const DNN_TARGET_HDDL: i32 = 8; + pub const DNN_TARGET_MYRIAD: i32 = 3; + pub const DNN_TARGET_NPU: i32 = 9; + pub const DNN_TARGET_OPENCL: i32 = 1; + pub const DNN_TARGET_OPENCL_FP16: i32 = 2; + pub const DNN_TARGET_VULKAN: i32 = 4; + pub const OPENCV_DNN_API_VERSION: i32 = 20251223; + pub const SoftNMSMethod_SOFTNMS_GAUSSIAN: i32 = 2; + pub const SoftNMSMethod_SOFTNMS_LINEAR: i32 = 1; + /// Enum of computation backends supported by layers. + /// ## See also + /// Net::setPreferableBackend + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum Backend { + /// DNN_BACKEND_DEFAULT equals to OPENCV_DNN_BACKEND_DEFAULT, which can be defined using CMake or a configuration parameter + DNN_BACKEND_DEFAULT = 0, + /// DNN_BACKEND_DEFAULT equals to OPENCV_DNN_BACKEND_DEFAULT, which can be defined using CMake or a configuration parameter + DNN_BACKEND_HALIDE = 1, + /// Intel OpenVINO computational backend, supported targets: CPU, OPENCL, OPENCL_FP16, MYRIAD, HDDL, NPU + /// + /// Note: Tutorial how to build OpenCV with OpenVINO: [tutorial_dnn_openvino] + DNN_BACKEND_INFERENCE_ENGINE = 2, + DNN_BACKEND_OPENCV = 3, + DNN_BACKEND_VKCOM = 4, + DNN_BACKEND_CUDA = 5, + DNN_BACKEND_WEBNN = 6, + DNN_BACKEND_TIMVX = 7, + DNN_BACKEND_CANN = 8, + } + + opencv_type_enum! { crate::dnn::Backend { DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE, DNN_BACKEND_INFERENCE_ENGINE, DNN_BACKEND_OPENCV, DNN_BACKEND_VKCOM, DNN_BACKEND_CUDA, DNN_BACKEND_WEBNN, DNN_BACKEND_TIMVX, DNN_BACKEND_CANN } } + + /// Enum of data layout for model inference. + /// ## See also + /// Image2BlobParams + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum DataLayout { + DNN_LAYOUT_UNKNOWN = 0, + /// OpenCV data layout for 2D data. + DNN_LAYOUT_ND = 1, + /// OpenCV data layout for 4D data. + DNN_LAYOUT_NCHW = 2, + /// OpenCV data layout for 5D data. + DNN_LAYOUT_NCDHW = 3, + /// Tensorflow-like data layout for 4D data. + DNN_LAYOUT_NHWC = 4, + /// Tensorflow-like data layout for 5D data. + DNN_LAYOUT_NDHWC = 5, + /// Tensorflow-like data layout, it should only be used at tf or tflite model parsing. + DNN_LAYOUT_PLANAR = 6, + } + + opencv_type_enum! { crate::dnn::DataLayout { DNN_LAYOUT_UNKNOWN, DNN_LAYOUT_ND, DNN_LAYOUT_NCHW, DNN_LAYOUT_NCDHW, DNN_LAYOUT_NHWC, DNN_LAYOUT_NDHWC, DNN_LAYOUT_PLANAR } } + + /// Enum of image processing mode. + /// To facilitate the specialization pre-processing requirements of the dnn model. + /// For example, the `letter box` often used in the Yolo series of models. + /// ## See also + /// Image2BlobParams + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum ImagePaddingMode { + DNN_PMODE_NULL = 0, + DNN_PMODE_CROP_CENTER = 1, + DNN_PMODE_LETTERBOX = 2, + } + + opencv_type_enum! { crate::dnn::ImagePaddingMode { DNN_PMODE_NULL, DNN_PMODE_CROP_CENTER, DNN_PMODE_LETTERBOX } } + + /// Enum of Soft NMS methods. + /// ## See also + /// softNMSBoxes + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum SoftNMSMethod { + SOFTNMS_LINEAR = 1, + SOFTNMS_GAUSSIAN = 2, + } + + opencv_type_enum! { crate::dnn::SoftNMSMethod { SOFTNMS_LINEAR, SOFTNMS_GAUSSIAN } } + + /// Enum of target devices for computations. + /// ## See also + /// Net::setPreferableTarget + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum Target { + DNN_TARGET_CPU = 0, + DNN_TARGET_OPENCL = 1, + DNN_TARGET_OPENCL_FP16 = 2, + DNN_TARGET_MYRIAD = 3, + DNN_TARGET_VULKAN = 4, + /// FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin. + DNN_TARGET_FPGA = 5, + DNN_TARGET_CUDA = 6, + DNN_TARGET_CUDA_FP16 = 7, + DNN_TARGET_HDDL = 8, + DNN_TARGET_NPU = 9, + DNN_TARGET_CPU_FP16 = 10, + } + + opencv_type_enum! { crate::dnn::Target { DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16, DNN_TARGET_MYRIAD, DNN_TARGET_VULKAN, DNN_TARGET_FPGA, DNN_TARGET_CUDA, DNN_TARGET_CUDA_FP16, DNN_TARGET_HDDL, DNN_TARGET_NPU, DNN_TARGET_CPU_FP16 } } + + /// Each Layer class must provide this function to the factory + pub type LayerFactory_Constructor = Option *mut c_void>; + pub type MatShape = core::Vector; + /// Container for strings and integers. + /// + /// + /// **Deprecated**: Use getLayerId() with int result. + #[deprecated = "Use getLayerId() with int result."] + pub type Net_LayerId = crate::dnn::DictValue; + /// ## Note + /// This alternative version of [nms_boxes_batched_1] function uses the following default values for its arguments: + /// * eta: 1.f + /// * top_k: 0 + #[inline] + pub fn nms_boxes_batched_1_def(bboxes: &core::Vector, scores: &core::Vector, class_ids: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_NMSBoxesBatched_const_vectorLRect2dGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR(bboxes.as_raw_VectorOfRect2d(), scores.as_raw_VectorOff32(), class_ids.as_raw_VectorOfi32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * eta: 1.f + /// * top_k: 0 + #[inline] + pub fn nms_boxes_batched_1(bboxes: &core::Vector, scores: &core::Vector, class_ids: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector, eta: f32, top_k: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_NMSBoxesBatched_const_vectorLRect2dGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR_const_float_const_int(bboxes.as_raw_VectorOfRect2d(), scores.as_raw_VectorOff32(), class_ids.as_raw_VectorOfi32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), eta, top_k, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs batched non maximum suppression on given boxes and corresponding scores across different classes. + /// + /// ## Parameters + /// * bboxes: a set of bounding boxes to apply NMS. + /// * scores: a set of corresponding confidences. + /// * class_ids: a set of corresponding class ids. Ids are integer and usually start from 0. + /// * score_threshold: a threshold used to filter boxes by score. + /// * nms_threshold: a threshold used in non maximum suppression. + /// * indices: the kept indices of bboxes after NMS. + /// * eta: a coefficient in adaptive threshold formula: ![inline formula](https://latex.codecogs.com/png.latex?nms%5C%5Fthreshold%5F%7Bi%2B1%7D%3Deta%5Ccdot%20nms%5C%5Fthreshold%5Fi). + /// * top_k: if `>0`, keep at most @p top_k picked indices. + /// + /// ## Note + /// This alternative version of [nms_boxes_batched] function uses the following default values for its arguments: + /// * eta: 1.f + /// * top_k: 0 + #[inline] + pub fn nms_boxes_batched_def(bboxes: &core::Vector, scores: &core::Vector, class_ids: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_NMSBoxesBatched_const_vectorLRectGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR(bboxes.as_raw_VectorOfRect(), scores.as_raw_VectorOff32(), class_ids.as_raw_VectorOfi32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs batched non maximum suppression on given boxes and corresponding scores across different classes. + /// + /// ## Parameters + /// * bboxes: a set of bounding boxes to apply NMS. + /// * scores: a set of corresponding confidences. + /// * class_ids: a set of corresponding class ids. Ids are integer and usually start from 0. + /// * score_threshold: a threshold used to filter boxes by score. + /// * nms_threshold: a threshold used in non maximum suppression. + /// * indices: the kept indices of bboxes after NMS. + /// * eta: a coefficient in adaptive threshold formula: ![inline formula](https://latex.codecogs.com/png.latex?nms%5C%5Fthreshold%5F%7Bi%2B1%7D%3Deta%5Ccdot%20nms%5C%5Fthreshold%5Fi). + /// * top_k: if `>0`, keep at most @p top_k picked indices. + /// + /// ## C++ default parameters + /// * eta: 1.f + /// * top_k: 0 + #[inline] + pub fn nms_boxes_batched(bboxes: &core::Vector, scores: &core::Vector, class_ids: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector, eta: f32, top_k: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_NMSBoxesBatched_const_vectorLRectGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR_const_float_const_int(bboxes.as_raw_VectorOfRect(), scores.as_raw_VectorOff32(), class_ids.as_raw_VectorOfi32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), eta, top_k, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [nms_boxes_f64] function uses the following default values for its arguments: + /// * eta: 1.f + /// * top_k: 0 + #[inline] + pub fn nms_boxes_f64_def(bboxes: &core::Vector, scores: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_NMSBoxes_const_vectorLRect2dGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR(bboxes.as_raw_VectorOfRect2d(), scores.as_raw_VectorOff32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * eta: 1.f + /// * top_k: 0 + #[inline] + pub fn nms_boxes_f64(bboxes: &core::Vector, scores: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector, eta: f32, top_k: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_NMSBoxes_const_vectorLRect2dGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR_const_float_const_int(bboxes.as_raw_VectorOfRect2d(), scores.as_raw_VectorOff32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), eta, top_k, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs non maximum suppression given boxes and corresponding scores. + /// + /// ## Parameters + /// * bboxes: a set of bounding boxes to apply NMS. + /// * scores: a set of corresponding confidences. + /// * score_threshold: a threshold used to filter boxes by score. + /// * nms_threshold: a threshold used in non maximum suppression. + /// * indices: the kept indices of bboxes after NMS. + /// * eta: a coefficient in adaptive threshold formula: ![inline formula](https://latex.codecogs.com/png.latex?nms%5C%5Fthreshold%5F%7Bi%2B1%7D%3Deta%5Ccdot%20nms%5C%5Fthreshold%5Fi). + /// * top_k: if `>0`, keep at most @p top_k picked indices. + /// + /// ## Note + /// This alternative version of [nms_boxes] function uses the following default values for its arguments: + /// * eta: 1.f + /// * top_k: 0 + #[inline] + pub fn nms_boxes_def(bboxes: &core::Vector, scores: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_NMSBoxes_const_vectorLRectGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR(bboxes.as_raw_VectorOfRect(), scores.as_raw_VectorOff32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs non maximum suppression given boxes and corresponding scores. + /// + /// ## Parameters + /// * bboxes: a set of bounding boxes to apply NMS. + /// * scores: a set of corresponding confidences. + /// * score_threshold: a threshold used to filter boxes by score. + /// * nms_threshold: a threshold used in non maximum suppression. + /// * indices: the kept indices of bboxes after NMS. + /// * eta: a coefficient in adaptive threshold formula: ![inline formula](https://latex.codecogs.com/png.latex?nms%5C%5Fthreshold%5F%7Bi%2B1%7D%3Deta%5Ccdot%20nms%5C%5Fthreshold%5Fi). + /// * top_k: if `>0`, keep at most @p top_k picked indices. + /// + /// ## C++ default parameters + /// * eta: 1.f + /// * top_k: 0 + #[inline] + pub fn nms_boxes(bboxes: &core::Vector, scores: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector, eta: f32, top_k: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_NMSBoxes_const_vectorLRectGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR_const_float_const_int(bboxes.as_raw_VectorOfRect(), scores.as_raw_VectorOff32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), eta, top_k, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [nms_boxes_rotated] function uses the following default values for its arguments: + /// * eta: 1.f + /// * top_k: 0 + #[inline] + pub fn nms_boxes_rotated_def(bboxes: &core::Vector, scores: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_NMSBoxes_const_vectorLRotatedRectGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR(bboxes.as_raw_VectorOfRotatedRect(), scores.as_raw_VectorOff32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * eta: 1.f + /// * top_k: 0 + #[inline] + pub fn nms_boxes_rotated(bboxes: &core::Vector, scores: &core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector, eta: f32, top_k: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_NMSBoxes_const_vectorLRotatedRectGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR_const_float_const_int(bboxes.as_raw_VectorOfRotatedRect(), scores.as_raw_VectorOff32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), eta, top_k, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates 4-dimensional blob from image with given params. + /// + /// @details This function is an extension of [blobFromImage] to meet more image preprocess needs. + /// Given input image and preprocessing parameters, and function outputs the blob. + /// + /// ## Parameters + /// * image: input image (all with 1-, 3- or 4-channels). + /// * param: struct of Image2BlobParams, contains all parameters needed by processing of image to blob. + /// ## Returns + /// 4-dimensional Mat. + /// + /// ## Note + /// This alternative version of [blob_from_image_with_params] function uses the following default values for its arguments: + /// * param: Image2BlobParams() + #[inline] + pub fn blob_from_image_with_params_def(image: &impl ToInputArray) -> Result { + input_array_arg!(image); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImageWithParams_const__InputArrayR(image.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates 4-dimensional blob from image with given params. + /// + /// @details This function is an extension of [blobFromImage] to meet more image preprocess needs. + /// Given input image and preprocessing parameters, and function outputs the blob. + /// + /// ## Parameters + /// * image: input image (all with 1-, 3- or 4-channels). + /// * param: struct of Image2BlobParams, contains all parameters needed by processing of image to blob. + /// ## Returns + /// 4-dimensional Mat. + /// + /// ## C++ default parameters + /// * param: Image2BlobParams() + #[inline] + pub fn blob_from_image_with_params(image: &impl ToInputArray, param: crate::dnn::Image2BlobParams) -> Result { + input_array_arg!(image); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImageWithParams_const__InputArrayR_const_Image2BlobParamsR(image.as_raw__InputArray(), ¶m, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates 4-dimensional blob from image with given params. + /// + /// @details This function is an extension of [blobFromImage] to meet more image preprocess needs. + /// Given input image and preprocessing parameters, and function outputs the blob. + /// + /// ## Parameters + /// * image: input image (all with 1-, 3- or 4-channels). + /// * param: struct of Image2BlobParams, contains all parameters needed by processing of image to blob. + /// ## Returns + /// 4-dimensional Mat. + /// + /// ## Overloaded parameters + /// + /// + /// ## Note + /// This alternative version of [blob_from_image_with_params_1] function uses the following default values for its arguments: + /// * param: Image2BlobParams() + #[inline] + pub fn blob_from_image_with_params_1_def(image: &impl ToInputArray, blob: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(image); + output_array_arg!(blob); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImageWithParams_const__InputArrayR_const__OutputArrayR(image.as_raw__InputArray(), blob.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates 4-dimensional blob from image with given params. + /// + /// @details This function is an extension of [blobFromImage] to meet more image preprocess needs. + /// Given input image and preprocessing parameters, and function outputs the blob. + /// + /// ## Parameters + /// * image: input image (all with 1-, 3- or 4-channels). + /// * param: struct of Image2BlobParams, contains all parameters needed by processing of image to blob. + /// ## Returns + /// 4-dimensional Mat. + /// + /// ## Overloaded parameters + /// + /// ## C++ default parameters + /// * param: Image2BlobParams() + #[inline] + pub fn blob_from_image_with_params_1(image: &impl ToInputArray, blob: &mut impl ToOutputArray, param: crate::dnn::Image2BlobParams) -> Result<()> { + input_array_arg!(image); + output_array_arg!(blob); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImageWithParams_const__InputArrayR_const__OutputArrayR_const_Image2BlobParamsR(image.as_raw__InputArray(), blob.as_raw__OutputArray(), ¶m, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center, + /// subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels. + /// ## Parameters + /// * image: input image (with 1-, 3- or 4-channels). + /// * scalefactor: multiplier for @p images values. + /// * size: spatial size for output image + /// * mean: scalar with mean values which are subtracted from channels. Values are intended + /// to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true. + /// * swapRB: flag which indicates that swap first and last channels + /// in 3-channel image is necessary. + /// * crop: flag which indicates whether image will be cropped after resize or not + /// * ddepth: Depth of output blob. Choose CV_32F or CV_8U. + /// @details if @p crop is true, input image is resized so one side after resize is equal to corresponding + /// dimension in @p size and another one is equal or larger. Then, crop from the center is performed. + /// If @p crop is false, direct resize without cropping and preserving aspect ratio is performed. + /// ## Returns + /// 4-dimensional Mat with NCHW dimensions order. + /// + /// + /// Note: + /// The order and usage of `scalefactor` and `mean` are (input - mean) * scalefactor. + /// + /// ## Note + /// This alternative version of [blob_from_image] function uses the following default values for its arguments: + /// * scalefactor: 1.0 + /// * size: Size() + /// * mean: Scalar() + /// * swap_rb: false + /// * crop: false + /// * ddepth: CV_32F + #[inline] + pub fn blob_from_image_def(image: &impl ToInputArray) -> Result { + input_array_arg!(image); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImage_const__InputArrayR(image.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates 4-dimensional blob from image. + /// @details This is an overloaded member function, provided for convenience. + /// It differs from the above function only in what argument(s) it accepts. + /// + /// ## Note + /// This alternative version of [blob_from_image_to] function uses the following default values for its arguments: + /// * scalefactor: 1.0 + /// * size: Size() + /// * mean: Scalar() + /// * swap_rb: false + /// * crop: false + /// * ddepth: CV_32F + #[inline] + pub fn blob_from_image_to_def(image: &impl ToInputArray, blob: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(image); + output_array_arg!(blob); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImage_const__InputArrayR_const__OutputArrayR(image.as_raw__InputArray(), blob.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates 4-dimensional blob from image. + /// @details This is an overloaded member function, provided for convenience. + /// It differs from the above function only in what argument(s) it accepts. + /// + /// ## C++ default parameters + /// * scalefactor: 1.0 + /// * size: Size() + /// * mean: Scalar() + /// * swap_rb: false + /// * crop: false + /// * ddepth: CV_32F + #[inline] + pub fn blob_from_image_to(image: &impl ToInputArray, blob: &mut impl ToOutputArray, scalefactor: f64, size: core::Size, mean: core::Scalar, swap_rb: bool, crop: bool, ddepth: i32) -> Result<()> { + input_array_arg!(image); + output_array_arg!(blob); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImage_const__InputArrayR_const__OutputArrayR_double_const_SizeR_const_ScalarR_bool_bool_int(image.as_raw__InputArray(), blob.as_raw__OutputArray(), scalefactor, &size, &mean, swap_rb, crop, ddepth, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center, + /// subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels. + /// ## Parameters + /// * image: input image (with 1-, 3- or 4-channels). + /// * scalefactor: multiplier for @p images values. + /// * size: spatial size for output image + /// * mean: scalar with mean values which are subtracted from channels. Values are intended + /// to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true. + /// * swapRB: flag which indicates that swap first and last channels + /// in 3-channel image is necessary. + /// * crop: flag which indicates whether image will be cropped after resize or not + /// * ddepth: Depth of output blob. Choose CV_32F or CV_8U. + /// @details if @p crop is true, input image is resized so one side after resize is equal to corresponding + /// dimension in @p size and another one is equal or larger. Then, crop from the center is performed. + /// If @p crop is false, direct resize without cropping and preserving aspect ratio is performed. + /// ## Returns + /// 4-dimensional Mat with NCHW dimensions order. + /// + /// + /// Note: + /// The order and usage of `scalefactor` and `mean` are (input - mean) * scalefactor. + /// + /// ## C++ default parameters + /// * scalefactor: 1.0 + /// * size: Size() + /// * mean: Scalar() + /// * swap_rb: false + /// * crop: false + /// * ddepth: CV_32F + #[inline] + pub fn blob_from_image(image: &impl ToInputArray, scalefactor: f64, size: core::Size, mean: core::Scalar, swap_rb: bool, crop: bool, ddepth: i32) -> Result { + input_array_arg!(image); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImage_const__InputArrayR_double_const_SizeR_const_ScalarR_bool_bool_int(image.as_raw__InputArray(), scalefactor, &size, &mean, swap_rb, crop, ddepth, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates 4-dimensional blob from series of images with given params. + /// + /// @details This function is an extension of [blobFromImages] to meet more image preprocess needs. + /// Given input image and preprocessing parameters, and function outputs the blob. + /// + /// ## Parameters + /// * images: input image (all with 1-, 3- or 4-channels). + /// * param: struct of Image2BlobParams, contains all parameters needed by processing of image to blob. + /// ## Returns + /// 4-dimensional Mat. + /// + /// ## Note + /// This alternative version of [blob_from_images_with_params] function uses the following default values for its arguments: + /// * param: Image2BlobParams() + #[inline] + pub fn blob_from_images_with_params_def(images: &impl ToInputArray) -> Result { + input_array_arg!(images); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImagesWithParams_const__InputArrayR(images.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates 4-dimensional blob from series of images with given params. + /// + /// @details This function is an extension of [blobFromImages] to meet more image preprocess needs. + /// Given input image and preprocessing parameters, and function outputs the blob. + /// + /// ## Parameters + /// * images: input image (all with 1-, 3- or 4-channels). + /// * param: struct of Image2BlobParams, contains all parameters needed by processing of image to blob. + /// ## Returns + /// 4-dimensional Mat. + /// + /// ## C++ default parameters + /// * param: Image2BlobParams() + #[inline] + pub fn blob_from_images_with_params(images: &impl ToInputArray, param: crate::dnn::Image2BlobParams) -> Result { + input_array_arg!(images); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImagesWithParams_const__InputArrayR_const_Image2BlobParamsR(images.as_raw__InputArray(), ¶m, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates 4-dimensional blob from series of images with given params. + /// + /// @details This function is an extension of [blobFromImages] to meet more image preprocess needs. + /// Given input image and preprocessing parameters, and function outputs the blob. + /// + /// ## Parameters + /// * images: input image (all with 1-, 3- or 4-channels). + /// * param: struct of Image2BlobParams, contains all parameters needed by processing of image to blob. + /// ## Returns + /// 4-dimensional Mat. + /// + /// ## Overloaded parameters + /// + /// + /// ## Note + /// This alternative version of [blob_from_images_with_params_1] function uses the following default values for its arguments: + /// * param: Image2BlobParams() + #[inline] + pub fn blob_from_images_with_params_1_def(images: &impl ToInputArray, blob: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(images); + output_array_arg!(blob); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImagesWithParams_const__InputArrayR_const__OutputArrayR(images.as_raw__InputArray(), blob.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates 4-dimensional blob from series of images with given params. + /// + /// @details This function is an extension of [blobFromImages] to meet more image preprocess needs. + /// Given input image and preprocessing parameters, and function outputs the blob. + /// + /// ## Parameters + /// * images: input image (all with 1-, 3- or 4-channels). + /// * param: struct of Image2BlobParams, contains all parameters needed by processing of image to blob. + /// ## Returns + /// 4-dimensional Mat. + /// + /// ## Overloaded parameters + /// + /// ## C++ default parameters + /// * param: Image2BlobParams() + #[inline] + pub fn blob_from_images_with_params_1(images: &impl ToInputArray, blob: &mut impl ToOutputArray, param: crate::dnn::Image2BlobParams) -> Result<()> { + input_array_arg!(images); + output_array_arg!(blob); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImagesWithParams_const__InputArrayR_const__OutputArrayR_const_Image2BlobParamsR(images.as_raw__InputArray(), blob.as_raw__OutputArray(), ¶m, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates 4-dimensional blob from series of images. Optionally resizes and + /// crops @p images from center, subtract @p mean values, scales values by @p scalefactor, + /// swap Blue and Red channels. + /// ## Parameters + /// * images: input images (all with 1-, 3- or 4-channels). + /// * size: spatial size for output image + /// * mean: scalar with mean values which are subtracted from channels. Values are intended + /// to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true. + /// * scalefactor: multiplier for @p images values. + /// * swapRB: flag which indicates that swap first and last channels + /// in 3-channel image is necessary. + /// * crop: flag which indicates whether image will be cropped after resize or not + /// * ddepth: Depth of output blob. Choose CV_32F or CV_8U. + /// @details if @p crop is true, input image is resized so one side after resize is equal to corresponding + /// dimension in @p size and another one is equal or larger. Then, crop from the center is performed. + /// If @p crop is false, direct resize without cropping and preserving aspect ratio is performed. + /// ## Returns + /// 4-dimensional Mat with NCHW dimensions order. + /// + /// + /// Note: + /// The order and usage of `scalefactor` and `mean` are (input - mean) * scalefactor. + /// + /// ## Note + /// This alternative version of [blob_from_images] function uses the following default values for its arguments: + /// * scalefactor: 1.0 + /// * size: Size() + /// * mean: Scalar() + /// * swap_rb: false + /// * crop: false + /// * ddepth: CV_32F + #[inline] + pub fn blob_from_images_def(images: &impl ToInputArray) -> Result { + input_array_arg!(images); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImages_const__InputArrayR(images.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates 4-dimensional blob from series of images. + /// @details This is an overloaded member function, provided for convenience. + /// It differs from the above function only in what argument(s) it accepts. + /// + /// ## Note + /// This alternative version of [blob_from_images_to] function uses the following default values for its arguments: + /// * scalefactor: 1.0 + /// * size: Size() + /// * mean: Scalar() + /// * swap_rb: false + /// * crop: false + /// * ddepth: CV_32F + #[inline] + pub fn blob_from_images_to_def(images: &impl ToInputArray, blob: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(images); + output_array_arg!(blob); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImages_const__InputArrayR_const__OutputArrayR(images.as_raw__InputArray(), blob.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates 4-dimensional blob from series of images. + /// @details This is an overloaded member function, provided for convenience. + /// It differs from the above function only in what argument(s) it accepts. + /// + /// ## C++ default parameters + /// * scalefactor: 1.0 + /// * size: Size() + /// * mean: Scalar() + /// * swap_rb: false + /// * crop: false + /// * ddepth: CV_32F + #[inline] + pub fn blob_from_images_to(images: &impl ToInputArray, blob: &mut impl ToOutputArray, scalefactor: f64, size: core::Size, mean: core::Scalar, swap_rb: bool, crop: bool, ddepth: i32) -> Result<()> { + input_array_arg!(images); + output_array_arg!(blob); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImages_const__InputArrayR_const__OutputArrayR_double_Size_const_ScalarR_bool_bool_int(images.as_raw__InputArray(), blob.as_raw__OutputArray(), scalefactor, &size, &mean, swap_rb, crop, ddepth, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates 4-dimensional blob from series of images. Optionally resizes and + /// crops @p images from center, subtract @p mean values, scales values by @p scalefactor, + /// swap Blue and Red channels. + /// ## Parameters + /// * images: input images (all with 1-, 3- or 4-channels). + /// * size: spatial size for output image + /// * mean: scalar with mean values which are subtracted from channels. Values are intended + /// to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true. + /// * scalefactor: multiplier for @p images values. + /// * swapRB: flag which indicates that swap first and last channels + /// in 3-channel image is necessary. + /// * crop: flag which indicates whether image will be cropped after resize or not + /// * ddepth: Depth of output blob. Choose CV_32F or CV_8U. + /// @details if @p crop is true, input image is resized so one side after resize is equal to corresponding + /// dimension in @p size and another one is equal or larger. Then, crop from the center is performed. + /// If @p crop is false, direct resize without cropping and preserving aspect ratio is performed. + /// ## Returns + /// 4-dimensional Mat with NCHW dimensions order. + /// + /// + /// Note: + /// The order and usage of `scalefactor` and `mean` are (input - mean) * scalefactor. + /// + /// ## C++ default parameters + /// * scalefactor: 1.0 + /// * size: Size() + /// * mean: Scalar() + /// * swap_rb: false + /// * crop: false + /// * ddepth: CV_32F + #[inline] + pub fn blob_from_images(images: &impl ToInputArray, scalefactor: f64, size: core::Size, mean: core::Scalar, swap_rb: bool, crop: bool, ddepth: i32) -> Result { + input_array_arg!(images); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_blobFromImages_const__InputArrayR_double_Size_const_ScalarR_bool_bool_int(images.as_raw__InputArray(), scalefactor, &size, &mean, swap_rb, crop, ddepth, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn concat(a: &crate::dnn::MatShape, b: &crate::dnn::MatShape) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_concat_const_MatShapeR_const_MatShapeR(a.as_raw_VectorOfi32(), b.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::MatShape::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Enables detailed logging of the DNN model loading with CV DNN API. + /// ## Parameters + /// * isDiagnosticsMode: Indicates whether diagnostic mode should be set. + /// + /// Diagnostic mode provides detailed logging of the model loading stage to explore + /// potential problems (ex.: not implemented layer type). + /// + /// + /// Note: In diagnostic mode series of assertions will be skipped, it can lead to the + /// expected application crash. + #[inline] + pub fn enable_model_diagnostics(is_diagnostics_mode: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_enableModelDiagnostics_bool(is_diagnostics_mode, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn get_available_backends() -> Result>> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_getAvailableBackends(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::>::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn get_available_targets(be: crate::dnn::Backend) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_getAvailableTargets_Backend(be, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns Inference Engine internal backend API. + /// + /// See values of `CV_DNN_BACKEND_INFERENCE_ENGINE_*` macros. + /// + /// `OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE` runtime parameter (environment variable) is ignored since 4.6.0. + /// + /// @deprecated + #[inline] + pub fn get_inference_engine_backend_type() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_getInferenceEngineBackendType(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns Inference Engine CPU type. + /// + /// Specify OpenVINO plugin: CPU or ARM. + #[inline] + pub fn get_inference_engine_cpu_type() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_getInferenceEngineCPUType(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns Inference Engine VPU type. + /// + /// See values of `CV_DNN_INFERENCE_ENGINE_VPU_TYPE_*` macros. + #[inline] + pub fn get_inference_engine_vpu_type() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_getInferenceEngineVPUType(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn get_plane(m: &impl core::MatTraitConst, n: i32, cn: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_getPlane_const_MatR_int_int(m.as_raw_Mat(), n, cn, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure + /// (std::vector). + /// ## Parameters + /// * blob_: 4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from + /// which you would like to extract the images. + /// * images_:[out] array of 2D Mat containing the images extracted from the blob in floating point precision + /// (CV_32F). They are non normalized neither mean added. The number of returned images equals the first dimension + /// of the blob (batch size). Every image has a number of channels equals to the second dimension of the blob (depth). + #[inline] + pub fn images_from_blob(blob_: &impl core::MatTraitConst, images_: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(images_); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_imagesFromBlob_const_MatR_const__OutputArrayR(blob_.as_raw_Mat(), images_.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Reads a network model stored in Caffe framework's format. + /// ## Parameters + /// * prototxt: path to the .prototxt file with text description of the network architecture. + /// * caffeModel: path to the .caffemodel file with learned network. + /// ## Returns + /// Net object. + /// + /// ## Note + /// This alternative version of [read_net_from_caffe] function uses the following default values for its arguments: + /// * caffe_model: String() + #[inline] + pub fn read_net_from_caffe_def(prototxt: &str) -> Result { + extern_container_arg!(prototxt); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromCaffe_const_StringR(prototxt.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in Caffe framework's format. + /// ## Parameters + /// * prototxt: path to the .prototxt file with text description of the network architecture. + /// * caffeModel: path to the .caffemodel file with learned network. + /// ## Returns + /// Net object. + /// + /// ## C++ default parameters + /// * caffe_model: String() + #[inline] + pub fn read_net_from_caffe(prototxt: &str, caffe_model: &str) -> Result { + extern_container_arg!(prototxt); + extern_container_arg!(caffe_model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromCaffe_const_StringR_const_StringR(prototxt.opencv_as_extern(), caffe_model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in Caffe model in memory. + /// @details This is an overloaded member function, provided for convenience. + /// It differs from the above function only in what argument(s) it accepts. + /// ## Parameters + /// * bufferProto: buffer containing the content of the .prototxt file + /// * lenProto: length of bufferProto + /// * bufferModel: buffer containing the content of the .caffemodel file + /// * lenModel: length of bufferModel + /// ## Returns + /// Net object. + /// + /// ## Note + /// This alternative version of [read_net_from_caffe_str] function uses the following default values for its arguments: + /// * buffer_model: NULL + /// * len_model: 0 + #[inline] + pub fn read_net_from_caffe_str_def(buffer_proto: &str, len_proto: size_t) -> Result { + extern_container_arg!(buffer_proto); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromCaffe_const_charX_size_t(buffer_proto.opencv_as_extern(), len_proto, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in Caffe model in memory. + /// @details This is an overloaded member function, provided for convenience. + /// It differs from the above function only in what argument(s) it accepts. + /// ## Parameters + /// * bufferProto: buffer containing the content of the .prototxt file + /// * lenProto: length of bufferProto + /// * bufferModel: buffer containing the content of the .caffemodel file + /// * lenModel: length of bufferModel + /// ## Returns + /// Net object. + /// + /// ## C++ default parameters + /// * buffer_model: NULL + /// * len_model: 0 + #[inline] + pub fn read_net_from_caffe_str(buffer_proto: &str, len_proto: size_t, buffer_model: &str, len_model: size_t) -> Result { + extern_container_arg!(buffer_proto); + extern_container_arg!(buffer_model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromCaffe_const_charX_size_t_const_charX_size_t(buffer_proto.opencv_as_extern(), len_proto, buffer_model.opencv_as_extern(), len_model, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in Caffe model in memory. + /// ## Parameters + /// * bufferProto: buffer containing the content of the .prototxt file + /// * bufferModel: buffer containing the content of the .caffemodel file + /// ## Returns + /// Net object. + /// + /// ## Note + /// This alternative version of [read_net_from_caffe_buffer] function uses the following default values for its arguments: + /// * buffer_model: std::vector() + #[inline] + pub fn read_net_from_caffe_buffer_def(buffer_proto: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromCaffe_const_vectorLunsigned_charGR(buffer_proto.as_raw_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in Caffe model in memory. + /// ## Parameters + /// * bufferProto: buffer containing the content of the .prototxt file + /// * bufferModel: buffer containing the content of the .caffemodel file + /// ## Returns + /// Net object. + /// + /// ## C++ default parameters + /// * buffer_model: std::vector() + #[inline] + pub fn read_net_from_caffe_buffer(buffer_proto: &core::Vector, buffer_model: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromCaffe_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(buffer_proto.as_raw_VectorOfu8(), buffer_model.as_raw_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in Darknet model files. + /// ## Parameters + /// * cfgFile: path to the .cfg file with text description of the network architecture. + /// * darknetModel: path to the .weights file with learned network. + /// ## Returns + /// Network object that ready to do forward, throw an exception in failure cases. + /// + /// ## Note + /// This alternative version of [read_net_from_darknet] function uses the following default values for its arguments: + /// * darknet_model: String() + #[inline] + pub fn read_net_from_darknet_def(cfg_file: &str) -> Result { + extern_container_arg!(cfg_file); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromDarknet_const_StringR(cfg_file.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in Darknet model files. + /// ## Parameters + /// * cfgFile: path to the .cfg file with text description of the network architecture. + /// * darknetModel: path to the .weights file with learned network. + /// ## Returns + /// Network object that ready to do forward, throw an exception in failure cases. + /// + /// ## C++ default parameters + /// * darknet_model: String() + #[inline] + pub fn read_net_from_darknet(cfg_file: &str, darknet_model: &str) -> Result { + extern_container_arg!(cfg_file); + extern_container_arg!(darknet_model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromDarknet_const_StringR_const_StringR(cfg_file.opencv_as_extern(), darknet_model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in Darknet model files. + /// ## Parameters + /// * bufferCfg: A buffer contains a content of .cfg file with text description of the network architecture. + /// * lenCfg: Number of bytes to read from bufferCfg + /// * bufferModel: A buffer contains a content of .weights file with learned network. + /// * lenModel: Number of bytes to read from bufferModel + /// ## Returns + /// Net object. + /// + /// ## Note + /// This alternative version of [read_net_from_darknet_str] function uses the following default values for its arguments: + /// * buffer_model: NULL + /// * len_model: 0 + #[inline] + pub fn read_net_from_darknet_str_def(buffer_cfg: &str, len_cfg: size_t) -> Result { + extern_container_arg!(buffer_cfg); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromDarknet_const_charX_size_t(buffer_cfg.opencv_as_extern(), len_cfg, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in Darknet model files. + /// ## Parameters + /// * bufferCfg: A buffer contains a content of .cfg file with text description of the network architecture. + /// * lenCfg: Number of bytes to read from bufferCfg + /// * bufferModel: A buffer contains a content of .weights file with learned network. + /// * lenModel: Number of bytes to read from bufferModel + /// ## Returns + /// Net object. + /// + /// ## C++ default parameters + /// * buffer_model: NULL + /// * len_model: 0 + #[inline] + pub fn read_net_from_darknet_str(buffer_cfg: &str, len_cfg: size_t, buffer_model: &str, len_model: size_t) -> Result { + extern_container_arg!(buffer_cfg); + extern_container_arg!(buffer_model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromDarknet_const_charX_size_t_const_charX_size_t(buffer_cfg.opencv_as_extern(), len_cfg, buffer_model.opencv_as_extern(), len_model, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in Darknet model files. + /// ## Parameters + /// * bufferCfg: A buffer contains a content of .cfg file with text description of the network architecture. + /// * bufferModel: A buffer contains a content of .weights file with learned network. + /// ## Returns + /// Net object. + /// + /// ## Note + /// This alternative version of [read_net_from_darknet_buffer] function uses the following default values for its arguments: + /// * buffer_model: std::vector() + #[inline] + pub fn read_net_from_darknet_buffer_def(buffer_cfg: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromDarknet_const_vectorLunsigned_charGR(buffer_cfg.as_raw_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in Darknet model files. + /// ## Parameters + /// * bufferCfg: A buffer contains a content of .cfg file with text description of the network architecture. + /// * bufferModel: A buffer contains a content of .weights file with learned network. + /// ## Returns + /// Net object. + /// + /// ## C++ default parameters + /// * buffer_model: std::vector() + #[inline] + pub fn read_net_from_darknet_buffer(buffer_cfg: &core::Vector, buffer_model: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromDarknet_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(buffer_cfg.as_raw_VectorOfu8(), buffer_model.as_raw_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Load a network from Intel's Model Optimizer intermediate representation. + /// ## Parameters + /// * xml: XML configuration file with network's topology. + /// * bin: Binary file with trained weights. + /// ## Returns + /// Net object. + /// Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine + /// backend. + /// + /// ## Note + /// This alternative version of [read_net_from_model_optimizer] function uses the following default values for its arguments: + /// * bin: "" + #[inline] + pub fn read_net_from_model_optimizer_def(xml: &str) -> Result { + extern_container_arg!(xml); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromModelOptimizer_const_StringR(xml.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Load a network from Intel's Model Optimizer intermediate representation. + /// ## Parameters + /// * xml: XML configuration file with network's topology. + /// * bin: Binary file with trained weights. + /// ## Returns + /// Net object. + /// Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine + /// backend. + /// + /// ## C++ default parameters + /// * bin: "" + #[inline] + pub fn read_net_from_model_optimizer(xml: &str, bin: &str) -> Result { + extern_container_arg!(xml); + extern_container_arg!(bin); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromModelOptimizer_const_StringR_const_StringR(xml.opencv_as_extern(), bin.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Load a network from Intel's Model Optimizer intermediate representation. + /// ## Parameters + /// * bufferModelConfigPtr: Pointer to buffer which contains XML configuration with network's topology. + /// * bufferModelConfigSize: Binary size of XML configuration data. + /// * bufferWeightsPtr: Pointer to buffer which contains binary data with trained weights. + /// * bufferWeightsSize: Binary size of trained weights data. + /// ## Returns + /// Net object. + /// Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine + /// backend. + #[inline] + pub fn read_net_from_model_optimizer_2(buffer_model_config_ptr: &[u8], buffer_weights_ptr: &[u8]) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromModelOptimizer_const_unsigned_charX_size_t_const_unsigned_charX_size_t(buffer_model_config_ptr.as_ptr(), buffer_model_config_ptr.len(), buffer_weights_ptr.as_ptr(), buffer_weights_ptr.len(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Load a network from Intel's Model Optimizer intermediate representation. + /// ## Parameters + /// * bufferModelConfig: Buffer contains XML configuration with network's topology. + /// * bufferWeights: Buffer contains binary data with trained weights. + /// ## Returns + /// Net object. + /// Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine + /// backend. + #[inline] + pub fn read_net_from_model_optimizer_1(buffer_model_config: &core::Vector, buffer_weights: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromModelOptimizer_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(buffer_model_config.as_raw_VectorOfu8(), buffer_weights.as_raw_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model ONNX. + /// ## Parameters + /// * onnxFile: path to the .onnx file with text description of the network architecture. + /// ## Returns + /// Network object that ready to do forward, throw an exception in failure cases. + #[inline] + pub fn read_net_from_onnx(onnx_file: &str) -> Result { + extern_container_arg!(onnx_file); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromONNX_const_StringR(onnx_file.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model from ONNX + /// in-memory buffer. + /// ## Parameters + /// * buffer: memory address of the first byte of the buffer. + /// * sizeBuffer: size of the buffer. + /// ## Returns + /// Network object that ready to do forward, throw an exception + /// in failure cases. + #[inline] + pub fn read_net_from_onnx_str(buffer: &str, size_buffer: size_t) -> Result { + extern_container_arg!(buffer); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromONNX_const_charX_size_t(buffer.opencv_as_extern(), size_buffer, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model from ONNX + /// in-memory buffer. + /// ## Parameters + /// * buffer: in-memory buffer that stores the ONNX model bytes. + /// ## Returns + /// Network object that ready to do forward, throw an exception + /// in failure cases. + #[inline] + pub fn read_net_from_onnx_buffer(buffer: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromONNX_const_vectorLunsigned_charGR(buffer.as_raw_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in TFLite framework's format. + /// ## Parameters + /// * model: path to the .tflite file with binary flatbuffers description of the network architecture + /// ## Returns + /// Net object. + #[inline] + pub fn read_net_from_tf_lite(model: &str) -> Result { + extern_container_arg!(model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromTFLite_const_StringR(model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in TFLite framework's format. + /// @details This is an overloaded member function, provided for convenience. + /// It differs from the above function only in what argument(s) it accepts. + /// ## Parameters + /// * bufferModel: buffer containing the content of the tflite file + /// * lenModel: length of bufferModel + #[inline] + pub fn read_net_from_tf_lite_2(buffer_model: &str, len_model: size_t) -> Result { + extern_container_arg!(buffer_model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromTFLite_const_charX_size_t(buffer_model.opencv_as_extern(), len_model, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in TFLite framework's format. + /// ## Parameters + /// * bufferModel: buffer containing the content of the tflite file + /// ## Returns + /// Net object. + #[inline] + pub fn read_net_from_tf_lite_1(buffer_model: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromTFLite_const_vectorLunsigned_charGR(buffer_model.as_raw_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in TensorFlow framework's format. + /// ## Parameters + /// * model: path to the .pb file with binary protobuf description of the network architecture + /// * config: path to the .pbtxt file that contains text graph definition in protobuf format. + /// Resulting Net object is built by text graph using weights from a binary one that + /// let us make it more flexible. + /// ## Returns + /// Net object. + /// + /// ## Note + /// This alternative version of [read_net_from_tensorflow] function uses the following default values for its arguments: + /// * config: String() + #[inline] + pub fn read_net_from_tensorflow_def(model: &str) -> Result { + extern_container_arg!(model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromTensorflow_const_StringR(model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in TensorFlow framework's format. + /// ## Parameters + /// * model: path to the .pb file with binary protobuf description of the network architecture + /// * config: path to the .pbtxt file that contains text graph definition in protobuf format. + /// Resulting Net object is built by text graph using weights from a binary one that + /// let us make it more flexible. + /// ## Returns + /// Net object. + /// + /// ## C++ default parameters + /// * config: String() + #[inline] + pub fn read_net_from_tensorflow(model: &str, config: &str) -> Result { + extern_container_arg!(model); + extern_container_arg!(config); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromTensorflow_const_StringR_const_StringR(model.opencv_as_extern(), config.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in TensorFlow framework's format. + /// @details This is an overloaded member function, provided for convenience. + /// It differs from the above function only in what argument(s) it accepts. + /// ## Parameters + /// * bufferModel: buffer containing the content of the pb file + /// * lenModel: length of bufferModel + /// * bufferConfig: buffer containing the content of the pbtxt file + /// * lenConfig: length of bufferConfig + /// + /// ## Note + /// This alternative version of [read_net_from_tensorflow_str] function uses the following default values for its arguments: + /// * buffer_config: NULL + /// * len_config: 0 + #[inline] + pub fn read_net_from_tensorflow_str_def(buffer_model: &str, len_model: size_t) -> Result { + extern_container_arg!(buffer_model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromTensorflow_const_charX_size_t(buffer_model.opencv_as_extern(), len_model, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in TensorFlow framework's format. + /// @details This is an overloaded member function, provided for convenience. + /// It differs from the above function only in what argument(s) it accepts. + /// ## Parameters + /// * bufferModel: buffer containing the content of the pb file + /// * lenModel: length of bufferModel + /// * bufferConfig: buffer containing the content of the pbtxt file + /// * lenConfig: length of bufferConfig + /// + /// ## C++ default parameters + /// * buffer_config: NULL + /// * len_config: 0 + #[inline] + pub fn read_net_from_tensorflow_str(buffer_model: &str, len_model: size_t, buffer_config: &str, len_config: size_t) -> Result { + extern_container_arg!(buffer_model); + extern_container_arg!(buffer_config); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromTensorflow_const_charX_size_t_const_charX_size_t(buffer_model.opencv_as_extern(), len_model, buffer_config.opencv_as_extern(), len_config, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in TensorFlow framework's format. + /// ## Parameters + /// * bufferModel: buffer containing the content of the pb file + /// * bufferConfig: buffer containing the content of the pbtxt file + /// ## Returns + /// Net object. + /// + /// ## Note + /// This alternative version of [read_net_from_tensorflow_buffer] function uses the following default values for its arguments: + /// * buffer_config: std::vector() + #[inline] + pub fn read_net_from_tensorflow_buffer_def(buffer_model: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromTensorflow_const_vectorLunsigned_charGR(buffer_model.as_raw_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in TensorFlow framework's format. + /// ## Parameters + /// * bufferModel: buffer containing the content of the pb file + /// * bufferConfig: buffer containing the content of the pbtxt file + /// ## Returns + /// Net object. + /// + /// ## C++ default parameters + /// * buffer_config: std::vector() + #[inline] + pub fn read_net_from_tensorflow_buffer(buffer_model: &core::Vector, buffer_config: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromTensorflow_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(buffer_model.as_raw_VectorOfu8(), buffer_config.as_raw_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in Torch7 framework's format. + /// ## Parameters + /// * model: path to the file, dumped from Torch by using torch.save() function. + /// * isBinary: specifies whether the network was serialized in ascii mode or binary. + /// * evaluate: specifies testing phase of network. If true, it's similar to evaluate() method in Torch. + /// ## Returns + /// Net object. + /// + /// + /// Note: Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type of C language, + /// which has various bit-length on different systems. + /// + /// The loading file must contain serialized nn.Module object + /// with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors. + /// + /// List of supported layers (i.e. object instances derived from Torch nn.Module class): + /// - nn.Sequential + /// - nn.Parallel + /// - nn.Concat + /// - nn.Linear + /// - nn.SpatialConvolution + /// - nn.SpatialMaxPooling, nn.SpatialAveragePooling + /// - nn.ReLU, nn.TanH, nn.Sigmoid + /// - nn.Reshape + /// - nn.SoftMax, nn.LogSoftMax + /// + /// Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported. + /// + /// ## Note + /// This alternative version of [read_net_from_torch] function uses the following default values for its arguments: + /// * is_binary: true + /// * evaluate: true + #[inline] + pub fn read_net_from_torch_def(model: &str) -> Result { + extern_container_arg!(model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromTorch_const_StringR(model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Reads a network model stored in Torch7 framework's format. + /// ## Parameters + /// * model: path to the file, dumped from Torch by using torch.save() function. + /// * isBinary: specifies whether the network was serialized in ascii mode or binary. + /// * evaluate: specifies testing phase of network. If true, it's similar to evaluate() method in Torch. + /// ## Returns + /// Net object. + /// + /// + /// Note: Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type of C language, + /// which has various bit-length on different systems. + /// + /// The loading file must contain serialized nn.Module object + /// with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors. + /// + /// List of supported layers (i.e. object instances derived from Torch nn.Module class): + /// - nn.Sequential + /// - nn.Parallel + /// - nn.Concat + /// - nn.Linear + /// - nn.SpatialConvolution + /// - nn.SpatialMaxPooling, nn.SpatialAveragePooling + /// - nn.ReLU, nn.TanH, nn.Sigmoid + /// - nn.Reshape + /// - nn.SoftMax, nn.LogSoftMax + /// + /// Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported. + /// + /// ## C++ default parameters + /// * is_binary: true + /// * evaluate: true + #[inline] + pub fn read_net_from_torch(model: &str, is_binary: bool, evaluate: bool) -> Result { + extern_container_arg!(model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNetFromTorch_const_StringR_bool_bool(model.opencv_as_extern(), is_binary, evaluate, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Read deep learning network represented in one of the supported formats. + /// ## Parameters + /// * model: Binary file contains trained weights. The following file + /// extensions are expected for models from different frameworks: + /// * `*.caffemodel` (Caffe, ) + /// * `*.pb` (TensorFlow, ) + /// * `*.t7` | `*.net` (Torch, ) + /// * `*.weights` (Darknet, ) + /// * `*.bin` | `*.onnx` (OpenVINO, ) + /// * `*.onnx` (ONNX, ) + /// * config: Text file contains network configuration. It could be a + /// file with the following extensions: + /// * `*.prototxt` (Caffe, ) + /// * `*.pbtxt` (TensorFlow, ) + /// * `*.cfg` (Darknet, ) + /// * `*.xml` (OpenVINO, ) + /// * framework: Explicit framework name tag to determine a format. + /// ## Returns + /// Net object. + /// + /// This function automatically detects an origin framework of trained model + /// and calls an appropriate function such [readNetFromCaffe], [readNetFromTensorflow], + /// [readNetFromTorch] or [readNetFromDarknet]. An order of @p model and @p config + /// arguments does not matter. + /// + /// ## Note + /// This alternative version of [read_net] function uses the following default values for its arguments: + /// * config: "" + /// * framework: "" + #[inline] + pub fn read_net_def(model: &str) -> Result { + extern_container_arg!(model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNet_const_StringR(model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Read deep learning network represented in one of the supported formats. + /// ## Parameters + /// * model: Binary file contains trained weights. The following file + /// extensions are expected for models from different frameworks: + /// * `*.caffemodel` (Caffe, ) + /// * `*.pb` (TensorFlow, ) + /// * `*.t7` | `*.net` (Torch, ) + /// * `*.weights` (Darknet, ) + /// * `*.bin` | `*.onnx` (OpenVINO, ) + /// * `*.onnx` (ONNX, ) + /// * config: Text file contains network configuration. It could be a + /// file with the following extensions: + /// * `*.prototxt` (Caffe, ) + /// * `*.pbtxt` (TensorFlow, ) + /// * `*.cfg` (Darknet, ) + /// * `*.xml` (OpenVINO, ) + /// * framework: Explicit framework name tag to determine a format. + /// ## Returns + /// Net object. + /// + /// This function automatically detects an origin framework of trained model + /// and calls an appropriate function such [readNetFromCaffe], [readNetFromTensorflow], + /// [readNetFromTorch] or [readNetFromDarknet]. An order of @p model and @p config + /// arguments does not matter. + /// + /// ## C++ default parameters + /// * config: "" + /// * framework: "" + #[inline] + pub fn read_net(model: &str, config: &str, framework: &str) -> Result { + extern_container_arg!(model); + extern_container_arg!(config); + extern_container_arg!(framework); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNet_const_StringR_const_StringR_const_StringR(model.opencv_as_extern(), config.opencv_as_extern(), framework.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Read deep learning network represented in one of the supported formats. + /// @details This is an overloaded member function, provided for convenience. + /// It differs from the above function only in what argument(s) it accepts. + /// ## Parameters + /// * framework: Name of origin framework. + /// * bufferModel: A buffer with a content of binary file with weights + /// * bufferConfig: A buffer with a content of text file contains network configuration. + /// ## Returns + /// Net object. + /// + /// ## Note + /// This alternative version of [read_net_1] function uses the following default values for its arguments: + /// * buffer_config: std::vector() + #[inline] + pub fn read_net_1_def(framework: &str, buffer_model: &core::Vector) -> Result { + extern_container_arg!(framework); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNet_const_StringR_const_vectorLunsigned_charGR(framework.opencv_as_extern(), buffer_model.as_raw_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Read deep learning network represented in one of the supported formats. + /// @details This is an overloaded member function, provided for convenience. + /// It differs from the above function only in what argument(s) it accepts. + /// ## Parameters + /// * framework: Name of origin framework. + /// * bufferModel: A buffer with a content of binary file with weights + /// * bufferConfig: A buffer with a content of text file contains network configuration. + /// ## Returns + /// Net object. + /// + /// ## C++ default parameters + /// * buffer_config: std::vector() + #[inline] + pub fn read_net_1(framework: &str, buffer_model: &core::Vector, buffer_config: &core::Vector) -> Result { + extern_container_arg!(framework); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readNet_const_StringR_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(framework.opencv_as_extern(), buffer_model.as_raw_VectorOfu8(), buffer_config.as_raw_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates blob from .pb file. + /// ## Parameters + /// * path: to the .pb file with input tensor. + /// ## Returns + /// Mat. + #[inline] + pub fn read_tensor_from_onnx(path: &str) -> Result { + extern_container_arg!(path); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readTensorFromONNX_const_StringR(path.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Loads blob which was serialized as torch.Tensor object of Torch7 framework. + /// @warning This function has the same limitations as readNetFromTorch(). + /// + /// ## Note + /// This alternative version of [read_torch_blob] function uses the following default values for its arguments: + /// * is_binary: true + #[inline] + pub fn read_torch_blob_def(filename: &str) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readTorchBlob_const_StringR(filename.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Loads blob which was serialized as torch.Tensor object of Torch7 framework. + /// @warning This function has the same limitations as readNetFromTorch(). + /// + /// ## C++ default parameters + /// * is_binary: true + #[inline] + pub fn read_torch_blob(filename: &str, is_binary: bool) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_readTorchBlob_const_StringR_bool(filename.opencv_as_extern(), is_binary, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Release a HDDL plugin. + #[inline] + pub fn release_hddl_plugin() -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_releaseHDDLPlugin(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Release a Myriad device (binded by OpenCV). + /// + /// Single Myriad device cannot be shared across multiple processes which uses + /// Inference Engine's Myriad plugin. + #[inline] + pub fn reset_myriad_device() -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_resetMyriadDevice(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Specify Inference Engine internal backend API. + /// + /// See values of `CV_DNN_BACKEND_INFERENCE_ENGINE_*` macros. + /// + /// ## Returns + /// previous value of internal backend API + /// + /// @deprecated + #[inline] + pub fn set_inference_engine_backend_type(new_backend_type: &str) -> Result { + extern_container_arg!(new_backend_type); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_setInferenceEngineBackendType_const_StringR(new_backend_type.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn shape_1(mat: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_shape_const_MatR(mat.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::MatShape::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn shape_2(sz: &impl core::MatSizeTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_shape_const_MatSizeR(sz.as_raw_MatSize(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::MatShape::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn shape_3(mat: &impl core::UMatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_shape_const_UMatR(mat.as_raw_UMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::MatShape::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn shape(dims: &i32, n: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_shape_const_intX_const_int(dims, n, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::MatShape::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [shape_4] function uses the following default values for its arguments: + /// * a1: -1 + /// * a2: -1 + /// * a3: -1 + #[inline] + pub fn shape_4_def(a0: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_shape_int(a0, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::MatShape::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * a1: -1 + /// * a2: -1 + /// * a3: -1 + #[inline] + pub fn shape_4(a0: i32, a1: i32, a2: i32, a3: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_shape_int_int_int_int(a0, a1, a2, a3, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::MatShape::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Convert all weights of Caffe network to half precision floating point. + /// ## Parameters + /// * src: Path to origin model from Caffe framework contains single + /// precision floating point weights (usually has `.caffemodel` extension). + /// * dst: Path to destination model with updated weights. + /// * layersTypes: Set of layers types which parameters will be converted. + /// By default, converts only Convolutional and Fully-Connected layers' + /// weights. + /// + /// + /// Note: Shrinked model has no origin float32 weights so it can't be used + /// in origin Caffe framework anymore. However the structure of data + /// is taken from NVidia's Caffe fork: . + /// So the resulting model may be used there. + /// + /// ## Note + /// This alternative version of [shrink_caffe_model] function uses the following default values for its arguments: + /// * layers_types: std::vector() + #[inline] + pub fn shrink_caffe_model_def(src: &str, dst: &str) -> Result<()> { + extern_container_arg!(src); + extern_container_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_shrinkCaffeModel_const_StringR_const_StringR(src.opencv_as_extern(), dst.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Convert all weights of Caffe network to half precision floating point. + /// ## Parameters + /// * src: Path to origin model from Caffe framework contains single + /// precision floating point weights (usually has `.caffemodel` extension). + /// * dst: Path to destination model with updated weights. + /// * layersTypes: Set of layers types which parameters will be converted. + /// By default, converts only Convolutional and Fully-Connected layers' + /// weights. + /// + /// + /// Note: Shrinked model has no origin float32 weights so it can't be used + /// in origin Caffe framework anymore. However the structure of data + /// is taken from NVidia's Caffe fork: . + /// So the resulting model may be used there. + /// + /// ## C++ default parameters + /// * layers_types: std::vector() + #[inline] + pub fn shrink_caffe_model(src: &str, dst: &str, layers_types: &core::Vector) -> Result<()> { + extern_container_arg!(src); + extern_container_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_shrinkCaffeModel_const_StringR_const_StringR_const_vectorLStringGR(src.opencv_as_extern(), dst.opencv_as_extern(), layers_types.as_raw_VectorOfString(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn slice(m: &impl core::MatTraitConst, r0: &impl crate::dnn::_RangeTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_slice_const_MatR_const__RangeR(m.as_raw_Mat(), r0.as_raw__Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn slice_1(m: &impl core::MatTraitConst, r0: &impl crate::dnn::_RangeTraitConst, r1: &impl crate::dnn::_RangeTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_slice_const_MatR_const__RangeR_const__RangeR(m.as_raw_Mat(), r0.as_raw__Range(), r1.as_raw__Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn slice_2(m: &impl core::MatTraitConst, r0: &impl crate::dnn::_RangeTraitConst, r1: &impl crate::dnn::_RangeTraitConst, r2: &impl crate::dnn::_RangeTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_slice_const_MatR_const__RangeR_const__RangeR_const__RangeR(m.as_raw_Mat(), r0.as_raw__Range(), r1.as_raw__Range(), r2.as_raw__Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn slice_3(m: &impl core::MatTraitConst, r0: &impl crate::dnn::_RangeTraitConst, r1: &impl crate::dnn::_RangeTraitConst, r2: &impl crate::dnn::_RangeTraitConst, r3: &impl crate::dnn::_RangeTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_slice_const_MatR_const__RangeR_const__RangeR_const__RangeR_const__RangeR(m.as_raw_Mat(), r0.as_raw__Range(), r1.as_raw__Range(), r2.as_raw__Range(), r3.as_raw__Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Performs soft non maximum suppression given boxes and corresponding scores. + /// Reference: + /// ## Parameters + /// * bboxes: a set of bounding boxes to apply Soft NMS. + /// * scores: a set of corresponding confidences. + /// * updated_scores: a set of corresponding updated confidences. + /// * score_threshold: a threshold used to filter boxes by score. + /// * nms_threshold: a threshold used in non maximum suppression. + /// * indices: the kept indices of bboxes after NMS. + /// * top_k: keep at most @p top_k picked indices. + /// * sigma: parameter of Gaussian weighting. + /// * method: Gaussian or linear. + /// ## See also + /// SoftNMSMethod + /// + /// ## Note + /// This alternative version of [soft_nms_boxes] function uses the following default values for its arguments: + /// * top_k: 0 + /// * sigma: 0.5 + /// * method: SoftNMSMethod::SOFTNMS_GAUSSIAN + #[inline] + pub fn soft_nms_boxes_def(bboxes: &core::Vector, scores: &core::Vector, updated_scores: &mut core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_softNMSBoxes_const_vectorLRectGR_const_vectorLfloatGR_vectorLfloatGR_const_float_const_float_vectorLintGR(bboxes.as_raw_VectorOfRect(), scores.as_raw_VectorOff32(), updated_scores.as_raw_mut_VectorOff32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs soft non maximum suppression given boxes and corresponding scores. + /// Reference: + /// ## Parameters + /// * bboxes: a set of bounding boxes to apply Soft NMS. + /// * scores: a set of corresponding confidences. + /// * updated_scores: a set of corresponding updated confidences. + /// * score_threshold: a threshold used to filter boxes by score. + /// * nms_threshold: a threshold used in non maximum suppression. + /// * indices: the kept indices of bboxes after NMS. + /// * top_k: keep at most @p top_k picked indices. + /// * sigma: parameter of Gaussian weighting. + /// * method: Gaussian or linear. + /// ## See also + /// SoftNMSMethod + /// + /// ## C++ default parameters + /// * top_k: 0 + /// * sigma: 0.5 + /// * method: SoftNMSMethod::SOFTNMS_GAUSSIAN + #[inline] + pub fn soft_nms_boxes(bboxes: &core::Vector, scores: &core::Vector, updated_scores: &mut core::Vector, score_threshold: f32, nms_threshold: f32, indices: &mut core::Vector, top_k: size_t, sigma: f32, method: crate::dnn::SoftNMSMethod) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_softNMSBoxes_const_vectorLRectGR_const_vectorLfloatGR_vectorLfloatGR_const_float_const_float_vectorLintGR_size_t_const_float_SoftNMSMethod(bboxes.as_raw_VectorOfRect(), scores.as_raw_VectorOff32(), updated_scores.as_raw_mut_VectorOff32(), score_threshold, nms_threshold, indices.as_raw_mut_VectorOfi32(), top_k, sigma, method, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [total_1] function uses the following default values for its arguments: + /// * start: -1 + /// * end: -1 + #[inline] + pub fn total_1_def(mat: &impl core::MatTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_total_const_MatR(mat.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * start: -1 + /// * end: -1 + #[inline] + pub fn total_1(mat: &impl core::MatTraitConst, start: i32, end: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_total_const_MatR_int_int(mat.as_raw_Mat(), start, end, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [total] function uses the following default values for its arguments: + /// * start: -1 + /// * end: -1 + #[inline] + pub fn total_def(shape: &crate::dnn::MatShape) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_total_const_MatShapeR(shape.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * start: -1 + /// * end: -1 + #[inline] + pub fn total(shape: &crate::dnn::MatShape, start: i32, end: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_total_const_MatShapeR_int_int(shape.as_raw_VectorOfi32(), start, end, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Create a text representation for a binary network stored in protocol buffer format. + /// ## Parameters + /// * model: A path to binary network. + /// * output: A path to output text file to be created. + /// + /// + /// Note: To reduce output file size, trained weights are not included. + #[inline] + pub fn write_text_graph(model: &str, output: &str) -> Result<()> { + extern_container_arg!(model); + extern_container_arg!(output); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_writeTextGraph_const_StringR_const_StringR(model.opencv_as_extern(), output.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + pub struct AbsLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { AbsLayer } + + impl Drop for AbsLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_AbsLayer_delete(self.as_raw_mut_AbsLayer()) }; + } + } + + unsafe impl Send for AbsLayer {} + + impl AbsLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::AbsLayer { + let ret = unsafe { sys::cv_dnn_AbsLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::AbsLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_AbsLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::AbsLayer] + pub trait AbsLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_AbsLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::AbsLayer] + pub trait AbsLayerTrait: crate::dnn::AbsLayerTraitConst + crate::dnn::ActivationLayerTrait { + fn as_raw_mut_AbsLayer(&mut self) -> *mut c_void; + + } + + impl Default for AbsLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for AbsLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("AbsLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { AbsLayer, crate::dnn::ActivationLayer, cv_dnn_AbsLayer_to_ActivationLayer } + + boxed_cast_base! { AbsLayer, core::Algorithm, cv_dnn_AbsLayer_to_Algorithm } + + boxed_cast_base! { AbsLayer, crate::dnn::Layer, cv_dnn_AbsLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for AbsLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for AbsLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AbsLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for AbsLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for AbsLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AbsLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for AbsLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for AbsLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AbsLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::AbsLayerTraitConst for AbsLayer { + #[inline] fn as_raw_AbsLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::AbsLayerTrait for AbsLayer { + #[inline] fn as_raw_mut_AbsLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AbsLayer, crate::dnn::AbsLayerTraitConst, as_raw_AbsLayer, crate::dnn::AbsLayerTrait, as_raw_mut_AbsLayer } + + pub struct AccumLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { AccumLayer } + + impl Drop for AccumLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_AccumLayer_delete(self.as_raw_mut_AccumLayer()) }; + } + } + + unsafe impl Send for AccumLayer {} + + impl AccumLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::AccumLayer { + let ret = unsafe { sys::cv_dnn_AccumLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::AccumLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_AccumLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::AccumLayer] + pub trait AccumLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_AccumLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::AccumLayer] + pub trait AccumLayerTrait: crate::dnn::AccumLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_AccumLayer(&mut self) -> *mut c_void; + + } + + impl Default for AccumLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for AccumLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("AccumLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { AccumLayer, core::Algorithm, cv_dnn_AccumLayer_to_Algorithm } + + boxed_cast_base! { AccumLayer, crate::dnn::Layer, cv_dnn_AccumLayer_to_Layer } + + impl core::AlgorithmTraitConst for AccumLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for AccumLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AccumLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for AccumLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for AccumLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AccumLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::AccumLayerTraitConst for AccumLayer { + #[inline] fn as_raw_AccumLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::AccumLayerTrait for AccumLayer { + #[inline] fn as_raw_mut_AccumLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AccumLayer, crate::dnn::AccumLayerTraitConst, as_raw_AccumLayer, crate::dnn::AccumLayerTrait, as_raw_mut_AccumLayer } + + pub struct AcosLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { AcosLayer } + + impl Drop for AcosLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_AcosLayer_delete(self.as_raw_mut_AcosLayer()) }; + } + } + + unsafe impl Send for AcosLayer {} + + impl AcosLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::AcosLayer { + let ret = unsafe { sys::cv_dnn_AcosLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::AcosLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_AcosLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::AcosLayer] + pub trait AcosLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_AcosLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::AcosLayer] + pub trait AcosLayerTrait: crate::dnn::AcosLayerTraitConst + crate::dnn::ActivationLayerTrait { + fn as_raw_mut_AcosLayer(&mut self) -> *mut c_void; + + } + + impl Default for AcosLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for AcosLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("AcosLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { AcosLayer, crate::dnn::ActivationLayer, cv_dnn_AcosLayer_to_ActivationLayer } + + boxed_cast_base! { AcosLayer, core::Algorithm, cv_dnn_AcosLayer_to_Algorithm } + + boxed_cast_base! { AcosLayer, crate::dnn::Layer, cv_dnn_AcosLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for AcosLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for AcosLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AcosLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for AcosLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for AcosLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AcosLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for AcosLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for AcosLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AcosLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::AcosLayerTraitConst for AcosLayer { + #[inline] fn as_raw_AcosLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::AcosLayerTrait for AcosLayer { + #[inline] fn as_raw_mut_AcosLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AcosLayer, crate::dnn::AcosLayerTraitConst, as_raw_AcosLayer, crate::dnn::AcosLayerTrait, as_raw_mut_AcosLayer } + + pub struct AcoshLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { AcoshLayer } + + impl Drop for AcoshLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_AcoshLayer_delete(self.as_raw_mut_AcoshLayer()) }; + } + } + + unsafe impl Send for AcoshLayer {} + + impl AcoshLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::AcoshLayer { + let ret = unsafe { sys::cv_dnn_AcoshLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::AcoshLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_AcoshLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::AcoshLayer] + pub trait AcoshLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_AcoshLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::AcoshLayer] + pub trait AcoshLayerTrait: crate::dnn::AcoshLayerTraitConst + crate::dnn::ActivationLayerTrait { + fn as_raw_mut_AcoshLayer(&mut self) -> *mut c_void; + + } + + impl Default for AcoshLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for AcoshLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("AcoshLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { AcoshLayer, crate::dnn::ActivationLayer, cv_dnn_AcoshLayer_to_ActivationLayer } + + boxed_cast_base! { AcoshLayer, core::Algorithm, cv_dnn_AcoshLayer_to_Algorithm } + + boxed_cast_base! { AcoshLayer, crate::dnn::Layer, cv_dnn_AcoshLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for AcoshLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for AcoshLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AcoshLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for AcoshLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for AcoshLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AcoshLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for AcoshLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for AcoshLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AcoshLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::AcoshLayerTraitConst for AcoshLayer { + #[inline] fn as_raw_AcoshLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::AcoshLayerTrait for AcoshLayer { + #[inline] fn as_raw_mut_AcoshLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AcoshLayer, crate::dnn::AcoshLayerTraitConst, as_raw_AcoshLayer, crate::dnn::AcoshLayerTrait, as_raw_mut_AcoshLayer } + + pub struct ActivationLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ActivationLayer } + + impl Drop for ActivationLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ActivationLayer_delete(self.as_raw_mut_ActivationLayer()) }; + } + } + + unsafe impl Send for ActivationLayer {} + + impl ActivationLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ActivationLayer { + let ret = unsafe { sys::cv_dnn_ActivationLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ActivationLayer::opencv_from_extern(ret) }; + ret + } + + } + + /// Constant methods for [crate::dnn::ActivationLayer] + pub trait ActivationLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ActivationLayer(&self) -> *const c_void; + + #[inline] + fn forward_slice(&self, src: &[f32], dst: &mut [f32], out_plane_size: size_t, cn0: i32, cn1: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ActivationLayer_forwardSlice_const_const_floatX_floatX_int_size_t_int_int(self.as_raw_ActivationLayer(), src.as_ptr(), dst.as_mut_ptr(), src.len().min(dst.len()).try_into()?, out_plane_size, cn0, cn1, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn forward_slice_1(&self, src: &[i32], lut: &[i32], dst: &mut [i32], out_plane_size: size_t, cn0: i32, cn1: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ActivationLayer_forwardSlice_const_const_intX_const_intX_intX_int_size_t_int_int(self.as_raw_ActivationLayer(), src.as_ptr(), lut.as_ptr(), dst.as_mut_ptr(), src.len().min(lut.len()).min(dst.len()).try_into()?, out_plane_size, cn0, cn1, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::dnn::ActivationLayer] + pub trait ActivationLayerTrait: crate::dnn::ActivationLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void; + + } + + impl Default for ActivationLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ActivationLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ActivationLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ActivationLayer, core::Algorithm, cv_dnn_ActivationLayer_to_Algorithm } + + boxed_cast_base! { ActivationLayer, crate::dnn::Layer, cv_dnn_ActivationLayer_to_Layer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::AbsLayer, cv_dnn_ActivationLayer_to_AbsLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::AcosLayer, cv_dnn_ActivationLayer_to_AcosLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::AcoshLayer, cv_dnn_ActivationLayer_to_AcoshLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::ActivationLayerInt8, cv_dnn_ActivationLayer_to_ActivationLayerInt8 } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::AsinLayer, cv_dnn_ActivationLayer_to_AsinLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::AsinhLayer, cv_dnn_ActivationLayer_to_AsinhLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::AtanLayer, cv_dnn_ActivationLayer_to_AtanLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::AtanhLayer, cv_dnn_ActivationLayer_to_AtanhLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::BNLLLayer, cv_dnn_ActivationLayer_to_BNLLLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::BatchNormLayer, cv_dnn_ActivationLayer_to_BatchNormLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::BatchNormLayerInt8, cv_dnn_ActivationLayer_to_BatchNormLayerInt8 } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::CeilLayer, cv_dnn_ActivationLayer_to_CeilLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::CeluLayer, cv_dnn_ActivationLayer_to_CeluLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::ChannelsPReLULayer, cv_dnn_ActivationLayer_to_ChannelsPReLULayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::CosLayer, cv_dnn_ActivationLayer_to_CosLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::CoshLayer, cv_dnn_ActivationLayer_to_CoshLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::ELULayer, cv_dnn_ActivationLayer_to_ELULayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::ErfLayer, cv_dnn_ActivationLayer_to_ErfLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::ExpLayer, cv_dnn_ActivationLayer_to_ExpLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::FloorLayer, cv_dnn_ActivationLayer_to_FloorLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::GeluApproximationLayer, cv_dnn_ActivationLayer_to_GeluApproximationLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::GeluLayer, cv_dnn_ActivationLayer_to_GeluLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::HardSigmoidLayer, cv_dnn_ActivationLayer_to_HardSigmoidLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::HardSwishLayer, cv_dnn_ActivationLayer_to_HardSwishLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::LogLayer, cv_dnn_ActivationLayer_to_LogLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::MishLayer, cv_dnn_ActivationLayer_to_MishLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::NotLayer, cv_dnn_ActivationLayer_to_NotLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::PowerLayer, cv_dnn_ActivationLayer_to_PowerLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::ReLU6Layer, cv_dnn_ActivationLayer_to_ReLU6Layer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::ReLULayer, cv_dnn_ActivationLayer_to_ReLULayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::ReciprocalLayer, cv_dnn_ActivationLayer_to_ReciprocalLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::RoundLayer, cv_dnn_ActivationLayer_to_RoundLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::SeluLayer, cv_dnn_ActivationLayer_to_SeluLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::ShrinkLayer, cv_dnn_ActivationLayer_to_ShrinkLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::SigmoidLayer, cv_dnn_ActivationLayer_to_SigmoidLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::SignLayer, cv_dnn_ActivationLayer_to_SignLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::SinLayer, cv_dnn_ActivationLayer_to_SinLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::SinhLayer, cv_dnn_ActivationLayer_to_SinhLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::SoftplusLayer, cv_dnn_ActivationLayer_to_SoftplusLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::SoftsignLayer, cv_dnn_ActivationLayer_to_SoftsignLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::SqrtLayer, cv_dnn_ActivationLayer_to_SqrtLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::SwishLayer, cv_dnn_ActivationLayer_to_SwishLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::TanHLayer, cv_dnn_ActivationLayer_to_TanHLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::TanLayer, cv_dnn_ActivationLayer_to_TanLayer } + + boxed_cast_descendant! { ActivationLayer, crate::dnn::ThresholdedReluLayer, cv_dnn_ActivationLayer_to_ThresholdedReluLayer } + + impl core::AlgorithmTraitConst for ActivationLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ActivationLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ActivationLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ActivationLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ActivationLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ActivationLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ActivationLayerTraitConst for ActivationLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for ActivationLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ActivationLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + pub struct ActivationLayerInt8 { + ptr: *mut c_void, + } + + opencv_type_boxed! { ActivationLayerInt8 } + + impl Drop for ActivationLayerInt8 { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ActivationLayerInt8_delete(self.as_raw_mut_ActivationLayerInt8()) }; + } + } + + unsafe impl Send for ActivationLayerInt8 {} + + impl ActivationLayerInt8 { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ActivationLayerInt8 { + let ret = unsafe { sys::cv_dnn_ActivationLayerInt8_defaultNew_const() }; + let ret = unsafe { crate::dnn::ActivationLayerInt8::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ActivationLayerInt8_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ActivationLayerInt8] + pub trait ActivationLayerInt8TraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_ActivationLayerInt8(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ActivationLayerInt8] + pub trait ActivationLayerInt8Trait: crate::dnn::ActivationLayerInt8TraitConst + crate::dnn::ActivationLayerTrait { + fn as_raw_mut_ActivationLayerInt8(&mut self) -> *mut c_void; + + } + + impl Default for ActivationLayerInt8 { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ActivationLayerInt8 { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ActivationLayerInt8") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ActivationLayerInt8, crate::dnn::ActivationLayer, cv_dnn_ActivationLayerInt8_to_ActivationLayer } + + boxed_cast_base! { ActivationLayerInt8, core::Algorithm, cv_dnn_ActivationLayerInt8_to_Algorithm } + + boxed_cast_base! { ActivationLayerInt8, crate::dnn::Layer, cv_dnn_ActivationLayerInt8_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for ActivationLayerInt8 { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for ActivationLayerInt8 { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ActivationLayerInt8, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for ActivationLayerInt8 { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ActivationLayerInt8 { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ActivationLayerInt8, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ActivationLayerInt8 { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ActivationLayerInt8 { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ActivationLayerInt8, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ActivationLayerInt8TraitConst for ActivationLayerInt8 { + #[inline] fn as_raw_ActivationLayerInt8(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerInt8Trait for ActivationLayerInt8 { + #[inline] fn as_raw_mut_ActivationLayerInt8(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ActivationLayerInt8, crate::dnn::ActivationLayerInt8TraitConst, as_raw_ActivationLayerInt8, crate::dnn::ActivationLayerInt8Trait, as_raw_mut_ActivationLayerInt8 } + + /// ArgMax/ArgMin layer + /// + /// Note: returns indices as floats, which means the supported range is [-2^24; 2^24] + pub struct ArgLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ArgLayer } + + impl Drop for ArgLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ArgLayer_delete(self.as_raw_mut_ArgLayer()) }; + } + } + + unsafe impl Send for ArgLayer {} + + impl ArgLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ArgLayer { + let ret = unsafe { sys::cv_dnn_ArgLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ArgLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ArgLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ArgLayer] + pub trait ArgLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ArgLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ArgLayer] + pub trait ArgLayerTrait: crate::dnn::ArgLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_ArgLayer(&mut self) -> *mut c_void; + + } + + impl Default for ArgLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ArgLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ArgLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ArgLayer, core::Algorithm, cv_dnn_ArgLayer_to_Algorithm } + + boxed_cast_base! { ArgLayer, crate::dnn::Layer, cv_dnn_ArgLayer_to_Layer } + + impl core::AlgorithmTraitConst for ArgLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ArgLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ArgLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ArgLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ArgLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ArgLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ArgLayerTraitConst for ArgLayer { + #[inline] fn as_raw_ArgLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ArgLayerTrait for ArgLayer { + #[inline] fn as_raw_mut_ArgLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ArgLayer, crate::dnn::ArgLayerTraitConst, as_raw_ArgLayer, crate::dnn::ArgLayerTrait, as_raw_mut_ArgLayer } + + pub struct AsinLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { AsinLayer } + + impl Drop for AsinLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_AsinLayer_delete(self.as_raw_mut_AsinLayer()) }; + } + } + + unsafe impl Send for AsinLayer {} + + impl AsinLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::AsinLayer { + let ret = unsafe { sys::cv_dnn_AsinLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::AsinLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_AsinLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::AsinLayer] + pub trait AsinLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_AsinLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::AsinLayer] + pub trait AsinLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::AsinLayerTraitConst { + fn as_raw_mut_AsinLayer(&mut self) -> *mut c_void; + + } + + impl Default for AsinLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for AsinLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("AsinLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { AsinLayer, crate::dnn::ActivationLayer, cv_dnn_AsinLayer_to_ActivationLayer } + + boxed_cast_base! { AsinLayer, core::Algorithm, cv_dnn_AsinLayer_to_Algorithm } + + boxed_cast_base! { AsinLayer, crate::dnn::Layer, cv_dnn_AsinLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for AsinLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for AsinLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AsinLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for AsinLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for AsinLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AsinLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for AsinLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for AsinLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AsinLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::AsinLayerTraitConst for AsinLayer { + #[inline] fn as_raw_AsinLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::AsinLayerTrait for AsinLayer { + #[inline] fn as_raw_mut_AsinLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AsinLayer, crate::dnn::AsinLayerTraitConst, as_raw_AsinLayer, crate::dnn::AsinLayerTrait, as_raw_mut_AsinLayer } + + pub struct AsinhLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { AsinhLayer } + + impl Drop for AsinhLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_AsinhLayer_delete(self.as_raw_mut_AsinhLayer()) }; + } + } + + unsafe impl Send for AsinhLayer {} + + impl AsinhLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::AsinhLayer { + let ret = unsafe { sys::cv_dnn_AsinhLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::AsinhLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_AsinhLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::AsinhLayer] + pub trait AsinhLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_AsinhLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::AsinhLayer] + pub trait AsinhLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::AsinhLayerTraitConst { + fn as_raw_mut_AsinhLayer(&mut self) -> *mut c_void; + + } + + impl Default for AsinhLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for AsinhLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("AsinhLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { AsinhLayer, crate::dnn::ActivationLayer, cv_dnn_AsinhLayer_to_ActivationLayer } + + boxed_cast_base! { AsinhLayer, core::Algorithm, cv_dnn_AsinhLayer_to_Algorithm } + + boxed_cast_base! { AsinhLayer, crate::dnn::Layer, cv_dnn_AsinhLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for AsinhLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for AsinhLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AsinhLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for AsinhLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for AsinhLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AsinhLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for AsinhLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for AsinhLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AsinhLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::AsinhLayerTraitConst for AsinhLayer { + #[inline] fn as_raw_AsinhLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::AsinhLayerTrait for AsinhLayer { + #[inline] fn as_raw_mut_AsinhLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AsinhLayer, crate::dnn::AsinhLayerTraitConst, as_raw_AsinhLayer, crate::dnn::AsinhLayerTrait, as_raw_mut_AsinhLayer } + + pub struct AtanLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { AtanLayer } + + impl Drop for AtanLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_AtanLayer_delete(self.as_raw_mut_AtanLayer()) }; + } + } + + unsafe impl Send for AtanLayer {} + + impl AtanLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::AtanLayer { + let ret = unsafe { sys::cv_dnn_AtanLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::AtanLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_AtanLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::AtanLayer] + pub trait AtanLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_AtanLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::AtanLayer] + pub trait AtanLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::AtanLayerTraitConst { + fn as_raw_mut_AtanLayer(&mut self) -> *mut c_void; + + } + + impl Default for AtanLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for AtanLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("AtanLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { AtanLayer, crate::dnn::ActivationLayer, cv_dnn_AtanLayer_to_ActivationLayer } + + boxed_cast_base! { AtanLayer, core::Algorithm, cv_dnn_AtanLayer_to_Algorithm } + + boxed_cast_base! { AtanLayer, crate::dnn::Layer, cv_dnn_AtanLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for AtanLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for AtanLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AtanLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for AtanLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for AtanLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AtanLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for AtanLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for AtanLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AtanLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::AtanLayerTraitConst for AtanLayer { + #[inline] fn as_raw_AtanLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::AtanLayerTrait for AtanLayer { + #[inline] fn as_raw_mut_AtanLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AtanLayer, crate::dnn::AtanLayerTraitConst, as_raw_AtanLayer, crate::dnn::AtanLayerTrait, as_raw_mut_AtanLayer } + + pub struct AtanhLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { AtanhLayer } + + impl Drop for AtanhLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_AtanhLayer_delete(self.as_raw_mut_AtanhLayer()) }; + } + } + + unsafe impl Send for AtanhLayer {} + + impl AtanhLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::AtanhLayer { + let ret = unsafe { sys::cv_dnn_AtanhLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::AtanhLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_AtanhLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::AtanhLayer] + pub trait AtanhLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_AtanhLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::AtanhLayer] + pub trait AtanhLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::AtanhLayerTraitConst { + fn as_raw_mut_AtanhLayer(&mut self) -> *mut c_void; + + } + + impl Default for AtanhLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for AtanhLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("AtanhLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { AtanhLayer, crate::dnn::ActivationLayer, cv_dnn_AtanhLayer_to_ActivationLayer } + + boxed_cast_base! { AtanhLayer, core::Algorithm, cv_dnn_AtanhLayer_to_Algorithm } + + boxed_cast_base! { AtanhLayer, crate::dnn::Layer, cv_dnn_AtanhLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for AtanhLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for AtanhLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AtanhLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for AtanhLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for AtanhLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AtanhLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for AtanhLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for AtanhLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AtanhLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::AtanhLayerTraitConst for AtanhLayer { + #[inline] fn as_raw_AtanhLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::AtanhLayerTrait for AtanhLayer { + #[inline] fn as_raw_mut_AtanhLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AtanhLayer, crate::dnn::AtanhLayerTraitConst, as_raw_AtanhLayer, crate::dnn::AtanhLayerTrait, as_raw_mut_AtanhLayer } + + pub struct AttentionLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { AttentionLayer } + + impl Drop for AttentionLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_AttentionLayer_delete(self.as_raw_mut_AttentionLayer()) }; + } + } + + unsafe impl Send for AttentionLayer {} + + impl AttentionLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::AttentionLayer { + let ret = unsafe { sys::cv_dnn_AttentionLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::AttentionLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_AttentionLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::AttentionLayer] + pub trait AttentionLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_AttentionLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::AttentionLayer] + pub trait AttentionLayerTrait: crate::dnn::AttentionLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_AttentionLayer(&mut self) -> *mut c_void; + + } + + impl Default for AttentionLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for AttentionLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("AttentionLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { AttentionLayer, core::Algorithm, cv_dnn_AttentionLayer_to_Algorithm } + + boxed_cast_base! { AttentionLayer, crate::dnn::Layer, cv_dnn_AttentionLayer_to_Layer } + + impl core::AlgorithmTraitConst for AttentionLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for AttentionLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AttentionLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for AttentionLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for AttentionLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AttentionLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::AttentionLayerTraitConst for AttentionLayer { + #[inline] fn as_raw_AttentionLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::AttentionLayerTrait for AttentionLayer { + #[inline] fn as_raw_mut_AttentionLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { AttentionLayer, crate::dnn::AttentionLayerTraitConst, as_raw_AttentionLayer, crate::dnn::AttentionLayerTrait, as_raw_mut_AttentionLayer } + + pub struct BNLLLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { BNLLLayer } + + impl Drop for BNLLLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_BNLLLayer_delete(self.as_raw_mut_BNLLLayer()) }; + } + } + + unsafe impl Send for BNLLLayer {} + + impl BNLLLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::BNLLLayer { + let ret = unsafe { sys::cv_dnn_BNLLLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::BNLLLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_BNLLLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::BNLLLayer] + pub trait BNLLLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_BNLLLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::BNLLLayer] + pub trait BNLLLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::BNLLLayerTraitConst { + fn as_raw_mut_BNLLLayer(&mut self) -> *mut c_void; + + } + + impl Default for BNLLLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for BNLLLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("BNLLLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { BNLLLayer, crate::dnn::ActivationLayer, cv_dnn_BNLLLayer_to_ActivationLayer } + + boxed_cast_base! { BNLLLayer, core::Algorithm, cv_dnn_BNLLLayer_to_Algorithm } + + boxed_cast_base! { BNLLLayer, crate::dnn::Layer, cv_dnn_BNLLLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for BNLLLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for BNLLLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BNLLLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for BNLLLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for BNLLLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BNLLLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for BNLLLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for BNLLLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BNLLLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::BNLLLayerTraitConst for BNLLLayer { + #[inline] fn as_raw_BNLLLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::BNLLLayerTrait for BNLLLayer { + #[inline] fn as_raw_mut_BNLLLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BNLLLayer, crate::dnn::BNLLLayerTraitConst, as_raw_BNLLLayer, crate::dnn::BNLLLayerTrait, as_raw_mut_BNLLLayer } + + /// Derivatives of this class encapsulates functions of certain backends. + pub struct BackendNode { + ptr: *mut c_void, + } + + opencv_type_boxed! { BackendNode } + + impl Drop for BackendNode { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_BackendNode_delete(self.as_raw_mut_BackendNode()) }; + } + } + + unsafe impl Send for BackendNode {} + + /// Constant methods for [crate::dnn::BackendNode] + pub trait BackendNodeTraitConst { + fn as_raw_BackendNode(&self) -> *const c_void; + + /// Backend identifier. + #[inline] + fn backend_id(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_BackendNode_propBackendId_const(self.as_raw_BackendNode()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::BackendNode] + pub trait BackendNodeTrait: crate::dnn::BackendNodeTraitConst { + fn as_raw_mut_BackendNode(&mut self) -> *mut c_void; + + /// Backend identifier. + #[inline] + fn set_backend_id(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_BackendNode_propBackendId_const_int(self.as_raw_mut_BackendNode(), val) }; + ret + } + + } + + impl std::fmt::Debug for BackendNode { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("BackendNode") + .field("backend_id", &crate::dnn::BackendNodeTraitConst::backend_id(self)) + .finish() + } + } + + impl crate::dnn::BackendNodeTraitConst for BackendNode { + #[inline] fn as_raw_BackendNode(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::BackendNodeTrait for BackendNode { + #[inline] fn as_raw_mut_BackendNode(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BackendNode, crate::dnn::BackendNodeTraitConst, as_raw_BackendNode, crate::dnn::BackendNodeTrait, as_raw_mut_BackendNode } + + /// Derivatives of this class wraps cv::Mat for different backends and targets. + pub struct BackendWrapper { + ptr: *mut c_void, + } + + opencv_type_boxed! { BackendWrapper } + + impl Drop for BackendWrapper { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_BackendWrapper_delete(self.as_raw_mut_BackendWrapper()) }; + } + } + + unsafe impl Send for BackendWrapper {} + + /// Constant methods for [crate::dnn::BackendWrapper] + pub trait BackendWrapperTraitConst { + fn as_raw_BackendWrapper(&self) -> *const c_void; + + /// Backend identifier. + #[inline] + fn backend_id(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_BackendWrapper_propBackendId_const(self.as_raw_BackendWrapper()) }; + ret + } + + /// Target identifier. + #[inline] + fn target_id(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_BackendWrapper_propTargetId_const(self.as_raw_BackendWrapper()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::BackendWrapper] + pub trait BackendWrapperTrait: crate::dnn::BackendWrapperTraitConst { + fn as_raw_mut_BackendWrapper(&mut self) -> *mut c_void; + + /// Backend identifier. + #[inline] + fn set_backend_id(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_BackendWrapper_propBackendId_const_int(self.as_raw_mut_BackendWrapper(), val) }; + ret + } + + /// Target identifier. + #[inline] + fn set_target_id(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_BackendWrapper_propTargetId_const_int(self.as_raw_mut_BackendWrapper(), val) }; + ret + } + + /// Transfer data to CPU host memory. + #[inline] + fn copy_to_host(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_BackendWrapper_copyToHost(self.as_raw_mut_BackendWrapper(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Indicate that an actual data is on CPU. + #[inline] + fn set_host_dirty(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_BackendWrapper_setHostDirty(self.as_raw_mut_BackendWrapper(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for BackendWrapper { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("BackendWrapper") + .field("backend_id", &crate::dnn::BackendWrapperTraitConst::backend_id(self)) + .field("target_id", &crate::dnn::BackendWrapperTraitConst::target_id(self)) + .finish() + } + } + + impl crate::dnn::BackendWrapperTraitConst for BackendWrapper { + #[inline] fn as_raw_BackendWrapper(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::BackendWrapperTrait for BackendWrapper { + #[inline] fn as_raw_mut_BackendWrapper(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BackendWrapper, crate::dnn::BackendWrapperTraitConst, as_raw_BackendWrapper, crate::dnn::BackendWrapperTrait, as_raw_mut_BackendWrapper } + + pub struct BaseConvolutionLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { BaseConvolutionLayer } + + impl Drop for BaseConvolutionLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_BaseConvolutionLayer_delete(self.as_raw_mut_BaseConvolutionLayer()) }; + } + } + + unsafe impl Send for BaseConvolutionLayer {} + + impl BaseConvolutionLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::BaseConvolutionLayer { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::BaseConvolutionLayer::opencv_from_extern(ret) }; + ret + } + + } + + /// Constant methods for [crate::dnn::BaseConvolutionLayer] + pub trait BaseConvolutionLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_BaseConvolutionLayer(&self) -> *const c_void; + + #[inline] + fn kernel(&self) -> core::Size { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_BaseConvolutionLayer_propKernel_const(self.as_raw_BaseConvolutionLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn stride(&self) -> core::Size { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_BaseConvolutionLayer_propStride_const(self.as_raw_BaseConvolutionLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn pad(&self) -> core::Size { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_BaseConvolutionLayer_propPad_const(self.as_raw_BaseConvolutionLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn dilation(&self) -> core::Size { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_BaseConvolutionLayer_propDilation_const(self.as_raw_BaseConvolutionLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn adjust_pad(&self) -> core::Size { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_BaseConvolutionLayer_propAdjustPad_const(self.as_raw_BaseConvolutionLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn adjust_pads(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propAdjust_pads_const(self.as_raw_BaseConvolutionLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn kernel_size(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propKernel_size_const(self.as_raw_BaseConvolutionLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn strides(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propStrides_const(self.as_raw_BaseConvolutionLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn dilations(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propDilations_const(self.as_raw_BaseConvolutionLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn pads_begin(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propPads_begin_const(self.as_raw_BaseConvolutionLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn pads_end(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propPads_end_const(self.as_raw_BaseConvolutionLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn pad_mode(&self) -> String { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propPadMode_const(self.as_raw_BaseConvolutionLayer()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn num_output(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propNumOutput_const(self.as_raw_BaseConvolutionLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::BaseConvolutionLayer] + pub trait BaseConvolutionLayerTrait: crate::dnn::BaseConvolutionLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_BaseConvolutionLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_kernel(&mut self, val: core::Size) { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propKernel_const_Size(self.as_raw_mut_BaseConvolutionLayer(), &val) }; + ret + } + + #[inline] + fn set_stride(&mut self, val: core::Size) { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propStride_const_Size(self.as_raw_mut_BaseConvolutionLayer(), &val) }; + ret + } + + #[inline] + fn set_pad(&mut self, val: core::Size) { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propPad_const_Size(self.as_raw_mut_BaseConvolutionLayer(), &val) }; + ret + } + + #[inline] + fn set_dilation(&mut self, val: core::Size) { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propDilation_const_Size(self.as_raw_mut_BaseConvolutionLayer(), &val) }; + ret + } + + #[inline] + fn set_adjust_pad(&mut self, val: core::Size) { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propAdjustPad_const_Size(self.as_raw_mut_BaseConvolutionLayer(), &val) }; + ret + } + + #[inline] + fn set_adjust_pads(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propAdjust_pads_const_vectorLsize_tG(self.as_raw_mut_BaseConvolutionLayer(), val.as_raw_VectorOfsize_t()) }; + ret + } + + #[inline] + fn set_kernel_size(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propKernel_size_const_vectorLsize_tG(self.as_raw_mut_BaseConvolutionLayer(), val.as_raw_VectorOfsize_t()) }; + ret + } + + #[inline] + fn set_strides(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propStrides_const_vectorLsize_tG(self.as_raw_mut_BaseConvolutionLayer(), val.as_raw_VectorOfsize_t()) }; + ret + } + + #[inline] + fn set_dilations(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propDilations_const_vectorLsize_tG(self.as_raw_mut_BaseConvolutionLayer(), val.as_raw_VectorOfsize_t()) }; + ret + } + + #[inline] + fn set_pads_begin(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propPads_begin_const_vectorLsize_tG(self.as_raw_mut_BaseConvolutionLayer(), val.as_raw_VectorOfsize_t()) }; + ret + } + + #[inline] + fn set_pads_end(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propPads_end_const_vectorLsize_tG(self.as_raw_mut_BaseConvolutionLayer(), val.as_raw_VectorOfsize_t()) }; + ret + } + + #[inline] + fn set_pad_mode(&mut self, val: &str) { + extern_container_arg!(nofail val); + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propPadMode_const_String(self.as_raw_mut_BaseConvolutionLayer(), val.opencv_as_extern()) }; + ret + } + + #[inline] + fn set_num_output(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_BaseConvolutionLayer_propNumOutput_const_int(self.as_raw_mut_BaseConvolutionLayer(), val) }; + ret + } + + } + + impl Default for BaseConvolutionLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for BaseConvolutionLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("BaseConvolutionLayer") + .field("kernel", &crate::dnn::BaseConvolutionLayerTraitConst::kernel(self)) + .field("stride", &crate::dnn::BaseConvolutionLayerTraitConst::stride(self)) + .field("pad", &crate::dnn::BaseConvolutionLayerTraitConst::pad(self)) + .field("dilation", &crate::dnn::BaseConvolutionLayerTraitConst::dilation(self)) + .field("adjust_pad", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pad(self)) + .field("adjust_pads", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pads(self)) + .field("kernel_size", &crate::dnn::BaseConvolutionLayerTraitConst::kernel_size(self)) + .field("strides", &crate::dnn::BaseConvolutionLayerTraitConst::strides(self)) + .field("dilations", &crate::dnn::BaseConvolutionLayerTraitConst::dilations(self)) + .field("pads_begin", &crate::dnn::BaseConvolutionLayerTraitConst::pads_begin(self)) + .field("pads_end", &crate::dnn::BaseConvolutionLayerTraitConst::pads_end(self)) + .field("pad_mode", &crate::dnn::BaseConvolutionLayerTraitConst::pad_mode(self)) + .field("num_output", &crate::dnn::BaseConvolutionLayerTraitConst::num_output(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { BaseConvolutionLayer, core::Algorithm, cv_dnn_BaseConvolutionLayer_to_Algorithm } + + boxed_cast_base! { BaseConvolutionLayer, crate::dnn::Layer, cv_dnn_BaseConvolutionLayer_to_Layer } + + impl core::AlgorithmTraitConst for BaseConvolutionLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for BaseConvolutionLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BaseConvolutionLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for BaseConvolutionLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for BaseConvolutionLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BaseConvolutionLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::BaseConvolutionLayerTraitConst for BaseConvolutionLayer { + #[inline] fn as_raw_BaseConvolutionLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::BaseConvolutionLayerTrait for BaseConvolutionLayer { + #[inline] fn as_raw_mut_BaseConvolutionLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BaseConvolutionLayer, crate::dnn::BaseConvolutionLayerTraitConst, as_raw_BaseConvolutionLayer, crate::dnn::BaseConvolutionLayerTrait, as_raw_mut_BaseConvolutionLayer } + + pub struct BatchNormLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { BatchNormLayer } + + impl Drop for BatchNormLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_BatchNormLayer_delete(self.as_raw_mut_BatchNormLayer()) }; + } + } + + unsafe impl Send for BatchNormLayer {} + + impl BatchNormLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::BatchNormLayer { + let ret = unsafe { sys::cv_dnn_BatchNormLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::BatchNormLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_BatchNormLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::BatchNormLayer] + pub trait BatchNormLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_BatchNormLayer(&self) -> *const c_void; + + #[inline] + fn has_weights(&self) -> bool { + let ret = unsafe { sys::cv_dnn_BatchNormLayer_propHasWeights_const(self.as_raw_BatchNormLayer()) }; + ret + } + + #[inline] + fn has_bias(&self) -> bool { + let ret = unsafe { sys::cv_dnn_BatchNormLayer_propHasBias_const(self.as_raw_BatchNormLayer()) }; + ret + } + + #[inline] + fn epsilon(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_BatchNormLayer_propEpsilon_const(self.as_raw_BatchNormLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::BatchNormLayer] + pub trait BatchNormLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::BatchNormLayerTraitConst { + fn as_raw_mut_BatchNormLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_has_weights(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_BatchNormLayer_propHasWeights_const_bool(self.as_raw_mut_BatchNormLayer(), val) }; + ret + } + + #[inline] + fn set_has_bias(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_BatchNormLayer_propHasBias_const_bool(self.as_raw_mut_BatchNormLayer(), val) }; + ret + } + + #[inline] + fn set_epsilon(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_BatchNormLayer_propEpsilon_const_float(self.as_raw_mut_BatchNormLayer(), val) }; + ret + } + + } + + impl Default for BatchNormLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for BatchNormLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("BatchNormLayer") + .field("has_weights", &crate::dnn::BatchNormLayerTraitConst::has_weights(self)) + .field("has_bias", &crate::dnn::BatchNormLayerTraitConst::has_bias(self)) + .field("epsilon", &crate::dnn::BatchNormLayerTraitConst::epsilon(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { BatchNormLayer, crate::dnn::ActivationLayer, cv_dnn_BatchNormLayer_to_ActivationLayer } + + boxed_cast_base! { BatchNormLayer, core::Algorithm, cv_dnn_BatchNormLayer_to_Algorithm } + + boxed_cast_base! { BatchNormLayer, crate::dnn::Layer, cv_dnn_BatchNormLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for BatchNormLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for BatchNormLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BatchNormLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for BatchNormLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for BatchNormLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BatchNormLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for BatchNormLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for BatchNormLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BatchNormLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::BatchNormLayerTraitConst for BatchNormLayer { + #[inline] fn as_raw_BatchNormLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::BatchNormLayerTrait for BatchNormLayer { + #[inline] fn as_raw_mut_BatchNormLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BatchNormLayer, crate::dnn::BatchNormLayerTraitConst, as_raw_BatchNormLayer, crate::dnn::BatchNormLayerTrait, as_raw_mut_BatchNormLayer } + + pub struct BatchNormLayerInt8 { + ptr: *mut c_void, + } + + opencv_type_boxed! { BatchNormLayerInt8 } + + impl Drop for BatchNormLayerInt8 { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_BatchNormLayerInt8_delete(self.as_raw_mut_BatchNormLayerInt8()) }; + } + } + + unsafe impl Send for BatchNormLayerInt8 {} + + impl BatchNormLayerInt8 { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::BatchNormLayerInt8 { + let ret = unsafe { sys::cv_dnn_BatchNormLayerInt8_defaultNew_const() }; + let ret = unsafe { crate::dnn::BatchNormLayerInt8::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_BatchNormLayerInt8_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::BatchNormLayerInt8] + pub trait BatchNormLayerInt8TraitConst: crate::dnn::BatchNormLayerTraitConst { + fn as_raw_BatchNormLayerInt8(&self) -> *const c_void; + + #[inline] + fn input_sc(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_BatchNormLayerInt8_propInput_sc_const(self.as_raw_BatchNormLayerInt8()) }; + ret + } + + #[inline] + fn output_sc(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_BatchNormLayerInt8_propOutput_sc_const(self.as_raw_BatchNormLayerInt8()) }; + ret + } + + #[inline] + fn input_zp(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_BatchNormLayerInt8_propInput_zp_const(self.as_raw_BatchNormLayerInt8()) }; + ret + } + + #[inline] + fn output_zp(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_BatchNormLayerInt8_propOutput_zp_const(self.as_raw_BatchNormLayerInt8()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::BatchNormLayerInt8] + pub trait BatchNormLayerInt8Trait: crate::dnn::BatchNormLayerInt8TraitConst + crate::dnn::BatchNormLayerTrait { + fn as_raw_mut_BatchNormLayerInt8(&mut self) -> *mut c_void; + + #[inline] + fn set_input_sc(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_BatchNormLayerInt8_propInput_sc_const_float(self.as_raw_mut_BatchNormLayerInt8(), val) }; + ret + } + + #[inline] + fn set_output_sc(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_BatchNormLayerInt8_propOutput_sc_const_float(self.as_raw_mut_BatchNormLayerInt8(), val) }; + ret + } + + #[inline] + fn set_input_zp(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_BatchNormLayerInt8_propInput_zp_const_int(self.as_raw_mut_BatchNormLayerInt8(), val) }; + ret + } + + #[inline] + fn set_output_zp(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_BatchNormLayerInt8_propOutput_zp_const_int(self.as_raw_mut_BatchNormLayerInt8(), val) }; + ret + } + + } + + impl Default for BatchNormLayerInt8 { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for BatchNormLayerInt8 { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("BatchNormLayerInt8") + .field("input_sc", &crate::dnn::BatchNormLayerInt8TraitConst::input_sc(self)) + .field("output_sc", &crate::dnn::BatchNormLayerInt8TraitConst::output_sc(self)) + .field("input_zp", &crate::dnn::BatchNormLayerInt8TraitConst::input_zp(self)) + .field("output_zp", &crate::dnn::BatchNormLayerInt8TraitConst::output_zp(self)) + .field("has_weights", &crate::dnn::BatchNormLayerTraitConst::has_weights(self)) + .field("has_bias", &crate::dnn::BatchNormLayerTraitConst::has_bias(self)) + .field("epsilon", &crate::dnn::BatchNormLayerTraitConst::epsilon(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { BatchNormLayerInt8, crate::dnn::ActivationLayer, cv_dnn_BatchNormLayerInt8_to_ActivationLayer } + + boxed_cast_base! { BatchNormLayerInt8, core::Algorithm, cv_dnn_BatchNormLayerInt8_to_Algorithm } + + boxed_cast_base! { BatchNormLayerInt8, crate::dnn::BatchNormLayer, cv_dnn_BatchNormLayerInt8_to_BatchNormLayer } + + boxed_cast_base! { BatchNormLayerInt8, crate::dnn::Layer, cv_dnn_BatchNormLayerInt8_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for BatchNormLayerInt8 { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for BatchNormLayerInt8 { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BatchNormLayerInt8, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for BatchNormLayerInt8 { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for BatchNormLayerInt8 { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BatchNormLayerInt8, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::BatchNormLayerTraitConst for BatchNormLayerInt8 { + #[inline] fn as_raw_BatchNormLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::BatchNormLayerTrait for BatchNormLayerInt8 { + #[inline] fn as_raw_mut_BatchNormLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BatchNormLayerInt8, crate::dnn::BatchNormLayerTraitConst, as_raw_BatchNormLayer, crate::dnn::BatchNormLayerTrait, as_raw_mut_BatchNormLayer } + + impl crate::dnn::LayerTraitConst for BatchNormLayerInt8 { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for BatchNormLayerInt8 { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BatchNormLayerInt8, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::BatchNormLayerInt8TraitConst for BatchNormLayerInt8 { + #[inline] fn as_raw_BatchNormLayerInt8(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::BatchNormLayerInt8Trait for BatchNormLayerInt8 { + #[inline] fn as_raw_mut_BatchNormLayerInt8(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BatchNormLayerInt8, crate::dnn::BatchNormLayerInt8TraitConst, as_raw_BatchNormLayerInt8, crate::dnn::BatchNormLayerInt8Trait, as_raw_mut_BatchNormLayerInt8 } + + /// # Partial List of Implemented Layers + /// This subsection of dnn module contains information about built-in layers and their descriptions. + /// + /// Classes listed here, in fact, provides C++ API for creating instances of built-in layers. + /// In addition to this way of layers instantiation, there is a more common factory API (see [dnnLayerFactory]), it allows to create layers dynamically (by name) and register new ones. + /// You can use both API, but factory API is less convenient for native C++ programming and basically designed for use inside importers (see [readNetFromCaffe](), [readNetFromTorch](), [readNetFromTensorflow]()). + /// + /// Built-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers. + /// In particular, the following layers and Caffe importer were tested to reproduce Caffe functionality: + /// - Convolution + /// - Deconvolution + /// - Pooling + /// - InnerProduct + /// - TanH, ReLU, Sigmoid, BNLL, Power, AbsVal + /// - Softmax + /// - Reshape, Flatten, Slice, Split + /// - LRN + /// - MVN + /// - Dropout (since it does nothing on forward pass -)) + pub struct BlankLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { BlankLayer } + + impl Drop for BlankLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_BlankLayer_delete(self.as_raw_mut_BlankLayer()) }; + } + } + + unsafe impl Send for BlankLayer {} + + impl BlankLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::BlankLayer { + let ret = unsafe { sys::cv_dnn_BlankLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::BlankLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_BlankLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::BlankLayer] + pub trait BlankLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_BlankLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::BlankLayer] + pub trait BlankLayerTrait: crate::dnn::BlankLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_BlankLayer(&mut self) -> *mut c_void; + + } + + impl Default for BlankLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for BlankLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("BlankLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { BlankLayer, core::Algorithm, cv_dnn_BlankLayer_to_Algorithm } + + boxed_cast_base! { BlankLayer, crate::dnn::Layer, cv_dnn_BlankLayer_to_Layer } + + impl core::AlgorithmTraitConst for BlankLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for BlankLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BlankLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for BlankLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for BlankLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BlankLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::BlankLayerTraitConst for BlankLayer { + #[inline] fn as_raw_BlankLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::BlankLayerTrait for BlankLayer { + #[inline] fn as_raw_mut_BlankLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { BlankLayer, crate::dnn::BlankLayerTraitConst, as_raw_BlankLayer, crate::dnn::BlankLayerTrait, as_raw_mut_BlankLayer } + + pub struct CeilLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { CeilLayer } + + impl Drop for CeilLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_CeilLayer_delete(self.as_raw_mut_CeilLayer()) }; + } + } + + unsafe impl Send for CeilLayer {} + + impl CeilLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::CeilLayer { + let ret = unsafe { sys::cv_dnn_CeilLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::CeilLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_CeilLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::CeilLayer] + pub trait CeilLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_CeilLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::CeilLayer] + pub trait CeilLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::CeilLayerTraitConst { + fn as_raw_mut_CeilLayer(&mut self) -> *mut c_void; + + } + + impl Default for CeilLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for CeilLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("CeilLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { CeilLayer, crate::dnn::ActivationLayer, cv_dnn_CeilLayer_to_ActivationLayer } + + boxed_cast_base! { CeilLayer, core::Algorithm, cv_dnn_CeilLayer_to_Algorithm } + + boxed_cast_base! { CeilLayer, crate::dnn::Layer, cv_dnn_CeilLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for CeilLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for CeilLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CeilLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for CeilLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for CeilLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CeilLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for CeilLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for CeilLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CeilLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::CeilLayerTraitConst for CeilLayer { + #[inline] fn as_raw_CeilLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::CeilLayerTrait for CeilLayer { + #[inline] fn as_raw_mut_CeilLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CeilLayer, crate::dnn::CeilLayerTraitConst, as_raw_CeilLayer, crate::dnn::CeilLayerTrait, as_raw_mut_CeilLayer } + + pub struct CeluLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { CeluLayer } + + impl Drop for CeluLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_CeluLayer_delete(self.as_raw_mut_CeluLayer()) }; + } + } + + unsafe impl Send for CeluLayer {} + + impl CeluLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::CeluLayer { + let ret = unsafe { sys::cv_dnn_CeluLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::CeluLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_CeluLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::CeluLayer] + pub trait CeluLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_CeluLayer(&self) -> *const c_void; + + #[inline] + fn alpha(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_CeluLayer_propAlpha_const(self.as_raw_CeluLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::CeluLayer] + pub trait CeluLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::CeluLayerTraitConst { + fn as_raw_mut_CeluLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_alpha(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_CeluLayer_propAlpha_const_float(self.as_raw_mut_CeluLayer(), val) }; + ret + } + + } + + impl Default for CeluLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for CeluLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("CeluLayer") + .field("alpha", &crate::dnn::CeluLayerTraitConst::alpha(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { CeluLayer, crate::dnn::ActivationLayer, cv_dnn_CeluLayer_to_ActivationLayer } + + boxed_cast_base! { CeluLayer, core::Algorithm, cv_dnn_CeluLayer_to_Algorithm } + + boxed_cast_base! { CeluLayer, crate::dnn::Layer, cv_dnn_CeluLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for CeluLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for CeluLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CeluLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for CeluLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for CeluLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CeluLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for CeluLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for CeluLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CeluLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::CeluLayerTraitConst for CeluLayer { + #[inline] fn as_raw_CeluLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::CeluLayerTrait for CeluLayer { + #[inline] fn as_raw_mut_CeluLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CeluLayer, crate::dnn::CeluLayerTraitConst, as_raw_CeluLayer, crate::dnn::CeluLayerTrait, as_raw_mut_CeluLayer } + + pub struct ChannelsPReLULayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ChannelsPReLULayer } + + impl Drop for ChannelsPReLULayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ChannelsPReLULayer_delete(self.as_raw_mut_ChannelsPReLULayer()) }; + } + } + + unsafe impl Send for ChannelsPReLULayer {} + + impl ChannelsPReLULayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ChannelsPReLULayer { + let ret = unsafe { sys::cv_dnn_ChannelsPReLULayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ChannelsPReLULayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ChannelsPReLULayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ChannelsPReLULayer] + pub trait ChannelsPReLULayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_ChannelsPReLULayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ChannelsPReLULayer] + pub trait ChannelsPReLULayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::ChannelsPReLULayerTraitConst { + fn as_raw_mut_ChannelsPReLULayer(&mut self) -> *mut c_void; + + } + + impl Default for ChannelsPReLULayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ChannelsPReLULayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ChannelsPReLULayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ChannelsPReLULayer, crate::dnn::ActivationLayer, cv_dnn_ChannelsPReLULayer_to_ActivationLayer } + + boxed_cast_base! { ChannelsPReLULayer, core::Algorithm, cv_dnn_ChannelsPReLULayer_to_Algorithm } + + boxed_cast_base! { ChannelsPReLULayer, crate::dnn::Layer, cv_dnn_ChannelsPReLULayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for ChannelsPReLULayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for ChannelsPReLULayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ChannelsPReLULayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for ChannelsPReLULayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ChannelsPReLULayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ChannelsPReLULayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ChannelsPReLULayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ChannelsPReLULayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ChannelsPReLULayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ChannelsPReLULayerTraitConst for ChannelsPReLULayer { + #[inline] fn as_raw_ChannelsPReLULayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ChannelsPReLULayerTrait for ChannelsPReLULayer { + #[inline] fn as_raw_mut_ChannelsPReLULayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ChannelsPReLULayer, crate::dnn::ChannelsPReLULayerTraitConst, as_raw_ChannelsPReLULayer, crate::dnn::ChannelsPReLULayerTrait, as_raw_mut_ChannelsPReLULayer } + + /// This class represents high-level API for classification models. + /// + /// ClassificationModel allows to set params for preprocessing input image. + /// ClassificationModel creates net from file with trained weights and config, + /// sets preprocessing input, runs forward pass and return top-1 prediction. + pub struct ClassificationModel { + ptr: *mut c_void, + } + + opencv_type_boxed! { ClassificationModel } + + impl Drop for ClassificationModel { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ClassificationModel_delete(self.as_raw_mut_ClassificationModel()) }; + } + } + + unsafe impl Send for ClassificationModel {} + + impl ClassificationModel { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ClassificationModel_ClassificationModel(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::ClassificationModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create classification model from network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## C++ default parameters + /// * config: "" + #[inline] + pub fn new(model: &str, config: &str) -> Result { + extern_container_arg!(model); + extern_container_arg!(config); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ClassificationModel_ClassificationModel_const_StringR_const_StringR(model.opencv_as_extern(), config.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::ClassificationModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create classification model from network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * config: "" + #[inline] + pub fn new_def(model: &str) -> Result { + extern_container_arg!(model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ClassificationModel_ClassificationModel_const_StringR(model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::ClassificationModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create model from deep learning network. + /// ## Parameters + /// * network: Net object. + #[inline] + pub fn new_1(network: &impl crate::dnn::NetTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ClassificationModel_ClassificationModel_const_NetR(network.as_raw_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::ClassificationModel::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ClassificationModel] + pub trait ClassificationModelTraitConst: crate::dnn::ModelTraitConst { + fn as_raw_ClassificationModel(&self) -> *const c_void; + + /// Get enable/disable softmax post processing option. + /// + /// This option defaults to false, softmax post processing is not applied within the classify() function. + #[inline] + fn get_enable_softmax_post_processing(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ClassificationModel_getEnableSoftmaxPostProcessing_const(self.as_raw_ClassificationModel(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::dnn::ClassificationModel] + pub trait ClassificationModelTrait: crate::dnn::ClassificationModelTraitConst + crate::dnn::ModelTrait { + fn as_raw_mut_ClassificationModel(&mut self) -> *mut c_void; + + /// Set enable/disable softmax post processing option. + /// + /// If this option is true, softmax is applied after forward inference within the classify() function + /// to convert the confidences range to [0.0-1.0]. + /// This function allows you to toggle this behavior. + /// Please turn true when not contain softmax layer in model. + /// ## Parameters + /// * enable: Set enable softmax post processing within the classify() function. + #[inline] + fn set_enable_softmax_post_processing(&mut self, enable: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ClassificationModel_setEnableSoftmaxPostProcessing_bool(self.as_raw_mut_ClassificationModel(), enable, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::ClassificationModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Given the @p input frame, create input blob, run net and return top-1 prediction. + /// ## Parameters + /// * frame: The input image. + #[inline] + fn classify(&mut self, frame: &impl ToInputArray) -> Result> { + input_array_arg!(frame); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ClassificationModel_classify_const__InputArrayR(self.as_raw_mut_ClassificationModel(), frame.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Tuple::<(i32, f32)>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Given the @p input frame, create input blob, run net and return top-1 prediction. + /// ## Parameters + /// * frame: The input image. + /// + /// ## Overloaded parameters + #[inline] + fn classify_1(&mut self, frame: &impl ToInputArray, class_id: &mut i32, conf: &mut f32) -> Result<()> { + input_array_arg!(frame); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ClassificationModel_classify_const__InputArrayR_intR_floatR(self.as_raw_mut_ClassificationModel(), frame.as_raw__InputArray(), class_id, conf, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl Clone for ClassificationModel { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_dnn_ClassificationModel_implicitClone_const(self.as_raw_ClassificationModel())) } + } + } + + impl std::fmt::Debug for ClassificationModel { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ClassificationModel") + .finish() + } + } + + boxed_cast_base! { ClassificationModel, crate::dnn::Model, cv_dnn_ClassificationModel_to_Model } + + impl crate::dnn::ModelTraitConst for ClassificationModel { + #[inline] fn as_raw_Model(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ModelTrait for ClassificationModel { + #[inline] fn as_raw_mut_Model(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ClassificationModel, crate::dnn::ModelTraitConst, as_raw_Model, crate::dnn::ModelTrait, as_raw_mut_Model } + + impl crate::dnn::ClassificationModelTraitConst for ClassificationModel { + #[inline] fn as_raw_ClassificationModel(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ClassificationModelTrait for ClassificationModel { + #[inline] fn as_raw_mut_ClassificationModel(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ClassificationModel, crate::dnn::ClassificationModelTraitConst, as_raw_ClassificationModel, crate::dnn::ClassificationModelTrait, as_raw_mut_ClassificationModel } + + pub struct CompareLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { CompareLayer } + + impl Drop for CompareLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_CompareLayer_delete(self.as_raw_mut_CompareLayer()) }; + } + } + + unsafe impl Send for CompareLayer {} + + impl CompareLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::CompareLayer { + let ret = unsafe { sys::cv_dnn_CompareLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::CompareLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_CompareLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::CompareLayer] + pub trait CompareLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_CompareLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::CompareLayer] + pub trait CompareLayerTrait: crate::dnn::CompareLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_CompareLayer(&mut self) -> *mut c_void; + + } + + impl Default for CompareLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for CompareLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("CompareLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { CompareLayer, core::Algorithm, cv_dnn_CompareLayer_to_Algorithm } + + boxed_cast_base! { CompareLayer, crate::dnn::Layer, cv_dnn_CompareLayer_to_Layer } + + impl core::AlgorithmTraitConst for CompareLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for CompareLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CompareLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for CompareLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for CompareLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CompareLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::CompareLayerTraitConst for CompareLayer { + #[inline] fn as_raw_CompareLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::CompareLayerTrait for CompareLayer { + #[inline] fn as_raw_mut_CompareLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CompareLayer, crate::dnn::CompareLayerTraitConst, as_raw_CompareLayer, crate::dnn::CompareLayerTrait, as_raw_mut_CompareLayer } + + pub struct ConcatLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ConcatLayer } + + impl Drop for ConcatLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ConcatLayer_delete(self.as_raw_mut_ConcatLayer()) }; + } + } + + unsafe impl Send for ConcatLayer {} + + impl ConcatLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ConcatLayer { + let ret = unsafe { sys::cv_dnn_ConcatLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ConcatLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ConcatLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ConcatLayer] + pub trait ConcatLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ConcatLayer(&self) -> *const c_void; + + #[inline] + fn axis(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_ConcatLayer_propAxis_const(self.as_raw_ConcatLayer()) }; + ret + } + + /// Add zero padding in case of concatenation of blobs with different + /// spatial sizes. + /// + /// Details: + #[inline] + fn padding(&self) -> bool { + let ret = unsafe { sys::cv_dnn_ConcatLayer_propPadding_const(self.as_raw_ConcatLayer()) }; + ret + } + + #[inline] + fn padding_value(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_ConcatLayer_propPaddingValue_const(self.as_raw_ConcatLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::ConcatLayer] + pub trait ConcatLayerTrait: crate::dnn::ConcatLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_ConcatLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_axis(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_ConcatLayer_propAxis_const_int(self.as_raw_mut_ConcatLayer(), val) }; + ret + } + + /// Add zero padding in case of concatenation of blobs with different + /// spatial sizes. + /// + /// Details: + #[inline] + fn set_padding(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_ConcatLayer_propPadding_const_bool(self.as_raw_mut_ConcatLayer(), val) }; + ret + } + + #[inline] + fn set_padding_value(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_ConcatLayer_propPaddingValue_const_int(self.as_raw_mut_ConcatLayer(), val) }; + ret + } + + } + + impl Default for ConcatLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ConcatLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ConcatLayer") + .field("axis", &crate::dnn::ConcatLayerTraitConst::axis(self)) + .field("padding", &crate::dnn::ConcatLayerTraitConst::padding(self)) + .field("padding_value", &crate::dnn::ConcatLayerTraitConst::padding_value(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ConcatLayer, core::Algorithm, cv_dnn_ConcatLayer_to_Algorithm } + + boxed_cast_base! { ConcatLayer, crate::dnn::Layer, cv_dnn_ConcatLayer_to_Layer } + + impl core::AlgorithmTraitConst for ConcatLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ConcatLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConcatLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ConcatLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ConcatLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConcatLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ConcatLayerTraitConst for ConcatLayer { + #[inline] fn as_raw_ConcatLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ConcatLayerTrait for ConcatLayer { + #[inline] fn as_raw_mut_ConcatLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConcatLayer, crate::dnn::ConcatLayerTraitConst, as_raw_ConcatLayer, crate::dnn::ConcatLayerTrait, as_raw_mut_ConcatLayer } + + /// Constant layer produces the same data blob at an every forward pass. + pub struct ConstLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ConstLayer } + + impl Drop for ConstLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ConstLayer_delete(self.as_raw_mut_ConstLayer()) }; + } + } + + unsafe impl Send for ConstLayer {} + + impl ConstLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ConstLayer { + let ret = unsafe { sys::cv_dnn_ConstLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ConstLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ConstLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ConstLayer] + pub trait ConstLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ConstLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ConstLayer] + pub trait ConstLayerTrait: crate::dnn::ConstLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_ConstLayer(&mut self) -> *mut c_void; + + } + + impl Default for ConstLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ConstLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ConstLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ConstLayer, core::Algorithm, cv_dnn_ConstLayer_to_Algorithm } + + boxed_cast_base! { ConstLayer, crate::dnn::Layer, cv_dnn_ConstLayer_to_Layer } + + impl core::AlgorithmTraitConst for ConstLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ConstLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConstLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ConstLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ConstLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConstLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ConstLayerTraitConst for ConstLayer { + #[inline] fn as_raw_ConstLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ConstLayerTrait for ConstLayer { + #[inline] fn as_raw_mut_ConstLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConstLayer, crate::dnn::ConstLayerTraitConst, as_raw_ConstLayer, crate::dnn::ConstLayerTrait, as_raw_mut_ConstLayer } + + pub struct ConvolutionLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ConvolutionLayer } + + impl Drop for ConvolutionLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ConvolutionLayer_delete(self.as_raw_mut_ConvolutionLayer()) }; + } + } + + unsafe impl Send for ConvolutionLayer {} + + impl ConvolutionLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ConvolutionLayer { + let ret = unsafe { sys::cv_dnn_ConvolutionLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ConvolutionLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ConvolutionLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ConvolutionLayer] + pub trait ConvolutionLayerTraitConst: crate::dnn::BaseConvolutionLayerTraitConst { + fn as_raw_ConvolutionLayer(&self) -> *const c_void; + + #[inline] + fn fused_activation(&self) -> bool { + let ret = unsafe { sys::cv_dnn_ConvolutionLayer_propFusedActivation_const(self.as_raw_ConvolutionLayer()) }; + ret + } + + #[inline] + fn fused_add(&self) -> bool { + let ret = unsafe { sys::cv_dnn_ConvolutionLayer_propFusedAdd_const(self.as_raw_ConvolutionLayer()) }; + ret + } + + #[inline] + fn use_winograd(&self) -> bool { + let ret = unsafe { sys::cv_dnn_ConvolutionLayer_propUseWinograd_const(self.as_raw_ConvolutionLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::ConvolutionLayer] + pub trait ConvolutionLayerTrait: crate::dnn::BaseConvolutionLayerTrait + crate::dnn::ConvolutionLayerTraitConst { + fn as_raw_mut_ConvolutionLayer(&mut self) -> *mut c_void; + + /// ## C++ default parameters + /// * val: false + #[inline] + fn set_fused_activation(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_ConvolutionLayer_propFusedActivation_const_bool(self.as_raw_mut_ConvolutionLayer(), val) }; + ret + } + + /// ## C++ default parameters + /// * val: false + #[inline] + fn set_fused_add(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_ConvolutionLayer_propFusedAdd_const_bool(self.as_raw_mut_ConvolutionLayer(), val) }; + ret + } + + /// ## C++ default parameters + /// * val: true + #[inline] + fn set_use_winograd(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_ConvolutionLayer_propUseWinograd_const_bool(self.as_raw_mut_ConvolutionLayer(), val) }; + ret + } + + } + + impl Default for ConvolutionLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ConvolutionLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ConvolutionLayer") + .field("fused_activation", &crate::dnn::ConvolutionLayerTraitConst::fused_activation(self)) + .field("fused_add", &crate::dnn::ConvolutionLayerTraitConst::fused_add(self)) + .field("use_winograd", &crate::dnn::ConvolutionLayerTraitConst::use_winograd(self)) + .field("kernel", &crate::dnn::BaseConvolutionLayerTraitConst::kernel(self)) + .field("stride", &crate::dnn::BaseConvolutionLayerTraitConst::stride(self)) + .field("pad", &crate::dnn::BaseConvolutionLayerTraitConst::pad(self)) + .field("dilation", &crate::dnn::BaseConvolutionLayerTraitConst::dilation(self)) + .field("adjust_pad", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pad(self)) + .field("adjust_pads", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pads(self)) + .field("kernel_size", &crate::dnn::BaseConvolutionLayerTraitConst::kernel_size(self)) + .field("strides", &crate::dnn::BaseConvolutionLayerTraitConst::strides(self)) + .field("dilations", &crate::dnn::BaseConvolutionLayerTraitConst::dilations(self)) + .field("pads_begin", &crate::dnn::BaseConvolutionLayerTraitConst::pads_begin(self)) + .field("pads_end", &crate::dnn::BaseConvolutionLayerTraitConst::pads_end(self)) + .field("pad_mode", &crate::dnn::BaseConvolutionLayerTraitConst::pad_mode(self)) + .field("num_output", &crate::dnn::BaseConvolutionLayerTraitConst::num_output(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ConvolutionLayer, core::Algorithm, cv_dnn_ConvolutionLayer_to_Algorithm } + + boxed_cast_base! { ConvolutionLayer, crate::dnn::BaseConvolutionLayer, cv_dnn_ConvolutionLayer_to_BaseConvolutionLayer } + + boxed_cast_base! { ConvolutionLayer, crate::dnn::Layer, cv_dnn_ConvolutionLayer_to_Layer } + + impl core::AlgorithmTraitConst for ConvolutionLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ConvolutionLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConvolutionLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::BaseConvolutionLayerTraitConst for ConvolutionLayer { + #[inline] fn as_raw_BaseConvolutionLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::BaseConvolutionLayerTrait for ConvolutionLayer { + #[inline] fn as_raw_mut_BaseConvolutionLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConvolutionLayer, crate::dnn::BaseConvolutionLayerTraitConst, as_raw_BaseConvolutionLayer, crate::dnn::BaseConvolutionLayerTrait, as_raw_mut_BaseConvolutionLayer } + + impl crate::dnn::LayerTraitConst for ConvolutionLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ConvolutionLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConvolutionLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ConvolutionLayerTraitConst for ConvolutionLayer { + #[inline] fn as_raw_ConvolutionLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ConvolutionLayerTrait for ConvolutionLayer { + #[inline] fn as_raw_mut_ConvolutionLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConvolutionLayer, crate::dnn::ConvolutionLayerTraitConst, as_raw_ConvolutionLayer, crate::dnn::ConvolutionLayerTrait, as_raw_mut_ConvolutionLayer } + + pub struct ConvolutionLayerInt8 { + ptr: *mut c_void, + } + + opencv_type_boxed! { ConvolutionLayerInt8 } + + impl Drop for ConvolutionLayerInt8 { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ConvolutionLayerInt8_delete(self.as_raw_mut_ConvolutionLayerInt8()) }; + } + } + + unsafe impl Send for ConvolutionLayerInt8 {} + + impl ConvolutionLayerInt8 { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ConvolutionLayerInt8 { + let ret = unsafe { sys::cv_dnn_ConvolutionLayerInt8_defaultNew_const() }; + let ret = unsafe { crate::dnn::ConvolutionLayerInt8::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ConvolutionLayerInt8_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ConvolutionLayerInt8] + pub trait ConvolutionLayerInt8TraitConst: crate::dnn::BaseConvolutionLayerTraitConst { + fn as_raw_ConvolutionLayerInt8(&self) -> *const c_void; + + #[inline] + fn input_zp(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_ConvolutionLayerInt8_propInput_zp_const(self.as_raw_ConvolutionLayerInt8()) }; + ret + } + + #[inline] + fn output_zp(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_ConvolutionLayerInt8_propOutput_zp_const(self.as_raw_ConvolutionLayerInt8()) }; + ret + } + + #[inline] + fn input_sc(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_ConvolutionLayerInt8_propInput_sc_const(self.as_raw_ConvolutionLayerInt8()) }; + ret + } + + #[inline] + fn output_sc(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_ConvolutionLayerInt8_propOutput_sc_const(self.as_raw_ConvolutionLayerInt8()) }; + ret + } + + #[inline] + fn per_channel(&self) -> bool { + let ret = unsafe { sys::cv_dnn_ConvolutionLayerInt8_propPer_channel_const(self.as_raw_ConvolutionLayerInt8()) }; + ret + } + + #[inline] + fn use_winograd(&self) -> bool { + let ret = unsafe { sys::cv_dnn_ConvolutionLayerInt8_propUseWinograd_const(self.as_raw_ConvolutionLayerInt8()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::ConvolutionLayerInt8] + pub trait ConvolutionLayerInt8Trait: crate::dnn::BaseConvolutionLayerTrait + crate::dnn::ConvolutionLayerInt8TraitConst { + fn as_raw_mut_ConvolutionLayerInt8(&mut self) -> *mut c_void; + + #[inline] + fn set_input_zp(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_ConvolutionLayerInt8_propInput_zp_const_int(self.as_raw_mut_ConvolutionLayerInt8(), val) }; + ret + } + + #[inline] + fn set_output_zp(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_ConvolutionLayerInt8_propOutput_zp_const_int(self.as_raw_mut_ConvolutionLayerInt8(), val) }; + ret + } + + #[inline] + fn set_input_sc(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_ConvolutionLayerInt8_propInput_sc_const_float(self.as_raw_mut_ConvolutionLayerInt8(), val) }; + ret + } + + #[inline] + fn set_output_sc(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_ConvolutionLayerInt8_propOutput_sc_const_float(self.as_raw_mut_ConvolutionLayerInt8(), val) }; + ret + } + + #[inline] + fn set_per_channel(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_ConvolutionLayerInt8_propPer_channel_const_bool(self.as_raw_mut_ConvolutionLayerInt8(), val) }; + ret + } + + /// ## C++ default parameters + /// * val: false + #[inline] + fn set_use_winograd(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_ConvolutionLayerInt8_propUseWinograd_const_bool(self.as_raw_mut_ConvolutionLayerInt8(), val) }; + ret + } + + } + + impl Default for ConvolutionLayerInt8 { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ConvolutionLayerInt8 { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ConvolutionLayerInt8") + .field("input_zp", &crate::dnn::ConvolutionLayerInt8TraitConst::input_zp(self)) + .field("output_zp", &crate::dnn::ConvolutionLayerInt8TraitConst::output_zp(self)) + .field("input_sc", &crate::dnn::ConvolutionLayerInt8TraitConst::input_sc(self)) + .field("output_sc", &crate::dnn::ConvolutionLayerInt8TraitConst::output_sc(self)) + .field("per_channel", &crate::dnn::ConvolutionLayerInt8TraitConst::per_channel(self)) + .field("use_winograd", &crate::dnn::ConvolutionLayerInt8TraitConst::use_winograd(self)) + .field("kernel", &crate::dnn::BaseConvolutionLayerTraitConst::kernel(self)) + .field("stride", &crate::dnn::BaseConvolutionLayerTraitConst::stride(self)) + .field("pad", &crate::dnn::BaseConvolutionLayerTraitConst::pad(self)) + .field("dilation", &crate::dnn::BaseConvolutionLayerTraitConst::dilation(self)) + .field("adjust_pad", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pad(self)) + .field("adjust_pads", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pads(self)) + .field("kernel_size", &crate::dnn::BaseConvolutionLayerTraitConst::kernel_size(self)) + .field("strides", &crate::dnn::BaseConvolutionLayerTraitConst::strides(self)) + .field("dilations", &crate::dnn::BaseConvolutionLayerTraitConst::dilations(self)) + .field("pads_begin", &crate::dnn::BaseConvolutionLayerTraitConst::pads_begin(self)) + .field("pads_end", &crate::dnn::BaseConvolutionLayerTraitConst::pads_end(self)) + .field("pad_mode", &crate::dnn::BaseConvolutionLayerTraitConst::pad_mode(self)) + .field("num_output", &crate::dnn::BaseConvolutionLayerTraitConst::num_output(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ConvolutionLayerInt8, core::Algorithm, cv_dnn_ConvolutionLayerInt8_to_Algorithm } + + boxed_cast_base! { ConvolutionLayerInt8, crate::dnn::BaseConvolutionLayer, cv_dnn_ConvolutionLayerInt8_to_BaseConvolutionLayer } + + boxed_cast_base! { ConvolutionLayerInt8, crate::dnn::Layer, cv_dnn_ConvolutionLayerInt8_to_Layer } + + impl core::AlgorithmTraitConst for ConvolutionLayerInt8 { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ConvolutionLayerInt8 { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConvolutionLayerInt8, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::BaseConvolutionLayerTraitConst for ConvolutionLayerInt8 { + #[inline] fn as_raw_BaseConvolutionLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::BaseConvolutionLayerTrait for ConvolutionLayerInt8 { + #[inline] fn as_raw_mut_BaseConvolutionLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConvolutionLayerInt8, crate::dnn::BaseConvolutionLayerTraitConst, as_raw_BaseConvolutionLayer, crate::dnn::BaseConvolutionLayerTrait, as_raw_mut_BaseConvolutionLayer } + + impl crate::dnn::LayerTraitConst for ConvolutionLayerInt8 { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ConvolutionLayerInt8 { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConvolutionLayerInt8, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ConvolutionLayerInt8TraitConst for ConvolutionLayerInt8 { + #[inline] fn as_raw_ConvolutionLayerInt8(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ConvolutionLayerInt8Trait for ConvolutionLayerInt8 { + #[inline] fn as_raw_mut_ConvolutionLayerInt8(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ConvolutionLayerInt8, crate::dnn::ConvolutionLayerInt8TraitConst, as_raw_ConvolutionLayerInt8, crate::dnn::ConvolutionLayerInt8Trait, as_raw_mut_ConvolutionLayerInt8 } + + pub struct CorrelationLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { CorrelationLayer } + + impl Drop for CorrelationLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_CorrelationLayer_delete(self.as_raw_mut_CorrelationLayer()) }; + } + } + + unsafe impl Send for CorrelationLayer {} + + impl CorrelationLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::CorrelationLayer { + let ret = unsafe { sys::cv_dnn_CorrelationLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::CorrelationLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_CorrelationLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::CorrelationLayer] + pub trait CorrelationLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_CorrelationLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::CorrelationLayer] + pub trait CorrelationLayerTrait: crate::dnn::CorrelationLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_CorrelationLayer(&mut self) -> *mut c_void; + + } + + impl Default for CorrelationLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for CorrelationLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("CorrelationLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { CorrelationLayer, core::Algorithm, cv_dnn_CorrelationLayer_to_Algorithm } + + boxed_cast_base! { CorrelationLayer, crate::dnn::Layer, cv_dnn_CorrelationLayer_to_Layer } + + impl core::AlgorithmTraitConst for CorrelationLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for CorrelationLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CorrelationLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for CorrelationLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for CorrelationLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CorrelationLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::CorrelationLayerTraitConst for CorrelationLayer { + #[inline] fn as_raw_CorrelationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::CorrelationLayerTrait for CorrelationLayer { + #[inline] fn as_raw_mut_CorrelationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CorrelationLayer, crate::dnn::CorrelationLayerTraitConst, as_raw_CorrelationLayer, crate::dnn::CorrelationLayerTrait, as_raw_mut_CorrelationLayer } + + pub struct CosLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { CosLayer } + + impl Drop for CosLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_CosLayer_delete(self.as_raw_mut_CosLayer()) }; + } + } + + unsafe impl Send for CosLayer {} + + impl CosLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::CosLayer { + let ret = unsafe { sys::cv_dnn_CosLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::CosLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_CosLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::CosLayer] + pub trait CosLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_CosLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::CosLayer] + pub trait CosLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::CosLayerTraitConst { + fn as_raw_mut_CosLayer(&mut self) -> *mut c_void; + + } + + impl Default for CosLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for CosLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("CosLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { CosLayer, crate::dnn::ActivationLayer, cv_dnn_CosLayer_to_ActivationLayer } + + boxed_cast_base! { CosLayer, core::Algorithm, cv_dnn_CosLayer_to_Algorithm } + + boxed_cast_base! { CosLayer, crate::dnn::Layer, cv_dnn_CosLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for CosLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for CosLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CosLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for CosLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for CosLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CosLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for CosLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for CosLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CosLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::CosLayerTraitConst for CosLayer { + #[inline] fn as_raw_CosLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::CosLayerTrait for CosLayer { + #[inline] fn as_raw_mut_CosLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CosLayer, crate::dnn::CosLayerTraitConst, as_raw_CosLayer, crate::dnn::CosLayerTrait, as_raw_mut_CosLayer } + + pub struct CoshLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { CoshLayer } + + impl Drop for CoshLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_CoshLayer_delete(self.as_raw_mut_CoshLayer()) }; + } + } + + unsafe impl Send for CoshLayer {} + + impl CoshLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::CoshLayer { + let ret = unsafe { sys::cv_dnn_CoshLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::CoshLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_CoshLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::CoshLayer] + pub trait CoshLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_CoshLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::CoshLayer] + pub trait CoshLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::CoshLayerTraitConst { + fn as_raw_mut_CoshLayer(&mut self) -> *mut c_void; + + } + + impl Default for CoshLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for CoshLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("CoshLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { CoshLayer, crate::dnn::ActivationLayer, cv_dnn_CoshLayer_to_ActivationLayer } + + boxed_cast_base! { CoshLayer, core::Algorithm, cv_dnn_CoshLayer_to_Algorithm } + + boxed_cast_base! { CoshLayer, crate::dnn::Layer, cv_dnn_CoshLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for CoshLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for CoshLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CoshLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for CoshLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for CoshLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CoshLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for CoshLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for CoshLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CoshLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::CoshLayerTraitConst for CoshLayer { + #[inline] fn as_raw_CoshLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::CoshLayerTrait for CoshLayer { + #[inline] fn as_raw_mut_CoshLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CoshLayer, crate::dnn::CoshLayerTraitConst, as_raw_CoshLayer, crate::dnn::CoshLayerTrait, as_raw_mut_CoshLayer } + + pub struct CropAndResizeLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { CropAndResizeLayer } + + impl Drop for CropAndResizeLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_CropAndResizeLayer_delete(self.as_raw_mut_CropAndResizeLayer()) }; + } + } + + unsafe impl Send for CropAndResizeLayer {} + + impl CropAndResizeLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::CropAndResizeLayer { + let ret = unsafe { sys::cv_dnn_CropAndResizeLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::CropAndResizeLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_CropAndResizeLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::CropAndResizeLayer] + pub trait CropAndResizeLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_CropAndResizeLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::CropAndResizeLayer] + pub trait CropAndResizeLayerTrait: crate::dnn::CropAndResizeLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_CropAndResizeLayer(&mut self) -> *mut c_void; + + } + + impl Default for CropAndResizeLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for CropAndResizeLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("CropAndResizeLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { CropAndResizeLayer, core::Algorithm, cv_dnn_CropAndResizeLayer_to_Algorithm } + + boxed_cast_base! { CropAndResizeLayer, crate::dnn::Layer, cv_dnn_CropAndResizeLayer_to_Layer } + + impl core::AlgorithmTraitConst for CropAndResizeLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for CropAndResizeLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CropAndResizeLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for CropAndResizeLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for CropAndResizeLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CropAndResizeLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::CropAndResizeLayerTraitConst for CropAndResizeLayer { + #[inline] fn as_raw_CropAndResizeLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::CropAndResizeLayerTrait for CropAndResizeLayer { + #[inline] fn as_raw_mut_CropAndResizeLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CropAndResizeLayer, crate::dnn::CropAndResizeLayerTraitConst, as_raw_CropAndResizeLayer, crate::dnn::CropAndResizeLayerTrait, as_raw_mut_CropAndResizeLayer } + + pub struct CropLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { CropLayer } + + impl Drop for CropLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_CropLayer_delete(self.as_raw_mut_CropLayer()) }; + } + } + + unsafe impl Send for CropLayer {} + + impl CropLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::CropLayer { + let ret = unsafe { sys::cv_dnn_CropLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::CropLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_CropLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::CropLayer] + pub trait CropLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_CropLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::CropLayer] + pub trait CropLayerTrait: crate::dnn::CropLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_CropLayer(&mut self) -> *mut c_void; + + } + + impl Default for CropLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for CropLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("CropLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { CropLayer, core::Algorithm, cv_dnn_CropLayer_to_Algorithm } + + boxed_cast_base! { CropLayer, crate::dnn::Layer, cv_dnn_CropLayer_to_Layer } + + impl core::AlgorithmTraitConst for CropLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for CropLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CropLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for CropLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for CropLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CropLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::CropLayerTraitConst for CropLayer { + #[inline] fn as_raw_CropLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::CropLayerTrait for CropLayer { + #[inline] fn as_raw_mut_CropLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CropLayer, crate::dnn::CropLayerTraitConst, as_raw_CropLayer, crate::dnn::CropLayerTrait, as_raw_mut_CropLayer } + + pub struct CumSumLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { CumSumLayer } + + impl Drop for CumSumLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_CumSumLayer_delete(self.as_raw_mut_CumSumLayer()) }; + } + } + + unsafe impl Send for CumSumLayer {} + + impl CumSumLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::CumSumLayer { + let ret = unsafe { sys::cv_dnn_CumSumLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::CumSumLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_CumSumLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::CumSumLayer] + pub trait CumSumLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_CumSumLayer(&self) -> *const c_void; + + #[inline] + fn exclusive(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_CumSumLayer_propExclusive_const(self.as_raw_CumSumLayer()) }; + ret + } + + #[inline] + fn reverse(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_CumSumLayer_propReverse_const(self.as_raw_CumSumLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::CumSumLayer] + pub trait CumSumLayerTrait: crate::dnn::CumSumLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_CumSumLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_exclusive(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_CumSumLayer_propExclusive_const_int(self.as_raw_mut_CumSumLayer(), val) }; + ret + } + + #[inline] + fn set_reverse(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_CumSumLayer_propReverse_const_int(self.as_raw_mut_CumSumLayer(), val) }; + ret + } + + } + + impl Default for CumSumLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for CumSumLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("CumSumLayer") + .field("exclusive", &crate::dnn::CumSumLayerTraitConst::exclusive(self)) + .field("reverse", &crate::dnn::CumSumLayerTraitConst::reverse(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { CumSumLayer, core::Algorithm, cv_dnn_CumSumLayer_to_Algorithm } + + boxed_cast_base! { CumSumLayer, crate::dnn::Layer, cv_dnn_CumSumLayer_to_Layer } + + impl core::AlgorithmTraitConst for CumSumLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for CumSumLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CumSumLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for CumSumLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for CumSumLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CumSumLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::CumSumLayerTraitConst for CumSumLayer { + #[inline] fn as_raw_CumSumLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::CumSumLayerTrait for CumSumLayer { + #[inline] fn as_raw_mut_CumSumLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CumSumLayer, crate::dnn::CumSumLayerTraitConst, as_raw_CumSumLayer, crate::dnn::CumSumLayerTrait, as_raw_mut_CumSumLayer } + + pub struct DataAugmentationLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { DataAugmentationLayer } + + impl Drop for DataAugmentationLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_DataAugmentationLayer_delete(self.as_raw_mut_DataAugmentationLayer()) }; + } + } + + unsafe impl Send for DataAugmentationLayer {} + + impl DataAugmentationLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::DataAugmentationLayer { + let ret = unsafe { sys::cv_dnn_DataAugmentationLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::DataAugmentationLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DataAugmentationLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::DataAugmentationLayer] + pub trait DataAugmentationLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_DataAugmentationLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::DataAugmentationLayer] + pub trait DataAugmentationLayerTrait: crate::dnn::DataAugmentationLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_DataAugmentationLayer(&mut self) -> *mut c_void; + + } + + impl Default for DataAugmentationLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for DataAugmentationLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("DataAugmentationLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { DataAugmentationLayer, core::Algorithm, cv_dnn_DataAugmentationLayer_to_Algorithm } + + boxed_cast_base! { DataAugmentationLayer, crate::dnn::Layer, cv_dnn_DataAugmentationLayer_to_Layer } + + impl core::AlgorithmTraitConst for DataAugmentationLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for DataAugmentationLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DataAugmentationLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for DataAugmentationLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for DataAugmentationLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DataAugmentationLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::DataAugmentationLayerTraitConst for DataAugmentationLayer { + #[inline] fn as_raw_DataAugmentationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::DataAugmentationLayerTrait for DataAugmentationLayer { + #[inline] fn as_raw_mut_DataAugmentationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DataAugmentationLayer, crate::dnn::DataAugmentationLayerTraitConst, as_raw_DataAugmentationLayer, crate::dnn::DataAugmentationLayerTrait, as_raw_mut_DataAugmentationLayer } + + pub struct DeconvolutionLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { DeconvolutionLayer } + + impl Drop for DeconvolutionLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_DeconvolutionLayer_delete(self.as_raw_mut_DeconvolutionLayer()) }; + } + } + + unsafe impl Send for DeconvolutionLayer {} + + impl DeconvolutionLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::DeconvolutionLayer { + let ret = unsafe { sys::cv_dnn_DeconvolutionLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::DeconvolutionLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DeconvolutionLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::DeconvolutionLayer] + pub trait DeconvolutionLayerTraitConst: crate::dnn::BaseConvolutionLayerTraitConst { + fn as_raw_DeconvolutionLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::DeconvolutionLayer] + pub trait DeconvolutionLayerTrait: crate::dnn::BaseConvolutionLayerTrait + crate::dnn::DeconvolutionLayerTraitConst { + fn as_raw_mut_DeconvolutionLayer(&mut self) -> *mut c_void; + + } + + impl Default for DeconvolutionLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for DeconvolutionLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("DeconvolutionLayer") + .field("kernel", &crate::dnn::BaseConvolutionLayerTraitConst::kernel(self)) + .field("stride", &crate::dnn::BaseConvolutionLayerTraitConst::stride(self)) + .field("pad", &crate::dnn::BaseConvolutionLayerTraitConst::pad(self)) + .field("dilation", &crate::dnn::BaseConvolutionLayerTraitConst::dilation(self)) + .field("adjust_pad", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pad(self)) + .field("adjust_pads", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pads(self)) + .field("kernel_size", &crate::dnn::BaseConvolutionLayerTraitConst::kernel_size(self)) + .field("strides", &crate::dnn::BaseConvolutionLayerTraitConst::strides(self)) + .field("dilations", &crate::dnn::BaseConvolutionLayerTraitConst::dilations(self)) + .field("pads_begin", &crate::dnn::BaseConvolutionLayerTraitConst::pads_begin(self)) + .field("pads_end", &crate::dnn::BaseConvolutionLayerTraitConst::pads_end(self)) + .field("pad_mode", &crate::dnn::BaseConvolutionLayerTraitConst::pad_mode(self)) + .field("num_output", &crate::dnn::BaseConvolutionLayerTraitConst::num_output(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { DeconvolutionLayer, core::Algorithm, cv_dnn_DeconvolutionLayer_to_Algorithm } + + boxed_cast_base! { DeconvolutionLayer, crate::dnn::BaseConvolutionLayer, cv_dnn_DeconvolutionLayer_to_BaseConvolutionLayer } + + boxed_cast_base! { DeconvolutionLayer, crate::dnn::Layer, cv_dnn_DeconvolutionLayer_to_Layer } + + impl core::AlgorithmTraitConst for DeconvolutionLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for DeconvolutionLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DeconvolutionLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::BaseConvolutionLayerTraitConst for DeconvolutionLayer { + #[inline] fn as_raw_BaseConvolutionLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::BaseConvolutionLayerTrait for DeconvolutionLayer { + #[inline] fn as_raw_mut_BaseConvolutionLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DeconvolutionLayer, crate::dnn::BaseConvolutionLayerTraitConst, as_raw_BaseConvolutionLayer, crate::dnn::BaseConvolutionLayerTrait, as_raw_mut_BaseConvolutionLayer } + + impl crate::dnn::LayerTraitConst for DeconvolutionLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for DeconvolutionLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DeconvolutionLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::DeconvolutionLayerTraitConst for DeconvolutionLayer { + #[inline] fn as_raw_DeconvolutionLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::DeconvolutionLayerTrait for DeconvolutionLayer { + #[inline] fn as_raw_mut_DeconvolutionLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DeconvolutionLayer, crate::dnn::DeconvolutionLayerTraitConst, as_raw_DeconvolutionLayer, crate::dnn::DeconvolutionLayerTrait, as_raw_mut_DeconvolutionLayer } + + pub struct DepthToSpaceLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { DepthToSpaceLayer } + + impl Drop for DepthToSpaceLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_DepthToSpaceLayer_delete(self.as_raw_mut_DepthToSpaceLayer()) }; + } + } + + unsafe impl Send for DepthToSpaceLayer {} + + impl DepthToSpaceLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::DepthToSpaceLayer { + let ret = unsafe { sys::cv_dnn_DepthToSpaceLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::DepthToSpaceLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DepthToSpaceLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::DepthToSpaceLayer] + pub trait DepthToSpaceLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_DepthToSpaceLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::DepthToSpaceLayer] + pub trait DepthToSpaceLayerTrait: crate::dnn::DepthToSpaceLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_DepthToSpaceLayer(&mut self) -> *mut c_void; + + } + + impl Default for DepthToSpaceLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for DepthToSpaceLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("DepthToSpaceLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { DepthToSpaceLayer, core::Algorithm, cv_dnn_DepthToSpaceLayer_to_Algorithm } + + boxed_cast_base! { DepthToSpaceLayer, crate::dnn::Layer, cv_dnn_DepthToSpaceLayer_to_Layer } + + impl core::AlgorithmTraitConst for DepthToSpaceLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for DepthToSpaceLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DepthToSpaceLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for DepthToSpaceLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for DepthToSpaceLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DepthToSpaceLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::DepthToSpaceLayerTraitConst for DepthToSpaceLayer { + #[inline] fn as_raw_DepthToSpaceLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::DepthToSpaceLayerTrait for DepthToSpaceLayer { + #[inline] fn as_raw_mut_DepthToSpaceLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DepthToSpaceLayer, crate::dnn::DepthToSpaceLayerTraitConst, as_raw_DepthToSpaceLayer, crate::dnn::DepthToSpaceLayerTrait, as_raw_mut_DepthToSpaceLayer } + + pub struct DequantizeLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { DequantizeLayer } + + impl Drop for DequantizeLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_DequantizeLayer_delete(self.as_raw_mut_DequantizeLayer()) }; + } + } + + unsafe impl Send for DequantizeLayer {} + + impl DequantizeLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::DequantizeLayer { + let ret = unsafe { sys::cv_dnn_DequantizeLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::DequantizeLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DequantizeLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::DequantizeLayer] + pub trait DequantizeLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_DequantizeLayer(&self) -> *const c_void; + + #[inline] + fn scales(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_DequantizeLayer_propScales_const(self.as_raw_DequantizeLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn zeropoints(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_DequantizeLayer_propZeropoints_const(self.as_raw_DequantizeLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::DequantizeLayer] + pub trait DequantizeLayerTrait: crate::dnn::DequantizeLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_DequantizeLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_scales(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_DequantizeLayer_propScales_const_vectorLfloatG(self.as_raw_mut_DequantizeLayer(), val.as_raw_VectorOff32()) }; + ret + } + + #[inline] + fn set_zeropoints(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_DequantizeLayer_propZeropoints_const_vectorLintG(self.as_raw_mut_DequantizeLayer(), val.as_raw_VectorOfi32()) }; + ret + } + + } + + impl Default for DequantizeLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for DequantizeLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("DequantizeLayer") + .field("scales", &crate::dnn::DequantizeLayerTraitConst::scales(self)) + .field("zeropoints", &crate::dnn::DequantizeLayerTraitConst::zeropoints(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { DequantizeLayer, core::Algorithm, cv_dnn_DequantizeLayer_to_Algorithm } + + boxed_cast_base! { DequantizeLayer, crate::dnn::Layer, cv_dnn_DequantizeLayer_to_Layer } + + impl core::AlgorithmTraitConst for DequantizeLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for DequantizeLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DequantizeLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for DequantizeLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for DequantizeLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DequantizeLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::DequantizeLayerTraitConst for DequantizeLayer { + #[inline] fn as_raw_DequantizeLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::DequantizeLayerTrait for DequantizeLayer { + #[inline] fn as_raw_mut_DequantizeLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DequantizeLayer, crate::dnn::DequantizeLayerTraitConst, as_raw_DequantizeLayer, crate::dnn::DequantizeLayerTrait, as_raw_mut_DequantizeLayer } + + /// This class represents high-level API for object detection networks. + /// + /// DetectionModel allows to set params for preprocessing input image. + /// DetectionModel creates net from file with trained weights and config, + /// sets preprocessing input, runs forward pass and return result detections. + /// For DetectionModel SSD, Faster R-CNN, YOLO topologies are supported. + pub struct DetectionModel { + ptr: *mut c_void, + } + + opencv_type_boxed! { DetectionModel } + + impl Drop for DetectionModel { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_DetectionModel_delete(self.as_raw_mut_DetectionModel()) }; + } + } + + unsafe impl Send for DetectionModel {} + + impl DetectionModel { + /// Create detection model from network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## C++ default parameters + /// * config: "" + #[inline] + pub fn new(model: &str, config: &str) -> Result { + extern_container_arg!(model); + extern_container_arg!(config); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DetectionModel_DetectionModel_const_StringR_const_StringR(model.opencv_as_extern(), config.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DetectionModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create detection model from network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * config: "" + #[inline] + pub fn new_def(model: &str) -> Result { + extern_container_arg!(model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DetectionModel_DetectionModel_const_StringR(model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DetectionModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create model from deep learning network. + /// ## Parameters + /// * network: Net object. + #[inline] + pub fn new_1(network: &impl crate::dnn::NetTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DetectionModel_DetectionModel_const_NetR(network.as_raw_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DetectionModel::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DetectionModel_DetectionModel(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DetectionModel::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::DetectionModel] + pub trait DetectionModelTraitConst: crate::dnn::ModelTraitConst { + fn as_raw_DetectionModel(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::DetectionModel] + pub trait DetectionModelTrait: crate::dnn::DetectionModelTraitConst + crate::dnn::ModelTrait { + fn as_raw_mut_DetectionModel(&mut self) -> *mut c_void; + + /// nmsAcrossClasses defaults to false, + /// such that when non max suppression is used during the detect() function, it will do so per-class. + /// This function allows you to toggle this behaviour. + /// ## Parameters + /// * value: The new value for nmsAcrossClasses + #[inline] + fn set_nms_across_classes(&mut self, value: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DetectionModel_setNmsAcrossClasses_bool(self.as_raw_mut_DetectionModel(), value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DetectionModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Getter for nmsAcrossClasses. This variable defaults to false, + /// such that when non max suppression is used during the detect() function, it will do so only per-class + #[inline] + fn get_nms_across_classes(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DetectionModel_getNmsAcrossClasses(self.as_raw_mut_DetectionModel(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Given the @p input frame, create input blob, run net and return result detections. + /// ## Parameters + /// * frame: The input image. + /// * classIds:[out] Class indexes in result detection. + /// * confidences:[out] A set of corresponding confidences. + /// * boxes:[out] A set of bounding boxes. + /// * confThreshold: A threshold used to filter boxes by confidences. + /// * nmsThreshold: A threshold used in non maximum suppression. + /// + /// ## C++ default parameters + /// * conf_threshold: 0.5f + /// * nms_threshold: 0.0f + #[inline] + fn detect(&mut self, frame: &impl ToInputArray, class_ids: &mut core::Vector, confidences: &mut core::Vector, boxes: &mut core::Vector, conf_threshold: f32, nms_threshold: f32) -> Result<()> { + input_array_arg!(frame); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DetectionModel_detect_const__InputArrayR_vectorLintGR_vectorLfloatGR_vectorLRectGR_float_float(self.as_raw_mut_DetectionModel(), frame.as_raw__InputArray(), class_ids.as_raw_mut_VectorOfi32(), confidences.as_raw_mut_VectorOff32(), boxes.as_raw_mut_VectorOfRect(), conf_threshold, nms_threshold, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Given the @p input frame, create input blob, run net and return result detections. + /// ## Parameters + /// * frame: The input image. + /// * classIds:[out] Class indexes in result detection. + /// * confidences:[out] A set of corresponding confidences. + /// * boxes:[out] A set of bounding boxes. + /// * confThreshold: A threshold used to filter boxes by confidences. + /// * nmsThreshold: A threshold used in non maximum suppression. + /// + /// ## Note + /// This alternative version of [DetectionModelTrait::detect] function uses the following default values for its arguments: + /// * conf_threshold: 0.5f + /// * nms_threshold: 0.0f + #[inline] + fn detect_def(&mut self, frame: &impl ToInputArray, class_ids: &mut core::Vector, confidences: &mut core::Vector, boxes: &mut core::Vector) -> Result<()> { + input_array_arg!(frame); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DetectionModel_detect_const__InputArrayR_vectorLintGR_vectorLfloatGR_vectorLRectGR(self.as_raw_mut_DetectionModel(), frame.as_raw__InputArray(), class_ids.as_raw_mut_VectorOfi32(), confidences.as_raw_mut_VectorOff32(), boxes.as_raw_mut_VectorOfRect(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl Clone for DetectionModel { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_dnn_DetectionModel_implicitClone_const(self.as_raw_DetectionModel())) } + } + } + + impl std::fmt::Debug for DetectionModel { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("DetectionModel") + .finish() + } + } + + boxed_cast_base! { DetectionModel, crate::dnn::Model, cv_dnn_DetectionModel_to_Model } + + impl crate::dnn::ModelTraitConst for DetectionModel { + #[inline] fn as_raw_Model(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ModelTrait for DetectionModel { + #[inline] fn as_raw_mut_Model(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DetectionModel, crate::dnn::ModelTraitConst, as_raw_Model, crate::dnn::ModelTrait, as_raw_mut_Model } + + impl crate::dnn::DetectionModelTraitConst for DetectionModel { + #[inline] fn as_raw_DetectionModel(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::DetectionModelTrait for DetectionModel { + #[inline] fn as_raw_mut_DetectionModel(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DetectionModel, crate::dnn::DetectionModelTraitConst, as_raw_DetectionModel, crate::dnn::DetectionModelTrait, as_raw_mut_DetectionModel } + + /// Detection output layer. + /// + /// The layer size is: @f$ (1 \times 1 \times N \times 7) @f$ + /// where N is [keep_top_k] parameter multiplied by batch size. Each row is: + /// [image_id, label, confidence, xmin, ymin, xmax, ymax] + /// where image_id is the index of image input in the batch. + pub struct DetectionOutputLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { DetectionOutputLayer } + + impl Drop for DetectionOutputLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_DetectionOutputLayer_delete(self.as_raw_mut_DetectionOutputLayer()) }; + } + } + + unsafe impl Send for DetectionOutputLayer {} + + impl DetectionOutputLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::DetectionOutputLayer { + let ret = unsafe { sys::cv_dnn_DetectionOutputLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::DetectionOutputLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DetectionOutputLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::DetectionOutputLayer] + pub trait DetectionOutputLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_DetectionOutputLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::DetectionOutputLayer] + pub trait DetectionOutputLayerTrait: crate::dnn::DetectionOutputLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_DetectionOutputLayer(&mut self) -> *mut c_void; + + } + + impl Default for DetectionOutputLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for DetectionOutputLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("DetectionOutputLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { DetectionOutputLayer, core::Algorithm, cv_dnn_DetectionOutputLayer_to_Algorithm } + + boxed_cast_base! { DetectionOutputLayer, crate::dnn::Layer, cv_dnn_DetectionOutputLayer_to_Layer } + + impl core::AlgorithmTraitConst for DetectionOutputLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for DetectionOutputLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DetectionOutputLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for DetectionOutputLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for DetectionOutputLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DetectionOutputLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::DetectionOutputLayerTraitConst for DetectionOutputLayer { + #[inline] fn as_raw_DetectionOutputLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::DetectionOutputLayerTrait for DetectionOutputLayer { + #[inline] fn as_raw_mut_DetectionOutputLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DetectionOutputLayer, crate::dnn::DetectionOutputLayerTraitConst, as_raw_DetectionOutputLayer, crate::dnn::DetectionOutputLayerTrait, as_raw_mut_DetectionOutputLayer } + + /// This class implements name-value dictionary, values are instances of DictValue. + pub struct Dict { + ptr: *mut c_void, + } + + opencv_type_boxed! { Dict } + + impl Drop for Dict { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_Dict_delete(self.as_raw_mut_Dict()) }; + } + } + + unsafe impl Send for Dict {} + + impl Dict { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::Dict { + let ret = unsafe { sys::cv_dnn_Dict_defaultNew_const() }; + let ret = unsafe { crate::dnn::Dict::opencv_from_extern(ret) }; + ret + } + + } + + /// Constant methods for [crate::dnn::Dict] + pub trait DictTraitConst { + fn as_raw_Dict(&self) -> *const c_void; + + /// Checks a presence of the @p key in the dictionary. + #[inline] + fn has(&self, key: &str) -> Result { + extern_container_arg!(key); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Dict_has_const_const_StringR(self.as_raw_Dict(), key.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// If the @p key in the dictionary then returns pointer to its value, else returns NULL. + /// + /// ## Overloaded parameters + #[inline] + unsafe fn ptr(&self, key: &str) -> Result { + extern_container_arg!(key); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Dict_ptr_const_const_StringR(self.as_raw_Dict(), key.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DictValue::opencv_from_extern(ret) }; + Ok(ret) + } + + /// If the @p key in the dictionary then returns its value, else an error will be generated. + #[inline] + fn get(&self, key: &str) -> Result { + extern_container_arg!(key); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Dict_get_const_const_StringR(self.as_raw_Dict(), key.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DictValue::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Mutable methods for [crate::dnn::Dict] + pub trait DictTrait: crate::dnn::DictTraitConst { + fn as_raw_mut_Dict(&mut self) -> *mut c_void; + + /// If the @p key in the dictionary then returns pointer to its value, else returns NULL. + #[inline] + unsafe fn ptr_mut(&mut self, key: &str) -> Result { + extern_container_arg!(key); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Dict_ptr_const_StringR(self.as_raw_mut_Dict(), key.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DictValue::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Sets new @p value for the @p key, or adds new key-value pair into the dictionary. + #[inline] + fn set_str(&mut self, key: &str, value: &str) -> Result { + extern_container_arg!(key); + extern_container_arg!(value); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Dict_set_const_cv_String_const_StringR_const_StringR(self.as_raw_mut_Dict(), key.opencv_as_extern(), value.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Sets new @p value for the @p key, or adds new key-value pair into the dictionary. + #[inline] + fn set(&mut self, key: &str, value: &impl crate::dnn::DictValueTraitConst) -> Result { + extern_container_arg!(key); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Dict_set_const_cv_dnn_DictValue_const_StringR_const_DictValueR(self.as_raw_mut_Dict(), key.opencv_as_extern(), value.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DictValue::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Sets new @p value for the @p key, or adds new key-value pair into the dictionary. + #[inline] + fn set_f64(&mut self, key: &str, value: &f64) -> Result { + extern_container_arg!(key); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Dict_set_const_double_const_StringR_const_doubleR(self.as_raw_mut_Dict(), key.opencv_as_extern(), value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets new @p value for the @p key, or adds new key-value pair into the dictionary. + #[inline] + fn set_i64(&mut self, key: &str, value: &i64) -> Result { + extern_container_arg!(key); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Dict_set_const_int64_t_const_StringR_const_int64_tR(self.as_raw_mut_Dict(), key.opencv_as_extern(), value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Erase @p key from the dictionary. + #[inline] + fn erase(&mut self, key: &str) -> Result<()> { + extern_container_arg!(key); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Dict_erase_const_StringR(self.as_raw_mut_Dict(), key.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl Default for Dict { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for Dict { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Dict") + .finish() + } + } + + impl crate::dnn::DictTraitConst for Dict { + #[inline] fn as_raw_Dict(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::DictTrait for Dict { + #[inline] fn as_raw_mut_Dict(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Dict, crate::dnn::DictTraitConst, as_raw_Dict, crate::dnn::DictTrait, as_raw_mut_Dict } + + /// This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64. + /// @todo Maybe int64 is useless because double type exactly stores at least 2^52 integers. + pub struct DictValue { + ptr: *mut c_void, + } + + opencv_type_boxed! { DictValue } + + impl Drop for DictValue { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_DictValue_delete(self.as_raw_mut_DictValue()) }; + } + } + + unsafe impl Send for DictValue {} + + impl DictValue { + #[inline] + pub fn copy(r: &impl crate::dnn::DictValueTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_DictValue_const_DictValueR(r.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DictValue::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn from_bool(i: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_DictValue_bool(i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DictValue::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * i: 0 + #[inline] + pub fn from_i64(i: i64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_DictValue_int64_t(i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DictValue::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [from_i64] function uses the following default values for its arguments: + /// * i: 0 + #[inline] + pub fn from_i64_def() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_DictValue(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DictValue::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn from_i32(i: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_DictValue_int(i, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DictValue::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn from_u32(p: u32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_DictValue_unsigned_int(p, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DictValue::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn from_f64(p: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_DictValue_double(p, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DictValue::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn from_str(s: &str) -> Result { + extern_container_arg!(s); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_DictValue_const_charX(s.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::DictValue::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::DictValue] + pub trait DictValueTraitConst { + fn as_raw_DictValue(&self) -> *const c_void; + + /// ## C++ default parameters + /// * idx: -1 + #[inline] + fn get_str(&self, idx: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_get_cv_String_const_int(self.as_raw_DictValue(), idx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [DictValueTraitConst::get_str] function uses the following default values for its arguments: + /// * idx: -1 + #[inline] + fn get_str_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_get_cv_String_const(self.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * idx: -1 + #[inline] + fn get_f64(&self, idx: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_get_double_const_int(self.as_raw_DictValue(), idx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [DictValueTraitConst::get_f64] function uses the following default values for its arguments: + /// * idx: -1 + #[inline] + fn get_f64_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_get_double_const(self.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * idx: -1 + #[inline] + fn get_i32(&self, idx: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_get_int_const_int(self.as_raw_DictValue(), idx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [DictValueTraitConst::get_i32] function uses the following default values for its arguments: + /// * idx: -1 + #[inline] + fn get_i32_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_get_int_const(self.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * idx: -1 + #[inline] + fn get_i64(&self, idx: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_get_int64_t_const_int(self.as_raw_DictValue(), idx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [DictValueTraitConst::get_i64] function uses the following default values for its arguments: + /// * idx: -1 + #[inline] + fn get_i64_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_get_int64_t_const(self.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_size_const(self.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_int(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_isInt_const(self.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_string(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_isString_const(self.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn is_real(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_isReal_const(self.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * idx: -1 + #[inline] + fn get_int_value(&self, idx: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_getIntValue_const_int(self.as_raw_DictValue(), idx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [DictValueTraitConst::get_int_value] function uses the following default values for its arguments: + /// * idx: -1 + #[inline] + fn get_int_value_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_getIntValue_const(self.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * idx: -1 + #[inline] + fn get_real_value(&self, idx: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_getRealValue_const_int(self.as_raw_DictValue(), idx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [DictValueTraitConst::get_real_value] function uses the following default values for its arguments: + /// * idx: -1 + #[inline] + fn get_real_value_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_getRealValue_const(self.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * idx: -1 + #[inline] + fn get_string_value(&self, idx: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_getStringValue_const_int(self.as_raw_DictValue(), idx, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [DictValueTraitConst::get_string_value] function uses the following default values for its arguments: + /// * idx: -1 + #[inline] + fn get_string_value_def(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_getStringValue_const(self.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Mutable methods for [crate::dnn::DictValue] + pub trait DictValueTrait: crate::dnn::DictValueTraitConst { + fn as_raw_mut_DictValue(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, r: &impl crate::dnn::DictValueTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_DictValue_operatorST_const_DictValueR(self.as_raw_mut_DictValue(), r.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl crate::dnn::DictValueTraitConst for DictValue { + #[inline] fn as_raw_DictValue(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::DictValueTrait for DictValue { + #[inline] fn as_raw_mut_DictValue(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { DictValue, crate::dnn::DictValueTraitConst, as_raw_DictValue, crate::dnn::DictValueTrait, as_raw_mut_DictValue } + + pub struct ELULayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ELULayer } + + impl Drop for ELULayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ELULayer_delete(self.as_raw_mut_ELULayer()) }; + } + } + + unsafe impl Send for ELULayer {} + + impl ELULayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ELULayer { + let ret = unsafe { sys::cv_dnn_ELULayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ELULayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ELULayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ELULayer] + pub trait ELULayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_ELULayer(&self) -> *const c_void; + + #[inline] + fn alpha(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_ELULayer_propAlpha_const(self.as_raw_ELULayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::ELULayer] + pub trait ELULayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::ELULayerTraitConst { + fn as_raw_mut_ELULayer(&mut self) -> *mut c_void; + + #[inline] + fn set_alpha(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_ELULayer_propAlpha_const_float(self.as_raw_mut_ELULayer(), val) }; + ret + } + + } + + impl Default for ELULayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ELULayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ELULayer") + .field("alpha", &crate::dnn::ELULayerTraitConst::alpha(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ELULayer, crate::dnn::ActivationLayer, cv_dnn_ELULayer_to_ActivationLayer } + + boxed_cast_base! { ELULayer, core::Algorithm, cv_dnn_ELULayer_to_Algorithm } + + boxed_cast_base! { ELULayer, crate::dnn::Layer, cv_dnn_ELULayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for ELULayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for ELULayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ELULayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for ELULayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ELULayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ELULayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ELULayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ELULayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ELULayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ELULayerTraitConst for ELULayer { + #[inline] fn as_raw_ELULayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ELULayerTrait for ELULayer { + #[inline] fn as_raw_mut_ELULayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ELULayer, crate::dnn::ELULayerTraitConst, as_raw_ELULayer, crate::dnn::ELULayerTrait, as_raw_mut_ELULayer } + + /// This function performs array summation based + /// on the Einstein summation convention. The function + /// allows for concise expressions of various mathematical + /// operations using subscripts. + /// + /// By default, the labels are placed in alphabetical + /// order at the end of the output. + /// For example: + /// if `c = einsum("i,j", a, b)`, then `c[i,j] == a[i]*b[j]`. + /// However, if `c = einsum("j,i", a, b)`, then `c[i,j] = a[j]*b[i]`. + /// Alternatively, you can control the output order or prevent + /// an axis from being summed/force an axis to be summed + /// by providing indices for the output. + /// For example: + /// `diag(a)` -> `einsum("ii->i", a)` + /// `sum(a, axis=0)` -> `einsum("i...->", a)` + /// Subscripts at the beginning and end may be specified + /// by putting an ellipsis "..." in the middle. + /// For instance, the function `einsum("i...i", a)` takes + /// the diagonal of the first and last dimensions of + /// the operand, and `einsum("ij...,jk...->ik...")` performs + /// the matrix product using the first two indices + /// of each operand instead of the last two. + /// When there is only one operand, no axes being summed, + /// and no output parameter, this function returns + /// a view into the operand instead of creating a copy. + pub struct EinsumLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { EinsumLayer } + + impl Drop for EinsumLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_EinsumLayer_delete(self.as_raw_mut_EinsumLayer()) }; + } + } + + unsafe impl Send for EinsumLayer {} + + impl EinsumLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::EinsumLayer { + let ret = unsafe { sys::cv_dnn_EinsumLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::EinsumLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_EinsumLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::EinsumLayer] + pub trait EinsumLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_EinsumLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::EinsumLayer] + pub trait EinsumLayerTrait: crate::dnn::EinsumLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_EinsumLayer(&mut self) -> *mut c_void; + + } + + impl Default for EinsumLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for EinsumLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("EinsumLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { EinsumLayer, core::Algorithm, cv_dnn_EinsumLayer_to_Algorithm } + + boxed_cast_base! { EinsumLayer, crate::dnn::Layer, cv_dnn_EinsumLayer_to_Layer } + + impl core::AlgorithmTraitConst for EinsumLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for EinsumLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { EinsumLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for EinsumLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for EinsumLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { EinsumLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::EinsumLayerTraitConst for EinsumLayer { + #[inline] fn as_raw_EinsumLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::EinsumLayerTrait for EinsumLayer { + #[inline] fn as_raw_mut_EinsumLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { EinsumLayer, crate::dnn::EinsumLayerTraitConst, as_raw_EinsumLayer, crate::dnn::EinsumLayerTrait, as_raw_mut_EinsumLayer } + + /// Element wise operation on inputs + /// + /// Extra optional parameters: + /// - "operation" as string. Values are "sum" (default), "prod", "max", "div", "min" + /// - "coeff" as float array. Specify weights of inputs for SUM operation + /// - "output_channels_mode" as string. Values are "same" (default, all input must have the same layout), "input_0", "input_0_truncate", "max_input_channels" + pub struct EltwiseLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { EltwiseLayer } + + impl Drop for EltwiseLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_EltwiseLayer_delete(self.as_raw_mut_EltwiseLayer()) }; + } + } + + unsafe impl Send for EltwiseLayer {} + + impl EltwiseLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::EltwiseLayer { + let ret = unsafe { sys::cv_dnn_EltwiseLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::EltwiseLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_EltwiseLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::EltwiseLayer] + pub trait EltwiseLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_EltwiseLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::EltwiseLayer] + pub trait EltwiseLayerTrait: crate::dnn::EltwiseLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_EltwiseLayer(&mut self) -> *mut c_void; + + } + + impl Default for EltwiseLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for EltwiseLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("EltwiseLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { EltwiseLayer, core::Algorithm, cv_dnn_EltwiseLayer_to_Algorithm } + + boxed_cast_base! { EltwiseLayer, crate::dnn::Layer, cv_dnn_EltwiseLayer_to_Layer } + + impl core::AlgorithmTraitConst for EltwiseLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for EltwiseLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { EltwiseLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for EltwiseLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for EltwiseLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { EltwiseLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::EltwiseLayerTraitConst for EltwiseLayer { + #[inline] fn as_raw_EltwiseLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::EltwiseLayerTrait for EltwiseLayer { + #[inline] fn as_raw_mut_EltwiseLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { EltwiseLayer, crate::dnn::EltwiseLayerTraitConst, as_raw_EltwiseLayer, crate::dnn::EltwiseLayerTrait, as_raw_mut_EltwiseLayer } + + pub struct EltwiseLayerInt8 { + ptr: *mut c_void, + } + + opencv_type_boxed! { EltwiseLayerInt8 } + + impl Drop for EltwiseLayerInt8 { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_EltwiseLayerInt8_delete(self.as_raw_mut_EltwiseLayerInt8()) }; + } + } + + unsafe impl Send for EltwiseLayerInt8 {} + + impl EltwiseLayerInt8 { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::EltwiseLayerInt8 { + let ret = unsafe { sys::cv_dnn_EltwiseLayerInt8_defaultNew_const() }; + let ret = unsafe { crate::dnn::EltwiseLayerInt8::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_EltwiseLayerInt8_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::EltwiseLayerInt8] + pub trait EltwiseLayerInt8TraitConst: crate::dnn::LayerTraitConst { + fn as_raw_EltwiseLayerInt8(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::EltwiseLayerInt8] + pub trait EltwiseLayerInt8Trait: crate::dnn::EltwiseLayerInt8TraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_EltwiseLayerInt8(&mut self) -> *mut c_void; + + } + + impl Default for EltwiseLayerInt8 { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for EltwiseLayerInt8 { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("EltwiseLayerInt8") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { EltwiseLayerInt8, core::Algorithm, cv_dnn_EltwiseLayerInt8_to_Algorithm } + + boxed_cast_base! { EltwiseLayerInt8, crate::dnn::Layer, cv_dnn_EltwiseLayerInt8_to_Layer } + + impl core::AlgorithmTraitConst for EltwiseLayerInt8 { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for EltwiseLayerInt8 { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { EltwiseLayerInt8, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for EltwiseLayerInt8 { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for EltwiseLayerInt8 { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { EltwiseLayerInt8, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::EltwiseLayerInt8TraitConst for EltwiseLayerInt8 { + #[inline] fn as_raw_EltwiseLayerInt8(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::EltwiseLayerInt8Trait for EltwiseLayerInt8 { + #[inline] fn as_raw_mut_EltwiseLayerInt8(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { EltwiseLayerInt8, crate::dnn::EltwiseLayerInt8TraitConst, as_raw_EltwiseLayerInt8, crate::dnn::EltwiseLayerInt8Trait, as_raw_mut_EltwiseLayerInt8 } + + pub struct ErfLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ErfLayer } + + impl Drop for ErfLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ErfLayer_delete(self.as_raw_mut_ErfLayer()) }; + } + } + + unsafe impl Send for ErfLayer {} + + impl ErfLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ErfLayer { + let ret = unsafe { sys::cv_dnn_ErfLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ErfLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ErfLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ErfLayer] + pub trait ErfLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_ErfLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ErfLayer] + pub trait ErfLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::ErfLayerTraitConst { + fn as_raw_mut_ErfLayer(&mut self) -> *mut c_void; + + } + + impl Default for ErfLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ErfLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ErfLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ErfLayer, crate::dnn::ActivationLayer, cv_dnn_ErfLayer_to_ActivationLayer } + + boxed_cast_base! { ErfLayer, core::Algorithm, cv_dnn_ErfLayer_to_Algorithm } + + boxed_cast_base! { ErfLayer, crate::dnn::Layer, cv_dnn_ErfLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for ErfLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for ErfLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ErfLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for ErfLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ErfLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ErfLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ErfLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ErfLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ErfLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ErfLayerTraitConst for ErfLayer { + #[inline] fn as_raw_ErfLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ErfLayerTrait for ErfLayer { + #[inline] fn as_raw_mut_ErfLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ErfLayer, crate::dnn::ErfLayerTraitConst, as_raw_ErfLayer, crate::dnn::ErfLayerTrait, as_raw_mut_ErfLayer } + + pub struct ExpLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ExpLayer } + + impl Drop for ExpLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ExpLayer_delete(self.as_raw_mut_ExpLayer()) }; + } + } + + unsafe impl Send for ExpLayer {} + + impl ExpLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ExpLayer { + let ret = unsafe { sys::cv_dnn_ExpLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ExpLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ExpLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ExpLayer] + pub trait ExpLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_ExpLayer(&self) -> *const c_void; + + #[inline] + fn base(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_ExpLayer_propBase_const(self.as_raw_ExpLayer()) }; + ret + } + + #[inline] + fn scale(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_ExpLayer_propScale_const(self.as_raw_ExpLayer()) }; + ret + } + + #[inline] + fn shift(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_ExpLayer_propShift_const(self.as_raw_ExpLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::ExpLayer] + pub trait ExpLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::ExpLayerTraitConst { + fn as_raw_mut_ExpLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_base(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_ExpLayer_propBase_const_float(self.as_raw_mut_ExpLayer(), val) }; + ret + } + + #[inline] + fn set_scale(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_ExpLayer_propScale_const_float(self.as_raw_mut_ExpLayer(), val) }; + ret + } + + #[inline] + fn set_shift(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_ExpLayer_propShift_const_float(self.as_raw_mut_ExpLayer(), val) }; + ret + } + + } + + impl Default for ExpLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ExpLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ExpLayer") + .field("base", &crate::dnn::ExpLayerTraitConst::base(self)) + .field("scale", &crate::dnn::ExpLayerTraitConst::scale(self)) + .field("shift", &crate::dnn::ExpLayerTraitConst::shift(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ExpLayer, crate::dnn::ActivationLayer, cv_dnn_ExpLayer_to_ActivationLayer } + + boxed_cast_base! { ExpLayer, core::Algorithm, cv_dnn_ExpLayer_to_Algorithm } + + boxed_cast_base! { ExpLayer, crate::dnn::Layer, cv_dnn_ExpLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for ExpLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for ExpLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ExpLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for ExpLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ExpLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ExpLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ExpLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ExpLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ExpLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ExpLayerTraitConst for ExpLayer { + #[inline] fn as_raw_ExpLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ExpLayerTrait for ExpLayer { + #[inline] fn as_raw_mut_ExpLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ExpLayer, crate::dnn::ExpLayerTraitConst, as_raw_ExpLayer, crate::dnn::ExpLayerTrait, as_raw_mut_ExpLayer } + + pub struct ExpandLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ExpandLayer } + + impl Drop for ExpandLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ExpandLayer_delete(self.as_raw_mut_ExpandLayer()) }; + } + } + + unsafe impl Send for ExpandLayer {} + + impl ExpandLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ExpandLayer { + let ret = unsafe { sys::cv_dnn_ExpandLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ExpandLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ExpandLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ExpandLayer] + pub trait ExpandLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ExpandLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ExpandLayer] + pub trait ExpandLayerTrait: crate::dnn::ExpandLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_ExpandLayer(&mut self) -> *mut c_void; + + } + + impl Default for ExpandLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ExpandLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ExpandLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ExpandLayer, core::Algorithm, cv_dnn_ExpandLayer_to_Algorithm } + + boxed_cast_base! { ExpandLayer, crate::dnn::Layer, cv_dnn_ExpandLayer_to_Layer } + + impl core::AlgorithmTraitConst for ExpandLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ExpandLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ExpandLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ExpandLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ExpandLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ExpandLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ExpandLayerTraitConst for ExpandLayer { + #[inline] fn as_raw_ExpandLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ExpandLayerTrait for ExpandLayer { + #[inline] fn as_raw_mut_ExpandLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ExpandLayer, crate::dnn::ExpandLayerTraitConst, as_raw_ExpandLayer, crate::dnn::ExpandLayerTrait, as_raw_mut_ExpandLayer } + + pub struct FlattenLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { FlattenLayer } + + impl Drop for FlattenLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_FlattenLayer_delete(self.as_raw_mut_FlattenLayer()) }; + } + } + + unsafe impl Send for FlattenLayer {} + + impl FlattenLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::FlattenLayer { + let ret = unsafe { sys::cv_dnn_FlattenLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::FlattenLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_FlattenLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::FlattenLayer] + pub trait FlattenLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_FlattenLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::FlattenLayer] + pub trait FlattenLayerTrait: crate::dnn::FlattenLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_FlattenLayer(&mut self) -> *mut c_void; + + } + + impl Default for FlattenLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for FlattenLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("FlattenLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { FlattenLayer, core::Algorithm, cv_dnn_FlattenLayer_to_Algorithm } + + boxed_cast_base! { FlattenLayer, crate::dnn::Layer, cv_dnn_FlattenLayer_to_Layer } + + impl core::AlgorithmTraitConst for FlattenLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for FlattenLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { FlattenLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for FlattenLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for FlattenLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { FlattenLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::FlattenLayerTraitConst for FlattenLayer { + #[inline] fn as_raw_FlattenLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::FlattenLayerTrait for FlattenLayer { + #[inline] fn as_raw_mut_FlattenLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { FlattenLayer, crate::dnn::FlattenLayerTraitConst, as_raw_FlattenLayer, crate::dnn::FlattenLayerTrait, as_raw_mut_FlattenLayer } + + pub struct FloorLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { FloorLayer } + + impl Drop for FloorLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_FloorLayer_delete(self.as_raw_mut_FloorLayer()) }; + } + } + + unsafe impl Send for FloorLayer {} + + impl FloorLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::FloorLayer { + let ret = unsafe { sys::cv_dnn_FloorLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::FloorLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_FloorLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::FloorLayer] + pub trait FloorLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_FloorLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::FloorLayer] + pub trait FloorLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::FloorLayerTraitConst { + fn as_raw_mut_FloorLayer(&mut self) -> *mut c_void; + + } + + impl Default for FloorLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for FloorLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("FloorLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { FloorLayer, crate::dnn::ActivationLayer, cv_dnn_FloorLayer_to_ActivationLayer } + + boxed_cast_base! { FloorLayer, core::Algorithm, cv_dnn_FloorLayer_to_Algorithm } + + boxed_cast_base! { FloorLayer, crate::dnn::Layer, cv_dnn_FloorLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for FloorLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for FloorLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { FloorLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for FloorLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for FloorLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { FloorLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for FloorLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for FloorLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { FloorLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::FloorLayerTraitConst for FloorLayer { + #[inline] fn as_raw_FloorLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::FloorLayerTrait for FloorLayer { + #[inline] fn as_raw_mut_FloorLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { FloorLayer, crate::dnn::FloorLayerTraitConst, as_raw_FloorLayer, crate::dnn::FloorLayerTrait, as_raw_mut_FloorLayer } + + pub struct FlowWarpLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { FlowWarpLayer } + + impl Drop for FlowWarpLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_FlowWarpLayer_delete(self.as_raw_mut_FlowWarpLayer()) }; + } + } + + unsafe impl Send for FlowWarpLayer {} + + impl FlowWarpLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::FlowWarpLayer { + let ret = unsafe { sys::cv_dnn_FlowWarpLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::FlowWarpLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_FlowWarpLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::FlowWarpLayer] + pub trait FlowWarpLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_FlowWarpLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::FlowWarpLayer] + pub trait FlowWarpLayerTrait: crate::dnn::FlowWarpLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_FlowWarpLayer(&mut self) -> *mut c_void; + + } + + impl Default for FlowWarpLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for FlowWarpLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("FlowWarpLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { FlowWarpLayer, core::Algorithm, cv_dnn_FlowWarpLayer_to_Algorithm } + + boxed_cast_base! { FlowWarpLayer, crate::dnn::Layer, cv_dnn_FlowWarpLayer_to_Layer } + + impl core::AlgorithmTraitConst for FlowWarpLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for FlowWarpLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { FlowWarpLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for FlowWarpLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for FlowWarpLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { FlowWarpLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::FlowWarpLayerTraitConst for FlowWarpLayer { + #[inline] fn as_raw_FlowWarpLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::FlowWarpLayerTrait for FlowWarpLayer { + #[inline] fn as_raw_mut_FlowWarpLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { FlowWarpLayer, crate::dnn::FlowWarpLayerTraitConst, as_raw_FlowWarpLayer, crate::dnn::FlowWarpLayerTrait, as_raw_mut_FlowWarpLayer } + + /// GRU recurrent one-layer + /// + /// Accepts input sequence and computes the final hidden state for each element in the batch. + /// + /// - input[0] containing the features of the input sequence. + /// input[0] should have shape [`T`, `N`, `data_dims`] where `T` is sequence length, `N` is batch size, `data_dims` is input size + /// - output would have shape [`T`, `N`, `D` * `hidden_size`] where `D = 2` if layer is bidirectional otherwise `D = 1` + /// + /// Depends on the following attributes: + /// - hidden_size - Number of neurons in the hidden layer + /// - direction - RNN could be bidirectional or forward + /// + /// The final hidden state @f$ h_t @f$ computes by the following formulas: + /// + /// @f{eqnarray*}{ + /// r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\ + /// z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\ + /// n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn})) \\ + /// h_t = (1 - z_t) \odot n_t + z_t \odot h_{(t-1)} \\ + /// @f} + /// Where @f$x_t@f$ is current input, @f$h_{(t-1)}@f$ is previous or initial hidden state. + /// + /// @f$W_{x?}@f$, @f$W_{h?}@f$ and @f$b_{?}@f$ are learned weights represented as matrices: + /// @f$W_{x?} \in R^{N_h \times N_x}@f$, @f$W_{h?} \in R^{N_h \times N_h}@f$, @f$b_? \in R^{N_h}@f$. + /// + /// @f$\odot@f$ is per-element multiply operation. + pub struct GRULayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { GRULayer } + + impl Drop for GRULayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_GRULayer_delete(self.as_raw_mut_GRULayer()) }; + } + } + + unsafe impl Send for GRULayer {} + + impl GRULayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::GRULayer { + let ret = unsafe { sys::cv_dnn_GRULayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::GRULayer::opencv_from_extern(ret) }; + ret + } + + /// Creates instance of GRU layer + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_GRULayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::GRULayer] + pub trait GRULayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_GRULayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::GRULayer] + pub trait GRULayerTrait: crate::dnn::GRULayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_GRULayer(&mut self) -> *mut c_void; + + } + + impl Default for GRULayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for GRULayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("GRULayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { GRULayer, core::Algorithm, cv_dnn_GRULayer_to_Algorithm } + + boxed_cast_base! { GRULayer, crate::dnn::Layer, cv_dnn_GRULayer_to_Layer } + + impl core::AlgorithmTraitConst for GRULayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for GRULayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GRULayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for GRULayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for GRULayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GRULayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::GRULayerTraitConst for GRULayer { + #[inline] fn as_raw_GRULayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::GRULayerTrait for GRULayer { + #[inline] fn as_raw_mut_GRULayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GRULayer, crate::dnn::GRULayerTraitConst, as_raw_GRULayer, crate::dnn::GRULayerTrait, as_raw_mut_GRULayer } + + /// GatherElements layer + /// GatherElements takes two inputs data and indices of the same rank r >= 1 and an optional attribute axis and works such that: + /// output[i][j][k] = data[index[i][j][k]][j][k] if axis = 0 and r = 3 + /// output[i][j][k] = data[i][index[i][j][k]][k] if axis = 1 and r = 3 + /// output[i][j][k] = data[i][j][index[i][j][k]] if axis = 2 and r = 3 + /// + /// Gather, on the other hand, takes a data tensor of rank r >= 1, and indices tensor of rank q, and works such that: + /// it gathers the enteries along axis dimension of the input data indexed by indices and concatenates them in an output tensor of rank q + (r - 1) + /// e.g. If axis = 0, let k = indices[i_{0}, ..., i_{q-1}] then output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]: + pub struct GatherElementsLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { GatherElementsLayer } + + impl Drop for GatherElementsLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_GatherElementsLayer_delete(self.as_raw_mut_GatherElementsLayer()) }; + } + } + + unsafe impl Send for GatherElementsLayer {} + + impl GatherElementsLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::GatherElementsLayer { + let ret = unsafe { sys::cv_dnn_GatherElementsLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::GatherElementsLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_GatherElementsLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::GatherElementsLayer] + pub trait GatherElementsLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_GatherElementsLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::GatherElementsLayer] + pub trait GatherElementsLayerTrait: crate::dnn::GatherElementsLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_GatherElementsLayer(&mut self) -> *mut c_void; + + } + + impl Default for GatherElementsLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for GatherElementsLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("GatherElementsLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { GatherElementsLayer, core::Algorithm, cv_dnn_GatherElementsLayer_to_Algorithm } + + boxed_cast_base! { GatherElementsLayer, crate::dnn::Layer, cv_dnn_GatherElementsLayer_to_Layer } + + impl core::AlgorithmTraitConst for GatherElementsLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for GatherElementsLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GatherElementsLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for GatherElementsLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for GatherElementsLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GatherElementsLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::GatherElementsLayerTraitConst for GatherElementsLayer { + #[inline] fn as_raw_GatherElementsLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::GatherElementsLayerTrait for GatherElementsLayer { + #[inline] fn as_raw_mut_GatherElementsLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GatherElementsLayer, crate::dnn::GatherElementsLayerTraitConst, as_raw_GatherElementsLayer, crate::dnn::GatherElementsLayerTrait, as_raw_mut_GatherElementsLayer } + + /// Gather layer + pub struct GatherLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { GatherLayer } + + impl Drop for GatherLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_GatherLayer_delete(self.as_raw_mut_GatherLayer()) }; + } + } + + unsafe impl Send for GatherLayer {} + + impl GatherLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::GatherLayer { + let ret = unsafe { sys::cv_dnn_GatherLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::GatherLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_GatherLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::GatherLayer] + pub trait GatherLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_GatherLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::GatherLayer] + pub trait GatherLayerTrait: crate::dnn::GatherLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_GatherLayer(&mut self) -> *mut c_void; + + } + + impl Default for GatherLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for GatherLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("GatherLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { GatherLayer, core::Algorithm, cv_dnn_GatherLayer_to_Algorithm } + + boxed_cast_base! { GatherLayer, crate::dnn::Layer, cv_dnn_GatherLayer_to_Layer } + + impl core::AlgorithmTraitConst for GatherLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for GatherLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GatherLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for GatherLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for GatherLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GatherLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::GatherLayerTraitConst for GatherLayer { + #[inline] fn as_raw_GatherLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::GatherLayerTrait for GatherLayer { + #[inline] fn as_raw_mut_GatherLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GatherLayer, crate::dnn::GatherLayerTraitConst, as_raw_GatherLayer, crate::dnn::GatherLayerTrait, as_raw_mut_GatherLayer } + + pub struct GeluApproximationLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { GeluApproximationLayer } + + impl Drop for GeluApproximationLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_GeluApproximationLayer_delete(self.as_raw_mut_GeluApproximationLayer()) }; + } + } + + unsafe impl Send for GeluApproximationLayer {} + + impl GeluApproximationLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::GeluApproximationLayer { + let ret = unsafe { sys::cv_dnn_GeluApproximationLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::GeluApproximationLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_GeluApproximationLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::GeluApproximationLayer] + pub trait GeluApproximationLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_GeluApproximationLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::GeluApproximationLayer] + pub trait GeluApproximationLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::GeluApproximationLayerTraitConst { + fn as_raw_mut_GeluApproximationLayer(&mut self) -> *mut c_void; + + } + + impl Default for GeluApproximationLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for GeluApproximationLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("GeluApproximationLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { GeluApproximationLayer, crate::dnn::ActivationLayer, cv_dnn_GeluApproximationLayer_to_ActivationLayer } + + boxed_cast_base! { GeluApproximationLayer, core::Algorithm, cv_dnn_GeluApproximationLayer_to_Algorithm } + + boxed_cast_base! { GeluApproximationLayer, crate::dnn::Layer, cv_dnn_GeluApproximationLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for GeluApproximationLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for GeluApproximationLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeluApproximationLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for GeluApproximationLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for GeluApproximationLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeluApproximationLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for GeluApproximationLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for GeluApproximationLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeluApproximationLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::GeluApproximationLayerTraitConst for GeluApproximationLayer { + #[inline] fn as_raw_GeluApproximationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::GeluApproximationLayerTrait for GeluApproximationLayer { + #[inline] fn as_raw_mut_GeluApproximationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeluApproximationLayer, crate::dnn::GeluApproximationLayerTraitConst, as_raw_GeluApproximationLayer, crate::dnn::GeluApproximationLayerTrait, as_raw_mut_GeluApproximationLayer } + + pub struct GeluLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { GeluLayer } + + impl Drop for GeluLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_GeluLayer_delete(self.as_raw_mut_GeluLayer()) }; + } + } + + unsafe impl Send for GeluLayer {} + + impl GeluLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::GeluLayer { + let ret = unsafe { sys::cv_dnn_GeluLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::GeluLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_GeluLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::GeluLayer] + pub trait GeluLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_GeluLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::GeluLayer] + pub trait GeluLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::GeluLayerTraitConst { + fn as_raw_mut_GeluLayer(&mut self) -> *mut c_void; + + } + + impl Default for GeluLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for GeluLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("GeluLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { GeluLayer, crate::dnn::ActivationLayer, cv_dnn_GeluLayer_to_ActivationLayer } + + boxed_cast_base! { GeluLayer, core::Algorithm, cv_dnn_GeluLayer_to_Algorithm } + + boxed_cast_base! { GeluLayer, crate::dnn::Layer, cv_dnn_GeluLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for GeluLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for GeluLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeluLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for GeluLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for GeluLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeluLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for GeluLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for GeluLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeluLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::GeluLayerTraitConst for GeluLayer { + #[inline] fn as_raw_GeluLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::GeluLayerTrait for GeluLayer { + #[inline] fn as_raw_mut_GeluLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeluLayer, crate::dnn::GeluLayerTraitConst, as_raw_GeluLayer, crate::dnn::GeluLayerTrait, as_raw_mut_GeluLayer } + + pub struct GemmLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { GemmLayer } + + impl Drop for GemmLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_GemmLayer_delete(self.as_raw_mut_GemmLayer()) }; + } + } + + unsafe impl Send for GemmLayer {} + + impl GemmLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::GemmLayer { + let ret = unsafe { sys::cv_dnn_GemmLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::GemmLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_GemmLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::GemmLayer] + pub trait GemmLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_GemmLayer(&self) -> *const c_void; + + #[inline] + fn trans_a(&self) -> bool { + let ret = unsafe { sys::cv_dnn_GemmLayer_propTrans_a_const(self.as_raw_GemmLayer()) }; + ret + } + + #[inline] + fn trans_b(&self) -> bool { + let ret = unsafe { sys::cv_dnn_GemmLayer_propTrans_b_const(self.as_raw_GemmLayer()) }; + ret + } + + #[inline] + fn alpha(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_GemmLayer_propAlpha_const(self.as_raw_GemmLayer()) }; + ret + } + + #[inline] + fn beta(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_GemmLayer_propBeta_const(self.as_raw_GemmLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::GemmLayer] + pub trait GemmLayerTrait: crate::dnn::GemmLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_GemmLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_trans_a(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_GemmLayer_propTrans_a_const_bool(self.as_raw_mut_GemmLayer(), val) }; + ret + } + + #[inline] + fn set_trans_b(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_GemmLayer_propTrans_b_const_bool(self.as_raw_mut_GemmLayer(), val) }; + ret + } + + #[inline] + fn set_alpha(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_GemmLayer_propAlpha_const_float(self.as_raw_mut_GemmLayer(), val) }; + ret + } + + #[inline] + fn set_beta(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_GemmLayer_propBeta_const_float(self.as_raw_mut_GemmLayer(), val) }; + ret + } + + } + + impl Default for GemmLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for GemmLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("GemmLayer") + .field("trans_a", &crate::dnn::GemmLayerTraitConst::trans_a(self)) + .field("trans_b", &crate::dnn::GemmLayerTraitConst::trans_b(self)) + .field("alpha", &crate::dnn::GemmLayerTraitConst::alpha(self)) + .field("beta", &crate::dnn::GemmLayerTraitConst::beta(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { GemmLayer, core::Algorithm, cv_dnn_GemmLayer_to_Algorithm } + + boxed_cast_base! { GemmLayer, crate::dnn::Layer, cv_dnn_GemmLayer_to_Layer } + + impl core::AlgorithmTraitConst for GemmLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for GemmLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GemmLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for GemmLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for GemmLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GemmLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::GemmLayerTraitConst for GemmLayer { + #[inline] fn as_raw_GemmLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::GemmLayerTrait for GemmLayer { + #[inline] fn as_raw_mut_GemmLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GemmLayer, crate::dnn::GemmLayerTraitConst, as_raw_GemmLayer, crate::dnn::GemmLayerTrait, as_raw_mut_GemmLayer } + + pub struct GroupNormLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { GroupNormLayer } + + impl Drop for GroupNormLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_GroupNormLayer_delete(self.as_raw_mut_GroupNormLayer()) }; + } + } + + unsafe impl Send for GroupNormLayer {} + + impl GroupNormLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::GroupNormLayer { + let ret = unsafe { sys::cv_dnn_GroupNormLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::GroupNormLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_GroupNormLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::GroupNormLayer] + pub trait GroupNormLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_GroupNormLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::GroupNormLayer] + pub trait GroupNormLayerTrait: crate::dnn::GroupNormLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_GroupNormLayer(&mut self) -> *mut c_void; + + } + + impl Default for GroupNormLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for GroupNormLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("GroupNormLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { GroupNormLayer, core::Algorithm, cv_dnn_GroupNormLayer_to_Algorithm } + + boxed_cast_base! { GroupNormLayer, crate::dnn::Layer, cv_dnn_GroupNormLayer_to_Layer } + + impl core::AlgorithmTraitConst for GroupNormLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for GroupNormLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GroupNormLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for GroupNormLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for GroupNormLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GroupNormLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::GroupNormLayerTraitConst for GroupNormLayer { + #[inline] fn as_raw_GroupNormLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::GroupNormLayerTrait for GroupNormLayer { + #[inline] fn as_raw_mut_GroupNormLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GroupNormLayer, crate::dnn::GroupNormLayerTraitConst, as_raw_GroupNormLayer, crate::dnn::GroupNormLayerTrait, as_raw_mut_GroupNormLayer } + + pub struct HardSigmoidLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { HardSigmoidLayer } + + impl Drop for HardSigmoidLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_HardSigmoidLayer_delete(self.as_raw_mut_HardSigmoidLayer()) }; + } + } + + unsafe impl Send for HardSigmoidLayer {} + + impl HardSigmoidLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::HardSigmoidLayer { + let ret = unsafe { sys::cv_dnn_HardSigmoidLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::HardSigmoidLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_HardSigmoidLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::HardSigmoidLayer] + pub trait HardSigmoidLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_HardSigmoidLayer(&self) -> *const c_void; + + #[inline] + fn alpha(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_HardSigmoidLayer_propAlpha_const(self.as_raw_HardSigmoidLayer()) }; + ret + } + + #[inline] + fn beta(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_HardSigmoidLayer_propBeta_const(self.as_raw_HardSigmoidLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::HardSigmoidLayer] + pub trait HardSigmoidLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::HardSigmoidLayerTraitConst { + fn as_raw_mut_HardSigmoidLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_alpha(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_HardSigmoidLayer_propAlpha_const_float(self.as_raw_mut_HardSigmoidLayer(), val) }; + ret + } + + #[inline] + fn set_beta(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_HardSigmoidLayer_propBeta_const_float(self.as_raw_mut_HardSigmoidLayer(), val) }; + ret + } + + } + + impl Default for HardSigmoidLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for HardSigmoidLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("HardSigmoidLayer") + .field("alpha", &crate::dnn::HardSigmoidLayerTraitConst::alpha(self)) + .field("beta", &crate::dnn::HardSigmoidLayerTraitConst::beta(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { HardSigmoidLayer, crate::dnn::ActivationLayer, cv_dnn_HardSigmoidLayer_to_ActivationLayer } + + boxed_cast_base! { HardSigmoidLayer, core::Algorithm, cv_dnn_HardSigmoidLayer_to_Algorithm } + + boxed_cast_base! { HardSigmoidLayer, crate::dnn::Layer, cv_dnn_HardSigmoidLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for HardSigmoidLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for HardSigmoidLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { HardSigmoidLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for HardSigmoidLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for HardSigmoidLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { HardSigmoidLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for HardSigmoidLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for HardSigmoidLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { HardSigmoidLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::HardSigmoidLayerTraitConst for HardSigmoidLayer { + #[inline] fn as_raw_HardSigmoidLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::HardSigmoidLayerTrait for HardSigmoidLayer { + #[inline] fn as_raw_mut_HardSigmoidLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { HardSigmoidLayer, crate::dnn::HardSigmoidLayerTraitConst, as_raw_HardSigmoidLayer, crate::dnn::HardSigmoidLayerTrait, as_raw_mut_HardSigmoidLayer } + + pub struct HardSwishLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { HardSwishLayer } + + impl Drop for HardSwishLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_HardSwishLayer_delete(self.as_raw_mut_HardSwishLayer()) }; + } + } + + unsafe impl Send for HardSwishLayer {} + + impl HardSwishLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::HardSwishLayer { + let ret = unsafe { sys::cv_dnn_HardSwishLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::HardSwishLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_HardSwishLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::HardSwishLayer] + pub trait HardSwishLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_HardSwishLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::HardSwishLayer] + pub trait HardSwishLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::HardSwishLayerTraitConst { + fn as_raw_mut_HardSwishLayer(&mut self) -> *mut c_void; + + } + + impl Default for HardSwishLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for HardSwishLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("HardSwishLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { HardSwishLayer, crate::dnn::ActivationLayer, cv_dnn_HardSwishLayer_to_ActivationLayer } + + boxed_cast_base! { HardSwishLayer, core::Algorithm, cv_dnn_HardSwishLayer_to_Algorithm } + + boxed_cast_base! { HardSwishLayer, crate::dnn::Layer, cv_dnn_HardSwishLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for HardSwishLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for HardSwishLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { HardSwishLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for HardSwishLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for HardSwishLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { HardSwishLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for HardSwishLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for HardSwishLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { HardSwishLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::HardSwishLayerTraitConst for HardSwishLayer { + #[inline] fn as_raw_HardSwishLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::HardSwishLayerTrait for HardSwishLayer { + #[inline] fn as_raw_mut_HardSwishLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { HardSwishLayer, crate::dnn::HardSwishLayerTraitConst, as_raw_HardSwishLayer, crate::dnn::HardSwishLayerTrait, as_raw_mut_HardSwishLayer } + + /// Processing params of image to blob. + /// + /// It includes all possible image processing operations and corresponding parameters. + /// ## See also + /// blobFromImageWithParams + /// + /// + /// Note: + /// The order and usage of `scalefactor` and `mean` are (input - mean) * scalefactor. + /// The order and usage of `scalefactor`, `size`, `mean`, `swapRB`, and `ddepth` are consistent + /// with the function of [blobFromImage]. + #[repr(C)] + #[derive(Copy, Clone, Debug, PartialEq)] + pub struct Image2BlobParams { + /// scalefactor multiplier for input image values. + pub scalefactor: core::Scalar, + /// Spatial size for output image. + pub size: core::Size, + /// Scalar with mean values which are subtracted from channels. + pub mean: core::Scalar, + /// Flag which indicates that swap first and last channels + pub swap_rb: bool, + /// Depth of output blob. Choose CV_32F or CV_8U. + pub ddepth: i32, + /// Order of output dimensions. Choose DNN_LAYOUT_NCHW or DNN_LAYOUT_NHWC. + pub datalayout: crate::dnn::DataLayout, + /// Image padding mode. see also: ImagePaddingMode. + pub paddingmode: crate::dnn::ImagePaddingMode, + /// Value used in padding mode for padding. + pub border_value: core::Scalar, + } + + opencv_type_simple! { crate::dnn::Image2BlobParams } + + impl Image2BlobParams { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Image2BlobParams_Image2BlobParams(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * size: Size() + /// * mean: Scalar() + /// * swap_rb: false + /// * ddepth: CV_32F + /// * datalayout: DNN_LAYOUT_NCHW + /// * mode: dnn::DNN_PMODE_NULL + /// * border_value: 0.0 + #[inline] + pub fn new(scalefactor: core::Scalar, size: core::Size, mean: core::Scalar, swap_rb: bool, ddepth: i32, datalayout: crate::dnn::DataLayout, mode: crate::dnn::ImagePaddingMode, border_value: core::Scalar) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Image2BlobParams_Image2BlobParams_const_ScalarR_const_SizeR_const_ScalarR_bool_int_DataLayout_ImagePaddingMode_Scalar(&scalefactor, &size, &mean, swap_rb, ddepth, datalayout, mode, &border_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * size: Size() + /// * mean: Scalar() + /// * swap_rb: false + /// * ddepth: CV_32F + /// * datalayout: DNN_LAYOUT_NCHW + /// * mode: dnn::DNN_PMODE_NULL + /// * border_value: 0.0 + #[inline] + pub fn new_def(scalefactor: core::Scalar) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Image2BlobParams_Image2BlobParams_const_ScalarR(&scalefactor, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Get rectangle coordinates in original image system from rectangle in blob coordinates. + /// ## Parameters + /// * rBlob: rect in blob coordinates. + /// * size: original input image size. + /// ## Returns + /// rectangle in original image coordinates. + #[inline] + pub fn blob_rect_to_image_rect(self, r_blob: core::Rect, size: core::Size) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Image2BlobParams_blobRectToImageRect_const_RectR_const_SizeR(&self, &r_blob, &size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Get rectangle coordinates in original image system from rectangle in blob coordinates. + /// ## Parameters + /// * rBlob: rect in blob coordinates. + /// * rImg: result rect in image coordinates. + /// * size: original input image size. + #[inline] + pub fn blob_rects_to_image_rects(self, r_blob: &core::Vector, r_img: &mut core::Vector, size: core::Size) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Image2BlobParams_blobRectsToImageRects_const_vectorLRectGR_vectorLRectGR_const_SizeR(&self, r_blob.as_raw_VectorOfRect(), r_img.as_raw_mut_VectorOfRect(), &size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// `InnerProduct`, `MatMul` and `Gemm` operations are all implemented by Fully Connected Layer. + /// Parameter `is_matmul` is used to distinguish `MatMul` and `Gemm` from `InnerProduct`. + pub struct InnerProductLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { InnerProductLayer } + + impl Drop for InnerProductLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_InnerProductLayer_delete(self.as_raw_mut_InnerProductLayer()) }; + } + } + + unsafe impl Send for InnerProductLayer {} + + impl InnerProductLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::InnerProductLayer { + let ret = unsafe { sys::cv_dnn_InnerProductLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::InnerProductLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_InnerProductLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::InnerProductLayer] + pub trait InnerProductLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_InnerProductLayer(&self) -> *const c_void; + + #[inline] + fn axis(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_InnerProductLayer_propAxis_const(self.as_raw_InnerProductLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::InnerProductLayer] + pub trait InnerProductLayerTrait: crate::dnn::InnerProductLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_InnerProductLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_axis(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_InnerProductLayer_propAxis_const_int(self.as_raw_mut_InnerProductLayer(), val) }; + ret + } + + } + + impl Default for InnerProductLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for InnerProductLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("InnerProductLayer") + .field("axis", &crate::dnn::InnerProductLayerTraitConst::axis(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { InnerProductLayer, core::Algorithm, cv_dnn_InnerProductLayer_to_Algorithm } + + boxed_cast_base! { InnerProductLayer, crate::dnn::Layer, cv_dnn_InnerProductLayer_to_Layer } + + impl core::AlgorithmTraitConst for InnerProductLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for InnerProductLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { InnerProductLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for InnerProductLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for InnerProductLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { InnerProductLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::InnerProductLayerTraitConst for InnerProductLayer { + #[inline] fn as_raw_InnerProductLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::InnerProductLayerTrait for InnerProductLayer { + #[inline] fn as_raw_mut_InnerProductLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { InnerProductLayer, crate::dnn::InnerProductLayerTraitConst, as_raw_InnerProductLayer, crate::dnn::InnerProductLayerTrait, as_raw_mut_InnerProductLayer } + + pub struct InnerProductLayerInt8 { + ptr: *mut c_void, + } + + opencv_type_boxed! { InnerProductLayerInt8 } + + impl Drop for InnerProductLayerInt8 { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_InnerProductLayerInt8_delete(self.as_raw_mut_InnerProductLayerInt8()) }; + } + } + + unsafe impl Send for InnerProductLayerInt8 {} + + impl InnerProductLayerInt8 { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::InnerProductLayerInt8 { + let ret = unsafe { sys::cv_dnn_InnerProductLayerInt8_defaultNew_const() }; + let ret = unsafe { crate::dnn::InnerProductLayerInt8::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_InnerProductLayerInt8_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::InnerProductLayerInt8] + pub trait InnerProductLayerInt8TraitConst: crate::dnn::InnerProductLayerTraitConst { + fn as_raw_InnerProductLayerInt8(&self) -> *const c_void; + + #[inline] + fn input_zp(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_InnerProductLayerInt8_propInput_zp_const(self.as_raw_InnerProductLayerInt8()) }; + ret + } + + #[inline] + fn output_zp(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_InnerProductLayerInt8_propOutput_zp_const(self.as_raw_InnerProductLayerInt8()) }; + ret + } + + #[inline] + fn input_sc(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_InnerProductLayerInt8_propInput_sc_const(self.as_raw_InnerProductLayerInt8()) }; + ret + } + + #[inline] + fn output_sc(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_InnerProductLayerInt8_propOutput_sc_const(self.as_raw_InnerProductLayerInt8()) }; + ret + } + + #[inline] + fn per_channel(&self) -> bool { + let ret = unsafe { sys::cv_dnn_InnerProductLayerInt8_propPer_channel_const(self.as_raw_InnerProductLayerInt8()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::InnerProductLayerInt8] + pub trait InnerProductLayerInt8Trait: crate::dnn::InnerProductLayerInt8TraitConst + crate::dnn::InnerProductLayerTrait { + fn as_raw_mut_InnerProductLayerInt8(&mut self) -> *mut c_void; + + #[inline] + fn set_input_zp(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_InnerProductLayerInt8_propInput_zp_const_int(self.as_raw_mut_InnerProductLayerInt8(), val) }; + ret + } + + #[inline] + fn set_output_zp(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_InnerProductLayerInt8_propOutput_zp_const_int(self.as_raw_mut_InnerProductLayerInt8(), val) }; + ret + } + + #[inline] + fn set_input_sc(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_InnerProductLayerInt8_propInput_sc_const_float(self.as_raw_mut_InnerProductLayerInt8(), val) }; + ret + } + + #[inline] + fn set_output_sc(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_InnerProductLayerInt8_propOutput_sc_const_float(self.as_raw_mut_InnerProductLayerInt8(), val) }; + ret + } + + #[inline] + fn set_per_channel(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_InnerProductLayerInt8_propPer_channel_const_bool(self.as_raw_mut_InnerProductLayerInt8(), val) }; + ret + } + + } + + impl Default for InnerProductLayerInt8 { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for InnerProductLayerInt8 { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("InnerProductLayerInt8") + .field("input_zp", &crate::dnn::InnerProductLayerInt8TraitConst::input_zp(self)) + .field("output_zp", &crate::dnn::InnerProductLayerInt8TraitConst::output_zp(self)) + .field("input_sc", &crate::dnn::InnerProductLayerInt8TraitConst::input_sc(self)) + .field("output_sc", &crate::dnn::InnerProductLayerInt8TraitConst::output_sc(self)) + .field("per_channel", &crate::dnn::InnerProductLayerInt8TraitConst::per_channel(self)) + .field("axis", &crate::dnn::InnerProductLayerTraitConst::axis(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { InnerProductLayerInt8, core::Algorithm, cv_dnn_InnerProductLayerInt8_to_Algorithm } + + boxed_cast_base! { InnerProductLayerInt8, crate::dnn::InnerProductLayer, cv_dnn_InnerProductLayerInt8_to_InnerProductLayer } + + boxed_cast_base! { InnerProductLayerInt8, crate::dnn::Layer, cv_dnn_InnerProductLayerInt8_to_Layer } + + impl core::AlgorithmTraitConst for InnerProductLayerInt8 { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for InnerProductLayerInt8 { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { InnerProductLayerInt8, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::InnerProductLayerTraitConst for InnerProductLayerInt8 { + #[inline] fn as_raw_InnerProductLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::InnerProductLayerTrait for InnerProductLayerInt8 { + #[inline] fn as_raw_mut_InnerProductLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { InnerProductLayerInt8, crate::dnn::InnerProductLayerTraitConst, as_raw_InnerProductLayer, crate::dnn::InnerProductLayerTrait, as_raw_mut_InnerProductLayer } + + impl crate::dnn::LayerTraitConst for InnerProductLayerInt8 { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for InnerProductLayerInt8 { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { InnerProductLayerInt8, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::InnerProductLayerInt8TraitConst for InnerProductLayerInt8 { + #[inline] fn as_raw_InnerProductLayerInt8(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::InnerProductLayerInt8Trait for InnerProductLayerInt8 { + #[inline] fn as_raw_mut_InnerProductLayerInt8(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { InnerProductLayerInt8, crate::dnn::InnerProductLayerInt8TraitConst, as_raw_InnerProductLayerInt8, crate::dnn::InnerProductLayerInt8Trait, as_raw_mut_InnerProductLayerInt8 } + + pub struct InstanceNormLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { InstanceNormLayer } + + impl Drop for InstanceNormLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_InstanceNormLayer_delete(self.as_raw_mut_InstanceNormLayer()) }; + } + } + + unsafe impl Send for InstanceNormLayer {} + + impl InstanceNormLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::InstanceNormLayer { + let ret = unsafe { sys::cv_dnn_InstanceNormLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::InstanceNormLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_InstanceNormLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::InstanceNormLayer] + pub trait InstanceNormLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_InstanceNormLayer(&self) -> *const c_void; + + #[inline] + fn epsilon(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_InstanceNormLayer_propEpsilon_const(self.as_raw_InstanceNormLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::InstanceNormLayer] + pub trait InstanceNormLayerTrait: crate::dnn::InstanceNormLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_InstanceNormLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_epsilon(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_InstanceNormLayer_propEpsilon_const_float(self.as_raw_mut_InstanceNormLayer(), val) }; + ret + } + + } + + impl Default for InstanceNormLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for InstanceNormLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("InstanceNormLayer") + .field("epsilon", &crate::dnn::InstanceNormLayerTraitConst::epsilon(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { InstanceNormLayer, core::Algorithm, cv_dnn_InstanceNormLayer_to_Algorithm } + + boxed_cast_base! { InstanceNormLayer, crate::dnn::Layer, cv_dnn_InstanceNormLayer_to_Layer } + + impl core::AlgorithmTraitConst for InstanceNormLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for InstanceNormLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { InstanceNormLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for InstanceNormLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for InstanceNormLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { InstanceNormLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::InstanceNormLayerTraitConst for InstanceNormLayer { + #[inline] fn as_raw_InstanceNormLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::InstanceNormLayerTrait for InstanceNormLayer { + #[inline] fn as_raw_mut_InstanceNormLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { InstanceNormLayer, crate::dnn::InstanceNormLayerTraitConst, as_raw_InstanceNormLayer, crate::dnn::InstanceNormLayerTrait, as_raw_mut_InstanceNormLayer } + + /// Bilinear resize layer from + /// + /// It differs from [ResizeLayer] in output shape and resize scales computations. + pub struct InterpLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { InterpLayer } + + impl Drop for InterpLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_InterpLayer_delete(self.as_raw_mut_InterpLayer()) }; + } + } + + unsafe impl Send for InterpLayer {} + + impl InterpLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::InterpLayer { + let ret = unsafe { sys::cv_dnn_InterpLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::InterpLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_InterpLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::InterpLayer] + pub trait InterpLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_InterpLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::InterpLayer] + pub trait InterpLayerTrait: crate::dnn::InterpLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_InterpLayer(&mut self) -> *mut c_void; + + } + + impl Default for InterpLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for InterpLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("InterpLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { InterpLayer, core::Algorithm, cv_dnn_InterpLayer_to_Algorithm } + + boxed_cast_base! { InterpLayer, crate::dnn::Layer, cv_dnn_InterpLayer_to_Layer } + + impl core::AlgorithmTraitConst for InterpLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for InterpLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { InterpLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for InterpLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for InterpLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { InterpLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::InterpLayerTraitConst for InterpLayer { + #[inline] fn as_raw_InterpLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::InterpLayerTrait for InterpLayer { + #[inline] fn as_raw_mut_InterpLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { InterpLayer, crate::dnn::InterpLayerTraitConst, as_raw_InterpLayer, crate::dnn::InterpLayerTrait, as_raw_mut_InterpLayer } + + /// This class represents high-level API for keypoints models + /// + /// KeypointsModel allows to set params for preprocessing input image. + /// KeypointsModel creates net from file with trained weights and config, + /// sets preprocessing input, runs forward pass and returns the x and y coordinates of each detected keypoint + pub struct KeypointsModel { + ptr: *mut c_void, + } + + opencv_type_boxed! { KeypointsModel } + + impl Drop for KeypointsModel { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_KeypointsModel_delete(self.as_raw_mut_KeypointsModel()) }; + } + } + + unsafe impl Send for KeypointsModel {} + + impl KeypointsModel { + /// Create keypoints model from network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## C++ default parameters + /// * config: "" + #[inline] + pub fn new(model: &str, config: &str) -> Result { + extern_container_arg!(model); + extern_container_arg!(config); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_KeypointsModel_KeypointsModel_const_StringR_const_StringR(model.opencv_as_extern(), config.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::KeypointsModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create keypoints model from network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * config: "" + #[inline] + pub fn new_def(model: &str) -> Result { + extern_container_arg!(model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_KeypointsModel_KeypointsModel_const_StringR(model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::KeypointsModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create model from deep learning network. + /// ## Parameters + /// * network: Net object. + #[inline] + pub fn new_1(network: &impl crate::dnn::NetTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_KeypointsModel_KeypointsModel_const_NetR(network.as_raw_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::KeypointsModel::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::KeypointsModel] + pub trait KeypointsModelTraitConst: crate::dnn::ModelTraitConst { + fn as_raw_KeypointsModel(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::KeypointsModel] + pub trait KeypointsModelTrait: crate::dnn::KeypointsModelTraitConst + crate::dnn::ModelTrait { + fn as_raw_mut_KeypointsModel(&mut self) -> *mut c_void; + + /// Given the @p input frame, create input blob, run net + /// ## Parameters + /// * frame: The input image. + /// * thresh: minimum confidence threshold to select a keypoint + /// ## Returns + /// a vector holding the x and y coordinates of each detected keypoint + /// + /// ## C++ default parameters + /// * thresh: 0.5 + #[inline] + fn estimate(&mut self, frame: &impl ToInputArray, thresh: f32) -> Result> { + input_array_arg!(frame); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_KeypointsModel_estimate_const__InputArrayR_float(self.as_raw_mut_KeypointsModel(), frame.as_raw__InputArray(), thresh, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Given the @p input frame, create input blob, run net + /// ## Parameters + /// * frame: The input image. + /// * thresh: minimum confidence threshold to select a keypoint + /// ## Returns + /// a vector holding the x and y coordinates of each detected keypoint + /// + /// ## Note + /// This alternative version of [KeypointsModelTrait::estimate] function uses the following default values for its arguments: + /// * thresh: 0.5 + #[inline] + fn estimate_def(&mut self, frame: &impl ToInputArray) -> Result> { + input_array_arg!(frame); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_KeypointsModel_estimate_const__InputArrayR(self.as_raw_mut_KeypointsModel(), frame.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl Clone for KeypointsModel { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_dnn_KeypointsModel_implicitClone_const(self.as_raw_KeypointsModel())) } + } + } + + impl std::fmt::Debug for KeypointsModel { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("KeypointsModel") + .finish() + } + } + + boxed_cast_base! { KeypointsModel, crate::dnn::Model, cv_dnn_KeypointsModel_to_Model } + + impl crate::dnn::ModelTraitConst for KeypointsModel { + #[inline] fn as_raw_Model(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ModelTrait for KeypointsModel { + #[inline] fn as_raw_mut_Model(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { KeypointsModel, crate::dnn::ModelTraitConst, as_raw_Model, crate::dnn::ModelTrait, as_raw_mut_Model } + + impl crate::dnn::KeypointsModelTraitConst for KeypointsModel { + #[inline] fn as_raw_KeypointsModel(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::KeypointsModelTrait for KeypointsModel { + #[inline] fn as_raw_mut_KeypointsModel(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { KeypointsModel, crate::dnn::KeypointsModelTraitConst, as_raw_KeypointsModel, crate::dnn::KeypointsModelTrait, as_raw_mut_KeypointsModel } + + pub struct LRNLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { LRNLayer } + + impl Drop for LRNLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_LRNLayer_delete(self.as_raw_mut_LRNLayer()) }; + } + } + + unsafe impl Send for LRNLayer {} + + impl LRNLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::LRNLayer { + let ret = unsafe { sys::cv_dnn_LRNLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::LRNLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LRNLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::LRNLayer] + pub trait LRNLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_LRNLayer(&self) -> *const c_void; + + #[inline] + fn typ(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_LRNLayer_propType_const(self.as_raw_LRNLayer()) }; + ret + } + + #[inline] + fn size(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_LRNLayer_propSize_const(self.as_raw_LRNLayer()) }; + ret + } + + #[inline] + fn alpha(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_LRNLayer_propAlpha_const(self.as_raw_LRNLayer()) }; + ret + } + + #[inline] + fn beta(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_LRNLayer_propBeta_const(self.as_raw_LRNLayer()) }; + ret + } + + #[inline] + fn bias(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_LRNLayer_propBias_const(self.as_raw_LRNLayer()) }; + ret + } + + #[inline] + fn norm_by_size(&self) -> bool { + let ret = unsafe { sys::cv_dnn_LRNLayer_propNormBySize_const(self.as_raw_LRNLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::LRNLayer] + pub trait LRNLayerTrait: crate::dnn::LRNLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_LRNLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_type(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_LRNLayer_propType_const_int(self.as_raw_mut_LRNLayer(), val) }; + ret + } + + #[inline] + fn set_size(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_LRNLayer_propSize_const_int(self.as_raw_mut_LRNLayer(), val) }; + ret + } + + #[inline] + fn set_alpha(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_LRNLayer_propAlpha_const_float(self.as_raw_mut_LRNLayer(), val) }; + ret + } + + #[inline] + fn set_beta(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_LRNLayer_propBeta_const_float(self.as_raw_mut_LRNLayer(), val) }; + ret + } + + #[inline] + fn set_bias(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_LRNLayer_propBias_const_float(self.as_raw_mut_LRNLayer(), val) }; + ret + } + + #[inline] + fn set_norm_by_size(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_LRNLayer_propNormBySize_const_bool(self.as_raw_mut_LRNLayer(), val) }; + ret + } + + } + + impl Default for LRNLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for LRNLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("LRNLayer") + .field("typ", &crate::dnn::LRNLayerTraitConst::typ(self)) + .field("size", &crate::dnn::LRNLayerTraitConst::size(self)) + .field("alpha", &crate::dnn::LRNLayerTraitConst::alpha(self)) + .field("beta", &crate::dnn::LRNLayerTraitConst::beta(self)) + .field("bias", &crate::dnn::LRNLayerTraitConst::bias(self)) + .field("norm_by_size", &crate::dnn::LRNLayerTraitConst::norm_by_size(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { LRNLayer, core::Algorithm, cv_dnn_LRNLayer_to_Algorithm } + + boxed_cast_base! { LRNLayer, crate::dnn::Layer, cv_dnn_LRNLayer_to_Layer } + + impl core::AlgorithmTraitConst for LRNLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for LRNLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LRNLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for LRNLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for LRNLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LRNLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::LRNLayerTraitConst for LRNLayer { + #[inline] fn as_raw_LRNLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LRNLayerTrait for LRNLayer { + #[inline] fn as_raw_mut_LRNLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LRNLayer, crate::dnn::LRNLayerTraitConst, as_raw_LRNLayer, crate::dnn::LRNLayerTrait, as_raw_mut_LRNLayer } + + /// LSTM recurrent layer + pub struct LSTMLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { LSTMLayer } + + impl Drop for LSTMLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_LSTMLayer_delete(self.as_raw_mut_LSTMLayer()) }; + } + } + + unsafe impl Send for LSTMLayer {} + + impl LSTMLayer { + /// Creates instance of LSTM layer + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LSTMLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::LSTMLayer] + pub trait LSTMLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_LSTMLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::LSTMLayer] + pub trait LSTMLayerTrait: crate::dnn::LSTMLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_LSTMLayer(&mut self) -> *mut c_void; + + /// + /// **Deprecated**: Use LayerParams::blobs instead. + /// Set trained weights for LSTM layer. + /// + /// LSTM behavior on each step is defined by current input, previous output, previous cell state and learned weights. + /// + /// Let @f$x_t@f$ be current input, @f$h_t@f$ be current output, @f$c_t@f$ be current state. + /// Than current output and current cell state is computed as follows: + /// @f{eqnarray*}{ + /// h_t &= o_t \odot tanh(c_t), \\ + /// c_t &= f_t \odot c_{t-1} + i_t \odot g_t, \\ + /// @f} + /// where @f$\odot@f$ is per-element multiply operation and @f$i_t, f_t, o_t, g_t@f$ is internal gates that are computed using learned weights. + /// + /// Gates are computed as follows: + /// @f{eqnarray*}{ + /// i_t &= sigmoid&(W_{xi} x_t + W_{hi} h_{t-1} + b_i), \\ + /// f_t &= sigmoid&(W_{xf} x_t + W_{hf} h_{t-1} + b_f), \\ + /// o_t &= sigmoid&(W_{xo} x_t + W_{ho} h_{t-1} + b_o), \\ + /// g_t &= tanh &(W_{xg} x_t + W_{hg} h_{t-1} + b_g), \\ + /// @f} + /// where @f$W_{x?}@f$, @f$W_{h?}@f$ and @f$b_{?}@f$ are learned weights represented as matrices: + /// @f$W_{x?} \in R^{N_h \times N_x}@f$, @f$W_{h?} \in R^{N_h \times N_h}@f$, @f$b_? \in R^{N_h}@f$. + /// + /// For simplicity and performance purposes we use @f$ W_x = [W_{xi}; W_{xf}; W_{xo}, W_{xg}] @f$ + /// (i.e. @f$W_x@f$ is vertical concatenation of @f$ W_{x?} @f$), @f$ W_x \in R^{4N_h \times N_x} @f$. + /// The same for @f$ W_h = [W_{hi}; W_{hf}; W_{ho}, W_{hg}], W_h \in R^{4N_h \times N_h} @f$ + /// and for @f$ b = [b_i; b_f, b_o, b_g]@f$, @f$b \in R^{4N_h} @f$. + /// + /// ## Parameters + /// * Wh: is matrix defining how previous output is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_h @f$) + /// * Wx: is matrix defining how current input is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_x @f$) + /// * b: is bias vector (i.e. according to above mentioned notation is @f$ b @f$) + #[deprecated = "Use LayerParams::blobs instead."] + #[inline] + fn set_weights(&mut self, wh: &impl core::MatTraitConst, wx: &impl core::MatTraitConst, b: &impl core::MatTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LSTMLayer_setWeights_const_MatR_const_MatR_const_MatR(self.as_raw_mut_LSTMLayer(), wh.as_raw_Mat(), wx.as_raw_Mat(), b.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Specifies shape of output blob which will be [[`T`], `N`] + @p outTailShape. + /// @details If this parameter is empty or unset then @p outTailShape = [`Wh`.size(0)] will be used, + /// where `Wh` is parameter from setWeights(). + /// + /// ## C++ default parameters + /// * out_tail_shape: MatShape() + #[inline] + fn set_out_shape(&mut self, out_tail_shape: &crate::dnn::MatShape) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LSTMLayer_setOutShape_const_MatShapeR(self.as_raw_mut_LSTMLayer(), out_tail_shape.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Specifies shape of output blob which will be [[`T`], `N`] + @p outTailShape. + /// @details If this parameter is empty or unset then @p outTailShape = [`Wh`.size(0)] will be used, + /// where `Wh` is parameter from setWeights(). + /// + /// ## Note + /// This alternative version of [LSTMLayerTrait::set_out_shape] function uses the following default values for its arguments: + /// * out_tail_shape: MatShape() + #[inline] + fn set_out_shape_def(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LSTMLayer_setOutShape(self.as_raw_mut_LSTMLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// + /// **Deprecated**: Use flag `produce_cell_output` in LayerParams. + /// Specifies either interpret first dimension of input blob as timestamp dimension either as sample. + /// + /// If flag is set to true then shape of input blob will be interpreted as [`T`, `N`, `[data dims]`] where `T` specifies number of timestamps, `N` is number of independent streams. + /// In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times. + /// + /// If flag is set to false then shape of input blob will be interpreted as [`N`, `[data dims]`]. + /// In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`]. + /// + /// ## C++ default parameters + /// * use_: true + #[deprecated = "Use flag `produce_cell_output` in LayerParams."] + #[inline] + fn set_use_timstamps_dim(&mut self, use_: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LSTMLayer_setUseTimstampsDim_bool(self.as_raw_mut_LSTMLayer(), use_, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// + /// **Deprecated**: Use flag `produce_cell_output` in LayerParams. + /// Specifies either interpret first dimension of input blob as timestamp dimension either as sample. + /// + /// If flag is set to true then shape of input blob will be interpreted as [`T`, `N`, `[data dims]`] where `T` specifies number of timestamps, `N` is number of independent streams. + /// In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times. + /// + /// If flag is set to false then shape of input blob will be interpreted as [`N`, `[data dims]`]. + /// In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`]. + /// + /// ## Note + /// This alternative version of [LSTMLayerTrait::set_use_timstamps_dim] function uses the following default values for its arguments: + /// * use_: true + #[deprecated = "Use flag `produce_cell_output` in LayerParams."] + #[inline] + fn set_use_timstamps_dim_def(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LSTMLayer_setUseTimstampsDim(self.as_raw_mut_LSTMLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// + /// **Deprecated**: Use flag `use_timestamp_dim` in LayerParams. + /// If this flag is set to true then layer will produce @f$ c_t @f$ as second output. + /// @details Shape of the second output is the same as first output. + /// + /// ## C++ default parameters + /// * produce: false + #[deprecated = "Use flag `use_timestamp_dim` in LayerParams."] + #[inline] + fn set_produce_cell_output(&mut self, produce: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LSTMLayer_setProduceCellOutput_bool(self.as_raw_mut_LSTMLayer(), produce, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// + /// **Deprecated**: Use flag `use_timestamp_dim` in LayerParams. + /// If this flag is set to true then layer will produce @f$ c_t @f$ as second output. + /// @details Shape of the second output is the same as first output. + /// + /// ## Note + /// This alternative version of [LSTMLayerTrait::set_produce_cell_output] function uses the following default values for its arguments: + /// * produce: false + #[deprecated = "Use flag `use_timestamp_dim` in LayerParams."] + #[inline] + fn set_produce_cell_output_def(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LSTMLayer_setProduceCellOutput(self.as_raw_mut_LSTMLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn input_name_to_index(&mut self, input_name: &str) -> Result { + extern_container_arg!(input_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LSTMLayer_inputNameToIndex_String(self.as_raw_mut_LSTMLayer(), input_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn output_name_to_index(&mut self, output_name: &str) -> Result { + extern_container_arg!(output_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LSTMLayer_outputNameToIndex_const_StringR(self.as_raw_mut_LSTMLayer(), output_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for LSTMLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("LSTMLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { LSTMLayer, core::Algorithm, cv_dnn_LSTMLayer_to_Algorithm } + + boxed_cast_base! { LSTMLayer, crate::dnn::Layer, cv_dnn_LSTMLayer_to_Layer } + + impl core::AlgorithmTraitConst for LSTMLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for LSTMLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LSTMLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for LSTMLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for LSTMLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LSTMLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::LSTMLayerTraitConst for LSTMLayer { + #[inline] fn as_raw_LSTMLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LSTMLayerTrait for LSTMLayer { + #[inline] fn as_raw_mut_LSTMLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LSTMLayer, crate::dnn::LSTMLayerTraitConst, as_raw_LSTMLayer, crate::dnn::LSTMLayerTrait, as_raw_mut_LSTMLayer } + + /// This interface class allows to build new Layers - are building blocks of networks. + /// + /// Each class, derived from Layer, must implement forward() method to compute outputs. + /// Also before using the new layer into networks you must register your layer by using one of [dnnLayerFactory] "LayerFactory" macros. + pub struct Layer { + ptr: *mut c_void, + } + + opencv_type_boxed! { Layer } + + impl Drop for Layer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_Layer_delete(self.as_raw_mut_Layer()) }; + } + } + + unsafe impl Send for Layer {} + + impl Layer { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_Layer(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Layer::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn new(params: &impl crate::dnn::LayerParamsTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_Layer_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Layer::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::Layer] + pub trait LayerTraitConst: core::AlgorithmTraitConst { + fn as_raw_Layer(&self) -> *const c_void; + + /// List of learned parameters must be stored here to allow read them by using Net::getParam(). + #[inline] + fn blobs(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_Layer_propBlobs_const(self.as_raw_Layer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + /// Name of the layer instance, can be used for logging or other internal purposes. + #[inline] + fn name(&self) -> String { + let ret = unsafe { sys::cv_dnn_Layer_propName_const(self.as_raw_Layer()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + /// Type name which was used for creating layer by layer factory. + #[inline] + fn typ(&self) -> String { + let ret = unsafe { sys::cv_dnn_Layer_propType_const(self.as_raw_Layer()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + /// prefer target for layer forwarding + #[inline] + fn preferable_target(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_Layer_propPreferableTarget_const(self.as_raw_Layer()) }; + ret + } + + /// Automatic Halide scheduling based on layer hyper-parameters. + /// ## Parameters + /// * node: Backend node with Halide functions. + /// * inputs: Blobs that will be used in forward invocations. + /// * outputs: Blobs that will be used in forward invocations. + /// * targetId: Target identifier + /// ## See also + /// BackendNode, Target + /// + /// Layer don't use own Halide::Func members because we can have applied + /// layers fusing. In this way the fused function should be scheduled. + #[inline] + fn apply_halide_scheduler(&self, node: &mut core::Ptr, inputs: &core::Vector, outputs: &core::Vector, target_id: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_applyHalideScheduler_const_PtrLBackendNodeGR_const_vectorLMatXGR_const_vectorLMatGR_int(self.as_raw_Layer(), node.as_raw_mut_PtrOfBackendNode(), inputs.as_raw_VectorOfMat(), outputs.as_raw_VectorOfMat(), target_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns parameters of layers with channel-wise multiplication and addition. + /// ## Parameters + /// * scale:[out] Channel-wise multipliers. Total number of values should + /// be equal to number of channels. + /// * shift:[out] Channel-wise offsets. Total number of values should + /// be equal to number of channels. + /// + /// Some layers can fuse their transformations with further layers. + /// In example, convolution + batch normalization. This way base layer + /// use weights from layer after it. Fused layer is skipped. + /// By default, @p scale and @p shift are empty that means layer has no + /// element-wise multiplications or additions. + #[inline] + fn get_scale_shift(&self, scale: &mut impl core::MatTrait, shift: &mut impl core::MatTrait) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_getScaleShift_const_MatR_MatR(self.as_raw_Layer(), scale.as_raw_mut_Mat(), shift.as_raw_mut_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns scale and zeropoint of layers + /// ## Parameters + /// * scale:[out] Output scale + /// * zeropoint:[out] Output zeropoint + /// + /// By default, @p scale is 1 and @p zeropoint is 0. + #[inline] + fn get_scale_zeropoint(&self, scale: &mut f32, zeropoint: &mut i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_getScaleZeropoint_const_floatR_intR(self.as_raw_Layer(), scale, zeropoint, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_memory_shapes(&self, inputs: &core::Vector, required_outputs: i32, outputs: &mut core::Vector, internals: &mut core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_getMemoryShapes_const_const_vectorLMatShapeGR_const_int_vectorLMatShapeGR_vectorLMatShapeGR(self.as_raw_Layer(), inputs.as_raw_VectorOfMatShape(), required_outputs, outputs.as_raw_mut_VectorOfMatShape(), internals.as_raw_mut_VectorOfMatShape(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_flops(&self, inputs: &core::Vector, outputs: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_getFLOPS_const_const_vectorLMatShapeGR_const_vectorLMatShapeGR(self.as_raw_Layer(), inputs.as_raw_VectorOfMatShape(), outputs.as_raw_VectorOfMatShape(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::dnn::Layer] + pub trait LayerTrait: core::AlgorithmTrait + crate::dnn::LayerTraitConst { + fn as_raw_mut_Layer(&mut self) -> *mut c_void; + + /// List of learned parameters must be stored here to allow read them by using Net::getParam(). + #[inline] + fn set_blobs(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_Layer_propBlobs_const_vectorLMatG(self.as_raw_mut_Layer(), val.as_raw_VectorOfMat()) }; + ret + } + + /// Name of the layer instance, can be used for logging or other internal purposes. + #[inline] + fn set_name(&mut self, val: &str) { + extern_container_arg!(nofail val); + let ret = unsafe { sys::cv_dnn_Layer_propName_const_String(self.as_raw_mut_Layer(), val.opencv_as_extern()) }; + ret + } + + /// Type name which was used for creating layer by layer factory. + #[inline] + fn set_type(&mut self, val: &str) { + extern_container_arg!(nofail val); + let ret = unsafe { sys::cv_dnn_Layer_propType_const_String(self.as_raw_mut_Layer(), val.opencv_as_extern()) }; + ret + } + + /// prefer target for layer forwarding + #[inline] + fn set_preferable_target(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_Layer_propPreferableTarget_const_int(self.as_raw_mut_Layer(), val) }; + ret + } + + /// Computes and sets internal parameters according to inputs, outputs and blobs. + /// ## Parameters + /// * inputs: vector of already allocated input blobs + /// * outputs:[out] vector of already allocated output blobs + /// + /// This method is called after network has allocated all memory for input and output blobs + /// and before inferencing. + #[inline] + fn finalize(&mut self, inputs: &impl ToInputArray, outputs: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(inputs); + output_array_arg!(outputs); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_finalize_const__InputArrayR_const__OutputArrayR(self.as_raw_mut_Layer(), inputs.as_raw__InputArray(), outputs.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Given the @p input blobs, computes the output @p blobs. + /// + /// **Deprecated**: Use Layer::forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) instead + /// ## Parameters + /// * input: the input blobs. + /// * output:[out] allocated output blobs, which will store results of the computation. + /// * internals:[out] allocated internal blobs + #[deprecated = "Use Layer::forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) instead"] + #[inline] + fn forward_mat(&mut self, input: &mut core::Vector, output: &mut core::Vector, internals: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_forward_vectorLMatXGR_vectorLMatGR_vectorLMatGR(self.as_raw_mut_Layer(), input.as_raw_mut_VectorOfMat(), output.as_raw_mut_VectorOfMat(), internals.as_raw_mut_VectorOfMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Given the @p input blobs, computes the output @p blobs. + /// ## Parameters + /// * inputs: the input blobs. + /// * outputs:[out] allocated output blobs, which will store results of the computation. + /// * internals:[out] allocated internal blobs + #[inline] + fn forward(&mut self, inputs: &impl ToInputArray, outputs: &mut impl ToOutputArray, internals: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(inputs); + output_array_arg!(outputs); + output_array_arg!(internals); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_forward_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(self.as_raw_mut_Layer(), inputs.as_raw__InputArray(), outputs.as_raw__OutputArray(), internals.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Tries to quantize the given layer and compute the quantization parameters required for fixed point implementation. + /// ## Parameters + /// * scales: input and output scales. + /// * zeropoints: input and output zeropoints. + /// * params:[out] Quantized parameters required for fixed point implementation of that layer. + /// ## Returns + /// True if layer can be quantized. + #[inline] + fn try_quantize(&mut self, scales: &core::Vector>, zeropoints: &core::Vector>, params: &mut impl crate::dnn::LayerParamsTrait) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_tryQuantize_const_vectorLvectorLfloatGGR_const_vectorLvectorLintGGR_LayerParamsR(self.as_raw_mut_Layer(), scales.as_raw_VectorOfVectorOff32(), zeropoints.as_raw_VectorOfVectorOfi32(), params.as_raw_mut_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Given the @p input blobs, computes the output @p blobs. + /// ## Parameters + /// * inputs: the input blobs. + /// * outputs:[out] allocated output blobs, which will store results of the computation. + /// * internals:[out] allocated internal blobs + #[inline] + fn forward_fallback(&mut self, inputs: &impl ToInputArray, outputs: &mut impl ToOutputArray, internals: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(inputs); + output_array_arg!(outputs); + output_array_arg!(internals); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_forward_fallback_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(self.as_raw_mut_Layer(), inputs.as_raw__InputArray(), outputs.as_raw__OutputArray(), internals.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// + /// Computes and sets internal parameters according to inputs, outputs and blobs. + /// ## Parameters + /// * inputs: vector of already allocated input blobs + /// * outputs:[out] vector of already allocated output blobs + /// + /// This method is called after network has allocated all memory for input and output blobs + /// and before inferencing. + /// + /// ## Overloaded parameters + /// + /// + /// **Deprecated**: Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead + #[deprecated = "Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead"] + #[inline] + fn finalize_mat_to(&mut self, inputs: &core::Vector, outputs: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_finalize_const_vectorLMatGR_vectorLMatGR(self.as_raw_mut_Layer(), inputs.as_raw_VectorOfMat(), outputs.as_raw_mut_VectorOfMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// + /// Computes and sets internal parameters according to inputs, outputs and blobs. + /// ## Parameters + /// * inputs: vector of already allocated input blobs + /// * outputs:[out] vector of already allocated output blobs + /// + /// This method is called after network has allocated all memory for input and output blobs + /// and before inferencing. + /// + /// ## Overloaded parameters + /// + /// + /// **Deprecated**: Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead + #[deprecated = "Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead"] + #[inline] + fn finalize_mat(&mut self, inputs: &core::Vector) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_finalize_const_vectorLMatGR(self.as_raw_mut_Layer(), inputs.as_raw_VectorOfMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Allocates layer and computes output. + /// + /// **Deprecated**: This method will be removed in the future release. + #[deprecated = "This method will be removed in the future release."] + #[inline] + fn run(&mut self, inputs: &core::Vector, outputs: &mut core::Vector, internals: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_run_const_vectorLMatGR_vectorLMatGR_vectorLMatGR(self.as_raw_mut_Layer(), inputs.as_raw_VectorOfMat(), outputs.as_raw_mut_VectorOfMat(), internals.as_raw_mut_VectorOfMat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns index of input blob into the input array. + /// ## Parameters + /// * inputName: label of input blob + /// + /// Each layer input and output can be labeled to easily identify them using "%[.output_name]" notation. + /// This method maps label of input blob to its index into input vector. + #[inline] + fn input_name_to_index(&mut self, input_name: &str) -> Result { + extern_container_arg!(input_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_inputNameToIndex_String(self.as_raw_mut_Layer(), input_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns index of output blob in output array. + /// ## See also + /// inputNameToIndex() + #[inline] + fn output_name_to_index(&mut self, output_name: &str) -> Result { + extern_container_arg!(output_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_outputNameToIndex_const_StringR(self.as_raw_mut_Layer(), output_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Ask layer if it support specific backend for doing computations. + /// ## Parameters + /// * backendId: computation backend identifier. + /// ## See also + /// Backend + #[inline] + fn support_backend(&mut self, backend_id: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_supportBackend_int(self.as_raw_mut_Layer(), backend_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns Halide backend node. + /// ## Parameters + /// * inputs: Input Halide buffers. + /// ## See also + /// BackendNode, BackendWrapper + /// + /// Input buffers should be exactly the same that will be used in forward invocations. + /// Despite we can use Halide::ImageParam based on input shape only, + /// it helps prevent some memory management issues (if something wrong, + /// Halide tests will be failed). + #[inline] + fn init_halide(&mut self, inputs: &core::Vector>) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_initHalide_const_vectorLPtrLBackendWrapperGGR(self.as_raw_mut_Layer(), inputs.as_raw_VectorOfPtrOfBackendWrapper(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn init_ngraph(&mut self, inputs: &core::Vector>, nodes: &core::Vector>) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_initNgraph_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendNodeGGR(self.as_raw_mut_Layer(), inputs.as_raw_VectorOfPtrOfBackendWrapper(), nodes.as_raw_VectorOfPtrOfBackendNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn init_vk_com(&mut self, inputs: &core::Vector>, outputs: &mut core::Vector>) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_initVkCom_const_vectorLPtrLBackendWrapperGGR_vectorLPtrLBackendWrapperGGR(self.as_raw_mut_Layer(), inputs.as_raw_VectorOfPtrOfBackendWrapper(), outputs.as_raw_mut_VectorOfPtrOfBackendWrapper(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn init_webnn(&mut self, inputs: &core::Vector>, nodes: &core::Vector>) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_initWebnn_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendNodeGGR(self.as_raw_mut_Layer(), inputs.as_raw_VectorOfPtrOfBackendWrapper(), nodes.as_raw_VectorOfPtrOfBackendNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns a CUDA backend node + /// + /// ## Parameters + /// * context: void pointer to CSLContext object + /// * inputs: layer inputs + /// * outputs: layer outputs + #[inline] + unsafe fn init_cuda(&mut self, context: *mut c_void, inputs: &core::Vector>, outputs: &core::Vector>) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_initCUDA_voidX_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendWrapperGGR(self.as_raw_mut_Layer(), context, inputs.as_raw_VectorOfPtrOfBackendWrapper(), outputs.as_raw_VectorOfPtrOfBackendWrapper(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns a TimVX backend node + /// + /// ## Parameters + /// * timVxInfo: void pointer to CSLContext object + /// * inputsWrapper: layer inputs + /// * outputsWrapper: layer outputs + /// * isLast: if the node is the last one of the TimVX Graph. + #[inline] + unsafe fn init_tim_vx(&mut self, tim_vx_info: *mut c_void, inputs_wrapper: &core::Vector>, outputs_wrapper: &core::Vector>, is_last: bool) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_initTimVX_voidX_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendWrapperGGR_bool(self.as_raw_mut_Layer(), tim_vx_info, inputs_wrapper.as_raw_VectorOfPtrOfBackendWrapper(), outputs_wrapper.as_raw_VectorOfPtrOfBackendWrapper(), is_last, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns a CANN backend node + /// + /// ## Parameters + /// * inputs: input tensors of CANN operator + /// * outputs: output tensors of CANN operator + /// * nodes: nodes of input tensors + #[inline] + fn init_cann(&mut self, inputs: &core::Vector>, outputs: &core::Vector>, nodes: &core::Vector>) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_initCann_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendNodeGGR(self.as_raw_mut_Layer(), inputs.as_raw_VectorOfPtrOfBackendWrapper(), outputs.as_raw_VectorOfPtrOfBackendWrapper(), nodes.as_raw_VectorOfPtrOfBackendNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Implement layers fusing. + /// ## Parameters + /// * node: Backend node of bottom layer. + /// ## See also + /// BackendNode + /// + /// Actual for graph-based backends. If layer attached successfully, + /// returns non-empty cv::Ptr to node of the same backend. + /// Fuse only over the last function. + #[inline] + fn try_attach(&mut self, node: &core::Ptr) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_tryAttach_const_PtrLBackendNodeGR(self.as_raw_mut_Layer(), node.as_raw_PtrOfBackendNode(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Try to fuse current layer with a next one + /// ## Parameters + /// * top: Next layer to be fused. + /// ## Returns + /// True if fusion was performed. + #[inline] + fn try_fuse(&mut self, top: &mut core::Ptr) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_tryFuse_PtrLLayerGR(self.as_raw_mut_Layer(), top.as_raw_mut_PtrOfLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// "Detaches" all the layers, attached to particular layer. + #[inline] + fn unset_attached(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_unsetAttached(self.as_raw_mut_Layer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn update_memory_shapes(&mut self, inputs: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_updateMemoryShapes_const_vectorLMatShapeGR(self.as_raw_mut_Layer(), inputs.as_raw_VectorOfMatShape(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_params_from(&mut self, params: &impl crate::dnn::LayerParamsTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Layer_setParamsFrom_const_LayerParamsR(self.as_raw_mut_Layer(), params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for Layer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Layer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { Layer, core::Algorithm, cv_dnn_Layer_to_Algorithm } + + boxed_cast_descendant! { Layer, crate::dnn::AbsLayer, cv_dnn_Layer_to_AbsLayer } + + boxed_cast_descendant! { Layer, crate::dnn::AccumLayer, cv_dnn_Layer_to_AccumLayer } + + boxed_cast_descendant! { Layer, crate::dnn::AcosLayer, cv_dnn_Layer_to_AcosLayer } + + boxed_cast_descendant! { Layer, crate::dnn::AcoshLayer, cv_dnn_Layer_to_AcoshLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ActivationLayer, cv_dnn_Layer_to_ActivationLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ActivationLayerInt8, cv_dnn_Layer_to_ActivationLayerInt8 } + + boxed_cast_descendant! { Layer, crate::dnn::ArgLayer, cv_dnn_Layer_to_ArgLayer } + + boxed_cast_descendant! { Layer, crate::dnn::AsinLayer, cv_dnn_Layer_to_AsinLayer } + + boxed_cast_descendant! { Layer, crate::dnn::AsinhLayer, cv_dnn_Layer_to_AsinhLayer } + + boxed_cast_descendant! { Layer, crate::dnn::AtanLayer, cv_dnn_Layer_to_AtanLayer } + + boxed_cast_descendant! { Layer, crate::dnn::AtanhLayer, cv_dnn_Layer_to_AtanhLayer } + + boxed_cast_descendant! { Layer, crate::dnn::AttentionLayer, cv_dnn_Layer_to_AttentionLayer } + + boxed_cast_descendant! { Layer, crate::dnn::BNLLLayer, cv_dnn_Layer_to_BNLLLayer } + + boxed_cast_descendant! { Layer, crate::dnn::BaseConvolutionLayer, cv_dnn_Layer_to_BaseConvolutionLayer } + + boxed_cast_descendant! { Layer, crate::dnn::BatchNormLayer, cv_dnn_Layer_to_BatchNormLayer } + + boxed_cast_descendant! { Layer, crate::dnn::BatchNormLayerInt8, cv_dnn_Layer_to_BatchNormLayerInt8 } + + boxed_cast_descendant! { Layer, crate::dnn::BlankLayer, cv_dnn_Layer_to_BlankLayer } + + boxed_cast_descendant! { Layer, crate::dnn::CeilLayer, cv_dnn_Layer_to_CeilLayer } + + boxed_cast_descendant! { Layer, crate::dnn::CeluLayer, cv_dnn_Layer_to_CeluLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ChannelsPReLULayer, cv_dnn_Layer_to_ChannelsPReLULayer } + + boxed_cast_descendant! { Layer, crate::dnn::CompareLayer, cv_dnn_Layer_to_CompareLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ConcatLayer, cv_dnn_Layer_to_ConcatLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ConstLayer, cv_dnn_Layer_to_ConstLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ConvolutionLayer, cv_dnn_Layer_to_ConvolutionLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ConvolutionLayerInt8, cv_dnn_Layer_to_ConvolutionLayerInt8 } + + boxed_cast_descendant! { Layer, crate::dnn::CorrelationLayer, cv_dnn_Layer_to_CorrelationLayer } + + boxed_cast_descendant! { Layer, crate::dnn::CosLayer, cv_dnn_Layer_to_CosLayer } + + boxed_cast_descendant! { Layer, crate::dnn::CoshLayer, cv_dnn_Layer_to_CoshLayer } + + boxed_cast_descendant! { Layer, crate::dnn::CropAndResizeLayer, cv_dnn_Layer_to_CropAndResizeLayer } + + boxed_cast_descendant! { Layer, crate::dnn::CropLayer, cv_dnn_Layer_to_CropLayer } + + boxed_cast_descendant! { Layer, crate::dnn::CumSumLayer, cv_dnn_Layer_to_CumSumLayer } + + boxed_cast_descendant! { Layer, crate::dnn::DataAugmentationLayer, cv_dnn_Layer_to_DataAugmentationLayer } + + boxed_cast_descendant! { Layer, crate::dnn::DeconvolutionLayer, cv_dnn_Layer_to_DeconvolutionLayer } + + boxed_cast_descendant! { Layer, crate::dnn::DepthToSpaceLayer, cv_dnn_Layer_to_DepthToSpaceLayer } + + boxed_cast_descendant! { Layer, crate::dnn::DequantizeLayer, cv_dnn_Layer_to_DequantizeLayer } + + boxed_cast_descendant! { Layer, crate::dnn::DetectionOutputLayer, cv_dnn_Layer_to_DetectionOutputLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ELULayer, cv_dnn_Layer_to_ELULayer } + + boxed_cast_descendant! { Layer, crate::dnn::EinsumLayer, cv_dnn_Layer_to_EinsumLayer } + + boxed_cast_descendant! { Layer, crate::dnn::EltwiseLayer, cv_dnn_Layer_to_EltwiseLayer } + + boxed_cast_descendant! { Layer, crate::dnn::EltwiseLayerInt8, cv_dnn_Layer_to_EltwiseLayerInt8 } + + boxed_cast_descendant! { Layer, crate::dnn::ErfLayer, cv_dnn_Layer_to_ErfLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ExpLayer, cv_dnn_Layer_to_ExpLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ExpandLayer, cv_dnn_Layer_to_ExpandLayer } + + boxed_cast_descendant! { Layer, crate::dnn::FlattenLayer, cv_dnn_Layer_to_FlattenLayer } + + boxed_cast_descendant! { Layer, crate::dnn::FloorLayer, cv_dnn_Layer_to_FloorLayer } + + boxed_cast_descendant! { Layer, crate::dnn::FlowWarpLayer, cv_dnn_Layer_to_FlowWarpLayer } + + boxed_cast_descendant! { Layer, crate::dnn::GRULayer, cv_dnn_Layer_to_GRULayer } + + boxed_cast_descendant! { Layer, crate::dnn::GatherElementsLayer, cv_dnn_Layer_to_GatherElementsLayer } + + boxed_cast_descendant! { Layer, crate::dnn::GatherLayer, cv_dnn_Layer_to_GatherLayer } + + boxed_cast_descendant! { Layer, crate::dnn::GeluApproximationLayer, cv_dnn_Layer_to_GeluApproximationLayer } + + boxed_cast_descendant! { Layer, crate::dnn::GeluLayer, cv_dnn_Layer_to_GeluLayer } + + boxed_cast_descendant! { Layer, crate::dnn::GemmLayer, cv_dnn_Layer_to_GemmLayer } + + boxed_cast_descendant! { Layer, crate::dnn::GroupNormLayer, cv_dnn_Layer_to_GroupNormLayer } + + boxed_cast_descendant! { Layer, crate::dnn::HardSigmoidLayer, cv_dnn_Layer_to_HardSigmoidLayer } + + boxed_cast_descendant! { Layer, crate::dnn::HardSwishLayer, cv_dnn_Layer_to_HardSwishLayer } + + boxed_cast_descendant! { Layer, crate::dnn::InnerProductLayer, cv_dnn_Layer_to_InnerProductLayer } + + boxed_cast_descendant! { Layer, crate::dnn::InnerProductLayerInt8, cv_dnn_Layer_to_InnerProductLayerInt8 } + + boxed_cast_descendant! { Layer, crate::dnn::InstanceNormLayer, cv_dnn_Layer_to_InstanceNormLayer } + + boxed_cast_descendant! { Layer, crate::dnn::InterpLayer, cv_dnn_Layer_to_InterpLayer } + + boxed_cast_descendant! { Layer, crate::dnn::LRNLayer, cv_dnn_Layer_to_LRNLayer } + + boxed_cast_descendant! { Layer, crate::dnn::LSTMLayer, cv_dnn_Layer_to_LSTMLayer } + + boxed_cast_descendant! { Layer, crate::dnn::LayerNormLayer, cv_dnn_Layer_to_LayerNormLayer } + + boxed_cast_descendant! { Layer, crate::dnn::LogLayer, cv_dnn_Layer_to_LogLayer } + + boxed_cast_descendant! { Layer, crate::dnn::MVNLayer, cv_dnn_Layer_to_MVNLayer } + + boxed_cast_descendant! { Layer, crate::dnn::MatMulLayer, cv_dnn_Layer_to_MatMulLayer } + + boxed_cast_descendant! { Layer, crate::dnn::MaxUnpoolLayer, cv_dnn_Layer_to_MaxUnpoolLayer } + + boxed_cast_descendant! { Layer, crate::dnn::MishLayer, cv_dnn_Layer_to_MishLayer } + + boxed_cast_descendant! { Layer, crate::dnn::NaryEltwiseLayer, cv_dnn_Layer_to_NaryEltwiseLayer } + + boxed_cast_descendant! { Layer, crate::dnn::NormalizeBBoxLayer, cv_dnn_Layer_to_NormalizeBBoxLayer } + + boxed_cast_descendant! { Layer, crate::dnn::NotLayer, cv_dnn_Layer_to_NotLayer } + + boxed_cast_descendant! { Layer, crate::dnn::PaddingLayer, cv_dnn_Layer_to_PaddingLayer } + + boxed_cast_descendant! { Layer, crate::dnn::PermuteLayer, cv_dnn_Layer_to_PermuteLayer } + + boxed_cast_descendant! { Layer, crate::dnn::PoolingLayer, cv_dnn_Layer_to_PoolingLayer } + + boxed_cast_descendant! { Layer, crate::dnn::PoolingLayerInt8, cv_dnn_Layer_to_PoolingLayerInt8 } + + boxed_cast_descendant! { Layer, crate::dnn::PowerLayer, cv_dnn_Layer_to_PowerLayer } + + boxed_cast_descendant! { Layer, crate::dnn::PriorBoxLayer, cv_dnn_Layer_to_PriorBoxLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ProposalLayer, cv_dnn_Layer_to_ProposalLayer } + + boxed_cast_descendant! { Layer, crate::dnn::QuantizeLayer, cv_dnn_Layer_to_QuantizeLayer } + + boxed_cast_descendant! { Layer, crate::dnn::RNNLayer, cv_dnn_Layer_to_RNNLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ReLU6Layer, cv_dnn_Layer_to_ReLU6Layer } + + boxed_cast_descendant! { Layer, crate::dnn::ReLULayer, cv_dnn_Layer_to_ReLULayer } + + boxed_cast_descendant! { Layer, crate::dnn::ReciprocalLayer, cv_dnn_Layer_to_ReciprocalLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ReduceLayer, cv_dnn_Layer_to_ReduceLayer } + + boxed_cast_descendant! { Layer, crate::dnn::RegionLayer, cv_dnn_Layer_to_RegionLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ReorgLayer, cv_dnn_Layer_to_ReorgLayer } + + boxed_cast_descendant! { Layer, crate::dnn::RequantizeLayer, cv_dnn_Layer_to_RequantizeLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ReshapeLayer, cv_dnn_Layer_to_ReshapeLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ResizeLayer, cv_dnn_Layer_to_ResizeLayer } + + boxed_cast_descendant! { Layer, crate::dnn::RoundLayer, cv_dnn_Layer_to_RoundLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ScaleLayer, cv_dnn_Layer_to_ScaleLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ScaleLayerInt8, cv_dnn_Layer_to_ScaleLayerInt8 } + + boxed_cast_descendant! { Layer, crate::dnn::ScatterLayer, cv_dnn_Layer_to_ScatterLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ScatterNDLayer, cv_dnn_Layer_to_ScatterNDLayer } + + boxed_cast_descendant! { Layer, crate::dnn::SeluLayer, cv_dnn_Layer_to_SeluLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ShiftLayer, cv_dnn_Layer_to_ShiftLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ShiftLayerInt8, cv_dnn_Layer_to_ShiftLayerInt8 } + + boxed_cast_descendant! { Layer, crate::dnn::ShrinkLayer, cv_dnn_Layer_to_ShrinkLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ShuffleChannelLayer, cv_dnn_Layer_to_ShuffleChannelLayer } + + boxed_cast_descendant! { Layer, crate::dnn::SigmoidLayer, cv_dnn_Layer_to_SigmoidLayer } + + boxed_cast_descendant! { Layer, crate::dnn::SignLayer, cv_dnn_Layer_to_SignLayer } + + boxed_cast_descendant! { Layer, crate::dnn::SinLayer, cv_dnn_Layer_to_SinLayer } + + boxed_cast_descendant! { Layer, crate::dnn::SinhLayer, cv_dnn_Layer_to_SinhLayer } + + boxed_cast_descendant! { Layer, crate::dnn::SliceLayer, cv_dnn_Layer_to_SliceLayer } + + boxed_cast_descendant! { Layer, crate::dnn::SoftmaxLayer, cv_dnn_Layer_to_SoftmaxLayer } + + boxed_cast_descendant! { Layer, crate::dnn::SoftmaxLayerInt8, cv_dnn_Layer_to_SoftmaxLayerInt8 } + + boxed_cast_descendant! { Layer, crate::dnn::SoftplusLayer, cv_dnn_Layer_to_SoftplusLayer } + + boxed_cast_descendant! { Layer, crate::dnn::SoftsignLayer, cv_dnn_Layer_to_SoftsignLayer } + + boxed_cast_descendant! { Layer, crate::dnn::SpaceToDepthLayer, cv_dnn_Layer_to_SpaceToDepthLayer } + + boxed_cast_descendant! { Layer, crate::dnn::SplitLayer, cv_dnn_Layer_to_SplitLayer } + + boxed_cast_descendant! { Layer, crate::dnn::SqrtLayer, cv_dnn_Layer_to_SqrtLayer } + + boxed_cast_descendant! { Layer, crate::dnn::SwishLayer, cv_dnn_Layer_to_SwishLayer } + + boxed_cast_descendant! { Layer, crate::dnn::TanHLayer, cv_dnn_Layer_to_TanHLayer } + + boxed_cast_descendant! { Layer, crate::dnn::TanLayer, cv_dnn_Layer_to_TanLayer } + + boxed_cast_descendant! { Layer, crate::dnn::ThresholdedReluLayer, cv_dnn_Layer_to_ThresholdedReluLayer } + + boxed_cast_descendant! { Layer, crate::dnn::TileLayer, cv_dnn_Layer_to_TileLayer } + + boxed_cast_descendant! { Layer, crate::dnn::TopKLayer, cv_dnn_Layer_to_TopKLayer } + + impl core::AlgorithmTraitConst for Layer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for Layer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Layer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for Layer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for Layer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Layer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + /// %Layer factory allows to create instances of registered layers. + pub struct LayerFactory { + ptr: *mut c_void, + } + + opencv_type_boxed! { LayerFactory } + + impl Drop for LayerFactory { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_LayerFactory_delete(self.as_raw_mut_LayerFactory()) }; + } + } + + unsafe impl Send for LayerFactory {} + + impl LayerFactory { + /// Registers the layer class with typename @p type and specified @p constructor. Thread-safe. + #[inline] + pub fn register_layer(typ: &str, constructor: crate::dnn::LayerFactory_Constructor) -> Result<()> { + extern_container_arg!(typ); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LayerFactory_registerLayer_const_StringR_Constructor(typ.opencv_as_extern(), constructor, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Unregisters registered layer with specified type name. Thread-safe. + #[inline] + pub fn unregister_layer(typ: &str) -> Result<()> { + extern_container_arg!(typ); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LayerFactory_unregisterLayer_const_StringR(typ.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Check if layer is registered. + #[inline] + pub fn is_layer_registered(typ: &str) -> Result { + extern_container_arg!(typ); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LayerFactory_isLayerRegistered_const_stringR(typ.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates instance of registered layer. + /// ## Parameters + /// * type: type name of creating layer. + /// * params: parameters which will be used for layer initialization. + /// + /// Note: Thread-safe. + #[inline] + pub fn create_layer_instance(typ: &str, params: &mut impl crate::dnn::LayerParamsTrait) -> Result> { + extern_container_arg!(typ); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LayerFactory_createLayerInstance_const_StringR_LayerParamsR(typ.opencv_as_extern(), params.as_raw_mut_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::LayerFactory] + pub trait LayerFactoryTraitConst { + fn as_raw_LayerFactory(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::LayerFactory] + pub trait LayerFactoryTrait: crate::dnn::LayerFactoryTraitConst { + fn as_raw_mut_LayerFactory(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for LayerFactory { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("LayerFactory") + .finish() + } + } + + impl crate::dnn::LayerFactoryTraitConst for LayerFactory { + #[inline] fn as_raw_LayerFactory(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerFactoryTrait for LayerFactory { + #[inline] fn as_raw_mut_LayerFactory(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LayerFactory, crate::dnn::LayerFactoryTraitConst, as_raw_LayerFactory, crate::dnn::LayerFactoryTrait, as_raw_mut_LayerFactory } + + pub struct LayerNormLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { LayerNormLayer } + + impl Drop for LayerNormLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_LayerNormLayer_delete(self.as_raw_mut_LayerNormLayer()) }; + } + } + + unsafe impl Send for LayerNormLayer {} + + impl LayerNormLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::LayerNormLayer { + let ret = unsafe { sys::cv_dnn_LayerNormLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::LayerNormLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LayerNormLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::LayerNormLayer] + pub trait LayerNormLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_LayerNormLayer(&self) -> *const c_void; + + #[inline] + fn has_bias(&self) -> bool { + let ret = unsafe { sys::cv_dnn_LayerNormLayer_propHasBias_const(self.as_raw_LayerNormLayer()) }; + ret + } + + #[inline] + fn axis(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_LayerNormLayer_propAxis_const(self.as_raw_LayerNormLayer()) }; + ret + } + + #[inline] + fn epsilon(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_LayerNormLayer_propEpsilon_const(self.as_raw_LayerNormLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::LayerNormLayer] + pub trait LayerNormLayerTrait: crate::dnn::LayerNormLayerTraitConst + crate::dnn::LayerTrait { + fn as_raw_mut_LayerNormLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_has_bias(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_LayerNormLayer_propHasBias_const_bool(self.as_raw_mut_LayerNormLayer(), val) }; + ret + } + + #[inline] + fn set_axis(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_LayerNormLayer_propAxis_const_int(self.as_raw_mut_LayerNormLayer(), val) }; + ret + } + + #[inline] + fn set_epsilon(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_LayerNormLayer_propEpsilon_const_float(self.as_raw_mut_LayerNormLayer(), val) }; + ret + } + + } + + impl Default for LayerNormLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for LayerNormLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("LayerNormLayer") + .field("has_bias", &crate::dnn::LayerNormLayerTraitConst::has_bias(self)) + .field("axis", &crate::dnn::LayerNormLayerTraitConst::axis(self)) + .field("epsilon", &crate::dnn::LayerNormLayerTraitConst::epsilon(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { LayerNormLayer, core::Algorithm, cv_dnn_LayerNormLayer_to_Algorithm } + + boxed_cast_base! { LayerNormLayer, crate::dnn::Layer, cv_dnn_LayerNormLayer_to_Layer } + + impl core::AlgorithmTraitConst for LayerNormLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for LayerNormLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LayerNormLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for LayerNormLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for LayerNormLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LayerNormLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::LayerNormLayerTraitConst for LayerNormLayer { + #[inline] fn as_raw_LayerNormLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerNormLayerTrait for LayerNormLayer { + #[inline] fn as_raw_mut_LayerNormLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LayerNormLayer, crate::dnn::LayerNormLayerTraitConst, as_raw_LayerNormLayer, crate::dnn::LayerNormLayerTrait, as_raw_mut_LayerNormLayer } + + /// This class provides all data needed to initialize layer. + /// + /// It includes dictionary with scalar params (which can be read by using Dict interface), + /// blob params [blobs] and optional meta information: [name] and [type] of layer instance. + pub struct LayerParams { + ptr: *mut c_void, + } + + opencv_type_boxed! { LayerParams } + + impl Drop for LayerParams { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_LayerParams_delete(self.as_raw_mut_LayerParams()) }; + } + } + + unsafe impl Send for LayerParams {} + + impl LayerParams { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::LayerParams { + let ret = unsafe { sys::cv_dnn_LayerParams_defaultNew_const() }; + let ret = unsafe { crate::dnn::LayerParams::opencv_from_extern(ret) }; + ret + } + + } + + /// Constant methods for [crate::dnn::LayerParams] + pub trait LayerParamsTraitConst: crate::dnn::DictTraitConst { + fn as_raw_LayerParams(&self) -> *const c_void; + + /// List of learned parameters stored as blobs. + #[inline] + fn blobs(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_LayerParams_propBlobs_const(self.as_raw_LayerParams()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + /// Name of the layer instance (optional, can be used internal purposes). + #[inline] + fn name(&self) -> String { + let ret = unsafe { sys::cv_dnn_LayerParams_propName_const(self.as_raw_LayerParams()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + /// Type name which was used for creating layer by layer factory (optional). + #[inline] + fn typ(&self) -> String { + let ret = unsafe { sys::cv_dnn_LayerParams_propType_const(self.as_raw_LayerParams()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::LayerParams] + pub trait LayerParamsTrait: crate::dnn::DictTrait + crate::dnn::LayerParamsTraitConst { + fn as_raw_mut_LayerParams(&mut self) -> *mut c_void; + + /// List of learned parameters stored as blobs. + #[inline] + fn set_blobs(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_LayerParams_propBlobs_const_vectorLMatG(self.as_raw_mut_LayerParams(), val.as_raw_VectorOfMat()) }; + ret + } + + /// Name of the layer instance (optional, can be used internal purposes). + #[inline] + fn set_name(&mut self, val: &str) { + extern_container_arg!(nofail val); + let ret = unsafe { sys::cv_dnn_LayerParams_propName_const_String(self.as_raw_mut_LayerParams(), val.opencv_as_extern()) }; + ret + } + + /// Type name which was used for creating layer by layer factory (optional). + #[inline] + fn set_type(&mut self, val: &str) { + extern_container_arg!(nofail val); + let ret = unsafe { sys::cv_dnn_LayerParams_propType_const_String(self.as_raw_mut_LayerParams(), val.opencv_as_extern()) }; + ret + } + + } + + impl Default for LayerParams { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for LayerParams { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("LayerParams") + .field("blobs", &crate::dnn::LayerParamsTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerParamsTraitConst::name(self)) + .field("typ", &crate::dnn::LayerParamsTraitConst::typ(self)) + .finish() + } + } + + boxed_cast_base! { LayerParams, crate::dnn::Dict, cv_dnn_LayerParams_to_Dict } + + impl crate::dnn::DictTraitConst for LayerParams { + #[inline] fn as_raw_Dict(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::DictTrait for LayerParams { + #[inline] fn as_raw_mut_Dict(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LayerParams, crate::dnn::DictTraitConst, as_raw_Dict, crate::dnn::DictTrait, as_raw_mut_Dict } + + impl crate::dnn::LayerParamsTraitConst for LayerParams { + #[inline] fn as_raw_LayerParams(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerParamsTrait for LayerParams { + #[inline] fn as_raw_mut_LayerParams(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LayerParams, crate::dnn::LayerParamsTraitConst, as_raw_LayerParams, crate::dnn::LayerParamsTrait, as_raw_mut_LayerParams } + + pub struct LogLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { LogLayer } + + impl Drop for LogLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_LogLayer_delete(self.as_raw_mut_LogLayer()) }; + } + } + + unsafe impl Send for LogLayer {} + + impl LogLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::LogLayer { + let ret = unsafe { sys::cv_dnn_LogLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::LogLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_LogLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::LogLayer] + pub trait LogLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_LogLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::LogLayer] + pub trait LogLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::LogLayerTraitConst { + fn as_raw_mut_LogLayer(&mut self) -> *mut c_void; + + } + + impl Default for LogLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for LogLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("LogLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { LogLayer, crate::dnn::ActivationLayer, cv_dnn_LogLayer_to_ActivationLayer } + + boxed_cast_base! { LogLayer, core::Algorithm, cv_dnn_LogLayer_to_Algorithm } + + boxed_cast_base! { LogLayer, crate::dnn::Layer, cv_dnn_LogLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for LogLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for LogLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LogLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for LogLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for LogLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LogLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for LogLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for LogLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LogLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::LogLayerTraitConst for LogLayer { + #[inline] fn as_raw_LogLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LogLayerTrait for LogLayer { + #[inline] fn as_raw_mut_LogLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LogLayer, crate::dnn::LogLayerTraitConst, as_raw_LogLayer, crate::dnn::LogLayerTrait, as_raw_mut_LogLayer } + + pub struct MVNLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { MVNLayer } + + impl Drop for MVNLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_MVNLayer_delete(self.as_raw_mut_MVNLayer()) }; + } + } + + unsafe impl Send for MVNLayer {} + + impl MVNLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::MVNLayer { + let ret = unsafe { sys::cv_dnn_MVNLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::MVNLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_MVNLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::MVNLayer] + pub trait MVNLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_MVNLayer(&self) -> *const c_void; + + #[inline] + fn eps(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_MVNLayer_propEps_const(self.as_raw_MVNLayer()) }; + ret + } + + #[inline] + fn norm_variance(&self) -> bool { + let ret = unsafe { sys::cv_dnn_MVNLayer_propNormVariance_const(self.as_raw_MVNLayer()) }; + ret + } + + #[inline] + fn across_channels(&self) -> bool { + let ret = unsafe { sys::cv_dnn_MVNLayer_propAcrossChannels_const(self.as_raw_MVNLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::MVNLayer] + pub trait MVNLayerTrait: crate::dnn::LayerTrait + crate::dnn::MVNLayerTraitConst { + fn as_raw_mut_MVNLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_eps(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_MVNLayer_propEps_const_float(self.as_raw_mut_MVNLayer(), val) }; + ret + } + + #[inline] + fn set_norm_variance(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_MVNLayer_propNormVariance_const_bool(self.as_raw_mut_MVNLayer(), val) }; + ret + } + + #[inline] + fn set_across_channels(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_MVNLayer_propAcrossChannels_const_bool(self.as_raw_mut_MVNLayer(), val) }; + ret + } + + } + + impl Default for MVNLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for MVNLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("MVNLayer") + .field("eps", &crate::dnn::MVNLayerTraitConst::eps(self)) + .field("norm_variance", &crate::dnn::MVNLayerTraitConst::norm_variance(self)) + .field("across_channels", &crate::dnn::MVNLayerTraitConst::across_channels(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { MVNLayer, core::Algorithm, cv_dnn_MVNLayer_to_Algorithm } + + boxed_cast_base! { MVNLayer, crate::dnn::Layer, cv_dnn_MVNLayer_to_Layer } + + impl core::AlgorithmTraitConst for MVNLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for MVNLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MVNLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for MVNLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for MVNLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MVNLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::MVNLayerTraitConst for MVNLayer { + #[inline] fn as_raw_MVNLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::MVNLayerTrait for MVNLayer { + #[inline] fn as_raw_mut_MVNLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MVNLayer, crate::dnn::MVNLayerTraitConst, as_raw_MVNLayer, crate::dnn::MVNLayerTrait, as_raw_mut_MVNLayer } + + pub struct MatMulLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { MatMulLayer } + + impl Drop for MatMulLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_MatMulLayer_delete(self.as_raw_mut_MatMulLayer()) }; + } + } + + unsafe impl Send for MatMulLayer {} + + impl MatMulLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::MatMulLayer { + let ret = unsafe { sys::cv_dnn_MatMulLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::MatMulLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_MatMulLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::MatMulLayer] + pub trait MatMulLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_MatMulLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::MatMulLayer] + pub trait MatMulLayerTrait: crate::dnn::LayerTrait + crate::dnn::MatMulLayerTraitConst { + fn as_raw_mut_MatMulLayer(&mut self) -> *mut c_void; + + } + + impl Default for MatMulLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for MatMulLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("MatMulLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { MatMulLayer, core::Algorithm, cv_dnn_MatMulLayer_to_Algorithm } + + boxed_cast_base! { MatMulLayer, crate::dnn::Layer, cv_dnn_MatMulLayer_to_Layer } + + impl core::AlgorithmTraitConst for MatMulLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for MatMulLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MatMulLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for MatMulLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for MatMulLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MatMulLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::MatMulLayerTraitConst for MatMulLayer { + #[inline] fn as_raw_MatMulLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::MatMulLayerTrait for MatMulLayer { + #[inline] fn as_raw_mut_MatMulLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MatMulLayer, crate::dnn::MatMulLayerTraitConst, as_raw_MatMulLayer, crate::dnn::MatMulLayerTrait, as_raw_mut_MatMulLayer } + + pub struct MaxUnpoolLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { MaxUnpoolLayer } + + impl Drop for MaxUnpoolLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_MaxUnpoolLayer_delete(self.as_raw_mut_MaxUnpoolLayer()) }; + } + } + + unsafe impl Send for MaxUnpoolLayer {} + + impl MaxUnpoolLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::MaxUnpoolLayer { + let ret = unsafe { sys::cv_dnn_MaxUnpoolLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::MaxUnpoolLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_MaxUnpoolLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::MaxUnpoolLayer] + pub trait MaxUnpoolLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_MaxUnpoolLayer(&self) -> *const c_void; + + #[inline] + fn pool_kernel(&self) -> core::Size { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_MaxUnpoolLayer_propPoolKernel_const(self.as_raw_MaxUnpoolLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn pool_pad(&self) -> core::Size { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_MaxUnpoolLayer_propPoolPad_const(self.as_raw_MaxUnpoolLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn pool_stride(&self) -> core::Size { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_MaxUnpoolLayer_propPoolStride_const(self.as_raw_MaxUnpoolLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + } + + /// Mutable methods for [crate::dnn::MaxUnpoolLayer] + pub trait MaxUnpoolLayerTrait: crate::dnn::LayerTrait + crate::dnn::MaxUnpoolLayerTraitConst { + fn as_raw_mut_MaxUnpoolLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_pool_kernel(&mut self, val: core::Size) { + let ret = unsafe { sys::cv_dnn_MaxUnpoolLayer_propPoolKernel_const_Size(self.as_raw_mut_MaxUnpoolLayer(), &val) }; + ret + } + + #[inline] + fn set_pool_pad(&mut self, val: core::Size) { + let ret = unsafe { sys::cv_dnn_MaxUnpoolLayer_propPoolPad_const_Size(self.as_raw_mut_MaxUnpoolLayer(), &val) }; + ret + } + + #[inline] + fn set_pool_stride(&mut self, val: core::Size) { + let ret = unsafe { sys::cv_dnn_MaxUnpoolLayer_propPoolStride_const_Size(self.as_raw_mut_MaxUnpoolLayer(), &val) }; + ret + } + + } + + impl Default for MaxUnpoolLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for MaxUnpoolLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("MaxUnpoolLayer") + .field("pool_kernel", &crate::dnn::MaxUnpoolLayerTraitConst::pool_kernel(self)) + .field("pool_pad", &crate::dnn::MaxUnpoolLayerTraitConst::pool_pad(self)) + .field("pool_stride", &crate::dnn::MaxUnpoolLayerTraitConst::pool_stride(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { MaxUnpoolLayer, core::Algorithm, cv_dnn_MaxUnpoolLayer_to_Algorithm } + + boxed_cast_base! { MaxUnpoolLayer, crate::dnn::Layer, cv_dnn_MaxUnpoolLayer_to_Layer } + + impl core::AlgorithmTraitConst for MaxUnpoolLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for MaxUnpoolLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MaxUnpoolLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for MaxUnpoolLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for MaxUnpoolLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MaxUnpoolLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::MaxUnpoolLayerTraitConst for MaxUnpoolLayer { + #[inline] fn as_raw_MaxUnpoolLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::MaxUnpoolLayerTrait for MaxUnpoolLayer { + #[inline] fn as_raw_mut_MaxUnpoolLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MaxUnpoolLayer, crate::dnn::MaxUnpoolLayerTraitConst, as_raw_MaxUnpoolLayer, crate::dnn::MaxUnpoolLayerTrait, as_raw_mut_MaxUnpoolLayer } + + pub struct MishLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { MishLayer } + + impl Drop for MishLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_MishLayer_delete(self.as_raw_mut_MishLayer()) }; + } + } + + unsafe impl Send for MishLayer {} + + impl MishLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::MishLayer { + let ret = unsafe { sys::cv_dnn_MishLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::MishLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_MishLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::MishLayer] + pub trait MishLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_MishLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::MishLayer] + pub trait MishLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::MishLayerTraitConst { + fn as_raw_mut_MishLayer(&mut self) -> *mut c_void; + + } + + impl Default for MishLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for MishLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("MishLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { MishLayer, crate::dnn::ActivationLayer, cv_dnn_MishLayer_to_ActivationLayer } + + boxed_cast_base! { MishLayer, core::Algorithm, cv_dnn_MishLayer_to_Algorithm } + + boxed_cast_base! { MishLayer, crate::dnn::Layer, cv_dnn_MishLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for MishLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for MishLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MishLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for MishLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for MishLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MishLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for MishLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for MishLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MishLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::MishLayerTraitConst for MishLayer { + #[inline] fn as_raw_MishLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::MishLayerTrait for MishLayer { + #[inline] fn as_raw_mut_MishLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { MishLayer, crate::dnn::MishLayerTraitConst, as_raw_MishLayer, crate::dnn::MishLayerTrait, as_raw_mut_MishLayer } + + /// This class is presented high-level API for neural networks. + /// + /// Model allows to set params for preprocessing input image. + /// Model creates net from file with trained weights and config, + /// sets preprocessing input and runs forward pass. + pub struct Model { + ptr: *mut c_void, + } + + opencv_type_boxed! { Model } + + impl Drop for Model { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_Model_delete(self.as_raw_mut_Model()) }; + } + } + + unsafe impl Send for Model {} + + impl Model { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_Model(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn copy(unnamed: &impl crate::dnn::ModelTraitConst) -> crate::dnn::Model { + let ret = unsafe { sys::cv_dnn_Model_Model_const_ModelR(unnamed.as_raw_Model()) }; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn copy_mut(mut unnamed: crate::dnn::Model) -> crate::dnn::Model { + let ret = unsafe { sys::cv_dnn_Model_Model_ModelRR(unnamed.as_raw_mut_Model()) }; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + ret + } + + /// Create model from deep learning network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## C++ default parameters + /// * config: "" + #[inline] + pub fn new(model: &str, config: &str) -> Result { + extern_container_arg!(model); + extern_container_arg!(config); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_Model_const_StringR_const_StringR(model.opencv_as_extern(), config.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create model from deep learning network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * config: "" + #[inline] + pub fn new_def(model: &str) -> Result { + extern_container_arg!(model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_Model_const_StringR(model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create model from deep learning network. + /// ## Parameters + /// * network: Net object. + #[inline] + pub fn new_1(network: &impl crate::dnn::NetTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_Model_const_NetR(network.as_raw_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::Model] + pub trait ModelTraitConst { + fn as_raw_Model(&self) -> *const c_void; + + /// Given the @p input frame, create input blob, run net and return the output @p blobs. + /// ## Parameters + /// * frame: The input image. + /// * outs:[out] Allocated output blobs, which will store results of the computation. + #[inline] + fn predict(&self, frame: &impl ToInputArray, outs: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(frame); + output_array_arg!(outs); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_predict_const_const__InputArrayR_const__OutputArrayR(self.as_raw_Model(), frame.as_raw__InputArray(), outs.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_network_(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_getNetwork__const(self.as_raw_Model(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Mutable methods for [crate::dnn::Model] + pub trait ModelTrait: crate::dnn::ModelTraitConst { + fn as_raw_mut_Model(&mut self) -> *mut c_void; + + #[inline] + fn set(&mut self, unnamed: &impl crate::dnn::ModelTraitConst) { + let ret = unsafe { sys::cv_dnn_Model_operatorST_const_ModelR(self.as_raw_mut_Model(), unnamed.as_raw_Model()) }; + ret + } + + #[inline] + fn set_1(&mut self, mut unnamed: crate::dnn::Model) { + let ret = unsafe { sys::cv_dnn_Model_operatorST_ModelRR(self.as_raw_mut_Model(), unnamed.as_raw_mut_Model()) }; + ret + } + + /// Set input size for frame. + /// ## Parameters + /// * size: New input size. + /// + /// Note: If shape of the new blob less than 0, then frame size not change. + #[inline] + fn set_input_size(&mut self, size: core::Size) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_setInputSize_const_SizeR(self.as_raw_mut_Model(), &size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Set input size for frame. + /// ## Parameters + /// * size: New input size. + /// + /// Note: If shape of the new blob less than 0, then frame size not change. + /// + /// ## Overloaded parameters + /// + /// * width: New input width. + /// * height: New input height. + #[inline] + fn set_input_size_1(&mut self, width: i32, height: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_setInputSize_int_int(self.as_raw_mut_Model(), width, height, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Set mean value for frame. + /// ## Parameters + /// * mean: Scalar with mean values which are subtracted from channels. + #[inline] + fn set_input_mean(&mut self, mean: core::Scalar) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_setInputMean_const_ScalarR(self.as_raw_mut_Model(), &mean, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Set scalefactor value for frame. + /// ## Parameters + /// * scale: Multiplier for frame values. + #[inline] + fn set_input_scale(&mut self, scale: core::Scalar) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_setInputScale_const_ScalarR(self.as_raw_mut_Model(), &scale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Set flag crop for frame. + /// ## Parameters + /// * crop: Flag which indicates whether image will be cropped after resize or not. + #[inline] + fn set_input_crop(&mut self, crop: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_setInputCrop_bool(self.as_raw_mut_Model(), crop, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Set flag swapRB for frame. + /// ## Parameters + /// * swapRB: Flag which indicates that swap first and last channels. + #[inline] + fn set_input_swap_rb(&mut self, swap_rb: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_setInputSwapRB_bool(self.as_raw_mut_Model(), swap_rb, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Set output names for frame. + /// ## Parameters + /// * outNames: Names for output layers. + #[inline] + fn set_output_names(&mut self, out_names: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_setOutputNames_const_vectorLStringGR(self.as_raw_mut_Model(), out_names.as_raw_VectorOfString(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Set preprocessing parameters for frame. + /// ## Parameters + /// * size: New input size. + /// * mean: Scalar with mean values which are subtracted from channels. + /// * scale: Multiplier for frame values. + /// * swapRB: Flag which indicates that swap first and last channels. + /// * crop: Flag which indicates whether image will be cropped after resize or not. + /// blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) ) + /// + /// ## C++ default parameters + /// * scale: 1.0 + /// * size: Size() + /// * mean: Scalar() + /// * swap_rb: false + /// * crop: false + #[inline] + fn set_input_params(&mut self, scale: f64, size: core::Size, mean: core::Scalar, swap_rb: bool, crop: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_setInputParams_double_const_SizeR_const_ScalarR_bool_bool(self.as_raw_mut_Model(), scale, &size, &mean, swap_rb, crop, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Set preprocessing parameters for frame. + /// ## Parameters + /// * size: New input size. + /// * mean: Scalar with mean values which are subtracted from channels. + /// * scale: Multiplier for frame values. + /// * swapRB: Flag which indicates that swap first and last channels. + /// * crop: Flag which indicates whether image will be cropped after resize or not. + /// blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) ) + /// + /// ## Note + /// This alternative version of [ModelTrait::set_input_params] function uses the following default values for its arguments: + /// * scale: 1.0 + /// * size: Size() + /// * mean: Scalar() + /// * swap_rb: false + /// * crop: false + #[inline] + fn set_input_params_def(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_setInputParams(self.as_raw_mut_Model(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## See also + /// Net::setPreferableBackend + #[inline] + fn set_preferable_backend(&mut self, backend_id: crate::dnn::Backend) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_setPreferableBackend_Backend(self.as_raw_mut_Model(), backend_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## See also + /// Net::setPreferableTarget + #[inline] + fn set_preferable_target(&mut self, target_id: crate::dnn::Target) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_setPreferableTarget_Target(self.as_raw_mut_Model(), target_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## See also + /// Net::enableWinograd + #[inline] + fn enable_winograd(&mut self, use_winograd: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_enableWinograd_bool(self.as_raw_mut_Model(), use_winograd, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Model::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn get_network__1(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Model_getNetwork_(self.as_raw_mut_Model(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl Clone for Model { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_dnn_Model_implicitClone_const(self.as_raw_Model())) } + } + } + + impl std::fmt::Debug for Model { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Model") + .finish() + } + } + + impl crate::dnn::ModelTraitConst for Model { + #[inline] fn as_raw_Model(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ModelTrait for Model { + #[inline] fn as_raw_mut_Model(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Model, crate::dnn::ModelTraitConst, as_raw_Model, crate::dnn::ModelTrait, as_raw_mut_Model } + + pub struct NaryEltwiseLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { NaryEltwiseLayer } + + impl Drop for NaryEltwiseLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_NaryEltwiseLayer_delete(self.as_raw_mut_NaryEltwiseLayer()) }; + } + } + + unsafe impl Send for NaryEltwiseLayer {} + + impl NaryEltwiseLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::NaryEltwiseLayer { + let ret = unsafe { sys::cv_dnn_NaryEltwiseLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::NaryEltwiseLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_NaryEltwiseLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::NaryEltwiseLayer] + pub trait NaryEltwiseLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_NaryEltwiseLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::NaryEltwiseLayer] + pub trait NaryEltwiseLayerTrait: crate::dnn::LayerTrait + crate::dnn::NaryEltwiseLayerTraitConst { + fn as_raw_mut_NaryEltwiseLayer(&mut self) -> *mut c_void; + + } + + impl Default for NaryEltwiseLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for NaryEltwiseLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("NaryEltwiseLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { NaryEltwiseLayer, core::Algorithm, cv_dnn_NaryEltwiseLayer_to_Algorithm } + + boxed_cast_base! { NaryEltwiseLayer, crate::dnn::Layer, cv_dnn_NaryEltwiseLayer_to_Layer } + + impl core::AlgorithmTraitConst for NaryEltwiseLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for NaryEltwiseLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { NaryEltwiseLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for NaryEltwiseLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for NaryEltwiseLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { NaryEltwiseLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::NaryEltwiseLayerTraitConst for NaryEltwiseLayer { + #[inline] fn as_raw_NaryEltwiseLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::NaryEltwiseLayerTrait for NaryEltwiseLayer { + #[inline] fn as_raw_mut_NaryEltwiseLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { NaryEltwiseLayer, crate::dnn::NaryEltwiseLayerTraitConst, as_raw_NaryEltwiseLayer, crate::dnn::NaryEltwiseLayerTrait, as_raw_mut_NaryEltwiseLayer } + + /// This class allows to create and manipulate comprehensive artificial neural networks. + /// + /// Neural network is presented as directed acyclic graph (DAG), where vertices are Layer instances, + /// and edges specify relationships between layers inputs and outputs. + /// + /// Each network layer has unique integer id and unique string name inside its network. + /// LayerId can store either layer name or layer id. + /// + /// This class supports reference counting of its instances, i. e. copies point to the same instance. + pub struct Net { + ptr: *mut c_void, + } + + opencv_type_boxed! { Net } + + impl Drop for Net { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_Net_delete(self.as_raw_mut_Net()) }; + } + } + + unsafe impl Send for Net {} + + impl Net { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_Net(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create a network from Intel's Model Optimizer intermediate representation (IR). + /// ## Parameters + /// * xml: XML configuration file with network's topology. + /// * bin: Binary file with trained weights. + /// Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine + /// backend. + #[inline] + pub fn read_from_model_optimizer(xml: &str, bin: &str) -> Result { + extern_container_arg!(xml); + extern_container_arg!(bin); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_readFromModelOptimizer_const_StringR_const_StringR(xml.opencv_as_extern(), bin.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR). + /// ## Parameters + /// * bufferModelConfig: buffer with model's configuration. + /// * bufferWeights: buffer with model's trained weights. + /// ## Returns + /// Net object. + #[inline] + pub fn read_from_model_optimizer_1(buffer_model_config: &core::Vector, buffer_weights: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_readFromModelOptimizer_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(buffer_model_config.as_raw_VectorOfu8(), buffer_weights.as_raw_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR). + /// ## Parameters + /// * bufferModelConfigPtr: buffer pointer of model's configuration. + /// * bufferModelConfigSize: buffer size of model's configuration. + /// * bufferWeightsPtr: buffer pointer of model's trained weights. + /// * bufferWeightsSize: buffer size of model's trained weights. + /// ## Returns + /// Net object. + #[inline] + pub fn read_from_model_optimizer_2(buffer_model_config_ptr: &[u8], buffer_weights_ptr: &[u8]) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_readFromModelOptimizer_const_unsigned_charX_size_t_const_unsigned_charX_size_t(buffer_model_config_ptr.as_ptr(), buffer_model_config_ptr.len(), buffer_weights_ptr.as_ptr(), buffer_weights_ptr.len(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::Net] + pub trait NetTraitConst { + fn as_raw_Net(&self) -> *const c_void; + + /// Returns true if there are no layers in the network. + #[inline] + fn empty(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_empty_const(self.as_raw_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Converts string name of the layer to the integer identifier. + /// ## Returns + /// id of the layer, or -1 if the layer wasn't found. + #[inline] + fn get_layer_id(&self, layer: &str) -> Result { + extern_container_arg!(layer); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getLayerId_const_const_StringR(self.as_raw_Net(), layer.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_layer_names(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getLayerNames_const(self.as_raw_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns pointer to layer with specified id or name which the network use. + #[inline] + fn get_layer(&self, layer_id: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getLayer_const_int(self.as_raw_Net(), layer_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns pointer to layer with specified id or name which the network use. + /// + /// ## Overloaded parameters + /// + /// + /// **Deprecated**: Use int getLayerId(const String &layer) + #[deprecated = "Use int getLayerId(const String &layer)"] + #[inline] + fn get_layer_1(&self, layer_name: &str) -> Result> { + extern_container_arg!(layer_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getLayer_const_const_StringR(self.as_raw_Net(), layer_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns pointer to layer with specified id or name which the network use. + /// + /// ## Overloaded parameters + /// + /// + /// **Deprecated**: to be removed + #[deprecated = "to be removed"] + #[inline] + fn get_layer_2(&self, layer_id: &impl crate::dnn::DictValueTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getLayer_const_const_LayerIdR(self.as_raw_Net(), layer_id.as_raw_DictValue(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns pointers to input layers of specific layer. + #[inline] + fn get_layer_inputs(&self, layer_id: i32) -> Result>> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getLayerInputs_const_int(self.as_raw_Net(), layer_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::>::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns input scale and zeropoint for a quantized Net. + /// ## Parameters + /// * scales: output parameter for returning input scales. + /// * zeropoints: output parameter for returning input zeropoints. + #[inline] + fn get_input_details(&self, scales: &mut core::Vector, zeropoints: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getInputDetails_const_vectorLfloatGR_vectorLintGR(self.as_raw_Net(), scales.as_raw_mut_VectorOff32(), zeropoints.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns output scale and zeropoint for a quantized Net. + /// ## Parameters + /// * scales: output parameter for returning output scales. + /// * zeropoints: output parameter for returning output zeropoints. + #[inline] + fn get_output_details(&self, scales: &mut core::Vector, zeropoints: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getOutputDetails_const_vectorLfloatGR_vectorLintGR(self.as_raw_Net(), scales.as_raw_mut_VectorOff32(), zeropoints.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns parameter blob of the layer. + /// ## Parameters + /// * layer: name or id of the layer. + /// * numParam: index of the layer parameter in the Layer::blobs array. + /// ## See also + /// Layer::blobs + /// + /// ## C++ default parameters + /// * num_param: 0 + #[inline] + fn get_param(&self, layer: i32, num_param: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getParam_const_int_int(self.as_raw_Net(), layer, num_param, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns parameter blob of the layer. + /// ## Parameters + /// * layer: name or id of the layer. + /// * numParam: index of the layer parameter in the Layer::blobs array. + /// ## See also + /// Layer::blobs + /// + /// ## Note + /// This alternative version of [NetTraitConst::get_param] function uses the following default values for its arguments: + /// * num_param: 0 + #[inline] + fn get_param_def(&self, layer: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getParam_const_int(self.as_raw_Net(), layer, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * num_param: 0 + #[inline] + fn get_param_1(&self, layer_name: &str, num_param: i32) -> Result { + extern_container_arg!(layer_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getParam_const_const_StringR_int(self.as_raw_Net(), layer_name.opencv_as_extern(), num_param, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [NetTraitConst::get_param] function uses the following default values for its arguments: + /// * num_param: 0 + #[inline] + fn get_param_def_1(&self, layer_name: &str) -> Result { + extern_container_arg!(layer_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getParam_const_const_StringR(self.as_raw_Net(), layer_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns indexes of layers with unconnected outputs. + /// + /// FIXIT: Rework API to registerOutput() approach, deprecate this call + #[inline] + fn get_unconnected_out_layers(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getUnconnectedOutLayers_const(self.as_raw_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns names of layers with unconnected outputs. + /// + /// FIXIT: Rework API to registerOutput() approach, deprecate this call + #[inline] + fn get_unconnected_out_layers_names(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getUnconnectedOutLayersNames_const(self.as_raw_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns input and output shapes for all layers in loaded model; + /// preliminary inferencing isn't necessary. + /// ## Parameters + /// * netInputShapes: shapes for all input blobs in net input layer. + /// * layersIds: output parameter for layer IDs. + /// * inLayersShapes: output parameter for input layers shapes; + /// order is the same as in layersIds + /// * outLayersShapes: output parameter for output layers shapes; + /// order is the same as in layersIds + #[inline] + fn get_layers_shapes(&self, net_input_shapes: &core::Vector, layers_ids: &mut core::Vector, in_layers_shapes: &mut core::Vector>, out_layers_shapes: &mut core::Vector>) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getLayersShapes_const_const_vectorLMatShapeGR_vectorLintGR_vectorLvectorLMatShapeGGR_vectorLvectorLMatShapeGGR(self.as_raw_Net(), net_input_shapes.as_raw_VectorOfMatShape(), layers_ids.as_raw_mut_VectorOfi32(), in_layers_shapes.as_raw_mut_VectorOfVectorOfMatShape(), out_layers_shapes.as_raw_mut_VectorOfVectorOfMatShape(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns input and output shapes for all layers in loaded model; + /// preliminary inferencing isn't necessary. + /// ## Parameters + /// * netInputShapes: shapes for all input blobs in net input layer. + /// * layersIds: output parameter for layer IDs. + /// * inLayersShapes: output parameter for input layers shapes; + /// order is the same as in layersIds + /// * outLayersShapes: output parameter for output layers shapes; + /// order is the same as in layersIds + /// + /// ## Overloaded parameters + #[inline] + fn get_layers_shapes_1(&self, net_input_shape: &crate::dnn::MatShape, layers_ids: &mut core::Vector, in_layers_shapes: &mut core::Vector>, out_layers_shapes: &mut core::Vector>) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getLayersShapes_const_const_MatShapeR_vectorLintGR_vectorLvectorLMatShapeGGR_vectorLvectorLMatShapeGGR(self.as_raw_Net(), net_input_shape.as_raw_VectorOfi32(), layers_ids.as_raw_mut_VectorOfi32(), in_layers_shapes.as_raw_mut_VectorOfVectorOfMatShape(), out_layers_shapes.as_raw_mut_VectorOfVectorOfMatShape(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns input and output shapes for layer with specified + /// id in loaded model; preliminary inferencing isn't necessary. + /// ## Parameters + /// * netInputShape: shape input blob in net input layer. + /// * layerId: id for layer. + /// * inLayerShapes: output parameter for input layers shapes; + /// order is the same as in layersIds + /// * outLayerShapes: output parameter for output layers shapes; + /// order is the same as in layersIds + #[inline] + fn get_layer_shapes(&self, net_input_shape: &crate::dnn::MatShape, layer_id: i32, in_layer_shapes: &mut core::Vector, out_layer_shapes: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getLayerShapes_const_const_MatShapeR_const_int_vectorLMatShapeGR_vectorLMatShapeGR(self.as_raw_Net(), net_input_shape.as_raw_VectorOfi32(), layer_id, in_layer_shapes.as_raw_mut_VectorOfMatShape(), out_layer_shapes.as_raw_mut_VectorOfMatShape(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns input and output shapes for layer with specified + /// id in loaded model; preliminary inferencing isn't necessary. + /// ## Parameters + /// * netInputShape: shape input blob in net input layer. + /// * layerId: id for layer. + /// * inLayerShapes: output parameter for input layers shapes; + /// order is the same as in layersIds + /// * outLayerShapes: output parameter for output layers shapes; + /// order is the same as in layersIds + /// + /// ## Overloaded parameters + #[inline] + fn get_layer_shapes_1(&self, net_input_shapes: &core::Vector, layer_id: i32, in_layer_shapes: &mut core::Vector, out_layer_shapes: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getLayerShapes_const_const_vectorLMatShapeGR_const_int_vectorLMatShapeGR_vectorLMatShapeGR(self.as_raw_Net(), net_input_shapes.as_raw_VectorOfMatShape(), layer_id, in_layer_shapes.as_raw_mut_VectorOfMatShape(), out_layer_shapes.as_raw_mut_VectorOfMatShape(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes FLOP for whole loaded model with specified input shapes. + /// ## Parameters + /// * netInputShapes: vector of shapes for all net inputs. + /// ## Returns + /// computed FLOP. + #[inline] + fn get_flops(&self, net_input_shapes: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getFLOPS_const_const_vectorLMatShapeGR(self.as_raw_Net(), net_input_shapes.as_raw_VectorOfMatShape(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes FLOP for whole loaded model with specified input shapes. + /// ## Parameters + /// * netInputShapes: vector of shapes for all net inputs. + /// ## Returns + /// computed FLOP. + /// + /// ## Overloaded parameters + #[inline] + fn get_flops_1(&self, net_input_shape: &crate::dnn::MatShape) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getFLOPS_const_const_MatShapeR(self.as_raw_Net(), net_input_shape.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes FLOP for whole loaded model with specified input shapes. + /// ## Parameters + /// * netInputShapes: vector of shapes for all net inputs. + /// ## Returns + /// computed FLOP. + /// + /// ## Overloaded parameters + #[inline] + fn get_flops_2(&self, layer_id: i32, net_input_shapes: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getFLOPS_const_const_int_const_vectorLMatShapeGR(self.as_raw_Net(), layer_id, net_input_shapes.as_raw_VectorOfMatShape(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes FLOP for whole loaded model with specified input shapes. + /// ## Parameters + /// * netInputShapes: vector of shapes for all net inputs. + /// ## Returns + /// computed FLOP. + /// + /// ## Overloaded parameters + #[inline] + fn get_flops_3(&self, layer_id: i32, net_input_shape: &crate::dnn::MatShape) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getFLOPS_const_const_int_const_MatShapeR(self.as_raw_Net(), layer_id, net_input_shape.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns list of types for layer used in model. + /// ## Parameters + /// * layersTypes: output parameter for returning types. + #[inline] + fn get_layer_types(&self, layers_types: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getLayerTypes_const_vectorLStringGR(self.as_raw_Net(), layers_types.as_raw_mut_VectorOfString(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns count of layers of specified type. + /// ## Parameters + /// * layerType: type. + /// ## Returns + /// count of layers + #[inline] + fn get_layers_count(&self, layer_type: &str) -> Result { + extern_container_arg!(layer_type); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getLayersCount_const_const_StringR(self.as_raw_Net(), layer_type.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes bytes number which are required to store + /// all weights and intermediate blobs for model. + /// ## Parameters + /// * netInputShapes: vector of shapes for all net inputs. + /// * weights: output parameter to store resulting bytes for weights. + /// * blobs: output parameter to store resulting bytes for intermediate blobs. + #[inline] + fn get_memory_consumption(&self, net_input_shapes: &core::Vector, weights: &mut size_t, blobs: &mut size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getMemoryConsumption_const_const_vectorLMatShapeGR_size_tR_size_tR(self.as_raw_Net(), net_input_shapes.as_raw_VectorOfMatShape(), weights, blobs, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes bytes number which are required to store + /// all weights and intermediate blobs for model. + /// ## Parameters + /// * netInputShapes: vector of shapes for all net inputs. + /// * weights: output parameter to store resulting bytes for weights. + /// * blobs: output parameter to store resulting bytes for intermediate blobs. + /// + /// ## Overloaded parameters + #[inline] + fn get_memory_consumption_1(&self, net_input_shape: &crate::dnn::MatShape, weights: &mut size_t, blobs: &mut size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getMemoryConsumption_const_const_MatShapeR_size_tR_size_tR(self.as_raw_Net(), net_input_shape.as_raw_VectorOfi32(), weights, blobs, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes bytes number which are required to store + /// all weights and intermediate blobs for model. + /// ## Parameters + /// * netInputShapes: vector of shapes for all net inputs. + /// * weights: output parameter to store resulting bytes for weights. + /// * blobs: output parameter to store resulting bytes for intermediate blobs. + /// + /// ## Overloaded parameters + #[inline] + fn get_memory_consumption_for_layer(&self, layer_id: i32, net_input_shapes: &core::Vector, weights: &mut size_t, blobs: &mut size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getMemoryConsumption_const_const_int_const_vectorLMatShapeGR_size_tR_size_tR(self.as_raw_Net(), layer_id, net_input_shapes.as_raw_VectorOfMatShape(), weights, blobs, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes bytes number which are required to store + /// all weights and intermediate blobs for model. + /// ## Parameters + /// * netInputShapes: vector of shapes for all net inputs. + /// * weights: output parameter to store resulting bytes for weights. + /// * blobs: output parameter to store resulting bytes for intermediate blobs. + /// + /// ## Overloaded parameters + #[inline] + fn get_memory_consumption_2(&self, layer_id: i32, net_input_shape: &crate::dnn::MatShape, weights: &mut size_t, blobs: &mut size_t) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getMemoryConsumption_const_const_int_const_MatShapeR_size_tR_size_tR(self.as_raw_Net(), layer_id, net_input_shape.as_raw_VectorOfi32(), weights, blobs, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes bytes number which are required to store + /// all weights and intermediate blobs for each layer. + /// ## Parameters + /// * netInputShapes: vector of shapes for all net inputs. + /// * layerIds: output vector to save layer IDs. + /// * weights: output parameter to store resulting bytes for weights. + /// * blobs: output parameter to store resulting bytes for intermediate blobs. + #[inline] + fn get_memory_consumption_for_layers(&self, net_input_shapes: &core::Vector, layer_ids: &mut core::Vector, weights: &mut core::Vector, blobs: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getMemoryConsumption_const_const_vectorLMatShapeGR_vectorLintGR_vectorLsize_tGR_vectorLsize_tGR(self.as_raw_Net(), net_input_shapes.as_raw_VectorOfMatShape(), layer_ids.as_raw_mut_VectorOfi32(), weights.as_raw_mut_VectorOfsize_t(), blobs.as_raw_mut_VectorOfsize_t(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes bytes number which are required to store + /// all weights and intermediate blobs for each layer. + /// ## Parameters + /// * netInputShapes: vector of shapes for all net inputs. + /// * layerIds: output vector to save layer IDs. + /// * weights: output parameter to store resulting bytes for weights. + /// * blobs: output parameter to store resulting bytes for intermediate blobs. + /// + /// ## Overloaded parameters + #[inline] + fn get_memory_consumption_3(&self, net_input_shape: &crate::dnn::MatShape, layer_ids: &mut core::Vector, weights: &mut core::Vector, blobs: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getMemoryConsumption_const_const_MatShapeR_vectorLintGR_vectorLsize_tGR_vectorLsize_tGR(self.as_raw_Net(), net_input_shape.as_raw_VectorOfi32(), layer_ids.as_raw_mut_VectorOfi32(), weights.as_raw_mut_VectorOfsize_t(), blobs.as_raw_mut_VectorOfsize_t(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::dnn::Net] + pub trait NetTrait: crate::dnn::NetTraitConst { + fn as_raw_mut_Net(&mut self) -> *mut c_void; + + /// Dump net to String + /// ## Returns + /// String with structure, hyperparameters, backend, target and fusion + /// Call method after setInput(). To see correct backend, target and fusion run after forward(). + #[inline] + fn dump(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_dump(self.as_raw_mut_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Dump net structure, hyperparameters, backend, target and fusion to dot file + /// ## Parameters + /// * path: path to output file with .dot extension + /// ## See also + /// dump() + #[inline] + fn dump_to_file(&mut self, path: &str) -> Result<()> { + extern_container_arg!(path); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_dumpToFile_const_StringR(self.as_raw_mut_Net(), path.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Dump net structure, hyperparameters, backend, target and fusion to pbtxt file + /// ## Parameters + /// * path: path to output file with .pbtxt extension + /// + /// Use Netron () to open the target file to visualize the model. + /// Call method after setInput(). To see correct backend, target and fusion run after forward(). + #[inline] + fn dump_to_pbtxt(&mut self, path: &str) -> Result<()> { + extern_container_arg!(path); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_dumpToPbtxt_const_StringR(self.as_raw_mut_Net(), path.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Adds new layer to the net. + /// ## Parameters + /// * name: unique name of the adding layer. + /// * type: typename of the adding layer (type must be registered in LayerRegister). + /// * dtype: datatype of output blobs. + /// * params: parameters which will be used to initialize the creating layer. + /// ## Returns + /// unique identifier of created layer, or -1 if a failure will happen. + #[inline] + fn add_layer_type(&mut self, name: &str, typ: &str, dtype: &i32, params: &mut impl crate::dnn::LayerParamsTrait) -> Result { + extern_container_arg!(name); + extern_container_arg!(typ); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_addLayer_const_StringR_const_StringR_const_intR_LayerParamsR(self.as_raw_mut_Net(), name.opencv_as_extern(), typ.opencv_as_extern(), dtype, params.as_raw_mut_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Adds new layer to the net. + /// ## Parameters + /// * name: unique name of the adding layer. + /// * type: typename of the adding layer (type must be registered in LayerRegister). + /// * dtype: datatype of output blobs. + /// * params: parameters which will be used to initialize the creating layer. + /// ## Returns + /// unique identifier of created layer, or -1 if a failure will happen. + /// + /// ## Overloaded parameters + /// Datatype of output blobs set to default CV_32F + #[inline] + fn add_layer(&mut self, name: &str, typ: &str, params: &mut impl crate::dnn::LayerParamsTrait) -> Result { + extern_container_arg!(name); + extern_container_arg!(typ); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_addLayer_const_StringR_const_StringR_LayerParamsR(self.as_raw_mut_Net(), name.opencv_as_extern(), typ.opencv_as_extern(), params.as_raw_mut_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Adds new layer and connects its first input to the first output of previously added layer. + /// ## See also + /// addLayer() + #[inline] + fn add_layer_to_prev_type(&mut self, name: &str, typ: &str, dtype: &i32, params: &mut impl crate::dnn::LayerParamsTrait) -> Result { + extern_container_arg!(name); + extern_container_arg!(typ); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_addLayerToPrev_const_StringR_const_StringR_const_intR_LayerParamsR(self.as_raw_mut_Net(), name.opencv_as_extern(), typ.opencv_as_extern(), dtype, params.as_raw_mut_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Adds new layer and connects its first input to the first output of previously added layer. + /// ## See also + /// addLayer() + /// + /// ## Overloaded parameters + #[inline] + fn add_layer_to_prev(&mut self, name: &str, typ: &str, params: &mut impl crate::dnn::LayerParamsTrait) -> Result { + extern_container_arg!(name); + extern_container_arg!(typ); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_addLayerToPrev_const_StringR_const_StringR_LayerParamsR(self.as_raw_mut_Net(), name.opencv_as_extern(), typ.opencv_as_extern(), params.as_raw_mut_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Connects output of the first layer to input of the second layer. + /// ## Parameters + /// * outPin: descriptor of the first layer output. + /// * inpPin: descriptor of the second layer input. + /// + /// Descriptors have the following template <layer_name>[.input_number]: + /// - the first part of the template layer_name is string name of the added layer. + /// If this part is empty then the network input pseudo layer will be used; + /// - the second optional part of the template input_number + /// is either number of the layer input, either label one. + /// If this part is omitted then the first layer input will be used. + /// ## See also + /// setNetInputs(), Layer::inputNameToIndex(), Layer::outputNameToIndex() + #[inline] + fn connect_first_second(&mut self, out_pin: &str, inp_pin: &str) -> Result<()> { + extern_container_arg!(out_pin); + extern_container_arg!(inp_pin); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_connect_String_String(self.as_raw_mut_Net(), out_pin.opencv_as_extern(), inp_pin.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Connects #@p outNum output of the first layer to #@p inNum input of the second layer. + /// ## Parameters + /// * outLayerId: identifier of the first layer + /// * outNum: number of the first layer output + /// * inpLayerId: identifier of the second layer + /// * inpNum: number of the second layer input + #[inline] + fn connect(&mut self, out_layer_id: i32, out_num: i32, inp_layer_id: i32, inp_num: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_connect_int_int_int_int(self.as_raw_mut_Net(), out_layer_id, out_num, inp_layer_id, inp_num, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Registers network output with name + /// + /// Function may create additional 'Identity' layer. + /// + /// ## Parameters + /// * outputName: identifier of the output + /// * layerId: identifier of the second layer + /// * outputPort: number of the second layer input + /// + /// ## Returns + /// index of bound layer (the same as layerId or newly created) + #[inline] + fn register_output(&mut self, output_name: &str, layer_id: i32, output_port: i32) -> Result { + extern_container_arg!(output_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_registerOutput_const_stringR_int_int(self.as_raw_mut_Net(), output_name.opencv_as_extern(), layer_id, output_port, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets outputs names of the network input pseudo layer. + /// + /// Each net always has special own the network input pseudo layer with id=0. + /// This layer stores the user blobs only and don't make any computations. + /// In fact, this layer provides the only way to pass user data into the network. + /// As any other layer, this layer can label its outputs and this function provides an easy way to do this. + #[inline] + fn set_inputs_names(&mut self, input_blob_names: &core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_setInputsNames_const_vectorLStringGR(self.as_raw_mut_Net(), input_blob_names.as_raw_VectorOfString(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Specify shape of network input. + #[inline] + fn set_input_shape(&mut self, input_name: &str, shape: &crate::dnn::MatShape) -> Result<()> { + extern_container_arg!(input_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_setInputShape_const_StringR_const_MatShapeR(self.as_raw_mut_Net(), input_name.opencv_as_extern(), shape.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Runs forward pass to compute output of layer with name @p outputName. + /// ## Parameters + /// * outputName: name for layer which output is needed to get + /// ## Returns + /// blob for first output of specified layer. + /// @details By default runs forward pass for the whole network. + /// + /// ## C++ default parameters + /// * output_name: String() + #[inline] + fn forward_single(&mut self, output_name: &str) -> Result { + extern_container_arg!(output_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_forward_const_StringR(self.as_raw_mut_Net(), output_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Runs forward pass to compute output of layer with name @p outputName. + /// ## Parameters + /// * outputName: name for layer which output is needed to get + /// ## Returns + /// blob for first output of specified layer. + /// @details By default runs forward pass for the whole network. + /// + /// ## Note + /// This alternative version of [NetTrait::forward_single] function uses the following default values for its arguments: + /// * output_name: String() + #[inline] + fn forward_single_def(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_forward(self.as_raw_mut_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Runs forward pass to compute output of layer with name @p outputName. + /// ## Parameters + /// * outputName: name for layer which output is needed to get + /// @details By default runs forward pass for the whole network. + /// + /// This is an asynchronous version of forward(const String&). + /// dnn::DNN_BACKEND_INFERENCE_ENGINE backend is required. + /// + /// ## C++ default parameters + /// * output_name: String() + #[inline] + fn forward_async(&mut self, output_name: &str) -> Result { + extern_container_arg!(output_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_forwardAsync_const_StringR(self.as_raw_mut_Net(), output_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::AsyncArray::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Runs forward pass to compute output of layer with name @p outputName. + /// ## Parameters + /// * outputName: name for layer which output is needed to get + /// @details By default runs forward pass for the whole network. + /// + /// This is an asynchronous version of forward(const String&). + /// dnn::DNN_BACKEND_INFERENCE_ENGINE backend is required. + /// + /// ## Note + /// This alternative version of [NetTrait::forward_async] function uses the following default values for its arguments: + /// * output_name: String() + #[inline] + fn forward_async_def(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_forwardAsync(self.as_raw_mut_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::AsyncArray::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Runs forward pass to compute output of layer with name @p outputName. + /// ## Parameters + /// * outputBlobs: contains all output blobs for specified layer. + /// * outputName: name for layer which output is needed to get + /// @details If @p outputName is empty, runs forward pass for the whole network. + /// + /// ## C++ default parameters + /// * output_name: String() + #[inline] + fn forward_layer(&mut self, output_blobs: &mut impl ToOutputArray, output_name: &str) -> Result<()> { + output_array_arg!(output_blobs); + extern_container_arg!(output_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_forward_const__OutputArrayR_const_StringR(self.as_raw_mut_Net(), output_blobs.as_raw__OutputArray(), output_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Runs forward pass to compute output of layer with name @p outputName. + /// ## Parameters + /// * outputBlobs: contains all output blobs for specified layer. + /// * outputName: name for layer which output is needed to get + /// @details If @p outputName is empty, runs forward pass for the whole network. + /// + /// ## Note + /// This alternative version of [NetTrait::forward_layer] function uses the following default values for its arguments: + /// * output_name: String() + #[inline] + fn forward_layer_def(&mut self, output_blobs: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(output_blobs); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_forward_const__OutputArrayR(self.as_raw_mut_Net(), output_blobs.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Runs forward pass to compute outputs of layers listed in @p outBlobNames. + /// ## Parameters + /// * outputBlobs: contains blobs for first outputs of specified layers. + /// * outBlobNames: names for layers which outputs are needed to get + #[inline] + fn forward(&mut self, output_blobs: &mut impl ToOutputArray, out_blob_names: &core::Vector) -> Result<()> { + output_array_arg!(output_blobs); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_forward_const__OutputArrayR_const_vectorLStringGR(self.as_raw_mut_Net(), output_blobs.as_raw__OutputArray(), out_blob_names.as_raw_VectorOfString(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Runs forward pass to compute outputs of layers listed in @p outBlobNames. + /// ## Parameters + /// * outputBlobs: contains all output blobs for each layer specified in @p outBlobNames. + /// * outBlobNames: names for layers which outputs are needed to get + #[inline] + fn forward_and_retrieve(&mut self, output_blobs: &mut core::Vector>, out_blob_names: &core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_forward_vectorLvectorLMatGGR_const_vectorLStringGR(self.as_raw_mut_Net(), output_blobs.as_raw_mut_VectorOfVectorOfMat(), out_blob_names.as_raw_VectorOfString(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a quantized Net from a floating-point Net. + /// ## Parameters + /// * calibData: Calibration data to compute the quantization parameters. + /// * inputsDtype: Datatype of quantized net's inputs. Can be CV_32F or CV_8S. + /// * outputsDtype: Datatype of quantized net's outputs. Can be CV_32F or CV_8S. + /// * perChannel: Quantization granularity of quantized Net. The default is true, that means quantize model + /// in per-channel way (channel-wise). Set it false to quantize model in per-tensor way (or tensor-wise). + /// + /// ## C++ default parameters + /// * per_channel: true + #[inline] + fn quantize(&mut self, calib_data: &impl ToInputArray, inputs_dtype: i32, outputs_dtype: i32, per_channel: bool) -> Result { + input_array_arg!(calib_data); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_quantize_const__InputArrayR_int_int_bool(self.as_raw_mut_Net(), calib_data.as_raw__InputArray(), inputs_dtype, outputs_dtype, per_channel, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns a quantized Net from a floating-point Net. + /// ## Parameters + /// * calibData: Calibration data to compute the quantization parameters. + /// * inputsDtype: Datatype of quantized net's inputs. Can be CV_32F or CV_8S. + /// * outputsDtype: Datatype of quantized net's outputs. Can be CV_32F or CV_8S. + /// * perChannel: Quantization granularity of quantized Net. The default is true, that means quantize model + /// in per-channel way (channel-wise). Set it false to quantize model in per-tensor way (or tensor-wise). + /// + /// ## Note + /// This alternative version of [NetTrait::quantize] function uses the following default values for its arguments: + /// * per_channel: true + #[inline] + fn quantize_def(&mut self, calib_data: &impl ToInputArray, inputs_dtype: i32, outputs_dtype: i32) -> Result { + input_array_arg!(calib_data); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_quantize_const__InputArrayR_int_int(self.as_raw_mut_Net(), calib_data.as_raw__InputArray(), inputs_dtype, outputs_dtype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::Net::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Compile Halide layers. + /// ## Parameters + /// * scheduler: Path to YAML file with scheduling directives. + /// ## See also + /// setPreferableBackend + /// + /// Schedule layers that support Halide backend. Then compile them for + /// specific target. For layers that not represented in scheduling file + /// or if no manual scheduling used at all, automatic scheduling will be applied. + #[inline] + fn set_halide_scheduler(&mut self, scheduler: &str) -> Result<()> { + extern_container_arg!(scheduler); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_setHalideScheduler_const_StringR(self.as_raw_mut_Net(), scheduler.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Ask network to use specific computation backend where it supported. + /// ## Parameters + /// * backendId: backend identifier. + /// ## See also + /// Backend + #[inline] + fn set_preferable_backend(&mut self, backend_id: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_setPreferableBackend_int(self.as_raw_mut_Net(), backend_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Ask network to make computations on specific target device. + /// ## Parameters + /// * targetId: target identifier. + /// ## See also + /// Target + /// + /// List of supported combinations backend / target: + /// | | DNN_BACKEND_OPENCV | DNN_BACKEND_INFERENCE_ENGINE | DNN_BACKEND_HALIDE | DNN_BACKEND_CUDA | + /// |------------------------|--------------------|------------------------------|--------------------|-------------------| + /// | DNN_TARGET_CPU | + | + | + | | + /// | DNN_TARGET_OPENCL | + | + | + | | + /// | DNN_TARGET_OPENCL_FP16 | + | + | | | + /// | DNN_TARGET_MYRIAD | | + | | | + /// | DNN_TARGET_FPGA | | + | | | + /// | DNN_TARGET_CUDA | | | | + | + /// | DNN_TARGET_CUDA_FP16 | | | | + | + /// | DNN_TARGET_HDDL | | + | | | + #[inline] + fn set_preferable_target(&mut self, target_id: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_setPreferableTarget_int(self.as_raw_mut_Net(), target_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets the new input value for the network + /// ## Parameters + /// * blob: A new blob. Should have CV_32F or CV_8U depth. + /// * name: A name of input layer. + /// * scalefactor: An optional normalization scale. + /// * mean: An optional mean subtraction values. + /// ## See also + /// connect(String, String) to know format of the descriptor. + /// + /// If scale or mean values are specified, a final input blob is computed + /// as: + /// ![block formula](https://latex.codecogs.com/png.latex?input%28n%2Cc%2Ch%2Cw%29%20%3D%20scalefactor%20%5Ctimes%20%28blob%28n%2Cc%2Ch%2Cw%29%20%2D%20mean%5Fc%29) + /// + /// ## C++ default parameters + /// * name: "" + /// * scalefactor: 1.0 + /// * mean: Scalar() + #[inline] + fn set_input(&mut self, blob: &impl ToInputArray, name: &str, scalefactor: f64, mean: core::Scalar) -> Result<()> { + input_array_arg!(blob); + extern_container_arg!(name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_setInput_const__InputArrayR_const_StringR_double_const_ScalarR(self.as_raw_mut_Net(), blob.as_raw__InputArray(), name.opencv_as_extern(), scalefactor, &mean, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets the new input value for the network + /// ## Parameters + /// * blob: A new blob. Should have CV_32F or CV_8U depth. + /// * name: A name of input layer. + /// * scalefactor: An optional normalization scale. + /// * mean: An optional mean subtraction values. + /// ## See also + /// connect(String, String) to know format of the descriptor. + /// + /// If scale or mean values are specified, a final input blob is computed + /// as: + /// ![block formula](https://latex.codecogs.com/png.latex?input%28n%2Cc%2Ch%2Cw%29%20%3D%20scalefactor%20%5Ctimes%20%28blob%28n%2Cc%2Ch%2Cw%29%20%2D%20mean%5Fc%29) + /// + /// ## Note + /// This alternative version of [NetTrait::set_input] function uses the following default values for its arguments: + /// * name: "" + /// * scalefactor: 1.0 + /// * mean: Scalar() + #[inline] + fn set_input_def(&mut self, blob: &impl ToInputArray) -> Result<()> { + input_array_arg!(blob); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_setInput_const__InputArrayR(self.as_raw_mut_Net(), blob.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets the new value for the learned param of the layer. + /// ## Parameters + /// * layer: name or id of the layer. + /// * numParam: index of the layer parameter in the Layer::blobs array. + /// * blob: the new value. + /// ## See also + /// Layer::blobs + /// + /// Note: If shape of the new blob differs from the previous shape, + /// then the following forward pass may fail. + #[inline] + fn set_param(&mut self, layer: i32, num_param: i32, blob: &impl core::MatTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_setParam_int_int_const_MatR(self.as_raw_mut_Net(), layer, num_param, blob.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn set_param_1(&mut self, layer_name: &str, num_param: i32, blob: &impl core::MatTraitConst) -> Result<()> { + extern_container_arg!(layer_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_setParam_const_StringR_int_const_MatR(self.as_raw_mut_Net(), layer_name.opencv_as_extern(), num_param, blob.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Enables or disables layer fusion in the network. + /// ## Parameters + /// * fusion: true to enable the fusion, false to disable. The fusion is enabled by default. + #[inline] + fn enable_fusion(&mut self, fusion: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_enableFusion_bool(self.as_raw_mut_Net(), fusion, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Enables or disables the Winograd compute branch. The Winograd compute branch can speed up + /// 3x3 Convolution at a small loss of accuracy. + /// ## Parameters + /// * useWinograd: true to enable the Winograd compute branch. The default is true. + #[inline] + fn enable_winograd(&mut self, use_winograd: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_enableWinograd_bool(self.as_raw_mut_Net(), use_winograd, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns overall time for inference and timings (in ticks) for layers. + /// + /// Indexes in returned vector correspond to layers ids. Some layers can be fused with others, + /// in this case zero ticks count will be return for that skipped layers. Supported by DNN_BACKEND_OPENCV on DNN_TARGET_CPU only. + /// + /// ## Parameters + /// * timings:[out] vector for tick timings for all layers. + /// ## Returns + /// overall ticks for model inference. + #[inline] + fn get_perf_profile(&mut self, timings: &mut core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_Net_getPerfProfile_vectorLdoubleGR(self.as_raw_mut_Net(), timings.as_raw_mut_VectorOff64(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl Clone for Net { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_dnn_Net_implicitClone_const(self.as_raw_Net())) } + } + } + + impl std::fmt::Debug for Net { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Net") + .finish() + } + } + + impl crate::dnn::NetTraitConst for Net { + #[inline] fn as_raw_Net(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::NetTrait for Net { + #[inline] fn as_raw_mut_Net(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Net, crate::dnn::NetTraitConst, as_raw_Net, crate::dnn::NetTrait, as_raw_mut_Net } + + /// ![inline formula](https://latex.codecogs.com/png.latex?%20L%5Fp%20) - normalization layer. + /// ## Parameters + /// * p: Normalization factor. The most common `p = 1` for ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F1%20) - + /// normalization or `p = 2` for ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F2%20) - normalization or a custom one. + /// * eps: Parameter ![inline formula](https://latex.codecogs.com/png.latex?%20%5Cepsilon%20) to prevent a division by zero. + /// * across_spatial: If true, normalize an input across all non-batch dimensions. + /// Otherwise normalize an every channel separately. + /// + /// Across spatial: + /// @f[ + /// norm = \sqrt[p]{\epsilon + \sum_{x, y, c} |src(x, y, c)|^p } \\ + /// dst(x, y, c) = \frac{ src(x, y, c) }{norm} + /// @f] + /// + /// Channel wise normalization: + /// @f[ + /// norm(c) = \sqrt[p]{\epsilon + \sum_{x, y} |src(x, y, c)|^p } \\ + /// dst(x, y, c) = \frac{ src(x, y, c) }{norm(c)} + /// @f] + /// + /// Where `x, y` - spatial coordinates, `c` - channel. + /// + /// An every sample in the batch is normalized separately. Optionally, + /// output is scaled by the trained parameters. + pub struct NormalizeBBoxLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { NormalizeBBoxLayer } + + impl Drop for NormalizeBBoxLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_NormalizeBBoxLayer_delete(self.as_raw_mut_NormalizeBBoxLayer()) }; + } + } + + unsafe impl Send for NormalizeBBoxLayer {} + + impl NormalizeBBoxLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::NormalizeBBoxLayer { + let ret = unsafe { sys::cv_dnn_NormalizeBBoxLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::NormalizeBBoxLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_NormalizeBBoxLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::NormalizeBBoxLayer] + pub trait NormalizeBBoxLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_NormalizeBBoxLayer(&self) -> *const c_void; + + #[inline] + fn pnorm(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_NormalizeBBoxLayer_propPnorm_const(self.as_raw_NormalizeBBoxLayer()) }; + ret + } + + #[inline] + fn epsilon(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_NormalizeBBoxLayer_propEpsilon_const(self.as_raw_NormalizeBBoxLayer()) }; + ret + } + + #[inline] + fn across_spatial(&self) -> bool { + let ret = unsafe { sys::cv_dnn_NormalizeBBoxLayer_propAcrossSpatial_const(self.as_raw_NormalizeBBoxLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::NormalizeBBoxLayer] + pub trait NormalizeBBoxLayerTrait: crate::dnn::LayerTrait + crate::dnn::NormalizeBBoxLayerTraitConst { + fn as_raw_mut_NormalizeBBoxLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_pnorm(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_NormalizeBBoxLayer_propPnorm_const_float(self.as_raw_mut_NormalizeBBoxLayer(), val) }; + ret + } + + #[inline] + fn set_epsilon(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_NormalizeBBoxLayer_propEpsilon_const_float(self.as_raw_mut_NormalizeBBoxLayer(), val) }; + ret + } + + #[inline] + fn set_across_spatial(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_NormalizeBBoxLayer_propAcrossSpatial_const_bool(self.as_raw_mut_NormalizeBBoxLayer(), val) }; + ret + } + + } + + impl Default for NormalizeBBoxLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for NormalizeBBoxLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("NormalizeBBoxLayer") + .field("pnorm", &crate::dnn::NormalizeBBoxLayerTraitConst::pnorm(self)) + .field("epsilon", &crate::dnn::NormalizeBBoxLayerTraitConst::epsilon(self)) + .field("across_spatial", &crate::dnn::NormalizeBBoxLayerTraitConst::across_spatial(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { NormalizeBBoxLayer, core::Algorithm, cv_dnn_NormalizeBBoxLayer_to_Algorithm } + + boxed_cast_base! { NormalizeBBoxLayer, crate::dnn::Layer, cv_dnn_NormalizeBBoxLayer_to_Layer } + + impl core::AlgorithmTraitConst for NormalizeBBoxLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for NormalizeBBoxLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { NormalizeBBoxLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for NormalizeBBoxLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for NormalizeBBoxLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { NormalizeBBoxLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::NormalizeBBoxLayerTraitConst for NormalizeBBoxLayer { + #[inline] fn as_raw_NormalizeBBoxLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::NormalizeBBoxLayerTrait for NormalizeBBoxLayer { + #[inline] fn as_raw_mut_NormalizeBBoxLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { NormalizeBBoxLayer, crate::dnn::NormalizeBBoxLayerTraitConst, as_raw_NormalizeBBoxLayer, crate::dnn::NormalizeBBoxLayerTrait, as_raw_mut_NormalizeBBoxLayer } + + pub struct NotLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { NotLayer } + + impl Drop for NotLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_NotLayer_delete(self.as_raw_mut_NotLayer()) }; + } + } + + unsafe impl Send for NotLayer {} + + impl NotLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::NotLayer { + let ret = unsafe { sys::cv_dnn_NotLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::NotLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_NotLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::NotLayer] + pub trait NotLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_NotLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::NotLayer] + pub trait NotLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::NotLayerTraitConst { + fn as_raw_mut_NotLayer(&mut self) -> *mut c_void; + + } + + impl Default for NotLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for NotLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("NotLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { NotLayer, crate::dnn::ActivationLayer, cv_dnn_NotLayer_to_ActivationLayer } + + boxed_cast_base! { NotLayer, core::Algorithm, cv_dnn_NotLayer_to_Algorithm } + + boxed_cast_base! { NotLayer, crate::dnn::Layer, cv_dnn_NotLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for NotLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for NotLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { NotLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for NotLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for NotLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { NotLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for NotLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for NotLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { NotLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::NotLayerTraitConst for NotLayer { + #[inline] fn as_raw_NotLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::NotLayerTrait for NotLayer { + #[inline] fn as_raw_mut_NotLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { NotLayer, crate::dnn::NotLayerTraitConst, as_raw_NotLayer, crate::dnn::NotLayerTrait, as_raw_mut_NotLayer } + + /// Adds extra values for specific axes. + /// ## Parameters + /// * paddings: Vector of paddings in format + /// ```C++ + /// [ pad_before, pad_after, // [0]th dimension + /// pad_before, pad_after, // [1]st dimension + /// ... + /// pad_before, pad_after ] // [n]th dimension + /// ``` + /// + /// that represents number of padded values at every dimension + /// starting from the first one. The rest of dimensions won't + /// be padded. + /// * value: Value to be padded. Defaults to zero. + /// * type: Padding type: 'constant', 'reflect' + /// * input_dims: Torch's parameter. If @p input_dims is not equal to the + /// actual input dimensionality then the `[0]th` dimension + /// is considered as a batch dimension and @p paddings are shifted + /// to a one dimension. Defaults to `-1` that means padding + /// corresponding to @p paddings. + pub struct PaddingLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { PaddingLayer } + + impl Drop for PaddingLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_PaddingLayer_delete(self.as_raw_mut_PaddingLayer()) }; + } + } + + unsafe impl Send for PaddingLayer {} + + impl PaddingLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::PaddingLayer { + let ret = unsafe { sys::cv_dnn_PaddingLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::PaddingLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_PaddingLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::PaddingLayer] + pub trait PaddingLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_PaddingLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::PaddingLayer] + pub trait PaddingLayerTrait: crate::dnn::LayerTrait + crate::dnn::PaddingLayerTraitConst { + fn as_raw_mut_PaddingLayer(&mut self) -> *mut c_void; + + } + + impl Default for PaddingLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for PaddingLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PaddingLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { PaddingLayer, core::Algorithm, cv_dnn_PaddingLayer_to_Algorithm } + + boxed_cast_base! { PaddingLayer, crate::dnn::Layer, cv_dnn_PaddingLayer_to_Layer } + + impl core::AlgorithmTraitConst for PaddingLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for PaddingLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PaddingLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for PaddingLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for PaddingLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PaddingLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::PaddingLayerTraitConst for PaddingLayer { + #[inline] fn as_raw_PaddingLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::PaddingLayerTrait for PaddingLayer { + #[inline] fn as_raw_mut_PaddingLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PaddingLayer, crate::dnn::PaddingLayerTraitConst, as_raw_PaddingLayer, crate::dnn::PaddingLayerTrait, as_raw_mut_PaddingLayer } + + pub struct PermuteLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { PermuteLayer } + + impl Drop for PermuteLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_PermuteLayer_delete(self.as_raw_mut_PermuteLayer()) }; + } + } + + unsafe impl Send for PermuteLayer {} + + impl PermuteLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::PermuteLayer { + let ret = unsafe { sys::cv_dnn_PermuteLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::PermuteLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_PermuteLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::PermuteLayer] + pub trait PermuteLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_PermuteLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::PermuteLayer] + pub trait PermuteLayerTrait: crate::dnn::LayerTrait + crate::dnn::PermuteLayerTraitConst { + fn as_raw_mut_PermuteLayer(&mut self) -> *mut c_void; + + } + + impl Default for PermuteLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for PermuteLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PermuteLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { PermuteLayer, core::Algorithm, cv_dnn_PermuteLayer_to_Algorithm } + + boxed_cast_base! { PermuteLayer, crate::dnn::Layer, cv_dnn_PermuteLayer_to_Layer } + + impl core::AlgorithmTraitConst for PermuteLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for PermuteLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PermuteLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for PermuteLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for PermuteLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PermuteLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::PermuteLayerTraitConst for PermuteLayer { + #[inline] fn as_raw_PermuteLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::PermuteLayerTrait for PermuteLayer { + #[inline] fn as_raw_mut_PermuteLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PermuteLayer, crate::dnn::PermuteLayerTraitConst, as_raw_PermuteLayer, crate::dnn::PermuteLayerTrait, as_raw_mut_PermuteLayer } + + pub struct PoolingLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { PoolingLayer } + + impl Drop for PoolingLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_PoolingLayer_delete(self.as_raw_mut_PoolingLayer()) }; + } + } + + unsafe impl Send for PoolingLayer {} + + impl PoolingLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::PoolingLayer { + let ret = unsafe { sys::cv_dnn_PoolingLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::PoolingLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_PoolingLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::PoolingLayer] + pub trait PoolingLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_PoolingLayer(&self) -> *const c_void; + + #[inline] + fn typ(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propType_const(self.as_raw_PoolingLayer()) }; + ret + } + + #[inline] + fn kernel_size(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propKernel_size_const(self.as_raw_PoolingLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn strides(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propStrides_const(self.as_raw_PoolingLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn pads_begin(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propPads_begin_const(self.as_raw_PoolingLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn pads_end(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propPads_end_const(self.as_raw_PoolingLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + /// Flag is true if at least one of the axes is global pooled. + #[inline] + fn global_pooling(&self) -> bool { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propGlobalPooling_const(self.as_raw_PoolingLayer()) }; + ret + } + + #[inline] + fn is_global_pooling(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propIsGlobalPooling_const(self.as_raw_PoolingLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn compute_max_idx(&self) -> bool { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propComputeMaxIdx_const(self.as_raw_PoolingLayer()) }; + ret + } + + #[inline] + fn pad_mode(&self) -> String { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propPadMode_const(self.as_raw_PoolingLayer()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn ceil_mode(&self) -> bool { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propCeilMode_const(self.as_raw_PoolingLayer()) }; + ret + } + + #[inline] + fn ave_pool_padded_area(&self) -> bool { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propAvePoolPaddedArea_const(self.as_raw_PoolingLayer()) }; + ret + } + + #[inline] + fn pooled_size(&self) -> core::Size { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_PoolingLayer_propPooledSize_const(self.as_raw_PoolingLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn spatial_scale(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propSpatialScale_const(self.as_raw_PoolingLayer()) }; + ret + } + + #[inline] + fn ps_roi_out_channels(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propPsRoiOutChannels_const(self.as_raw_PoolingLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::PoolingLayer] + pub trait PoolingLayerTrait: crate::dnn::LayerTrait + crate::dnn::PoolingLayerTraitConst { + fn as_raw_mut_PoolingLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_type(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propType_const_int(self.as_raw_mut_PoolingLayer(), val) }; + ret + } + + #[inline] + fn set_kernel_size(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propKernel_size_const_vectorLsize_tG(self.as_raw_mut_PoolingLayer(), val.as_raw_VectorOfsize_t()) }; + ret + } + + #[inline] + fn set_strides(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propStrides_const_vectorLsize_tG(self.as_raw_mut_PoolingLayer(), val.as_raw_VectorOfsize_t()) }; + ret + } + + #[inline] + fn set_pads_begin(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propPads_begin_const_vectorLsize_tG(self.as_raw_mut_PoolingLayer(), val.as_raw_VectorOfsize_t()) }; + ret + } + + #[inline] + fn set_pads_end(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propPads_end_const_vectorLsize_tG(self.as_raw_mut_PoolingLayer(), val.as_raw_VectorOfsize_t()) }; + ret + } + + /// Flag is true if at least one of the axes is global pooled. + #[inline] + fn set_global_pooling(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propGlobalPooling_const_bool(self.as_raw_mut_PoolingLayer(), val) }; + ret + } + + #[inline] + fn set_is_global_pooling(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propIsGlobalPooling_const_vectorLboolG(self.as_raw_mut_PoolingLayer(), val.as_raw_VectorOfbool()) }; + ret + } + + #[inline] + fn set_compute_max_idx(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propComputeMaxIdx_const_bool(self.as_raw_mut_PoolingLayer(), val) }; + ret + } + + #[inline] + fn set_pad_mode(&mut self, val: &str) { + extern_container_arg!(nofail val); + let ret = unsafe { sys::cv_dnn_PoolingLayer_propPadMode_const_String(self.as_raw_mut_PoolingLayer(), val.opencv_as_extern()) }; + ret + } + + #[inline] + fn set_ceil_mode(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propCeilMode_const_bool(self.as_raw_mut_PoolingLayer(), val) }; + ret + } + + #[inline] + fn set_ave_pool_padded_area(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propAvePoolPaddedArea_const_bool(self.as_raw_mut_PoolingLayer(), val) }; + ret + } + + #[inline] + fn set_pooled_size(&mut self, val: core::Size) { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propPooledSize_const_Size(self.as_raw_mut_PoolingLayer(), &val) }; + ret + } + + #[inline] + fn set_spatial_scale(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propSpatialScale_const_float(self.as_raw_mut_PoolingLayer(), val) }; + ret + } + + #[inline] + fn set_ps_roi_out_channels(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_PoolingLayer_propPsRoiOutChannels_const_int(self.as_raw_mut_PoolingLayer(), val) }; + ret + } + + } + + impl Default for PoolingLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for PoolingLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PoolingLayer") + .field("typ", &crate::dnn::PoolingLayerTraitConst::typ(self)) + .field("kernel_size", &crate::dnn::PoolingLayerTraitConst::kernel_size(self)) + .field("strides", &crate::dnn::PoolingLayerTraitConst::strides(self)) + .field("pads_begin", &crate::dnn::PoolingLayerTraitConst::pads_begin(self)) + .field("pads_end", &crate::dnn::PoolingLayerTraitConst::pads_end(self)) + .field("global_pooling", &crate::dnn::PoolingLayerTraitConst::global_pooling(self)) + .field("is_global_pooling", &crate::dnn::PoolingLayerTraitConst::is_global_pooling(self)) + .field("compute_max_idx", &crate::dnn::PoolingLayerTraitConst::compute_max_idx(self)) + .field("pad_mode", &crate::dnn::PoolingLayerTraitConst::pad_mode(self)) + .field("ceil_mode", &crate::dnn::PoolingLayerTraitConst::ceil_mode(self)) + .field("ave_pool_padded_area", &crate::dnn::PoolingLayerTraitConst::ave_pool_padded_area(self)) + .field("pooled_size", &crate::dnn::PoolingLayerTraitConst::pooled_size(self)) + .field("spatial_scale", &crate::dnn::PoolingLayerTraitConst::spatial_scale(self)) + .field("ps_roi_out_channels", &crate::dnn::PoolingLayerTraitConst::ps_roi_out_channels(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { PoolingLayer, core::Algorithm, cv_dnn_PoolingLayer_to_Algorithm } + + boxed_cast_base! { PoolingLayer, crate::dnn::Layer, cv_dnn_PoolingLayer_to_Layer } + + impl core::AlgorithmTraitConst for PoolingLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for PoolingLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PoolingLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for PoolingLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for PoolingLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PoolingLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::PoolingLayerTraitConst for PoolingLayer { + #[inline] fn as_raw_PoolingLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::PoolingLayerTrait for PoolingLayer { + #[inline] fn as_raw_mut_PoolingLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PoolingLayer, crate::dnn::PoolingLayerTraitConst, as_raw_PoolingLayer, crate::dnn::PoolingLayerTrait, as_raw_mut_PoolingLayer } + + pub struct PoolingLayerInt8 { + ptr: *mut c_void, + } + + opencv_type_boxed! { PoolingLayerInt8 } + + impl Drop for PoolingLayerInt8 { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_PoolingLayerInt8_delete(self.as_raw_mut_PoolingLayerInt8()) }; + } + } + + unsafe impl Send for PoolingLayerInt8 {} + + impl PoolingLayerInt8 { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::PoolingLayerInt8 { + let ret = unsafe { sys::cv_dnn_PoolingLayerInt8_defaultNew_const() }; + let ret = unsafe { crate::dnn::PoolingLayerInt8::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_PoolingLayerInt8_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::PoolingLayerInt8] + pub trait PoolingLayerInt8TraitConst: crate::dnn::PoolingLayerTraitConst { + fn as_raw_PoolingLayerInt8(&self) -> *const c_void; + + #[inline] + fn input_zp(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_PoolingLayerInt8_propInput_zp_const(self.as_raw_PoolingLayerInt8()) }; + ret + } + + #[inline] + fn output_zp(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_PoolingLayerInt8_propOutput_zp_const(self.as_raw_PoolingLayerInt8()) }; + ret + } + + #[inline] + fn input_sc(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_PoolingLayerInt8_propInput_sc_const(self.as_raw_PoolingLayerInt8()) }; + ret + } + + #[inline] + fn output_sc(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_PoolingLayerInt8_propOutput_sc_const(self.as_raw_PoolingLayerInt8()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::PoolingLayerInt8] + pub trait PoolingLayerInt8Trait: crate::dnn::PoolingLayerInt8TraitConst + crate::dnn::PoolingLayerTrait { + fn as_raw_mut_PoolingLayerInt8(&mut self) -> *mut c_void; + + #[inline] + fn set_input_zp(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_PoolingLayerInt8_propInput_zp_const_int(self.as_raw_mut_PoolingLayerInt8(), val) }; + ret + } + + #[inline] + fn set_output_zp(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_PoolingLayerInt8_propOutput_zp_const_int(self.as_raw_mut_PoolingLayerInt8(), val) }; + ret + } + + #[inline] + fn set_input_sc(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_PoolingLayerInt8_propInput_sc_const_float(self.as_raw_mut_PoolingLayerInt8(), val) }; + ret + } + + #[inline] + fn set_output_sc(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_PoolingLayerInt8_propOutput_sc_const_float(self.as_raw_mut_PoolingLayerInt8(), val) }; + ret + } + + } + + impl Default for PoolingLayerInt8 { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for PoolingLayerInt8 { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PoolingLayerInt8") + .field("input_zp", &crate::dnn::PoolingLayerInt8TraitConst::input_zp(self)) + .field("output_zp", &crate::dnn::PoolingLayerInt8TraitConst::output_zp(self)) + .field("input_sc", &crate::dnn::PoolingLayerInt8TraitConst::input_sc(self)) + .field("output_sc", &crate::dnn::PoolingLayerInt8TraitConst::output_sc(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .field("typ", &crate::dnn::PoolingLayerTraitConst::typ(self)) + .field("kernel_size", &crate::dnn::PoolingLayerTraitConst::kernel_size(self)) + .field("strides", &crate::dnn::PoolingLayerTraitConst::strides(self)) + .field("pads_begin", &crate::dnn::PoolingLayerTraitConst::pads_begin(self)) + .field("pads_end", &crate::dnn::PoolingLayerTraitConst::pads_end(self)) + .field("global_pooling", &crate::dnn::PoolingLayerTraitConst::global_pooling(self)) + .field("is_global_pooling", &crate::dnn::PoolingLayerTraitConst::is_global_pooling(self)) + .field("compute_max_idx", &crate::dnn::PoolingLayerTraitConst::compute_max_idx(self)) + .field("pad_mode", &crate::dnn::PoolingLayerTraitConst::pad_mode(self)) + .field("ceil_mode", &crate::dnn::PoolingLayerTraitConst::ceil_mode(self)) + .field("ave_pool_padded_area", &crate::dnn::PoolingLayerTraitConst::ave_pool_padded_area(self)) + .field("pooled_size", &crate::dnn::PoolingLayerTraitConst::pooled_size(self)) + .field("spatial_scale", &crate::dnn::PoolingLayerTraitConst::spatial_scale(self)) + .field("ps_roi_out_channels", &crate::dnn::PoolingLayerTraitConst::ps_roi_out_channels(self)) + .finish() + } + } + + boxed_cast_base! { PoolingLayerInt8, core::Algorithm, cv_dnn_PoolingLayerInt8_to_Algorithm } + + boxed_cast_base! { PoolingLayerInt8, crate::dnn::Layer, cv_dnn_PoolingLayerInt8_to_Layer } + + boxed_cast_base! { PoolingLayerInt8, crate::dnn::PoolingLayer, cv_dnn_PoolingLayerInt8_to_PoolingLayer } + + impl core::AlgorithmTraitConst for PoolingLayerInt8 { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for PoolingLayerInt8 { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PoolingLayerInt8, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for PoolingLayerInt8 { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for PoolingLayerInt8 { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PoolingLayerInt8, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::PoolingLayerTraitConst for PoolingLayerInt8 { + #[inline] fn as_raw_PoolingLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::PoolingLayerTrait for PoolingLayerInt8 { + #[inline] fn as_raw_mut_PoolingLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PoolingLayerInt8, crate::dnn::PoolingLayerTraitConst, as_raw_PoolingLayer, crate::dnn::PoolingLayerTrait, as_raw_mut_PoolingLayer } + + impl crate::dnn::PoolingLayerInt8TraitConst for PoolingLayerInt8 { + #[inline] fn as_raw_PoolingLayerInt8(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::PoolingLayerInt8Trait for PoolingLayerInt8 { + #[inline] fn as_raw_mut_PoolingLayerInt8(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PoolingLayerInt8, crate::dnn::PoolingLayerInt8TraitConst, as_raw_PoolingLayerInt8, crate::dnn::PoolingLayerInt8Trait, as_raw_mut_PoolingLayerInt8 } + + pub struct PowerLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { PowerLayer } + + impl Drop for PowerLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_PowerLayer_delete(self.as_raw_mut_PowerLayer()) }; + } + } + + unsafe impl Send for PowerLayer {} + + impl PowerLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::PowerLayer { + let ret = unsafe { sys::cv_dnn_PowerLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::PowerLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_PowerLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::PowerLayer] + pub trait PowerLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_PowerLayer(&self) -> *const c_void; + + #[inline] + fn power(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_PowerLayer_propPower_const(self.as_raw_PowerLayer()) }; + ret + } + + #[inline] + fn scale(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_PowerLayer_propScale_const(self.as_raw_PowerLayer()) }; + ret + } + + #[inline] + fn shift(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_PowerLayer_propShift_const(self.as_raw_PowerLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::PowerLayer] + pub trait PowerLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::PowerLayerTraitConst { + fn as_raw_mut_PowerLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_power(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_PowerLayer_propPower_const_float(self.as_raw_mut_PowerLayer(), val) }; + ret + } + + #[inline] + fn set_scale(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_PowerLayer_propScale_const_float(self.as_raw_mut_PowerLayer(), val) }; + ret + } + + #[inline] + fn set_shift(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_PowerLayer_propShift_const_float(self.as_raw_mut_PowerLayer(), val) }; + ret + } + + } + + impl Default for PowerLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for PowerLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PowerLayer") + .field("power", &crate::dnn::PowerLayerTraitConst::power(self)) + .field("scale", &crate::dnn::PowerLayerTraitConst::scale(self)) + .field("shift", &crate::dnn::PowerLayerTraitConst::shift(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { PowerLayer, crate::dnn::ActivationLayer, cv_dnn_PowerLayer_to_ActivationLayer } + + boxed_cast_base! { PowerLayer, core::Algorithm, cv_dnn_PowerLayer_to_Algorithm } + + boxed_cast_base! { PowerLayer, crate::dnn::Layer, cv_dnn_PowerLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for PowerLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for PowerLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PowerLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for PowerLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for PowerLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PowerLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for PowerLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for PowerLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PowerLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::PowerLayerTraitConst for PowerLayer { + #[inline] fn as_raw_PowerLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::PowerLayerTrait for PowerLayer { + #[inline] fn as_raw_mut_PowerLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PowerLayer, crate::dnn::PowerLayerTraitConst, as_raw_PowerLayer, crate::dnn::PowerLayerTrait, as_raw_mut_PowerLayer } + + pub struct PriorBoxLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { PriorBoxLayer } + + impl Drop for PriorBoxLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_PriorBoxLayer_delete(self.as_raw_mut_PriorBoxLayer()) }; + } + } + + unsafe impl Send for PriorBoxLayer {} + + impl PriorBoxLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::PriorBoxLayer { + let ret = unsafe { sys::cv_dnn_PriorBoxLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::PriorBoxLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_PriorBoxLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::PriorBoxLayer] + pub trait PriorBoxLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_PriorBoxLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::PriorBoxLayer] + pub trait PriorBoxLayerTrait: crate::dnn::LayerTrait + crate::dnn::PriorBoxLayerTraitConst { + fn as_raw_mut_PriorBoxLayer(&mut self) -> *mut c_void; + + } + + impl Default for PriorBoxLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for PriorBoxLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PriorBoxLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { PriorBoxLayer, core::Algorithm, cv_dnn_PriorBoxLayer_to_Algorithm } + + boxed_cast_base! { PriorBoxLayer, crate::dnn::Layer, cv_dnn_PriorBoxLayer_to_Layer } + + impl core::AlgorithmTraitConst for PriorBoxLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for PriorBoxLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PriorBoxLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for PriorBoxLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for PriorBoxLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PriorBoxLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::PriorBoxLayerTraitConst for PriorBoxLayer { + #[inline] fn as_raw_PriorBoxLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::PriorBoxLayerTrait for PriorBoxLayer { + #[inline] fn as_raw_mut_PriorBoxLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { PriorBoxLayer, crate::dnn::PriorBoxLayerTraitConst, as_raw_PriorBoxLayer, crate::dnn::PriorBoxLayerTrait, as_raw_mut_PriorBoxLayer } + + pub struct ProposalLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ProposalLayer } + + impl Drop for ProposalLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ProposalLayer_delete(self.as_raw_mut_ProposalLayer()) }; + } + } + + unsafe impl Send for ProposalLayer {} + + impl ProposalLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ProposalLayer { + let ret = unsafe { sys::cv_dnn_ProposalLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ProposalLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ProposalLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ProposalLayer] + pub trait ProposalLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ProposalLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ProposalLayer] + pub trait ProposalLayerTrait: crate::dnn::LayerTrait + crate::dnn::ProposalLayerTraitConst { + fn as_raw_mut_ProposalLayer(&mut self) -> *mut c_void; + + } + + impl Default for ProposalLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ProposalLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ProposalLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ProposalLayer, core::Algorithm, cv_dnn_ProposalLayer_to_Algorithm } + + boxed_cast_base! { ProposalLayer, crate::dnn::Layer, cv_dnn_ProposalLayer_to_Layer } + + impl core::AlgorithmTraitConst for ProposalLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ProposalLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ProposalLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ProposalLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ProposalLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ProposalLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ProposalLayerTraitConst for ProposalLayer { + #[inline] fn as_raw_ProposalLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ProposalLayerTrait for ProposalLayer { + #[inline] fn as_raw_mut_ProposalLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ProposalLayer, crate::dnn::ProposalLayerTraitConst, as_raw_ProposalLayer, crate::dnn::ProposalLayerTrait, as_raw_mut_ProposalLayer } + + pub struct QuantizeLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { QuantizeLayer } + + impl Drop for QuantizeLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_QuantizeLayer_delete(self.as_raw_mut_QuantizeLayer()) }; + } + } + + unsafe impl Send for QuantizeLayer {} + + impl QuantizeLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::QuantizeLayer { + let ret = unsafe { sys::cv_dnn_QuantizeLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::QuantizeLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_QuantizeLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::QuantizeLayer] + pub trait QuantizeLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_QuantizeLayer(&self) -> *const c_void; + + #[inline] + fn scales(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_QuantizeLayer_propScales_const(self.as_raw_QuantizeLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn zeropoints(&self) -> core::Vector { + let ret = unsafe { sys::cv_dnn_QuantizeLayer_propZeropoints_const(self.as_raw_QuantizeLayer()) }; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::QuantizeLayer] + pub trait QuantizeLayerTrait: crate::dnn::LayerTrait + crate::dnn::QuantizeLayerTraitConst { + fn as_raw_mut_QuantizeLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_scales(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_QuantizeLayer_propScales_const_vectorLfloatG(self.as_raw_mut_QuantizeLayer(), val.as_raw_VectorOff32()) }; + ret + } + + #[inline] + fn set_zeropoints(&mut self, val: core::Vector) { + let ret = unsafe { sys::cv_dnn_QuantizeLayer_propZeropoints_const_vectorLintG(self.as_raw_mut_QuantizeLayer(), val.as_raw_VectorOfi32()) }; + ret + } + + } + + impl Default for QuantizeLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for QuantizeLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("QuantizeLayer") + .field("scales", &crate::dnn::QuantizeLayerTraitConst::scales(self)) + .field("zeropoints", &crate::dnn::QuantizeLayerTraitConst::zeropoints(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { QuantizeLayer, core::Algorithm, cv_dnn_QuantizeLayer_to_Algorithm } + + boxed_cast_base! { QuantizeLayer, crate::dnn::Layer, cv_dnn_QuantizeLayer_to_Layer } + + impl core::AlgorithmTraitConst for QuantizeLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for QuantizeLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { QuantizeLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for QuantizeLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for QuantizeLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { QuantizeLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::QuantizeLayerTraitConst for QuantizeLayer { + #[inline] fn as_raw_QuantizeLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::QuantizeLayerTrait for QuantizeLayer { + #[inline] fn as_raw_mut_QuantizeLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { QuantizeLayer, crate::dnn::QuantizeLayerTraitConst, as_raw_QuantizeLayer, crate::dnn::QuantizeLayerTrait, as_raw_mut_QuantizeLayer } + + /// Classical recurrent layer + /// + /// Accepts two inputs @f$x_t@f$ and @f$h_{t-1}@f$ and compute two outputs @f$o_t@f$ and @f$h_t@f$. + /// + /// - input: should contain packed input @f$x_t@f$. + /// - output: should contain output @f$o_t@f$ (and @f$h_t@f$ if setProduceHiddenOutput() is set to true). + /// + /// input[0] should have shape [`T`, `N`, `data_dims`] where `T` and `N` is number of timestamps and number of independent samples of @f$x_t@f$ respectively. + /// + /// output[0] will have shape [`T`, `N`, @f$N_o@f$], where @f$N_o@f$ is number of rows in @f$ W_{xo} @f$ matrix. + /// + /// If setProduceHiddenOutput() is set to true then @p output[1] will contain a Mat with shape [`T`, `N`, @f$N_h@f$], where @f$N_h@f$ is number of rows in @f$ W_{hh} @f$ matrix. + pub struct RNNLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { RNNLayer } + + impl Drop for RNNLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_RNNLayer_delete(self.as_raw_mut_RNNLayer()) }; + } + } + + unsafe impl Send for RNNLayer {} + + impl RNNLayer { + /// Creates instance of RNNLayer + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_RNNLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::RNNLayer] + pub trait RNNLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_RNNLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::RNNLayer] + pub trait RNNLayerTrait: crate::dnn::LayerTrait + crate::dnn::RNNLayerTraitConst { + fn as_raw_mut_RNNLayer(&mut self) -> *mut c_void; + + /// Setups learned weights. + /// + /// Recurrent-layer behavior on each step is defined by current input @f$ x_t @f$, previous state @f$ h_t @f$ and learned weights as follows: + /// @f{eqnarray*}{ + /// h_t &= tanh&(W_{hh} h_{t-1} + W_{xh} x_t + b_h), \\ + /// o_t &= tanh&(W_{ho} h_t + b_o), + /// @f} + /// + /// ## Parameters + /// * Wxh: is @f$ W_{xh} @f$ matrix + /// * bh: is @f$ b_{h} @f$ vector + /// * Whh: is @f$ W_{hh} @f$ matrix + /// * Who: is @f$ W_{xo} @f$ matrix + /// * bo: is @f$ b_{o} @f$ vector + #[inline] + fn set_weights(&mut self, wxh: &impl core::MatTraitConst, bh: &impl core::MatTraitConst, whh: &impl core::MatTraitConst, who: &impl core::MatTraitConst, bo: &impl core::MatTraitConst) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_RNNLayer_setWeights_const_MatR_const_MatR_const_MatR_const_MatR_const_MatR(self.as_raw_mut_RNNLayer(), wxh.as_raw_Mat(), bh.as_raw_Mat(), whh.as_raw_Mat(), who.as_raw_Mat(), bo.as_raw_Mat(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// If this flag is set to true then layer will produce @f$ h_t @f$ as second output. + /// @details Shape of the second output is the same as first output. + /// + /// ## C++ default parameters + /// * produce: false + #[inline] + fn set_produce_hidden_output(&mut self, produce: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_RNNLayer_setProduceHiddenOutput_bool(self.as_raw_mut_RNNLayer(), produce, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// If this flag is set to true then layer will produce @f$ h_t @f$ as second output. + /// @details Shape of the second output is the same as first output. + /// + /// ## Note + /// This alternative version of [RNNLayerTrait::set_produce_hidden_output] function uses the following default values for its arguments: + /// * produce: false + #[inline] + fn set_produce_hidden_output_def(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_RNNLayer_setProduceHiddenOutput(self.as_raw_mut_RNNLayer(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for RNNLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("RNNLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { RNNLayer, core::Algorithm, cv_dnn_RNNLayer_to_Algorithm } + + boxed_cast_base! { RNNLayer, crate::dnn::Layer, cv_dnn_RNNLayer_to_Layer } + + impl core::AlgorithmTraitConst for RNNLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for RNNLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RNNLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for RNNLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for RNNLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RNNLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::RNNLayerTraitConst for RNNLayer { + #[inline] fn as_raw_RNNLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::RNNLayerTrait for RNNLayer { + #[inline] fn as_raw_mut_RNNLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RNNLayer, crate::dnn::RNNLayerTraitConst, as_raw_RNNLayer, crate::dnn::RNNLayerTrait, as_raw_mut_RNNLayer } + + pub struct ReLU6Layer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ReLU6Layer } + + impl Drop for ReLU6Layer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ReLU6Layer_delete(self.as_raw_mut_ReLU6Layer()) }; + } + } + + unsafe impl Send for ReLU6Layer {} + + impl ReLU6Layer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ReLU6Layer { + let ret = unsafe { sys::cv_dnn_ReLU6Layer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ReLU6Layer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ReLU6Layer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ReLU6Layer] + pub trait ReLU6LayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_ReLU6Layer(&self) -> *const c_void; + + #[inline] + fn min_value(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_ReLU6Layer_propMinValue_const(self.as_raw_ReLU6Layer()) }; + ret + } + + #[inline] + fn max_value(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_ReLU6Layer_propMaxValue_const(self.as_raw_ReLU6Layer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::ReLU6Layer] + pub trait ReLU6LayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::ReLU6LayerTraitConst { + fn as_raw_mut_ReLU6Layer(&mut self) -> *mut c_void; + + #[inline] + fn set_min_value(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_ReLU6Layer_propMinValue_const_float(self.as_raw_mut_ReLU6Layer(), val) }; + ret + } + + #[inline] + fn set_max_value(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_ReLU6Layer_propMaxValue_const_float(self.as_raw_mut_ReLU6Layer(), val) }; + ret + } + + } + + impl Default for ReLU6Layer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ReLU6Layer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ReLU6Layer") + .field("min_value", &crate::dnn::ReLU6LayerTraitConst::min_value(self)) + .field("max_value", &crate::dnn::ReLU6LayerTraitConst::max_value(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ReLU6Layer, crate::dnn::ActivationLayer, cv_dnn_ReLU6Layer_to_ActivationLayer } + + boxed_cast_base! { ReLU6Layer, core::Algorithm, cv_dnn_ReLU6Layer_to_Algorithm } + + boxed_cast_base! { ReLU6Layer, crate::dnn::Layer, cv_dnn_ReLU6Layer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for ReLU6Layer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for ReLU6Layer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReLU6Layer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for ReLU6Layer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ReLU6Layer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReLU6Layer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ReLU6Layer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ReLU6Layer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReLU6Layer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ReLU6LayerTraitConst for ReLU6Layer { + #[inline] fn as_raw_ReLU6Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ReLU6LayerTrait for ReLU6Layer { + #[inline] fn as_raw_mut_ReLU6Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReLU6Layer, crate::dnn::ReLU6LayerTraitConst, as_raw_ReLU6Layer, crate::dnn::ReLU6LayerTrait, as_raw_mut_ReLU6Layer } + + pub struct ReLULayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ReLULayer } + + impl Drop for ReLULayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ReLULayer_delete(self.as_raw_mut_ReLULayer()) }; + } + } + + unsafe impl Send for ReLULayer {} + + impl ReLULayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ReLULayer { + let ret = unsafe { sys::cv_dnn_ReLULayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ReLULayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ReLULayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ReLULayer] + pub trait ReLULayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_ReLULayer(&self) -> *const c_void; + + #[inline] + fn negative_slope(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_ReLULayer_propNegativeSlope_const(self.as_raw_ReLULayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::ReLULayer] + pub trait ReLULayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::ReLULayerTraitConst { + fn as_raw_mut_ReLULayer(&mut self) -> *mut c_void; + + #[inline] + fn set_negative_slope(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_ReLULayer_propNegativeSlope_const_float(self.as_raw_mut_ReLULayer(), val) }; + ret + } + + } + + impl Default for ReLULayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ReLULayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ReLULayer") + .field("negative_slope", &crate::dnn::ReLULayerTraitConst::negative_slope(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ReLULayer, crate::dnn::ActivationLayer, cv_dnn_ReLULayer_to_ActivationLayer } + + boxed_cast_base! { ReLULayer, core::Algorithm, cv_dnn_ReLULayer_to_Algorithm } + + boxed_cast_base! { ReLULayer, crate::dnn::Layer, cv_dnn_ReLULayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for ReLULayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for ReLULayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReLULayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for ReLULayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ReLULayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReLULayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ReLULayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ReLULayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReLULayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ReLULayerTraitConst for ReLULayer { + #[inline] fn as_raw_ReLULayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ReLULayerTrait for ReLULayer { + #[inline] fn as_raw_mut_ReLULayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReLULayer, crate::dnn::ReLULayerTraitConst, as_raw_ReLULayer, crate::dnn::ReLULayerTrait, as_raw_mut_ReLULayer } + + pub struct ReciprocalLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ReciprocalLayer } + + impl Drop for ReciprocalLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ReciprocalLayer_delete(self.as_raw_mut_ReciprocalLayer()) }; + } + } + + unsafe impl Send for ReciprocalLayer {} + + impl ReciprocalLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ReciprocalLayer { + let ret = unsafe { sys::cv_dnn_ReciprocalLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ReciprocalLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ReciprocalLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ReciprocalLayer] + pub trait ReciprocalLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_ReciprocalLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ReciprocalLayer] + pub trait ReciprocalLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::ReciprocalLayerTraitConst { + fn as_raw_mut_ReciprocalLayer(&mut self) -> *mut c_void; + + } + + impl Default for ReciprocalLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ReciprocalLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ReciprocalLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ReciprocalLayer, crate::dnn::ActivationLayer, cv_dnn_ReciprocalLayer_to_ActivationLayer } + + boxed_cast_base! { ReciprocalLayer, core::Algorithm, cv_dnn_ReciprocalLayer_to_Algorithm } + + boxed_cast_base! { ReciprocalLayer, crate::dnn::Layer, cv_dnn_ReciprocalLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for ReciprocalLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for ReciprocalLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReciprocalLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for ReciprocalLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ReciprocalLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReciprocalLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ReciprocalLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ReciprocalLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReciprocalLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ReciprocalLayerTraitConst for ReciprocalLayer { + #[inline] fn as_raw_ReciprocalLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ReciprocalLayerTrait for ReciprocalLayer { + #[inline] fn as_raw_mut_ReciprocalLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReciprocalLayer, crate::dnn::ReciprocalLayerTraitConst, as_raw_ReciprocalLayer, crate::dnn::ReciprocalLayerTrait, as_raw_mut_ReciprocalLayer } + + pub struct ReduceLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ReduceLayer } + + impl Drop for ReduceLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ReduceLayer_delete(self.as_raw_mut_ReduceLayer()) }; + } + } + + unsafe impl Send for ReduceLayer {} + + impl ReduceLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ReduceLayer { + let ret = unsafe { sys::cv_dnn_ReduceLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ReduceLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ReduceLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ReduceLayer] + pub trait ReduceLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ReduceLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ReduceLayer] + pub trait ReduceLayerTrait: crate::dnn::LayerTrait + crate::dnn::ReduceLayerTraitConst { + fn as_raw_mut_ReduceLayer(&mut self) -> *mut c_void; + + } + + impl Default for ReduceLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ReduceLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ReduceLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ReduceLayer, core::Algorithm, cv_dnn_ReduceLayer_to_Algorithm } + + boxed_cast_base! { ReduceLayer, crate::dnn::Layer, cv_dnn_ReduceLayer_to_Layer } + + impl core::AlgorithmTraitConst for ReduceLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ReduceLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReduceLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ReduceLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ReduceLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReduceLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ReduceLayerTraitConst for ReduceLayer { + #[inline] fn as_raw_ReduceLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ReduceLayerTrait for ReduceLayer { + #[inline] fn as_raw_mut_ReduceLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReduceLayer, crate::dnn::ReduceLayerTraitConst, as_raw_ReduceLayer, crate::dnn::ReduceLayerTrait, as_raw_mut_ReduceLayer } + + pub struct RegionLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { RegionLayer } + + impl Drop for RegionLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_RegionLayer_delete(self.as_raw_mut_RegionLayer()) }; + } + } + + unsafe impl Send for RegionLayer {} + + impl RegionLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::RegionLayer { + let ret = unsafe { sys::cv_dnn_RegionLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::RegionLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_RegionLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::RegionLayer] + pub trait RegionLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_RegionLayer(&self) -> *const c_void; + + #[inline] + fn nms_threshold(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_RegionLayer_propNmsThreshold_const(self.as_raw_RegionLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::RegionLayer] + pub trait RegionLayerTrait: crate::dnn::LayerTrait + crate::dnn::RegionLayerTraitConst { + fn as_raw_mut_RegionLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_nms_threshold(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_RegionLayer_propNmsThreshold_const_float(self.as_raw_mut_RegionLayer(), val) }; + ret + } + + } + + impl Default for RegionLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for RegionLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("RegionLayer") + .field("nms_threshold", &crate::dnn::RegionLayerTraitConst::nms_threshold(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { RegionLayer, core::Algorithm, cv_dnn_RegionLayer_to_Algorithm } + + boxed_cast_base! { RegionLayer, crate::dnn::Layer, cv_dnn_RegionLayer_to_Layer } + + impl core::AlgorithmTraitConst for RegionLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for RegionLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RegionLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for RegionLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for RegionLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RegionLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::RegionLayerTraitConst for RegionLayer { + #[inline] fn as_raw_RegionLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::RegionLayerTrait for RegionLayer { + #[inline] fn as_raw_mut_RegionLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RegionLayer, crate::dnn::RegionLayerTraitConst, as_raw_RegionLayer, crate::dnn::RegionLayerTrait, as_raw_mut_RegionLayer } + + pub struct ReorgLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ReorgLayer } + + impl Drop for ReorgLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ReorgLayer_delete(self.as_raw_mut_ReorgLayer()) }; + } + } + + unsafe impl Send for ReorgLayer {} + + impl ReorgLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ReorgLayer { + let ret = unsafe { sys::cv_dnn_ReorgLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ReorgLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ReorgLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ReorgLayer] + pub trait ReorgLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ReorgLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ReorgLayer] + pub trait ReorgLayerTrait: crate::dnn::LayerTrait + crate::dnn::ReorgLayerTraitConst { + fn as_raw_mut_ReorgLayer(&mut self) -> *mut c_void; + + } + + impl Default for ReorgLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ReorgLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ReorgLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ReorgLayer, core::Algorithm, cv_dnn_ReorgLayer_to_Algorithm } + + boxed_cast_base! { ReorgLayer, crate::dnn::Layer, cv_dnn_ReorgLayer_to_Layer } + + impl core::AlgorithmTraitConst for ReorgLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ReorgLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReorgLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ReorgLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ReorgLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReorgLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ReorgLayerTraitConst for ReorgLayer { + #[inline] fn as_raw_ReorgLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ReorgLayerTrait for ReorgLayer { + #[inline] fn as_raw_mut_ReorgLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReorgLayer, crate::dnn::ReorgLayerTraitConst, as_raw_ReorgLayer, crate::dnn::ReorgLayerTrait, as_raw_mut_ReorgLayer } + + pub struct RequantizeLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { RequantizeLayer } + + impl Drop for RequantizeLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_RequantizeLayer_delete(self.as_raw_mut_RequantizeLayer()) }; + } + } + + unsafe impl Send for RequantizeLayer {} + + impl RequantizeLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::RequantizeLayer { + let ret = unsafe { sys::cv_dnn_RequantizeLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::RequantizeLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_RequantizeLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::RequantizeLayer] + pub trait RequantizeLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_RequantizeLayer(&self) -> *const c_void; + + #[inline] + fn scale(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_RequantizeLayer_propScale_const(self.as_raw_RequantizeLayer()) }; + ret + } + + #[inline] + fn shift(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_RequantizeLayer_propShift_const(self.as_raw_RequantizeLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::RequantizeLayer] + pub trait RequantizeLayerTrait: crate::dnn::LayerTrait + crate::dnn::RequantizeLayerTraitConst { + fn as_raw_mut_RequantizeLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_scale(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_RequantizeLayer_propScale_const_float(self.as_raw_mut_RequantizeLayer(), val) }; + ret + } + + #[inline] + fn set_shift(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_RequantizeLayer_propShift_const_float(self.as_raw_mut_RequantizeLayer(), val) }; + ret + } + + } + + impl Default for RequantizeLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for RequantizeLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("RequantizeLayer") + .field("scale", &crate::dnn::RequantizeLayerTraitConst::scale(self)) + .field("shift", &crate::dnn::RequantizeLayerTraitConst::shift(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { RequantizeLayer, core::Algorithm, cv_dnn_RequantizeLayer_to_Algorithm } + + boxed_cast_base! { RequantizeLayer, crate::dnn::Layer, cv_dnn_RequantizeLayer_to_Layer } + + impl core::AlgorithmTraitConst for RequantizeLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for RequantizeLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RequantizeLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for RequantizeLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for RequantizeLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RequantizeLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::RequantizeLayerTraitConst for RequantizeLayer { + #[inline] fn as_raw_RequantizeLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::RequantizeLayerTrait for RequantizeLayer { + #[inline] fn as_raw_mut_RequantizeLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RequantizeLayer, crate::dnn::RequantizeLayerTraitConst, as_raw_RequantizeLayer, crate::dnn::RequantizeLayerTrait, as_raw_mut_RequantizeLayer } + + pub struct ReshapeLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ReshapeLayer } + + impl Drop for ReshapeLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ReshapeLayer_delete(self.as_raw_mut_ReshapeLayer()) }; + } + } + + unsafe impl Send for ReshapeLayer {} + + impl ReshapeLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ReshapeLayer { + let ret = unsafe { sys::cv_dnn_ReshapeLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ReshapeLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ReshapeLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ReshapeLayer] + pub trait ReshapeLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ReshapeLayer(&self) -> *const c_void; + + #[inline] + fn new_shape_desc(&self) -> crate::dnn::MatShape { + let ret = unsafe { sys::cv_dnn_ReshapeLayer_propNewShapeDesc_const(self.as_raw_ReshapeLayer()) }; + let ret = unsafe { crate::dnn::MatShape::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn new_shape_range(&self) -> core::Range { + let ret = unsafe { sys::cv_dnn_ReshapeLayer_propNewShapeRange_const(self.as_raw_ReshapeLayer()) }; + let ret = unsafe { core::Range::opencv_from_extern(ret) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::ReshapeLayer] + pub trait ReshapeLayerTrait: crate::dnn::LayerTrait + crate::dnn::ReshapeLayerTraitConst { + fn as_raw_mut_ReshapeLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_new_shape_desc(&mut self, val: crate::dnn::MatShape) { + let ret = unsafe { sys::cv_dnn_ReshapeLayer_propNewShapeDesc_const_MatShape(self.as_raw_mut_ReshapeLayer(), val.as_raw_VectorOfi32()) }; + ret + } + + #[inline] + fn set_new_shape_range(&mut self, val: core::Range) { + let ret = unsafe { sys::cv_dnn_ReshapeLayer_propNewShapeRange_const_Range(self.as_raw_mut_ReshapeLayer(), val.as_raw_Range()) }; + ret + } + + } + + impl Default for ReshapeLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ReshapeLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ReshapeLayer") + .field("new_shape_desc", &crate::dnn::ReshapeLayerTraitConst::new_shape_desc(self)) + .field("new_shape_range", &crate::dnn::ReshapeLayerTraitConst::new_shape_range(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ReshapeLayer, core::Algorithm, cv_dnn_ReshapeLayer_to_Algorithm } + + boxed_cast_base! { ReshapeLayer, crate::dnn::Layer, cv_dnn_ReshapeLayer_to_Layer } + + impl core::AlgorithmTraitConst for ReshapeLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ReshapeLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReshapeLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ReshapeLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ReshapeLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReshapeLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ReshapeLayerTraitConst for ReshapeLayer { + #[inline] fn as_raw_ReshapeLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ReshapeLayerTrait for ReshapeLayer { + #[inline] fn as_raw_mut_ReshapeLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ReshapeLayer, crate::dnn::ReshapeLayerTraitConst, as_raw_ReshapeLayer, crate::dnn::ReshapeLayerTrait, as_raw_mut_ReshapeLayer } + + /// Resize input 4-dimensional blob by nearest neighbor or bilinear strategy. + /// + /// Layer is used to support TensorFlow's resize_nearest_neighbor and resize_bilinear ops. + pub struct ResizeLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ResizeLayer } + + impl Drop for ResizeLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ResizeLayer_delete(self.as_raw_mut_ResizeLayer()) }; + } + } + + unsafe impl Send for ResizeLayer {} + + impl ResizeLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ResizeLayer { + let ret = unsafe { sys::cv_dnn_ResizeLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ResizeLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ResizeLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ResizeLayer] + pub trait ResizeLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ResizeLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ResizeLayer] + pub trait ResizeLayerTrait: crate::dnn::LayerTrait + crate::dnn::ResizeLayerTraitConst { + fn as_raw_mut_ResizeLayer(&mut self) -> *mut c_void; + + } + + impl Default for ResizeLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ResizeLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ResizeLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ResizeLayer, core::Algorithm, cv_dnn_ResizeLayer_to_Algorithm } + + boxed_cast_base! { ResizeLayer, crate::dnn::Layer, cv_dnn_ResizeLayer_to_Layer } + + impl core::AlgorithmTraitConst for ResizeLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ResizeLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ResizeLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ResizeLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ResizeLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ResizeLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ResizeLayerTraitConst for ResizeLayer { + #[inline] fn as_raw_ResizeLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ResizeLayerTrait for ResizeLayer { + #[inline] fn as_raw_mut_ResizeLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ResizeLayer, crate::dnn::ResizeLayerTraitConst, as_raw_ResizeLayer, crate::dnn::ResizeLayerTrait, as_raw_mut_ResizeLayer } + + pub struct RoundLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { RoundLayer } + + impl Drop for RoundLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_RoundLayer_delete(self.as_raw_mut_RoundLayer()) }; + } + } + + unsafe impl Send for RoundLayer {} + + impl RoundLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::RoundLayer { + let ret = unsafe { sys::cv_dnn_RoundLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::RoundLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_RoundLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::RoundLayer] + pub trait RoundLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_RoundLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::RoundLayer] + pub trait RoundLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::RoundLayerTraitConst { + fn as_raw_mut_RoundLayer(&mut self) -> *mut c_void; + + } + + impl Default for RoundLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for RoundLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("RoundLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { RoundLayer, crate::dnn::ActivationLayer, cv_dnn_RoundLayer_to_ActivationLayer } + + boxed_cast_base! { RoundLayer, core::Algorithm, cv_dnn_RoundLayer_to_Algorithm } + + boxed_cast_base! { RoundLayer, crate::dnn::Layer, cv_dnn_RoundLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for RoundLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for RoundLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RoundLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for RoundLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for RoundLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RoundLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for RoundLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for RoundLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RoundLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::RoundLayerTraitConst for RoundLayer { + #[inline] fn as_raw_RoundLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::RoundLayerTrait for RoundLayer { + #[inline] fn as_raw_mut_RoundLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { RoundLayer, crate::dnn::RoundLayerTraitConst, as_raw_RoundLayer, crate::dnn::RoundLayerTrait, as_raw_mut_RoundLayer } + + pub struct ScaleLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ScaleLayer } + + impl Drop for ScaleLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ScaleLayer_delete(self.as_raw_mut_ScaleLayer()) }; + } + } + + unsafe impl Send for ScaleLayer {} + + impl ScaleLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ScaleLayer { + let ret = unsafe { sys::cv_dnn_ScaleLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ScaleLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ScaleLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ScaleLayer] + pub trait ScaleLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ScaleLayer(&self) -> *const c_void; + + #[inline] + fn has_bias(&self) -> bool { + let ret = unsafe { sys::cv_dnn_ScaleLayer_propHasBias_const(self.as_raw_ScaleLayer()) }; + ret + } + + #[inline] + fn axis(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_ScaleLayer_propAxis_const(self.as_raw_ScaleLayer()) }; + ret + } + + #[inline] + fn mode(&self) -> String { + let ret = unsafe { sys::cv_dnn_ScaleLayer_propMode_const(self.as_raw_ScaleLayer()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::ScaleLayer] + pub trait ScaleLayerTrait: crate::dnn::LayerTrait + crate::dnn::ScaleLayerTraitConst { + fn as_raw_mut_ScaleLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_has_bias(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_ScaleLayer_propHasBias_const_bool(self.as_raw_mut_ScaleLayer(), val) }; + ret + } + + #[inline] + fn set_axis(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_ScaleLayer_propAxis_const_int(self.as_raw_mut_ScaleLayer(), val) }; + ret + } + + #[inline] + fn set_mode(&mut self, val: &str) { + extern_container_arg!(nofail val); + let ret = unsafe { sys::cv_dnn_ScaleLayer_propMode_const_String(self.as_raw_mut_ScaleLayer(), val.opencv_as_extern()) }; + ret + } + + } + + impl Default for ScaleLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ScaleLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ScaleLayer") + .field("has_bias", &crate::dnn::ScaleLayerTraitConst::has_bias(self)) + .field("axis", &crate::dnn::ScaleLayerTraitConst::axis(self)) + .field("mode", &crate::dnn::ScaleLayerTraitConst::mode(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ScaleLayer, core::Algorithm, cv_dnn_ScaleLayer_to_Algorithm } + + boxed_cast_base! { ScaleLayer, crate::dnn::Layer, cv_dnn_ScaleLayer_to_Layer } + + impl core::AlgorithmTraitConst for ScaleLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ScaleLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ScaleLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ScaleLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ScaleLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ScaleLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ScaleLayerTraitConst for ScaleLayer { + #[inline] fn as_raw_ScaleLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ScaleLayerTrait for ScaleLayer { + #[inline] fn as_raw_mut_ScaleLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ScaleLayer, crate::dnn::ScaleLayerTraitConst, as_raw_ScaleLayer, crate::dnn::ScaleLayerTrait, as_raw_mut_ScaleLayer } + + pub struct ScaleLayerInt8 { + ptr: *mut c_void, + } + + opencv_type_boxed! { ScaleLayerInt8 } + + impl Drop for ScaleLayerInt8 { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ScaleLayerInt8_delete(self.as_raw_mut_ScaleLayerInt8()) }; + } + } + + unsafe impl Send for ScaleLayerInt8 {} + + impl ScaleLayerInt8 { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ScaleLayerInt8 { + let ret = unsafe { sys::cv_dnn_ScaleLayerInt8_defaultNew_const() }; + let ret = unsafe { crate::dnn::ScaleLayerInt8::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ScaleLayerInt8_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ScaleLayerInt8] + pub trait ScaleLayerInt8TraitConst: crate::dnn::ScaleLayerTraitConst { + fn as_raw_ScaleLayerInt8(&self) -> *const c_void; + + #[inline] + fn output_sc(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_ScaleLayerInt8_propOutput_sc_const(self.as_raw_ScaleLayerInt8()) }; + ret + } + + #[inline] + fn output_zp(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_ScaleLayerInt8_propOutput_zp_const(self.as_raw_ScaleLayerInt8()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::ScaleLayerInt8] + pub trait ScaleLayerInt8Trait: crate::dnn::ScaleLayerInt8TraitConst + crate::dnn::ScaleLayerTrait { + fn as_raw_mut_ScaleLayerInt8(&mut self) -> *mut c_void; + + #[inline] + fn set_output_sc(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_ScaleLayerInt8_propOutput_sc_const_float(self.as_raw_mut_ScaleLayerInt8(), val) }; + ret + } + + #[inline] + fn set_output_zp(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_ScaleLayerInt8_propOutput_zp_const_int(self.as_raw_mut_ScaleLayerInt8(), val) }; + ret + } + + } + + impl Default for ScaleLayerInt8 { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ScaleLayerInt8 { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ScaleLayerInt8") + .field("output_sc", &crate::dnn::ScaleLayerInt8TraitConst::output_sc(self)) + .field("output_zp", &crate::dnn::ScaleLayerInt8TraitConst::output_zp(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .field("has_bias", &crate::dnn::ScaleLayerTraitConst::has_bias(self)) + .field("axis", &crate::dnn::ScaleLayerTraitConst::axis(self)) + .field("mode", &crate::dnn::ScaleLayerTraitConst::mode(self)) + .finish() + } + } + + boxed_cast_base! { ScaleLayerInt8, core::Algorithm, cv_dnn_ScaleLayerInt8_to_Algorithm } + + boxed_cast_base! { ScaleLayerInt8, crate::dnn::Layer, cv_dnn_ScaleLayerInt8_to_Layer } + + boxed_cast_base! { ScaleLayerInt8, crate::dnn::ScaleLayer, cv_dnn_ScaleLayerInt8_to_ScaleLayer } + + impl core::AlgorithmTraitConst for ScaleLayerInt8 { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ScaleLayerInt8 { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ScaleLayerInt8, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ScaleLayerInt8 { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ScaleLayerInt8 { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ScaleLayerInt8, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ScaleLayerTraitConst for ScaleLayerInt8 { + #[inline] fn as_raw_ScaleLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ScaleLayerTrait for ScaleLayerInt8 { + #[inline] fn as_raw_mut_ScaleLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ScaleLayerInt8, crate::dnn::ScaleLayerTraitConst, as_raw_ScaleLayer, crate::dnn::ScaleLayerTrait, as_raw_mut_ScaleLayer } + + impl crate::dnn::ScaleLayerInt8TraitConst for ScaleLayerInt8 { + #[inline] fn as_raw_ScaleLayerInt8(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ScaleLayerInt8Trait for ScaleLayerInt8 { + #[inline] fn as_raw_mut_ScaleLayerInt8(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ScaleLayerInt8, crate::dnn::ScaleLayerInt8TraitConst, as_raw_ScaleLayerInt8, crate::dnn::ScaleLayerInt8Trait, as_raw_mut_ScaleLayerInt8 } + + pub struct ScatterLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ScatterLayer } + + impl Drop for ScatterLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ScatterLayer_delete(self.as_raw_mut_ScatterLayer()) }; + } + } + + unsafe impl Send for ScatterLayer {} + + impl ScatterLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ScatterLayer { + let ret = unsafe { sys::cv_dnn_ScatterLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ScatterLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ScatterLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ScatterLayer] + pub trait ScatterLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ScatterLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ScatterLayer] + pub trait ScatterLayerTrait: crate::dnn::LayerTrait + crate::dnn::ScatterLayerTraitConst { + fn as_raw_mut_ScatterLayer(&mut self) -> *mut c_void; + + } + + impl Default for ScatterLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ScatterLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ScatterLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ScatterLayer, core::Algorithm, cv_dnn_ScatterLayer_to_Algorithm } + + boxed_cast_base! { ScatterLayer, crate::dnn::Layer, cv_dnn_ScatterLayer_to_Layer } + + impl core::AlgorithmTraitConst for ScatterLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ScatterLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ScatterLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ScatterLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ScatterLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ScatterLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ScatterLayerTraitConst for ScatterLayer { + #[inline] fn as_raw_ScatterLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ScatterLayerTrait for ScatterLayer { + #[inline] fn as_raw_mut_ScatterLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ScatterLayer, crate::dnn::ScatterLayerTraitConst, as_raw_ScatterLayer, crate::dnn::ScatterLayerTrait, as_raw_mut_ScatterLayer } + + pub struct ScatterNDLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ScatterNDLayer } + + impl Drop for ScatterNDLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ScatterNDLayer_delete(self.as_raw_mut_ScatterNDLayer()) }; + } + } + + unsafe impl Send for ScatterNDLayer {} + + impl ScatterNDLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ScatterNDLayer { + let ret = unsafe { sys::cv_dnn_ScatterNDLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ScatterNDLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ScatterNDLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ScatterNDLayer] + pub trait ScatterNDLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ScatterNDLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ScatterNDLayer] + pub trait ScatterNDLayerTrait: crate::dnn::LayerTrait + crate::dnn::ScatterNDLayerTraitConst { + fn as_raw_mut_ScatterNDLayer(&mut self) -> *mut c_void; + + } + + impl Default for ScatterNDLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ScatterNDLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ScatterNDLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ScatterNDLayer, core::Algorithm, cv_dnn_ScatterNDLayer_to_Algorithm } + + boxed_cast_base! { ScatterNDLayer, crate::dnn::Layer, cv_dnn_ScatterNDLayer_to_Layer } + + impl core::AlgorithmTraitConst for ScatterNDLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ScatterNDLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ScatterNDLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ScatterNDLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ScatterNDLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ScatterNDLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ScatterNDLayerTraitConst for ScatterNDLayer { + #[inline] fn as_raw_ScatterNDLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ScatterNDLayerTrait for ScatterNDLayer { + #[inline] fn as_raw_mut_ScatterNDLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ScatterNDLayer, crate::dnn::ScatterNDLayerTraitConst, as_raw_ScatterNDLayer, crate::dnn::ScatterNDLayerTrait, as_raw_mut_ScatterNDLayer } + + /// This class represents high-level API for segmentation models + /// + /// SegmentationModel allows to set params for preprocessing input image. + /// SegmentationModel creates net from file with trained weights and config, + /// sets preprocessing input, runs forward pass and returns the class prediction for each pixel. + pub struct SegmentationModel { + ptr: *mut c_void, + } + + opencv_type_boxed! { SegmentationModel } + + impl Drop for SegmentationModel { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SegmentationModel_delete(self.as_raw_mut_SegmentationModel()) }; + } + } + + unsafe impl Send for SegmentationModel {} + + impl SegmentationModel { + /// Create segmentation model from network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## C++ default parameters + /// * config: "" + #[inline] + pub fn new(model: &str, config: &str) -> Result { + extern_container_arg!(model); + extern_container_arg!(config); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SegmentationModel_SegmentationModel_const_StringR_const_StringR(model.opencv_as_extern(), config.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::SegmentationModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create segmentation model from network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * config: "" + #[inline] + pub fn new_def(model: &str) -> Result { + extern_container_arg!(model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SegmentationModel_SegmentationModel_const_StringR(model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::SegmentationModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create model from deep learning network. + /// ## Parameters + /// * network: Net object. + #[inline] + pub fn new_1(network: &impl crate::dnn::NetTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SegmentationModel_SegmentationModel_const_NetR(network.as_raw_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::SegmentationModel::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SegmentationModel] + pub trait SegmentationModelTraitConst: crate::dnn::ModelTraitConst { + fn as_raw_SegmentationModel(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::SegmentationModel] + pub trait SegmentationModelTrait: crate::dnn::ModelTrait + crate::dnn::SegmentationModelTraitConst { + fn as_raw_mut_SegmentationModel(&mut self) -> *mut c_void; + + /// Given the @p input frame, create input blob, run net + /// ## Parameters + /// * frame: The input image. + /// * mask:[out] Allocated class prediction for each pixel + #[inline] + fn segment(&mut self, frame: &impl ToInputArray, mask: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(frame); + output_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SegmentationModel_segment_const__InputArrayR_const__OutputArrayR(self.as_raw_mut_SegmentationModel(), frame.as_raw__InputArray(), mask.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl Clone for SegmentationModel { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_dnn_SegmentationModel_implicitClone_const(self.as_raw_SegmentationModel())) } + } + } + + impl std::fmt::Debug for SegmentationModel { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SegmentationModel") + .finish() + } + } + + boxed_cast_base! { SegmentationModel, crate::dnn::Model, cv_dnn_SegmentationModel_to_Model } + + impl crate::dnn::ModelTraitConst for SegmentationModel { + #[inline] fn as_raw_Model(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ModelTrait for SegmentationModel { + #[inline] fn as_raw_mut_Model(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SegmentationModel, crate::dnn::ModelTraitConst, as_raw_Model, crate::dnn::ModelTrait, as_raw_mut_Model } + + impl crate::dnn::SegmentationModelTraitConst for SegmentationModel { + #[inline] fn as_raw_SegmentationModel(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SegmentationModelTrait for SegmentationModel { + #[inline] fn as_raw_mut_SegmentationModel(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SegmentationModel, crate::dnn::SegmentationModelTraitConst, as_raw_SegmentationModel, crate::dnn::SegmentationModelTrait, as_raw_mut_SegmentationModel } + + pub struct SeluLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { SeluLayer } + + impl Drop for SeluLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SeluLayer_delete(self.as_raw_mut_SeluLayer()) }; + } + } + + unsafe impl Send for SeluLayer {} + + impl SeluLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SeluLayer { + let ret = unsafe { sys::cv_dnn_SeluLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::SeluLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SeluLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SeluLayer] + pub trait SeluLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_SeluLayer(&self) -> *const c_void; + + #[inline] + fn alpha(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_SeluLayer_propAlpha_const(self.as_raw_SeluLayer()) }; + ret + } + + #[inline] + fn gamma(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_SeluLayer_propGamma_const(self.as_raw_SeluLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::SeluLayer] + pub trait SeluLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::SeluLayerTraitConst { + fn as_raw_mut_SeluLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_alpha(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_SeluLayer_propAlpha_const_float(self.as_raw_mut_SeluLayer(), val) }; + ret + } + + #[inline] + fn set_gamma(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_SeluLayer_propGamma_const_float(self.as_raw_mut_SeluLayer(), val) }; + ret + } + + } + + impl Default for SeluLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SeluLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SeluLayer") + .field("alpha", &crate::dnn::SeluLayerTraitConst::alpha(self)) + .field("gamma", &crate::dnn::SeluLayerTraitConst::gamma(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { SeluLayer, crate::dnn::ActivationLayer, cv_dnn_SeluLayer_to_ActivationLayer } + + boxed_cast_base! { SeluLayer, core::Algorithm, cv_dnn_SeluLayer_to_Algorithm } + + boxed_cast_base! { SeluLayer, crate::dnn::Layer, cv_dnn_SeluLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for SeluLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for SeluLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SeluLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for SeluLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SeluLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SeluLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SeluLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SeluLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SeluLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SeluLayerTraitConst for SeluLayer { + #[inline] fn as_raw_SeluLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SeluLayerTrait for SeluLayer { + #[inline] fn as_raw_mut_SeluLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SeluLayer, crate::dnn::SeluLayerTraitConst, as_raw_SeluLayer, crate::dnn::SeluLayerTrait, as_raw_mut_SeluLayer } + + pub struct ShiftLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ShiftLayer } + + impl Drop for ShiftLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ShiftLayer_delete(self.as_raw_mut_ShiftLayer()) }; + } + } + + unsafe impl Send for ShiftLayer {} + + impl ShiftLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ShiftLayer { + let ret = unsafe { sys::cv_dnn_ShiftLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ShiftLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ShiftLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ShiftLayer] + pub trait ShiftLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ShiftLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ShiftLayer] + pub trait ShiftLayerTrait: crate::dnn::LayerTrait + crate::dnn::ShiftLayerTraitConst { + fn as_raw_mut_ShiftLayer(&mut self) -> *mut c_void; + + } + + impl Default for ShiftLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ShiftLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ShiftLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ShiftLayer, core::Algorithm, cv_dnn_ShiftLayer_to_Algorithm } + + boxed_cast_base! { ShiftLayer, crate::dnn::Layer, cv_dnn_ShiftLayer_to_Layer } + + impl core::AlgorithmTraitConst for ShiftLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ShiftLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ShiftLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ShiftLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ShiftLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ShiftLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ShiftLayerTraitConst for ShiftLayer { + #[inline] fn as_raw_ShiftLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ShiftLayerTrait for ShiftLayer { + #[inline] fn as_raw_mut_ShiftLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ShiftLayer, crate::dnn::ShiftLayerTraitConst, as_raw_ShiftLayer, crate::dnn::ShiftLayerTrait, as_raw_mut_ShiftLayer } + + pub struct ShiftLayerInt8 { + ptr: *mut c_void, + } + + opencv_type_boxed! { ShiftLayerInt8 } + + impl Drop for ShiftLayerInt8 { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ShiftLayerInt8_delete(self.as_raw_mut_ShiftLayerInt8()) }; + } + } + + unsafe impl Send for ShiftLayerInt8 {} + + impl ShiftLayerInt8 { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ShiftLayerInt8 { + let ret = unsafe { sys::cv_dnn_ShiftLayerInt8_defaultNew_const() }; + let ret = unsafe { crate::dnn::ShiftLayerInt8::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ShiftLayerInt8_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ShiftLayerInt8] + pub trait ShiftLayerInt8TraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ShiftLayerInt8(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::ShiftLayerInt8] + pub trait ShiftLayerInt8Trait: crate::dnn::LayerTrait + crate::dnn::ShiftLayerInt8TraitConst { + fn as_raw_mut_ShiftLayerInt8(&mut self) -> *mut c_void; + + } + + impl Default for ShiftLayerInt8 { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ShiftLayerInt8 { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ShiftLayerInt8") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ShiftLayerInt8, core::Algorithm, cv_dnn_ShiftLayerInt8_to_Algorithm } + + boxed_cast_base! { ShiftLayerInt8, crate::dnn::Layer, cv_dnn_ShiftLayerInt8_to_Layer } + + impl core::AlgorithmTraitConst for ShiftLayerInt8 { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ShiftLayerInt8 { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ShiftLayerInt8, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ShiftLayerInt8 { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ShiftLayerInt8 { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ShiftLayerInt8, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ShiftLayerInt8TraitConst for ShiftLayerInt8 { + #[inline] fn as_raw_ShiftLayerInt8(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ShiftLayerInt8Trait for ShiftLayerInt8 { + #[inline] fn as_raw_mut_ShiftLayerInt8(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ShiftLayerInt8, crate::dnn::ShiftLayerInt8TraitConst, as_raw_ShiftLayerInt8, crate::dnn::ShiftLayerInt8Trait, as_raw_mut_ShiftLayerInt8 } + + pub struct ShrinkLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ShrinkLayer } + + impl Drop for ShrinkLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ShrinkLayer_delete(self.as_raw_mut_ShrinkLayer()) }; + } + } + + unsafe impl Send for ShrinkLayer {} + + impl ShrinkLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ShrinkLayer { + let ret = unsafe { sys::cv_dnn_ShrinkLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ShrinkLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ShrinkLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ShrinkLayer] + pub trait ShrinkLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_ShrinkLayer(&self) -> *const c_void; + + #[inline] + fn bias(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_ShrinkLayer_propBias_const(self.as_raw_ShrinkLayer()) }; + ret + } + + #[inline] + fn lambd(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_ShrinkLayer_propLambd_const(self.as_raw_ShrinkLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::ShrinkLayer] + pub trait ShrinkLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::ShrinkLayerTraitConst { + fn as_raw_mut_ShrinkLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_bias(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_ShrinkLayer_propBias_const_float(self.as_raw_mut_ShrinkLayer(), val) }; + ret + } + + #[inline] + fn set_lambd(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_ShrinkLayer_propLambd_const_float(self.as_raw_mut_ShrinkLayer(), val) }; + ret + } + + } + + impl Default for ShrinkLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ShrinkLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ShrinkLayer") + .field("bias", &crate::dnn::ShrinkLayerTraitConst::bias(self)) + .field("lambd", &crate::dnn::ShrinkLayerTraitConst::lambd(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ShrinkLayer, crate::dnn::ActivationLayer, cv_dnn_ShrinkLayer_to_ActivationLayer } + + boxed_cast_base! { ShrinkLayer, core::Algorithm, cv_dnn_ShrinkLayer_to_Algorithm } + + boxed_cast_base! { ShrinkLayer, crate::dnn::Layer, cv_dnn_ShrinkLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for ShrinkLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for ShrinkLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ShrinkLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for ShrinkLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ShrinkLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ShrinkLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ShrinkLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ShrinkLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ShrinkLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ShrinkLayerTraitConst for ShrinkLayer { + #[inline] fn as_raw_ShrinkLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ShrinkLayerTrait for ShrinkLayer { + #[inline] fn as_raw_mut_ShrinkLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ShrinkLayer, crate::dnn::ShrinkLayerTraitConst, as_raw_ShrinkLayer, crate::dnn::ShrinkLayerTrait, as_raw_mut_ShrinkLayer } + + /// Permute channels of 4-dimensional input blob. + /// ## Parameters + /// * group: Number of groups to split input channels and pick in turns + /// into output blob. + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%20groupSize%20%3D%20%5Cfrac%7Bnumber%5C%20of%5C%20channels%7D%7Bgroup%7D%20) + /// ![block formula](https://latex.codecogs.com/png.latex?%20output%28n%2C%20c%2C%20h%2C%20w%29%20%3D%20input%28n%2C%20groupSize%20%5Ctimes%20%28c%20%5C%25%20group%29%20%2B%20%5Clfloor%20%5Cfrac%7Bc%7D%7Bgroup%7D%20%5Crfloor%2C%20h%2C%20w%29%20) + /// Read more at + pub struct ShuffleChannelLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ShuffleChannelLayer } + + impl Drop for ShuffleChannelLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ShuffleChannelLayer_delete(self.as_raw_mut_ShuffleChannelLayer()) }; + } + } + + unsafe impl Send for ShuffleChannelLayer {} + + impl ShuffleChannelLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ShuffleChannelLayer { + let ret = unsafe { sys::cv_dnn_ShuffleChannelLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ShuffleChannelLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ShuffleChannelLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ShuffleChannelLayer] + pub trait ShuffleChannelLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_ShuffleChannelLayer(&self) -> *const c_void; + + #[inline] + fn group(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_ShuffleChannelLayer_propGroup_const(self.as_raw_ShuffleChannelLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::ShuffleChannelLayer] + pub trait ShuffleChannelLayerTrait: crate::dnn::LayerTrait + crate::dnn::ShuffleChannelLayerTraitConst { + fn as_raw_mut_ShuffleChannelLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_group(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_ShuffleChannelLayer_propGroup_const_int(self.as_raw_mut_ShuffleChannelLayer(), val) }; + ret + } + + } + + impl Default for ShuffleChannelLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ShuffleChannelLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ShuffleChannelLayer") + .field("group", &crate::dnn::ShuffleChannelLayerTraitConst::group(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ShuffleChannelLayer, core::Algorithm, cv_dnn_ShuffleChannelLayer_to_Algorithm } + + boxed_cast_base! { ShuffleChannelLayer, crate::dnn::Layer, cv_dnn_ShuffleChannelLayer_to_Layer } + + impl core::AlgorithmTraitConst for ShuffleChannelLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ShuffleChannelLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ShuffleChannelLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ShuffleChannelLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ShuffleChannelLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ShuffleChannelLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ShuffleChannelLayerTraitConst for ShuffleChannelLayer { + #[inline] fn as_raw_ShuffleChannelLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ShuffleChannelLayerTrait for ShuffleChannelLayer { + #[inline] fn as_raw_mut_ShuffleChannelLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ShuffleChannelLayer, crate::dnn::ShuffleChannelLayerTraitConst, as_raw_ShuffleChannelLayer, crate::dnn::ShuffleChannelLayerTrait, as_raw_mut_ShuffleChannelLayer } + + pub struct SigmoidLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { SigmoidLayer } + + impl Drop for SigmoidLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SigmoidLayer_delete(self.as_raw_mut_SigmoidLayer()) }; + } + } + + unsafe impl Send for SigmoidLayer {} + + impl SigmoidLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SigmoidLayer { + let ret = unsafe { sys::cv_dnn_SigmoidLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::SigmoidLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SigmoidLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SigmoidLayer] + pub trait SigmoidLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_SigmoidLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::SigmoidLayer] + pub trait SigmoidLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::SigmoidLayerTraitConst { + fn as_raw_mut_SigmoidLayer(&mut self) -> *mut c_void; + + } + + impl Default for SigmoidLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SigmoidLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SigmoidLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { SigmoidLayer, crate::dnn::ActivationLayer, cv_dnn_SigmoidLayer_to_ActivationLayer } + + boxed_cast_base! { SigmoidLayer, core::Algorithm, cv_dnn_SigmoidLayer_to_Algorithm } + + boxed_cast_base! { SigmoidLayer, crate::dnn::Layer, cv_dnn_SigmoidLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for SigmoidLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for SigmoidLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SigmoidLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for SigmoidLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SigmoidLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SigmoidLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SigmoidLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SigmoidLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SigmoidLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SigmoidLayerTraitConst for SigmoidLayer { + #[inline] fn as_raw_SigmoidLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SigmoidLayerTrait for SigmoidLayer { + #[inline] fn as_raw_mut_SigmoidLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SigmoidLayer, crate::dnn::SigmoidLayerTraitConst, as_raw_SigmoidLayer, crate::dnn::SigmoidLayerTrait, as_raw_mut_SigmoidLayer } + + pub struct SignLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { SignLayer } + + impl Drop for SignLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SignLayer_delete(self.as_raw_mut_SignLayer()) }; + } + } + + unsafe impl Send for SignLayer {} + + impl SignLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SignLayer { + let ret = unsafe { sys::cv_dnn_SignLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::SignLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SignLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SignLayer] + pub trait SignLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_SignLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::SignLayer] + pub trait SignLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::SignLayerTraitConst { + fn as_raw_mut_SignLayer(&mut self) -> *mut c_void; + + } + + impl Default for SignLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SignLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SignLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { SignLayer, crate::dnn::ActivationLayer, cv_dnn_SignLayer_to_ActivationLayer } + + boxed_cast_base! { SignLayer, core::Algorithm, cv_dnn_SignLayer_to_Algorithm } + + boxed_cast_base! { SignLayer, crate::dnn::Layer, cv_dnn_SignLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for SignLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for SignLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SignLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for SignLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SignLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SignLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SignLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SignLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SignLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SignLayerTraitConst for SignLayer { + #[inline] fn as_raw_SignLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SignLayerTrait for SignLayer { + #[inline] fn as_raw_mut_SignLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SignLayer, crate::dnn::SignLayerTraitConst, as_raw_SignLayer, crate::dnn::SignLayerTrait, as_raw_mut_SignLayer } + + pub struct SinLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { SinLayer } + + impl Drop for SinLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SinLayer_delete(self.as_raw_mut_SinLayer()) }; + } + } + + unsafe impl Send for SinLayer {} + + impl SinLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SinLayer { + let ret = unsafe { sys::cv_dnn_SinLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::SinLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SinLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SinLayer] + pub trait SinLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_SinLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::SinLayer] + pub trait SinLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::SinLayerTraitConst { + fn as_raw_mut_SinLayer(&mut self) -> *mut c_void; + + } + + impl Default for SinLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SinLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SinLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { SinLayer, crate::dnn::ActivationLayer, cv_dnn_SinLayer_to_ActivationLayer } + + boxed_cast_base! { SinLayer, core::Algorithm, cv_dnn_SinLayer_to_Algorithm } + + boxed_cast_base! { SinLayer, crate::dnn::Layer, cv_dnn_SinLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for SinLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for SinLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SinLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for SinLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SinLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SinLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SinLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SinLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SinLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SinLayerTraitConst for SinLayer { + #[inline] fn as_raw_SinLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SinLayerTrait for SinLayer { + #[inline] fn as_raw_mut_SinLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SinLayer, crate::dnn::SinLayerTraitConst, as_raw_SinLayer, crate::dnn::SinLayerTrait, as_raw_mut_SinLayer } + + pub struct SinhLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { SinhLayer } + + impl Drop for SinhLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SinhLayer_delete(self.as_raw_mut_SinhLayer()) }; + } + } + + unsafe impl Send for SinhLayer {} + + impl SinhLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SinhLayer { + let ret = unsafe { sys::cv_dnn_SinhLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::SinhLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SinhLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SinhLayer] + pub trait SinhLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_SinhLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::SinhLayer] + pub trait SinhLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::SinhLayerTraitConst { + fn as_raw_mut_SinhLayer(&mut self) -> *mut c_void; + + } + + impl Default for SinhLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SinhLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SinhLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { SinhLayer, crate::dnn::ActivationLayer, cv_dnn_SinhLayer_to_ActivationLayer } + + boxed_cast_base! { SinhLayer, core::Algorithm, cv_dnn_SinhLayer_to_Algorithm } + + boxed_cast_base! { SinhLayer, crate::dnn::Layer, cv_dnn_SinhLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for SinhLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for SinhLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SinhLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for SinhLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SinhLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SinhLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SinhLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SinhLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SinhLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SinhLayerTraitConst for SinhLayer { + #[inline] fn as_raw_SinhLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SinhLayerTrait for SinhLayer { + #[inline] fn as_raw_mut_SinhLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SinhLayer, crate::dnn::SinhLayerTraitConst, as_raw_SinhLayer, crate::dnn::SinhLayerTrait, as_raw_mut_SinhLayer } + + /// Slice layer has several modes: + /// 1. Caffe mode + /// ## Parameters + /// * axis: Axis of split operation + /// * slice_point: Array of split points + /// + /// Number of output blobs equals to number of split points plus one. The + /// first blob is a slice on input from 0 to @p slice_point[0] - 1 by @p axis, + /// the second output blob is a slice of input from @p slice_point[0] to + /// @p slice_point[1] - 1 by @p axis and the last output blob is a slice of + /// input from @p slice_point[-1] up to the end of @p axis size. + /// + /// 2. TensorFlow mode + /// * begin: Vector of start indices + /// * size: Vector of sizes + /// + /// More convenient numpy-like slice. One and only output blob + /// is a slice `input[begin[0]:begin[0]+size[0], begin[1]:begin[1]+size[1], ...]` + /// + /// 3. Torch mode + /// * axis: Axis of split operation + /// + /// Split input blob on the equal parts by @p axis. + pub struct SliceLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { SliceLayer } + + impl Drop for SliceLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SliceLayer_delete(self.as_raw_mut_SliceLayer()) }; + } + } + + unsafe impl Send for SliceLayer {} + + impl SliceLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SliceLayer { + let ret = unsafe { sys::cv_dnn_SliceLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::SliceLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SliceLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SliceLayer] + pub trait SliceLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_SliceLayer(&self) -> *const c_void; + + /// Vector of slice ranges. + /// + /// The first dimension equals number of output blobs. + /// Inner vector has slice ranges for the first number of input dimensions. + #[inline] + fn slice_ranges(&self) -> core::Vector> { + let ret = unsafe { sys::cv_dnn_SliceLayer_propSliceRanges_const(self.as_raw_SliceLayer()) }; + let ret = unsafe { core::Vector::>::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn slice_steps(&self) -> core::Vector> { + let ret = unsafe { sys::cv_dnn_SliceLayer_propSliceSteps_const(self.as_raw_SliceLayer()) }; + let ret = unsafe { core::Vector::>::opencv_from_extern(ret) }; + ret + } + + #[inline] + fn axis(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_SliceLayer_propAxis_const(self.as_raw_SliceLayer()) }; + ret + } + + #[inline] + fn num_split(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_SliceLayer_propNum_split_const(self.as_raw_SliceLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::SliceLayer] + pub trait SliceLayerTrait: crate::dnn::LayerTrait + crate::dnn::SliceLayerTraitConst { + fn as_raw_mut_SliceLayer(&mut self) -> *mut c_void; + + /// Vector of slice ranges. + /// + /// The first dimension equals number of output blobs. + /// Inner vector has slice ranges for the first number of input dimensions. + #[inline] + fn set_slice_ranges(&mut self, val: core::Vector>) { + let ret = unsafe { sys::cv_dnn_SliceLayer_propSliceRanges_const_vectorLvectorLRangeGG(self.as_raw_mut_SliceLayer(), val.as_raw_VectorOfVectorOfRange()) }; + ret + } + + #[inline] + fn set_slice_steps(&mut self, val: core::Vector>) { + let ret = unsafe { sys::cv_dnn_SliceLayer_propSliceSteps_const_vectorLvectorLintGG(self.as_raw_mut_SliceLayer(), val.as_raw_VectorOfVectorOfi32()) }; + ret + } + + #[inline] + fn set_axis(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_SliceLayer_propAxis_const_int(self.as_raw_mut_SliceLayer(), val) }; + ret + } + + #[inline] + fn set_num_split(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_SliceLayer_propNum_split_const_int(self.as_raw_mut_SliceLayer(), val) }; + ret + } + + } + + impl Default for SliceLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SliceLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SliceLayer") + .field("slice_ranges", &crate::dnn::SliceLayerTraitConst::slice_ranges(self)) + .field("slice_steps", &crate::dnn::SliceLayerTraitConst::slice_steps(self)) + .field("axis", &crate::dnn::SliceLayerTraitConst::axis(self)) + .field("num_split", &crate::dnn::SliceLayerTraitConst::num_split(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { SliceLayer, core::Algorithm, cv_dnn_SliceLayer_to_Algorithm } + + boxed_cast_base! { SliceLayer, crate::dnn::Layer, cv_dnn_SliceLayer_to_Layer } + + impl core::AlgorithmTraitConst for SliceLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SliceLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SliceLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SliceLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SliceLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SliceLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SliceLayerTraitConst for SliceLayer { + #[inline] fn as_raw_SliceLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SliceLayerTrait for SliceLayer { + #[inline] fn as_raw_mut_SliceLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SliceLayer, crate::dnn::SliceLayerTraitConst, as_raw_SliceLayer, crate::dnn::SliceLayerTrait, as_raw_mut_SliceLayer } + + pub struct SoftmaxLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { SoftmaxLayer } + + impl Drop for SoftmaxLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SoftmaxLayer_delete(self.as_raw_mut_SoftmaxLayer()) }; + } + } + + unsafe impl Send for SoftmaxLayer {} + + impl SoftmaxLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SoftmaxLayer { + let ret = unsafe { sys::cv_dnn_SoftmaxLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::SoftmaxLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SoftmaxLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SoftmaxLayer] + pub trait SoftmaxLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_SoftmaxLayer(&self) -> *const c_void; + + #[inline] + fn log_soft_max(&self) -> bool { + let ret = unsafe { sys::cv_dnn_SoftmaxLayer_propLogSoftMax_const(self.as_raw_SoftmaxLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::SoftmaxLayer] + pub trait SoftmaxLayerTrait: crate::dnn::LayerTrait + crate::dnn::SoftmaxLayerTraitConst { + fn as_raw_mut_SoftmaxLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_log_soft_max(&mut self, val: bool) { + let ret = unsafe { sys::cv_dnn_SoftmaxLayer_propLogSoftMax_const_bool(self.as_raw_mut_SoftmaxLayer(), val) }; + ret + } + + } + + impl Default for SoftmaxLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SoftmaxLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SoftmaxLayer") + .field("log_soft_max", &crate::dnn::SoftmaxLayerTraitConst::log_soft_max(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { SoftmaxLayer, core::Algorithm, cv_dnn_SoftmaxLayer_to_Algorithm } + + boxed_cast_base! { SoftmaxLayer, crate::dnn::Layer, cv_dnn_SoftmaxLayer_to_Layer } + + impl core::AlgorithmTraitConst for SoftmaxLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SoftmaxLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftmaxLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SoftmaxLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SoftmaxLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftmaxLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SoftmaxLayerTraitConst for SoftmaxLayer { + #[inline] fn as_raw_SoftmaxLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SoftmaxLayerTrait for SoftmaxLayer { + #[inline] fn as_raw_mut_SoftmaxLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftmaxLayer, crate::dnn::SoftmaxLayerTraitConst, as_raw_SoftmaxLayer, crate::dnn::SoftmaxLayerTrait, as_raw_mut_SoftmaxLayer } + + pub struct SoftmaxLayerInt8 { + ptr: *mut c_void, + } + + opencv_type_boxed! { SoftmaxLayerInt8 } + + impl Drop for SoftmaxLayerInt8 { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SoftmaxLayerInt8_delete(self.as_raw_mut_SoftmaxLayerInt8()) }; + } + } + + unsafe impl Send for SoftmaxLayerInt8 {} + + impl SoftmaxLayerInt8 { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SoftmaxLayerInt8 { + let ret = unsafe { sys::cv_dnn_SoftmaxLayerInt8_defaultNew_const() }; + let ret = unsafe { crate::dnn::SoftmaxLayerInt8::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SoftmaxLayerInt8_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SoftmaxLayerInt8] + pub trait SoftmaxLayerInt8TraitConst: crate::dnn::SoftmaxLayerTraitConst { + fn as_raw_SoftmaxLayerInt8(&self) -> *const c_void; + + #[inline] + fn output_sc(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_SoftmaxLayerInt8_propOutput_sc_const(self.as_raw_SoftmaxLayerInt8()) }; + ret + } + + #[inline] + fn output_zp(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_SoftmaxLayerInt8_propOutput_zp_const(self.as_raw_SoftmaxLayerInt8()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::SoftmaxLayerInt8] + pub trait SoftmaxLayerInt8Trait: crate::dnn::SoftmaxLayerInt8TraitConst + crate::dnn::SoftmaxLayerTrait { + fn as_raw_mut_SoftmaxLayerInt8(&mut self) -> *mut c_void; + + #[inline] + fn set_output_sc(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_SoftmaxLayerInt8_propOutput_sc_const_float(self.as_raw_mut_SoftmaxLayerInt8(), val) }; + ret + } + + #[inline] + fn set_output_zp(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_SoftmaxLayerInt8_propOutput_zp_const_int(self.as_raw_mut_SoftmaxLayerInt8(), val) }; + ret + } + + } + + impl Default for SoftmaxLayerInt8 { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SoftmaxLayerInt8 { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SoftmaxLayerInt8") + .field("output_sc", &crate::dnn::SoftmaxLayerInt8TraitConst::output_sc(self)) + .field("output_zp", &crate::dnn::SoftmaxLayerInt8TraitConst::output_zp(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .field("log_soft_max", &crate::dnn::SoftmaxLayerTraitConst::log_soft_max(self)) + .finish() + } + } + + boxed_cast_base! { SoftmaxLayerInt8, core::Algorithm, cv_dnn_SoftmaxLayerInt8_to_Algorithm } + + boxed_cast_base! { SoftmaxLayerInt8, crate::dnn::Layer, cv_dnn_SoftmaxLayerInt8_to_Layer } + + boxed_cast_base! { SoftmaxLayerInt8, crate::dnn::SoftmaxLayer, cv_dnn_SoftmaxLayerInt8_to_SoftmaxLayer } + + impl core::AlgorithmTraitConst for SoftmaxLayerInt8 { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SoftmaxLayerInt8 { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftmaxLayerInt8, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SoftmaxLayerInt8 { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SoftmaxLayerInt8 { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftmaxLayerInt8, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SoftmaxLayerTraitConst for SoftmaxLayerInt8 { + #[inline] fn as_raw_SoftmaxLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SoftmaxLayerTrait for SoftmaxLayerInt8 { + #[inline] fn as_raw_mut_SoftmaxLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftmaxLayerInt8, crate::dnn::SoftmaxLayerTraitConst, as_raw_SoftmaxLayer, crate::dnn::SoftmaxLayerTrait, as_raw_mut_SoftmaxLayer } + + impl crate::dnn::SoftmaxLayerInt8TraitConst for SoftmaxLayerInt8 { + #[inline] fn as_raw_SoftmaxLayerInt8(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SoftmaxLayerInt8Trait for SoftmaxLayerInt8 { + #[inline] fn as_raw_mut_SoftmaxLayerInt8(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftmaxLayerInt8, crate::dnn::SoftmaxLayerInt8TraitConst, as_raw_SoftmaxLayerInt8, crate::dnn::SoftmaxLayerInt8Trait, as_raw_mut_SoftmaxLayerInt8 } + + pub struct SoftplusLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { SoftplusLayer } + + impl Drop for SoftplusLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SoftplusLayer_delete(self.as_raw_mut_SoftplusLayer()) }; + } + } + + unsafe impl Send for SoftplusLayer {} + + impl SoftplusLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SoftplusLayer { + let ret = unsafe { sys::cv_dnn_SoftplusLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::SoftplusLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SoftplusLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SoftplusLayer] + pub trait SoftplusLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_SoftplusLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::SoftplusLayer] + pub trait SoftplusLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::SoftplusLayerTraitConst { + fn as_raw_mut_SoftplusLayer(&mut self) -> *mut c_void; + + } + + impl Default for SoftplusLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SoftplusLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SoftplusLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { SoftplusLayer, crate::dnn::ActivationLayer, cv_dnn_SoftplusLayer_to_ActivationLayer } + + boxed_cast_base! { SoftplusLayer, core::Algorithm, cv_dnn_SoftplusLayer_to_Algorithm } + + boxed_cast_base! { SoftplusLayer, crate::dnn::Layer, cv_dnn_SoftplusLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for SoftplusLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for SoftplusLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftplusLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for SoftplusLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SoftplusLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftplusLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SoftplusLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SoftplusLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftplusLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SoftplusLayerTraitConst for SoftplusLayer { + #[inline] fn as_raw_SoftplusLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SoftplusLayerTrait for SoftplusLayer { + #[inline] fn as_raw_mut_SoftplusLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftplusLayer, crate::dnn::SoftplusLayerTraitConst, as_raw_SoftplusLayer, crate::dnn::SoftplusLayerTrait, as_raw_mut_SoftplusLayer } + + pub struct SoftsignLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { SoftsignLayer } + + impl Drop for SoftsignLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SoftsignLayer_delete(self.as_raw_mut_SoftsignLayer()) }; + } + } + + unsafe impl Send for SoftsignLayer {} + + impl SoftsignLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SoftsignLayer { + let ret = unsafe { sys::cv_dnn_SoftsignLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::SoftsignLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SoftsignLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SoftsignLayer] + pub trait SoftsignLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_SoftsignLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::SoftsignLayer] + pub trait SoftsignLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::SoftsignLayerTraitConst { + fn as_raw_mut_SoftsignLayer(&mut self) -> *mut c_void; + + } + + impl Default for SoftsignLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SoftsignLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SoftsignLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { SoftsignLayer, crate::dnn::ActivationLayer, cv_dnn_SoftsignLayer_to_ActivationLayer } + + boxed_cast_base! { SoftsignLayer, core::Algorithm, cv_dnn_SoftsignLayer_to_Algorithm } + + boxed_cast_base! { SoftsignLayer, crate::dnn::Layer, cv_dnn_SoftsignLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for SoftsignLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for SoftsignLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftsignLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for SoftsignLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SoftsignLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftsignLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SoftsignLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SoftsignLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftsignLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SoftsignLayerTraitConst for SoftsignLayer { + #[inline] fn as_raw_SoftsignLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SoftsignLayerTrait for SoftsignLayer { + #[inline] fn as_raw_mut_SoftsignLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SoftsignLayer, crate::dnn::SoftsignLayerTraitConst, as_raw_SoftsignLayer, crate::dnn::SoftsignLayerTrait, as_raw_mut_SoftsignLayer } + + pub struct SpaceToDepthLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { SpaceToDepthLayer } + + impl Drop for SpaceToDepthLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SpaceToDepthLayer_delete(self.as_raw_mut_SpaceToDepthLayer()) }; + } + } + + unsafe impl Send for SpaceToDepthLayer {} + + impl SpaceToDepthLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SpaceToDepthLayer { + let ret = unsafe { sys::cv_dnn_SpaceToDepthLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::SpaceToDepthLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SpaceToDepthLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SpaceToDepthLayer] + pub trait SpaceToDepthLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_SpaceToDepthLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::SpaceToDepthLayer] + pub trait SpaceToDepthLayerTrait: crate::dnn::LayerTrait + crate::dnn::SpaceToDepthLayerTraitConst { + fn as_raw_mut_SpaceToDepthLayer(&mut self) -> *mut c_void; + + } + + impl Default for SpaceToDepthLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SpaceToDepthLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SpaceToDepthLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { SpaceToDepthLayer, core::Algorithm, cv_dnn_SpaceToDepthLayer_to_Algorithm } + + boxed_cast_base! { SpaceToDepthLayer, crate::dnn::Layer, cv_dnn_SpaceToDepthLayer_to_Layer } + + impl core::AlgorithmTraitConst for SpaceToDepthLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SpaceToDepthLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SpaceToDepthLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SpaceToDepthLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SpaceToDepthLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SpaceToDepthLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SpaceToDepthLayerTraitConst for SpaceToDepthLayer { + #[inline] fn as_raw_SpaceToDepthLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SpaceToDepthLayerTrait for SpaceToDepthLayer { + #[inline] fn as_raw_mut_SpaceToDepthLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SpaceToDepthLayer, crate::dnn::SpaceToDepthLayerTraitConst, as_raw_SpaceToDepthLayer, crate::dnn::SpaceToDepthLayerTrait, as_raw_mut_SpaceToDepthLayer } + + pub struct SplitLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { SplitLayer } + + impl Drop for SplitLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SplitLayer_delete(self.as_raw_mut_SplitLayer()) }; + } + } + + unsafe impl Send for SplitLayer {} + + impl SplitLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SplitLayer { + let ret = unsafe { sys::cv_dnn_SplitLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::SplitLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SplitLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SplitLayer] + pub trait SplitLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_SplitLayer(&self) -> *const c_void; + + /// Number of copies that will be produced (is ignored when negative). + #[inline] + fn outputs_count(&self) -> i32 { + let ret = unsafe { sys::cv_dnn_SplitLayer_propOutputsCount_const(self.as_raw_SplitLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::SplitLayer] + pub trait SplitLayerTrait: crate::dnn::LayerTrait + crate::dnn::SplitLayerTraitConst { + fn as_raw_mut_SplitLayer(&mut self) -> *mut c_void; + + /// Number of copies that will be produced (is ignored when negative). + #[inline] + fn set_outputs_count(&mut self, val: i32) { + let ret = unsafe { sys::cv_dnn_SplitLayer_propOutputsCount_const_int(self.as_raw_mut_SplitLayer(), val) }; + ret + } + + } + + impl Default for SplitLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SplitLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SplitLayer") + .field("outputs_count", &crate::dnn::SplitLayerTraitConst::outputs_count(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { SplitLayer, core::Algorithm, cv_dnn_SplitLayer_to_Algorithm } + + boxed_cast_base! { SplitLayer, crate::dnn::Layer, cv_dnn_SplitLayer_to_Layer } + + impl core::AlgorithmTraitConst for SplitLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SplitLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SplitLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SplitLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SplitLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SplitLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SplitLayerTraitConst for SplitLayer { + #[inline] fn as_raw_SplitLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SplitLayerTrait for SplitLayer { + #[inline] fn as_raw_mut_SplitLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SplitLayer, crate::dnn::SplitLayerTraitConst, as_raw_SplitLayer, crate::dnn::SplitLayerTrait, as_raw_mut_SplitLayer } + + pub struct SqrtLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { SqrtLayer } + + impl Drop for SqrtLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SqrtLayer_delete(self.as_raw_mut_SqrtLayer()) }; + } + } + + unsafe impl Send for SqrtLayer {} + + impl SqrtLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SqrtLayer { + let ret = unsafe { sys::cv_dnn_SqrtLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::SqrtLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SqrtLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SqrtLayer] + pub trait SqrtLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_SqrtLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::SqrtLayer] + pub trait SqrtLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::SqrtLayerTraitConst { + fn as_raw_mut_SqrtLayer(&mut self) -> *mut c_void; + + } + + impl Default for SqrtLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SqrtLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SqrtLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { SqrtLayer, crate::dnn::ActivationLayer, cv_dnn_SqrtLayer_to_ActivationLayer } + + boxed_cast_base! { SqrtLayer, core::Algorithm, cv_dnn_SqrtLayer_to_Algorithm } + + boxed_cast_base! { SqrtLayer, crate::dnn::Layer, cv_dnn_SqrtLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for SqrtLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for SqrtLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SqrtLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for SqrtLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SqrtLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SqrtLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SqrtLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SqrtLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SqrtLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SqrtLayerTraitConst for SqrtLayer { + #[inline] fn as_raw_SqrtLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SqrtLayerTrait for SqrtLayer { + #[inline] fn as_raw_mut_SqrtLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SqrtLayer, crate::dnn::SqrtLayerTraitConst, as_raw_SqrtLayer, crate::dnn::SqrtLayerTrait, as_raw_mut_SqrtLayer } + + pub struct SwishLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { SwishLayer } + + impl Drop for SwishLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_SwishLayer_delete(self.as_raw_mut_SwishLayer()) }; + } + } + + unsafe impl Send for SwishLayer {} + + impl SwishLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::SwishLayer { + let ret = unsafe { sys::cv_dnn_SwishLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::SwishLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_SwishLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::SwishLayer] + pub trait SwishLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_SwishLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::SwishLayer] + pub trait SwishLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::SwishLayerTraitConst { + fn as_raw_mut_SwishLayer(&mut self) -> *mut c_void; + + } + + impl Default for SwishLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for SwishLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("SwishLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { SwishLayer, crate::dnn::ActivationLayer, cv_dnn_SwishLayer_to_ActivationLayer } + + boxed_cast_base! { SwishLayer, core::Algorithm, cv_dnn_SwishLayer_to_Algorithm } + + boxed_cast_base! { SwishLayer, crate::dnn::Layer, cv_dnn_SwishLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for SwishLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for SwishLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SwishLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for SwishLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for SwishLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SwishLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for SwishLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for SwishLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SwishLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::SwishLayerTraitConst for SwishLayer { + #[inline] fn as_raw_SwishLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::SwishLayerTrait for SwishLayer { + #[inline] fn as_raw_mut_SwishLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { SwishLayer, crate::dnn::SwishLayerTraitConst, as_raw_SwishLayer, crate::dnn::SwishLayerTrait, as_raw_mut_SwishLayer } + + pub struct TanHLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { TanHLayer } + + impl Drop for TanHLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_TanHLayer_delete(self.as_raw_mut_TanHLayer()) }; + } + } + + unsafe impl Send for TanHLayer {} + + impl TanHLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::TanHLayer { + let ret = unsafe { sys::cv_dnn_TanHLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::TanHLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TanHLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::TanHLayer] + pub trait TanHLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_TanHLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::TanHLayer] + pub trait TanHLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::TanHLayerTraitConst { + fn as_raw_mut_TanHLayer(&mut self) -> *mut c_void; + + } + + impl Default for TanHLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for TanHLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("TanHLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { TanHLayer, crate::dnn::ActivationLayer, cv_dnn_TanHLayer_to_ActivationLayer } + + boxed_cast_base! { TanHLayer, core::Algorithm, cv_dnn_TanHLayer_to_Algorithm } + + boxed_cast_base! { TanHLayer, crate::dnn::Layer, cv_dnn_TanHLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for TanHLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for TanHLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TanHLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for TanHLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for TanHLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TanHLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for TanHLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for TanHLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TanHLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::TanHLayerTraitConst for TanHLayer { + #[inline] fn as_raw_TanHLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::TanHLayerTrait for TanHLayer { + #[inline] fn as_raw_mut_TanHLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TanHLayer, crate::dnn::TanHLayerTraitConst, as_raw_TanHLayer, crate::dnn::TanHLayerTrait, as_raw_mut_TanHLayer } + + pub struct TanLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { TanLayer } + + impl Drop for TanLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_TanLayer_delete(self.as_raw_mut_TanLayer()) }; + } + } + + unsafe impl Send for TanLayer {} + + impl TanLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::TanLayer { + let ret = unsafe { sys::cv_dnn_TanLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::TanLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TanLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::TanLayer] + pub trait TanLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_TanLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::TanLayer] + pub trait TanLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::TanLayerTraitConst { + fn as_raw_mut_TanLayer(&mut self) -> *mut c_void; + + } + + impl Default for TanLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for TanLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("TanLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { TanLayer, crate::dnn::ActivationLayer, cv_dnn_TanLayer_to_ActivationLayer } + + boxed_cast_base! { TanLayer, core::Algorithm, cv_dnn_TanLayer_to_Algorithm } + + boxed_cast_base! { TanLayer, crate::dnn::Layer, cv_dnn_TanLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for TanLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for TanLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TanLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for TanLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for TanLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TanLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for TanLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for TanLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TanLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::TanLayerTraitConst for TanLayer { + #[inline] fn as_raw_TanLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::TanLayerTrait for TanLayer { + #[inline] fn as_raw_mut_TanLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TanLayer, crate::dnn::TanLayerTraitConst, as_raw_TanLayer, crate::dnn::TanLayerTrait, as_raw_mut_TanLayer } + + /// Base class for text detection networks + pub struct TextDetectionModel { + ptr: *mut c_void, + } + + opencv_type_boxed! { TextDetectionModel } + + impl Drop for TextDetectionModel { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_TextDetectionModel_delete(self.as_raw_mut_TextDetectionModel()) }; + } + } + + unsafe impl Send for TextDetectionModel {} + + /// Constant methods for [crate::dnn::TextDetectionModel] + pub trait TextDetectionModelTraitConst: crate::dnn::ModelTraitConst { + fn as_raw_TextDetectionModel(&self) -> *const c_void; + + /// Performs detection + /// + /// Given the input @p frame, prepare network input, run network inference, post-process network output and return result detections. + /// + /// Each result is quadrangle's 4 points in this order: + /// - bottom-left + /// - top-left + /// - top-right + /// - bottom-right + /// + /// Use cv::getPerspectiveTransform function to retrieve image region without perspective transformations. + /// + /// + /// Note: If DL model doesn't support that kind of output then result may be derived from detectTextRectangles() output. + /// + /// ## Parameters + /// * frame: The input image + /// * detections:[out] array with detections' quadrangles (4 points per result) + /// * confidences:[out] array with detection confidences + #[inline] + fn detect_with_confidences(&self, frame: &impl ToInputArray, detections: &mut core::Vector>, confidences: &mut core::Vector) -> Result<()> { + input_array_arg!(frame); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_detect_const_const__InputArrayR_vectorLvectorLPointGGR_vectorLfloatGR(self.as_raw_TextDetectionModel(), frame.as_raw__InputArray(), detections.as_raw_mut_VectorOfVectorOfPoint(), confidences.as_raw_mut_VectorOff32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs detection + /// + /// Given the input @p frame, prepare network input, run network inference, post-process network output and return result detections. + /// + /// Each result is quadrangle's 4 points in this order: + /// - bottom-left + /// - top-left + /// - top-right + /// - bottom-right + /// + /// Use cv::getPerspectiveTransform function to retrieve image region without perspective transformations. + /// + /// + /// Note: If DL model doesn't support that kind of output then result may be derived from detectTextRectangles() output. + /// + /// ## Parameters + /// * frame: The input image + /// * detections:[out] array with detections' quadrangles (4 points per result) + /// * confidences:[out] array with detection confidences + /// + /// ## Overloaded parameters + #[inline] + fn detect(&self, frame: &impl ToInputArray, detections: &mut core::Vector>) -> Result<()> { + input_array_arg!(frame); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_detect_const_const__InputArrayR_vectorLvectorLPointGGR(self.as_raw_TextDetectionModel(), frame.as_raw__InputArray(), detections.as_raw_mut_VectorOfVectorOfPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs detection + /// + /// Given the input @p frame, prepare network input, run network inference, post-process network output and return result detections. + /// + /// Each result is rotated rectangle. + /// + /// + /// Note: Result may be inaccurate in case of strong perspective transformations. + /// + /// ## Parameters + /// * frame: the input image + /// * detections:[out] array with detections' RotationRect results + /// * confidences:[out] array with detection confidences + #[inline] + fn detect_text_rectangles(&self, frame: &impl ToInputArray, detections: &mut core::Vector, confidences: &mut core::Vector) -> Result<()> { + input_array_arg!(frame); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_detectTextRectangles_const_const__InputArrayR_vectorLRotatedRectGR_vectorLfloatGR(self.as_raw_TextDetectionModel(), frame.as_raw__InputArray(), detections.as_raw_mut_VectorOfRotatedRect(), confidences.as_raw_mut_VectorOff32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs detection + /// + /// Given the input @p frame, prepare network input, run network inference, post-process network output and return result detections. + /// + /// Each result is rotated rectangle. + /// + /// + /// Note: Result may be inaccurate in case of strong perspective transformations. + /// + /// ## Parameters + /// * frame: the input image + /// * detections:[out] array with detections' RotationRect results + /// * confidences:[out] array with detection confidences + /// + /// ## Overloaded parameters + #[inline] + fn detect_text_rectangles_1(&self, frame: &impl ToInputArray, detections: &mut core::Vector) -> Result<()> { + input_array_arg!(frame); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_detectTextRectangles_const_const__InputArrayR_vectorLRotatedRectGR(self.as_raw_TextDetectionModel(), frame.as_raw__InputArray(), detections.as_raw_mut_VectorOfRotatedRect(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::dnn::TextDetectionModel] + pub trait TextDetectionModelTrait: crate::dnn::ModelTrait + crate::dnn::TextDetectionModelTraitConst { + fn as_raw_mut_TextDetectionModel(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for TextDetectionModel { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("TextDetectionModel") + .finish() + } + } + + boxed_cast_base! { TextDetectionModel, crate::dnn::Model, cv_dnn_TextDetectionModel_to_Model } + + impl crate::dnn::ModelTraitConst for TextDetectionModel { + #[inline] fn as_raw_Model(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ModelTrait for TextDetectionModel { + #[inline] fn as_raw_mut_Model(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TextDetectionModel, crate::dnn::ModelTraitConst, as_raw_Model, crate::dnn::ModelTrait, as_raw_mut_Model } + + impl crate::dnn::TextDetectionModelTraitConst for TextDetectionModel { + #[inline] fn as_raw_TextDetectionModel(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::TextDetectionModelTrait for TextDetectionModel { + #[inline] fn as_raw_mut_TextDetectionModel(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TextDetectionModel, crate::dnn::TextDetectionModelTraitConst, as_raw_TextDetectionModel, crate::dnn::TextDetectionModelTrait, as_raw_mut_TextDetectionModel } + + /// This class represents high-level API for text detection DL networks compatible with DB model. + /// + /// Related publications: [liao2020real](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_liao2020real) + /// Paper: + /// For more information about the hyper-parameters setting, please refer to + /// + /// Configurable parameters: + /// - (float) binaryThreshold - The threshold of the binary map. It is usually set to 0.3. + /// - (float) polygonThreshold - The threshold of text polygons. It is usually set to 0.5, 0.6, and 0.7. Default is 0.5f + /// - (double) unclipRatio - The unclip ratio of the detected text region, which determines the output size. It is usually set to 2.0. + /// - (int) maxCandidates - The max number of the output results. + pub struct TextDetectionModel_DB { + ptr: *mut c_void, + } + + opencv_type_boxed! { TextDetectionModel_DB } + + impl Drop for TextDetectionModel_DB { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_TextDetectionModel_DB_delete(self.as_raw_mut_TextDetectionModel_DB()) }; + } + } + + unsafe impl Send for TextDetectionModel_DB {} + + impl TextDetectionModel_DB { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_DB_TextDetectionModel_DB(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_DB::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create text detection algorithm from deep learning network. + /// ## Parameters + /// * network: Net object. + #[inline] + pub fn new(network: &impl crate::dnn::NetTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_DB_TextDetectionModel_DB_const_NetR(network.as_raw_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_DB::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create text detection model from network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## C++ default parameters + /// * config: "" + #[inline] + pub fn new_1(model: &str, config: &str) -> Result { + extern_container_arg!(model); + extern_container_arg!(config); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_DB_TextDetectionModel_DB_const_stringR_const_stringR(model.opencv_as_extern(), config.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_DB::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create text detection model from network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * config: "" + #[inline] + pub fn new_def(model: &str) -> Result { + extern_container_arg!(model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_DB_TextDetectionModel_DB_const_stringR(model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_DB::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::TextDetectionModel_DB] + pub trait TextDetectionModel_DBTraitConst: crate::dnn::TextDetectionModelTraitConst { + fn as_raw_TextDetectionModel_DB(&self) -> *const c_void; + + #[inline] + fn get_binary_threshold(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_DB_getBinaryThreshold_const(self.as_raw_TextDetectionModel_DB(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_polygon_threshold(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_DB_getPolygonThreshold_const(self.as_raw_TextDetectionModel_DB(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_unclip_ratio(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_DB_getUnclipRatio_const(self.as_raw_TextDetectionModel_DB(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_max_candidates(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_DB_getMaxCandidates_const(self.as_raw_TextDetectionModel_DB(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::dnn::TextDetectionModel_DB] + pub trait TextDetectionModel_DBTrait: crate::dnn::TextDetectionModelTrait + crate::dnn::TextDetectionModel_DBTraitConst { + fn as_raw_mut_TextDetectionModel_DB(&mut self) -> *mut c_void; + + #[inline] + fn set_binary_threshold(&mut self, binary_threshold: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_DB_setBinaryThreshold_float(self.as_raw_mut_TextDetectionModel_DB(), binary_threshold, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_DB::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn set_polygon_threshold(&mut self, polygon_threshold: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_DB_setPolygonThreshold_float(self.as_raw_mut_TextDetectionModel_DB(), polygon_threshold, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_DB::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn set_unclip_ratio(&mut self, unclip_ratio: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_DB_setUnclipRatio_double(self.as_raw_mut_TextDetectionModel_DB(), unclip_ratio, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_DB::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + fn set_max_candidates(&mut self, max_candidates: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_DB_setMaxCandidates_int(self.as_raw_mut_TextDetectionModel_DB(), max_candidates, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_DB::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl Clone for TextDetectionModel_DB { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_dnn_TextDetectionModel_DB_implicitClone_const(self.as_raw_TextDetectionModel_DB())) } + } + } + + impl std::fmt::Debug for TextDetectionModel_DB { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("TextDetectionModel_DB") + .finish() + } + } + + boxed_cast_base! { TextDetectionModel_DB, crate::dnn::Model, cv_dnn_TextDetectionModel_DB_to_Model } + + boxed_cast_base! { TextDetectionModel_DB, crate::dnn::TextDetectionModel, cv_dnn_TextDetectionModel_DB_to_TextDetectionModel } + + impl crate::dnn::ModelTraitConst for TextDetectionModel_DB { + #[inline] fn as_raw_Model(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ModelTrait for TextDetectionModel_DB { + #[inline] fn as_raw_mut_Model(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TextDetectionModel_DB, crate::dnn::ModelTraitConst, as_raw_Model, crate::dnn::ModelTrait, as_raw_mut_Model } + + impl crate::dnn::TextDetectionModelTraitConst for TextDetectionModel_DB { + #[inline] fn as_raw_TextDetectionModel(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::TextDetectionModelTrait for TextDetectionModel_DB { + #[inline] fn as_raw_mut_TextDetectionModel(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TextDetectionModel_DB, crate::dnn::TextDetectionModelTraitConst, as_raw_TextDetectionModel, crate::dnn::TextDetectionModelTrait, as_raw_mut_TextDetectionModel } + + impl crate::dnn::TextDetectionModel_DBTraitConst for TextDetectionModel_DB { + #[inline] fn as_raw_TextDetectionModel_DB(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::TextDetectionModel_DBTrait for TextDetectionModel_DB { + #[inline] fn as_raw_mut_TextDetectionModel_DB(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TextDetectionModel_DB, crate::dnn::TextDetectionModel_DBTraitConst, as_raw_TextDetectionModel_DB, crate::dnn::TextDetectionModel_DBTrait, as_raw_mut_TextDetectionModel_DB } + + /// This class represents high-level API for text detection DL networks compatible with EAST model. + /// + /// Configurable parameters: + /// - (float) confThreshold - used to filter boxes by confidences, default: 0.5f + /// - (float) nmsThreshold - used in non maximum suppression, default: 0.0f + pub struct TextDetectionModel_EAST { + ptr: *mut c_void, + } + + opencv_type_boxed! { TextDetectionModel_EAST } + + impl Drop for TextDetectionModel_EAST { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_TextDetectionModel_EAST_delete(self.as_raw_mut_TextDetectionModel_EAST()) }; + } + } + + unsafe impl Send for TextDetectionModel_EAST {} + + impl TextDetectionModel_EAST { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_EAST_TextDetectionModel_EAST(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_EAST::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create text detection algorithm from deep learning network + /// ## Parameters + /// * network: Net object + #[inline] + pub fn new(network: &impl crate::dnn::NetTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_EAST_TextDetectionModel_EAST_const_NetR(network.as_raw_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_EAST::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create text detection model from network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## C++ default parameters + /// * config: "" + #[inline] + pub fn from_file(model: &str, config: &str) -> Result { + extern_container_arg!(model); + extern_container_arg!(config); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_EAST_TextDetectionModel_EAST_const_stringR_const_stringR(model.opencv_as_extern(), config.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_EAST::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create text detection model from network represented in one of the supported formats. + /// An order of @p model and @p config arguments does not matter. + /// ## Parameters + /// * model: Binary file contains trained weights. + /// * config: Text file contains network configuration. + /// + /// ## Note + /// This alternative version of [from_file] function uses the following default values for its arguments: + /// * config: "" + #[inline] + pub fn from_file_def(model: &str) -> Result { + extern_container_arg!(model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_EAST_TextDetectionModel_EAST_const_stringR(model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_EAST::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::TextDetectionModel_EAST] + pub trait TextDetectionModel_EASTTraitConst: crate::dnn::TextDetectionModelTraitConst { + fn as_raw_TextDetectionModel_EAST(&self) -> *const c_void; + + /// Get the detection confidence threshold + #[inline] + fn get_confidence_threshold(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_EAST_getConfidenceThreshold_const(self.as_raw_TextDetectionModel_EAST(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Get the detection confidence threshold + #[inline] + fn get_nms_threshold(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_EAST_getNMSThreshold_const(self.as_raw_TextDetectionModel_EAST(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::dnn::TextDetectionModel_EAST] + pub trait TextDetectionModel_EASTTrait: crate::dnn::TextDetectionModelTrait + crate::dnn::TextDetectionModel_EASTTraitConst { + fn as_raw_mut_TextDetectionModel_EAST(&mut self) -> *mut c_void; + + /// Set the detection confidence threshold + /// ## Parameters + /// * confThreshold: A threshold used to filter boxes by confidences + #[inline] + fn set_confidence_threshold(&mut self, conf_threshold: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_EAST_setConfidenceThreshold_float(self.as_raw_mut_TextDetectionModel_EAST(), conf_threshold, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_EAST::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Set the detection NMS filter threshold + /// ## Parameters + /// * nmsThreshold: A threshold used in non maximum suppression + #[inline] + fn set_nms_threshold(&mut self, nms_threshold: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextDetectionModel_EAST_setNMSThreshold_float(self.as_raw_mut_TextDetectionModel_EAST(), nms_threshold, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextDetectionModel_EAST::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl Clone for TextDetectionModel_EAST { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_dnn_TextDetectionModel_EAST_implicitClone_const(self.as_raw_TextDetectionModel_EAST())) } + } + } + + impl std::fmt::Debug for TextDetectionModel_EAST { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("TextDetectionModel_EAST") + .finish() + } + } + + boxed_cast_base! { TextDetectionModel_EAST, crate::dnn::Model, cv_dnn_TextDetectionModel_EAST_to_Model } + + boxed_cast_base! { TextDetectionModel_EAST, crate::dnn::TextDetectionModel, cv_dnn_TextDetectionModel_EAST_to_TextDetectionModel } + + impl crate::dnn::ModelTraitConst for TextDetectionModel_EAST { + #[inline] fn as_raw_Model(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ModelTrait for TextDetectionModel_EAST { + #[inline] fn as_raw_mut_Model(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TextDetectionModel_EAST, crate::dnn::ModelTraitConst, as_raw_Model, crate::dnn::ModelTrait, as_raw_mut_Model } + + impl crate::dnn::TextDetectionModelTraitConst for TextDetectionModel_EAST { + #[inline] fn as_raw_TextDetectionModel(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::TextDetectionModelTrait for TextDetectionModel_EAST { + #[inline] fn as_raw_mut_TextDetectionModel(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TextDetectionModel_EAST, crate::dnn::TextDetectionModelTraitConst, as_raw_TextDetectionModel, crate::dnn::TextDetectionModelTrait, as_raw_mut_TextDetectionModel } + + impl crate::dnn::TextDetectionModel_EASTTraitConst for TextDetectionModel_EAST { + #[inline] fn as_raw_TextDetectionModel_EAST(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::TextDetectionModel_EASTTrait for TextDetectionModel_EAST { + #[inline] fn as_raw_mut_TextDetectionModel_EAST(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TextDetectionModel_EAST, crate::dnn::TextDetectionModel_EASTTraitConst, as_raw_TextDetectionModel_EAST, crate::dnn::TextDetectionModel_EASTTrait, as_raw_mut_TextDetectionModel_EAST } + + /// This class represents high-level API for text recognition networks. + /// + /// TextRecognitionModel allows to set params for preprocessing input image. + /// TextRecognitionModel creates net from file with trained weights and config, + /// sets preprocessing input, runs forward pass and return recognition result. + /// For TextRecognitionModel, CRNN-CTC is supported. + pub struct TextRecognitionModel { + ptr: *mut c_void, + } + + opencv_type_boxed! { TextRecognitionModel } + + impl Drop for TextRecognitionModel { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_TextRecognitionModel_delete(self.as_raw_mut_TextRecognitionModel()) }; + } + } + + unsafe impl Send for TextRecognitionModel {} + + impl TextRecognitionModel { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextRecognitionModel_TextRecognitionModel(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextRecognitionModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create Text Recognition model from deep learning network + /// Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method + /// ## Parameters + /// * network: Net object + #[inline] + pub fn new(network: &impl crate::dnn::NetTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextRecognitionModel_TextRecognitionModel_const_NetR(network.as_raw_Net(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextRecognitionModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create text recognition model from network represented in one of the supported formats + /// Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method + /// ## Parameters + /// * model: Binary file contains trained weights + /// * config: Text file contains network configuration + /// + /// ## C++ default parameters + /// * config: "" + #[inline] + pub fn from_file(model: &str, config: &str) -> Result { + extern_container_arg!(model); + extern_container_arg!(config); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextRecognitionModel_TextRecognitionModel_const_stringR_const_stringR(model.opencv_as_extern(), config.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextRecognitionModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Create text recognition model from network represented in one of the supported formats + /// Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method + /// ## Parameters + /// * model: Binary file contains trained weights + /// * config: Text file contains network configuration + /// + /// ## Note + /// This alternative version of [from_file] function uses the following default values for its arguments: + /// * config: "" + #[inline] + pub fn from_file_def(model: &str) -> Result { + extern_container_arg!(model); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextRecognitionModel_TextRecognitionModel_const_stringR(model.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextRecognitionModel::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::TextRecognitionModel] + pub trait TextRecognitionModelTraitConst: crate::dnn::ModelTraitConst { + fn as_raw_TextRecognitionModel(&self) -> *const c_void; + + /// Get the decoding method + /// ## Returns + /// the decoding method + #[inline] + fn get_decode_type(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextRecognitionModel_getDecodeType_const(self.as_raw_TextRecognitionModel(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Get the vocabulary for recognition. + /// ## Returns + /// vocabulary the associated vocabulary + #[inline] + fn get_vocabulary(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextRecognitionModel_getVocabulary_const(self.as_raw_TextRecognitionModel(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Given the @p input frame, create input blob, run net and return recognition result + /// ## Parameters + /// * frame: The input image + /// ## Returns + /// The text recognition result + #[inline] + fn recognize(&self, frame: &impl ToInputArray) -> Result { + input_array_arg!(frame); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextRecognitionModel_recognize_const_const__InputArrayR(self.as_raw_TextRecognitionModel(), frame.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Given the @p input frame, create input blob, run net and return recognition result + /// ## Parameters + /// * frame: The input image + /// * roiRects: List of text detection regions of interest (cv::Rect, CV_32SC4). ROIs is be cropped as the network inputs + /// * results:[out] A set of text recognition results. + #[inline] + fn recognize_1(&self, frame: &impl ToInputArray, roi_rects: &impl ToInputArray, results: &mut core::Vector) -> Result<()> { + input_array_arg!(frame); + input_array_arg!(roi_rects); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextRecognitionModel_recognize_const_const__InputArrayR_const__InputArrayR_vectorLstringGR(self.as_raw_TextRecognitionModel(), frame.as_raw__InputArray(), roi_rects.as_raw__InputArray(), results.as_raw_mut_VectorOfString(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::dnn::TextRecognitionModel] + pub trait TextRecognitionModelTrait: crate::dnn::ModelTrait + crate::dnn::TextRecognitionModelTraitConst { + fn as_raw_mut_TextRecognitionModel(&mut self) -> *mut c_void; + + /// Set the decoding method of translating the network output into string + /// ## Parameters + /// * decodeType: The decoding method of translating the network output into string, currently supported type: + /// - `"CTC-greedy"` greedy decoding for the output of CTC-based methods + /// - `"CTC-prefix-beam-search"` Prefix beam search decoding for the output of CTC-based methods + #[inline] + fn set_decode_type(&mut self, decode_type: &str) -> Result { + extern_container_arg!(decode_type); + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextRecognitionModel_setDecodeType_const_stringR(self.as_raw_mut_TextRecognitionModel(), decode_type.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextRecognitionModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Set the decoding method options for `"CTC-prefix-beam-search"` decode usage + /// ## Parameters + /// * beamSize: Beam size for search + /// * vocPruneSize: Parameter to optimize big vocabulary search, + /// only take top @p vocPruneSize tokens in each search step, @p vocPruneSize <= 0 stands for disable this prune. + /// + /// ## C++ default parameters + /// * voc_prune_size: 0 + #[inline] + fn set_decode_opts_ctc_prefix_beam_search(&mut self, beam_size: i32, voc_prune_size: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextRecognitionModel_setDecodeOptsCTCPrefixBeamSearch_int_int(self.as_raw_mut_TextRecognitionModel(), beam_size, voc_prune_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextRecognitionModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Set the decoding method options for `"CTC-prefix-beam-search"` decode usage + /// ## Parameters + /// * beamSize: Beam size for search + /// * vocPruneSize: Parameter to optimize big vocabulary search, + /// only take top @p vocPruneSize tokens in each search step, @p vocPruneSize <= 0 stands for disable this prune. + /// + /// ## Note + /// This alternative version of [TextRecognitionModelTrait::set_decode_opts_ctc_prefix_beam_search] function uses the following default values for its arguments: + /// * voc_prune_size: 0 + #[inline] + fn set_decode_opts_ctc_prefix_beam_search_def(&mut self, beam_size: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextRecognitionModel_setDecodeOptsCTCPrefixBeamSearch_int(self.as_raw_mut_TextRecognitionModel(), beam_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextRecognitionModel::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Set the vocabulary for recognition. + /// ## Parameters + /// * vocabulary: the associated vocabulary of the network. + #[inline] + fn set_vocabulary(&mut self, vocabulary: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TextRecognitionModel_setVocabulary_const_vectorLstringGR(self.as_raw_mut_TextRecognitionModel(), vocabulary.as_raw_VectorOfString(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::TextRecognitionModel::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl Clone for TextRecognitionModel { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_dnn_TextRecognitionModel_implicitClone_const(self.as_raw_TextRecognitionModel())) } + } + } + + impl std::fmt::Debug for TextRecognitionModel { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("TextRecognitionModel") + .finish() + } + } + + boxed_cast_base! { TextRecognitionModel, crate::dnn::Model, cv_dnn_TextRecognitionModel_to_Model } + + impl crate::dnn::ModelTraitConst for TextRecognitionModel { + #[inline] fn as_raw_Model(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ModelTrait for TextRecognitionModel { + #[inline] fn as_raw_mut_Model(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TextRecognitionModel, crate::dnn::ModelTraitConst, as_raw_Model, crate::dnn::ModelTrait, as_raw_mut_Model } + + impl crate::dnn::TextRecognitionModelTraitConst for TextRecognitionModel { + #[inline] fn as_raw_TextRecognitionModel(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::TextRecognitionModelTrait for TextRecognitionModel { + #[inline] fn as_raw_mut_TextRecognitionModel(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TextRecognitionModel, crate::dnn::TextRecognitionModelTraitConst, as_raw_TextRecognitionModel, crate::dnn::TextRecognitionModelTrait, as_raw_mut_TextRecognitionModel } + + pub struct ThresholdedReluLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { ThresholdedReluLayer } + + impl Drop for ThresholdedReluLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_ThresholdedReluLayer_delete(self.as_raw_mut_ThresholdedReluLayer()) }; + } + } + + unsafe impl Send for ThresholdedReluLayer {} + + impl ThresholdedReluLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::ThresholdedReluLayer { + let ret = unsafe { sys::cv_dnn_ThresholdedReluLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::ThresholdedReluLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_ThresholdedReluLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::ThresholdedReluLayer] + pub trait ThresholdedReluLayerTraitConst: crate::dnn::ActivationLayerTraitConst { + fn as_raw_ThresholdedReluLayer(&self) -> *const c_void; + + #[inline] + fn alpha(&self) -> f32 { + let ret = unsafe { sys::cv_dnn_ThresholdedReluLayer_propAlpha_const(self.as_raw_ThresholdedReluLayer()) }; + ret + } + + } + + /// Mutable methods for [crate::dnn::ThresholdedReluLayer] + pub trait ThresholdedReluLayerTrait: crate::dnn::ActivationLayerTrait + crate::dnn::ThresholdedReluLayerTraitConst { + fn as_raw_mut_ThresholdedReluLayer(&mut self) -> *mut c_void; + + #[inline] + fn set_alpha(&mut self, val: f32) { + let ret = unsafe { sys::cv_dnn_ThresholdedReluLayer_propAlpha_const_float(self.as_raw_mut_ThresholdedReluLayer(), val) }; + ret + } + + } + + impl Default for ThresholdedReluLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for ThresholdedReluLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("ThresholdedReluLayer") + .field("alpha", &crate::dnn::ThresholdedReluLayerTraitConst::alpha(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { ThresholdedReluLayer, crate::dnn::ActivationLayer, cv_dnn_ThresholdedReluLayer_to_ActivationLayer } + + boxed_cast_base! { ThresholdedReluLayer, core::Algorithm, cv_dnn_ThresholdedReluLayer_to_Algorithm } + + boxed_cast_base! { ThresholdedReluLayer, crate::dnn::Layer, cv_dnn_ThresholdedReluLayer_to_Layer } + + impl crate::dnn::ActivationLayerTraitConst for ThresholdedReluLayer { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for ThresholdedReluLayer { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ThresholdedReluLayer, crate::dnn::ActivationLayerTraitConst, as_raw_ActivationLayer, crate::dnn::ActivationLayerTrait, as_raw_mut_ActivationLayer } + + impl core::AlgorithmTraitConst for ThresholdedReluLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for ThresholdedReluLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ThresholdedReluLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for ThresholdedReluLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for ThresholdedReluLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ThresholdedReluLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::ThresholdedReluLayerTraitConst for ThresholdedReluLayer { + #[inline] fn as_raw_ThresholdedReluLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::ThresholdedReluLayerTrait for ThresholdedReluLayer { + #[inline] fn as_raw_mut_ThresholdedReluLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { ThresholdedReluLayer, crate::dnn::ThresholdedReluLayerTraitConst, as_raw_ThresholdedReluLayer, crate::dnn::ThresholdedReluLayerTrait, as_raw_mut_ThresholdedReluLayer } + + pub struct TileLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { TileLayer } + + impl Drop for TileLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_TileLayer_delete(self.as_raw_mut_TileLayer()) }; + } + } + + unsafe impl Send for TileLayer {} + + impl TileLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::TileLayer { + let ret = unsafe { sys::cv_dnn_TileLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::TileLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TileLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::TileLayer] + pub trait TileLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_TileLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::TileLayer] + pub trait TileLayerTrait: crate::dnn::LayerTrait + crate::dnn::TileLayerTraitConst { + fn as_raw_mut_TileLayer(&mut self) -> *mut c_void; + + } + + impl Default for TileLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for TileLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("TileLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { TileLayer, core::Algorithm, cv_dnn_TileLayer_to_Algorithm } + + boxed_cast_base! { TileLayer, crate::dnn::Layer, cv_dnn_TileLayer_to_Layer } + + impl core::AlgorithmTraitConst for TileLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for TileLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TileLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for TileLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for TileLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TileLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::TileLayerTraitConst for TileLayer { + #[inline] fn as_raw_TileLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::TileLayerTrait for TileLayer { + #[inline] fn as_raw_mut_TileLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TileLayer, crate::dnn::TileLayerTraitConst, as_raw_TileLayer, crate::dnn::TileLayerTrait, as_raw_mut_TileLayer } + + pub struct TopKLayer { + ptr: *mut c_void, + } + + opencv_type_boxed! { TopKLayer } + + impl Drop for TopKLayer { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn_TopKLayer_delete(self.as_raw_mut_TopKLayer()) }; + } + } + + unsafe impl Send for TopKLayer {} + + impl TopKLayer { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::dnn::TopKLayer { + let ret = unsafe { sys::cv_dnn_TopKLayer_defaultNew_const() }; + let ret = unsafe { crate::dnn::TopKLayer::opencv_from_extern(ret) }; + ret + } + + #[inline] + pub fn create(params: &impl crate::dnn::LayerParamsTraitConst) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn_TopKLayer_create_const_LayerParamsR(params.as_raw_LayerParams(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::TopKLayer] + pub trait TopKLayerTraitConst: crate::dnn::LayerTraitConst { + fn as_raw_TopKLayer(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::TopKLayer] + pub trait TopKLayerTrait: crate::dnn::LayerTrait + crate::dnn::TopKLayerTraitConst { + fn as_raw_mut_TopKLayer(&mut self) -> *mut c_void; + + } + + impl Default for TopKLayer { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for TopKLayer { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("TopKLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + boxed_cast_base! { TopKLayer, core::Algorithm, cv_dnn_TopKLayer_to_Algorithm } + + boxed_cast_base! { TopKLayer, crate::dnn::Layer, cv_dnn_TopKLayer_to_Layer } + + impl core::AlgorithmTraitConst for TopKLayer { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for TopKLayer { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TopKLayer, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::dnn::LayerTraitConst for TopKLayer { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::LayerTrait for TopKLayer { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TopKLayer, crate::dnn::LayerTraitConst, as_raw_Layer, crate::dnn::LayerTrait, as_raw_mut_Layer } + + impl crate::dnn::TopKLayerTraitConst for TopKLayer { + #[inline] fn as_raw_TopKLayer(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::TopKLayerTrait for TopKLayer { + #[inline] fn as_raw_mut_TopKLayer(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { TopKLayer, crate::dnn::TopKLayerTraitConst, as_raw_TopKLayer, crate::dnn::TopKLayerTrait, as_raw_mut_TopKLayer } + + pub struct _Range { + ptr: *mut c_void, + } + + opencv_type_boxed! { _Range } + + impl Drop for _Range { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_dnn__Range_delete(self.as_raw_mut__Range()) }; + } + } + + unsafe impl Send for _Range {} + + impl _Range { + #[inline] + pub fn from_base(r: &impl core::RangeTraitConst) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn__Range__Range_const_RangeR(r.as_raw_Range(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::_Range::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * size_: 1 + #[inline] + pub fn new(start_: i32, size_: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn__Range__Range_int_int(start_, size_, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::_Range::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * size_: 1 + #[inline] + pub fn new_def(start_: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_dnn__Range__Range_int(start_, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::dnn::_Range::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::dnn::_Range] + pub trait _RangeTraitConst: core::RangeTraitConst { + fn as_raw__Range(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::dnn::_Range] + pub trait _RangeTrait: core::RangeTrait + crate::dnn::_RangeTraitConst { + fn as_raw_mut__Range(&mut self) -> *mut c_void; + + } + + impl std::fmt::Debug for _Range { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("_Range") + .field("start", &core::RangeTraitConst::start(self)) + .field("end", &core::RangeTraitConst::end(self)) + .finish() + } + } + + boxed_cast_base! { _Range, core::Range, cv_dnn__Range_to_Range } + + impl core::RangeTraitConst for _Range { + #[inline] fn as_raw_Range(&self) -> *const c_void { self.as_raw() } + } + + impl core::RangeTrait for _Range { + #[inline] fn as_raw_mut_Range(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { _Range, core::RangeTraitConst, as_raw_Range, core::RangeTrait, as_raw_mut_Range } + + impl crate::dnn::_RangeTraitConst for _Range { + #[inline] fn as_raw__Range(&self) -> *const c_void { self.as_raw() } + } + + impl crate::dnn::_RangeTrait for _Range { + #[inline] fn as_raw_mut__Range(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { _Range, crate::dnn::_RangeTraitConst, as_raw__Range, crate::dnn::_RangeTrait, as_raw_mut__Range } + +pub use crate::manual::dnn::*; +} diff --git a/target/debug/build/opencv-4e105b5546afc119/out/opencv/highgui.rs b/target/debug/build/opencv-4e105b5546afc119/out/opencv/highgui.rs new file mode 100644 index 0000000..097a65a --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/opencv/highgui.rs @@ -0,0 +1,1884 @@ +pub mod highgui { + //! # High-level GUI + //! + //! While OpenCV was designed for use in full-scale applications and can be used within functionally + //! rich UI frameworks (such as Qt\*, WinForms\*, or Cocoa\*) or without any UI at all, sometimes there + //! it is required to try functionality quickly and visualize the results. This is what the HighGUI + //! module has been designed for. + //! + //! It provides easy interface to: + //! + //! * Create and manipulate windows that can display images and "remember" their content (no need to + //! handle repaint events from OS). + //! * Add trackbars to the windows, handle simple mouse events as well as keyboard commands. + //! # Flags related creating and manipulating HighGUI windows and mouse events + //! # OpenGL support + //! # Qt New Functions + //! + //! ![image](https://docs.opencv.org/4.13.0/qtgui.png) + //! + //! This figure explains new functionality implemented with Qt\* GUI. The new GUI provides a statusbar, + //! a toolbar, and a control panel. The control panel can have trackbars and buttonbars attached to it. + //! If you cannot see the control panel, press Ctrl+P or right-click any Qt window and select **Display + //! properties window**. + //! + //! * To attach a trackbar, the window name parameter must be NULL. + //! + //! * To attach a buttonbar, a button must be created. If the last bar attached to the control panel + //! is a buttonbar, the new button is added to the right of the last button. If the last bar + //! attached to the control panel is a trackbar, or the control panel is empty, a new buttonbar is + //! created. Then, a new button is attached to it. + //! + //! See below the example used to generate the figure: + //! + //! @include highgui_qt.cpp + //! + //! # WinRT support + //! + //! This figure explains new functionality implemented with WinRT GUI. The new GUI provides an Image control, + //! and a slider panel. Slider panel holds trackbars attached to it. + //! + //! Sliders are attached below the image control. Every new slider is added below the previous one. + //! + //! See below the example used to generate the figure: + //! ```C++ + //! void sample_app::MainPage::ShowWindow() + //! { + //! static cv::String windowName("sample"); + //! cv::winrt_initContainer(this->cvContainer); + //! cv::namedWindow(windowName); // not required + //! + //! cv::Mat image = cv::imread("Assets/sample.jpg"); + //! cv::Mat converted = cv::Mat(image.rows, image.cols, CV_8UC4); + //! cv::cvtColor(image, converted, COLOR_BGR2BGRA); + //! cv::imshow(windowName, converted); // this will create window if it hasn't been created before + //! + //! int state = 42; + //! cv::TrackbarCallback callback = [](int pos, void* userdata) + //! { + //! if (pos == 0) { + //! cv::destroyWindow(windowName); + //! } + //! }; + //! cv::TrackbarCallback callbackTwin = [](int pos, void* userdata) + //! { + //! if (pos >= 70) { + //! cv::destroyAllWindows(); + //! } + //! }; + //! cv::createTrackbar("Sample trackbar", windowName, &state, 100, callback); + //! cv::createTrackbar("Twin brother", windowName, &state, 100, callbackTwin); + //! } + //! ``` + //! + use crate::mod_prelude::*; + use crate::{core, sys, types}; + pub mod prelude { + pub use super::{QtFontTrait, QtFontTraitConst}; + } + + /// indicates that ALT Key is pressed. + pub const EVENT_FLAG_ALTKEY: i32 = 32; + /// indicates that CTRL Key is pressed. + pub const EVENT_FLAG_CTRLKEY: i32 = 8; + /// indicates that the left mouse button is down. + pub const EVENT_FLAG_LBUTTON: i32 = 1; + /// indicates that the middle mouse button is down. + pub const EVENT_FLAG_MBUTTON: i32 = 4; + /// indicates that the right mouse button is down. + pub const EVENT_FLAG_RBUTTON: i32 = 2; + /// indicates that SHIFT Key is pressed. + pub const EVENT_FLAG_SHIFTKEY: i32 = 16; + /// indicates that left mouse button is double clicked. + pub const EVENT_LBUTTONDBLCLK: i32 = 7; + /// indicates that the left mouse button is pressed. + pub const EVENT_LBUTTONDOWN: i32 = 1; + /// indicates that left mouse button is released. + pub const EVENT_LBUTTONUP: i32 = 4; + /// indicates that middle mouse button is double clicked. + pub const EVENT_MBUTTONDBLCLK: i32 = 9; + /// indicates that the middle mouse button is pressed. + pub const EVENT_MBUTTONDOWN: i32 = 3; + /// indicates that middle mouse button is released. + pub const EVENT_MBUTTONUP: i32 = 6; + /// positive and negative values mean right and left scrolling, respectively. + pub const EVENT_MOUSEHWHEEL: i32 = 11; + /// indicates that the mouse pointer has moved over the window. + pub const EVENT_MOUSEMOVE: i32 = 0; + /// positive and negative values mean forward and backward scrolling, respectively. + pub const EVENT_MOUSEWHEEL: i32 = 10; + /// indicates that right mouse button is double clicked. + pub const EVENT_RBUTTONDBLCLK: i32 = 8; + /// indicates that the right mouse button is pressed. + pub const EVENT_RBUTTONDOWN: i32 = 2; + /// indicates that right mouse button is released. + pub const EVENT_RBUTTONUP: i32 = 5; + /// Checkbox button. + pub const QT_CHECKBOX: i32 = 1; + /// Weight of 87 + pub const QT_FONT_BLACK: i32 = 87; + /// Weight of 75 + pub const QT_FONT_BOLD: i32 = 75; + /// Weight of 63 + pub const QT_FONT_DEMIBOLD: i32 = 63; + /// Weight of 25 + pub const QT_FONT_LIGHT: i32 = 25; + /// Weight of 50 + pub const QT_FONT_NORMAL: i32 = 50; + /// Button should create a new buttonbar + pub const QT_NEW_BUTTONBAR: i32 = 1024; + /// Push button. + pub const QT_PUSH_BUTTON: i32 = 0; + /// Radiobox button. + pub const QT_RADIOBOX: i32 = 2; + /// Italic font. + pub const QT_STYLE_ITALIC: i32 = 1; + /// Normal font. + pub const QT_STYLE_NORMAL: i32 = 0; + /// Oblique font. + pub const QT_STYLE_OBLIQUE: i32 = 2; + /// the user cannot resize the window, the size is constrainted by the image displayed. + pub const WINDOW_AUTOSIZE: i32 = 1; + /// the image expends as much as it can (no ratio constraint). + pub const WINDOW_FREERATIO: i32 = 256; + /// change the window to fullscreen. + pub const WINDOW_FULLSCREEN: i32 = 1; + /// status bar and tool bar + pub const WINDOW_GUI_EXPANDED: i32 = 0; + /// old fashious way + pub const WINDOW_GUI_NORMAL: i32 = 16; + /// the ratio of the image is respected. + pub const WINDOW_KEEPRATIO: i32 = 0; + /// the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size. + pub const WINDOW_NORMAL: i32 = 0; + /// window with opengl support. + pub const WINDOW_OPENGL: i32 = 4096; + /// window's aspect ration (can be set to WINDOW_FREERATIO or WINDOW_KEEPRATIO). + pub const WND_PROP_ASPECT_RATIO: i32 = 2; + /// autosize property (can be WINDOW_NORMAL or WINDOW_AUTOSIZE). + pub const WND_PROP_AUTOSIZE: i32 = 1; + /// fullscreen property (can be WINDOW_NORMAL or WINDOW_FULLSCREEN). + pub const WND_PROP_FULLSCREEN: i32 = 0; + /// opengl support. + pub const WND_PROP_OPENGL: i32 = 3; + /// property to toggle normal window being topmost or not + pub const WND_PROP_TOPMOST: i32 = 5; + /// checks whether the window exists and is visible + pub const WND_PROP_VISIBLE: i32 = 4; + /// enable or disable VSYNC (in OpenGL mode) + pub const WND_PROP_VSYNC: i32 = 6; + /// Mouse Event Flags see cv::MouseCallback + #[repr(transparent)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub struct MouseEventFlags(i32); + + impl MouseEventFlags { + /// No flags are set, might not make sense for all enums + pub const NONE: Self = Self(0); + /// indicates that the left mouse button is down. + pub const EVENT_FLAG_LBUTTON: Self = Self(1); + /// indicates that the right mouse button is down. + pub const EVENT_FLAG_RBUTTON: Self = Self(2); + /// indicates that the middle mouse button is down. + pub const EVENT_FLAG_MBUTTON: Self = Self(4); + /// indicates that CTRL Key is pressed. + pub const EVENT_FLAG_CTRLKEY: Self = Self(8); + /// indicates that SHIFT Key is pressed. + pub const EVENT_FLAG_SHIFTKEY: Self = Self(16); + /// indicates that ALT Key is pressed. + pub const EVENT_FLAG_ALTKEY: Self = Self(32); + } + + opencv_type_bitfield_enum! { crate::highgui::MouseEventFlags { NONE, EVENT_FLAG_LBUTTON, EVENT_FLAG_RBUTTON, EVENT_FLAG_MBUTTON, EVENT_FLAG_CTRLKEY, EVENT_FLAG_SHIFTKEY, EVENT_FLAG_ALTKEY } } + + /// Mouse Events see cv::MouseCallback + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum MouseEventTypes { + /// indicates that the mouse pointer has moved over the window. + EVENT_MOUSEMOVE = 0, + /// indicates that the left mouse button is pressed. + EVENT_LBUTTONDOWN = 1, + /// indicates that the right mouse button is pressed. + EVENT_RBUTTONDOWN = 2, + /// indicates that the middle mouse button is pressed. + EVENT_MBUTTONDOWN = 3, + /// indicates that left mouse button is released. + EVENT_LBUTTONUP = 4, + /// indicates that right mouse button is released. + EVENT_RBUTTONUP = 5, + /// indicates that middle mouse button is released. + EVENT_MBUTTONUP = 6, + /// indicates that left mouse button is double clicked. + EVENT_LBUTTONDBLCLK = 7, + /// indicates that right mouse button is double clicked. + EVENT_RBUTTONDBLCLK = 8, + /// indicates that middle mouse button is double clicked. + EVENT_MBUTTONDBLCLK = 9, + /// positive and negative values mean forward and backward scrolling, respectively. + EVENT_MOUSEWHEEL = 10, + /// positive and negative values mean right and left scrolling, respectively. + EVENT_MOUSEHWHEEL = 11, + } + + opencv_type_enum! { crate::highgui::MouseEventTypes { EVENT_MOUSEMOVE, EVENT_LBUTTONDOWN, EVENT_RBUTTONDOWN, EVENT_MBUTTONDOWN, EVENT_LBUTTONUP, EVENT_RBUTTONUP, EVENT_MBUTTONUP, EVENT_LBUTTONDBLCLK, EVENT_RBUTTONDBLCLK, EVENT_MBUTTONDBLCLK, EVENT_MOUSEWHEEL, EVENT_MOUSEHWHEEL } } + + /// Qt "button" type + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum QtButtonTypes { + /// Push button. + QT_PUSH_BUTTON = 0, + /// Checkbox button. + QT_CHECKBOX = 1, + /// Radiobox button. + QT_RADIOBOX = 2, + /// Button should create a new buttonbar + QT_NEW_BUTTONBAR = 1024, + } + + opencv_type_enum! { crate::highgui::QtButtonTypes { QT_PUSH_BUTTON, QT_CHECKBOX, QT_RADIOBOX, QT_NEW_BUTTONBAR } } + + /// Qt font style + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum QtFontStyles { + /// Normal font. + QT_STYLE_NORMAL = 0, + /// Italic font. + QT_STYLE_ITALIC = 1, + /// Oblique font. + QT_STYLE_OBLIQUE = 2, + } + + opencv_type_enum! { crate::highgui::QtFontStyles { QT_STYLE_NORMAL, QT_STYLE_ITALIC, QT_STYLE_OBLIQUE } } + + /// Qt font weight + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum QtFontWeights { + /// Weight of 25 + QT_FONT_LIGHT = 25, + /// Weight of 50 + QT_FONT_NORMAL = 50, + /// Weight of 63 + QT_FONT_DEMIBOLD = 63, + /// Weight of 75 + QT_FONT_BOLD = 75, + /// Weight of 87 + QT_FONT_BLACK = 87, + } + + opencv_type_enum! { crate::highgui::QtFontWeights { QT_FONT_LIGHT, QT_FONT_NORMAL, QT_FONT_DEMIBOLD, QT_FONT_BOLD, QT_FONT_BLACK } } + + /// Flags for cv::namedWindow + #[repr(transparent)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub struct WindowFlags(i32); + + impl WindowFlags { + /// the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size. + pub const WINDOW_NORMAL: Self = Self(0); + /// the user cannot resize the window, the size is constrainted by the image displayed. + pub const WINDOW_AUTOSIZE: Self = Self(1); + /// window with opengl support. + pub const WINDOW_OPENGL: Self = Self(4096); + /// change the window to fullscreen. + pub const WINDOW_FULLSCREEN: Self = Self(1); + /// the image expends as much as it can (no ratio constraint). + pub const WINDOW_FREERATIO: Self = Self(256); + /// the ratio of the image is respected. + pub const WINDOW_KEEPRATIO: Self = Self(0); + /// status bar and tool bar + pub const WINDOW_GUI_EXPANDED: Self = Self(0); + /// old fashious way + pub const WINDOW_GUI_NORMAL: Self = Self(16); + } + + opencv_type_bitfield_enum! { crate::highgui::WindowFlags { WINDOW_NORMAL, WINDOW_AUTOSIZE, WINDOW_OPENGL, WINDOW_FREERATIO, WINDOW_GUI_NORMAL } } + + /// Flags for cv::setWindowProperty / cv::getWindowProperty + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum WindowPropertyFlags { + /// fullscreen property (can be WINDOW_NORMAL or WINDOW_FULLSCREEN). + WND_PROP_FULLSCREEN = 0, + /// autosize property (can be WINDOW_NORMAL or WINDOW_AUTOSIZE). + WND_PROP_AUTOSIZE = 1, + /// window's aspect ration (can be set to WINDOW_FREERATIO or WINDOW_KEEPRATIO). + WND_PROP_ASPECT_RATIO = 2, + /// opengl support. + WND_PROP_OPENGL = 3, + /// checks whether the window exists and is visible + WND_PROP_VISIBLE = 4, + /// property to toggle normal window being topmost or not + WND_PROP_TOPMOST = 5, + /// enable or disable VSYNC (in OpenGL mode) + WND_PROP_VSYNC = 6, + } + + opencv_type_enum! { crate::highgui::WindowPropertyFlags { WND_PROP_FULLSCREEN, WND_PROP_AUTOSIZE, WND_PROP_ASPECT_RATIO, WND_PROP_OPENGL, WND_PROP_VISIBLE, WND_PROP_TOPMOST, WND_PROP_VSYNC } } + + /// Callback function for a button created by cv::createButton + /// ## Parameters + /// * state: current state of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button. + /// * userdata: The optional parameter. + pub type ButtonCallback = Option () + Send + Sync + 'static>>; + /// Callback function for mouse events. see cv::setMouseCallback + /// ## Parameters + /// * event: one of the cv::MouseEventTypes constants. + /// * x: The x-coordinate of the mouse event. + /// * y: The y-coordinate of the mouse event. + /// * flags: one of the cv::MouseEventFlags constants. + /// * userdata: The optional parameter. + pub type MouseCallback = Option () + Send + Sync + 'static>>; + /// Callback function defined to be called every frame. See cv::setOpenGlDrawCallback + /// ## Parameters + /// * userdata: The optional parameter. + pub type OpenGlDrawCallback = Option () + Send + Sync + 'static>>; + /// Callback function for Trackbar see cv::createTrackbar + /// ## Parameters + /// * pos: current position of the specified trackbar. + /// * userdata: The optional parameter. + pub type TrackbarCallback = Option () + Send + Sync + 'static>>; + /// Draws a text on the image. + /// + /// The function addText draws *text* on the image *img* using a specific font *font* (see example cv::fontQt + /// ) + /// + /// ## Parameters + /// * img: 8-bit 3-channel image where the text should be drawn. + /// * text: Text to write on an image. + /// * org: Point(x,y) where the text should start on an image. + /// * font: Font to use to draw a text. + #[inline] + pub fn add_text(img: &impl core::MatTraitConst, text: &str, org: core::Point, font: &impl crate::highgui::QtFontTraitConst) -> Result<()> { + extern_container_arg!(text); + return_send!(via ocvrs_return); + unsafe { sys::cv_addText_const_MatR_const_StringR_Point_const_QtFontR(img.as_raw_Mat(), text.opencv_as_extern(), &org, font.as_raw_QtFont(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a text on the image. + /// + /// ## Parameters + /// * img: 8-bit 3-channel image where the text should be drawn. + /// * text: Text to write on an image. + /// * org: Point(x,y) where the text should start on an image. + /// * nameFont: Name of the font. The name should match the name of a system font (such as + /// *Times*). If the font is not found, a default one is used. + /// * pointSize: Size of the font. If not specified, equal zero or negative, the point size of the + /// font is set to a system-dependent default value. Generally, this is 12 points. + /// * color: Color of the font in BGRA where A = 255 is fully transparent. + /// * weight: Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control. + /// * style: Font style. Available operation flags are : cv::QtFontStyles + /// * spacing: Spacing between characters. It can be negative or positive. + /// + /// ## Note + /// This alternative version of [add_text_with_font] function uses the following default values for its arguments: + /// * point_size: -1 + /// * color: Scalar::all(0) + /// * weight: QT_FONT_NORMAL + /// * style: QT_STYLE_NORMAL + /// * spacing: 0 + #[inline] + pub fn add_text_with_font_def(img: &impl core::MatTraitConst, text: &str, org: core::Point, name_font: &str) -> Result<()> { + extern_container_arg!(text); + extern_container_arg!(name_font); + return_send!(via ocvrs_return); + unsafe { sys::cv_addText_const_MatR_const_StringR_Point_const_StringR(img.as_raw_Mat(), text.opencv_as_extern(), &org, name_font.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a text on the image. + /// + /// ## Parameters + /// * img: 8-bit 3-channel image where the text should be drawn. + /// * text: Text to write on an image. + /// * org: Point(x,y) where the text should start on an image. + /// * nameFont: Name of the font. The name should match the name of a system font (such as + /// *Times*). If the font is not found, a default one is used. + /// * pointSize: Size of the font. If not specified, equal zero or negative, the point size of the + /// font is set to a system-dependent default value. Generally, this is 12 points. + /// * color: Color of the font in BGRA where A = 255 is fully transparent. + /// * weight: Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control. + /// * style: Font style. Available operation flags are : cv::QtFontStyles + /// * spacing: Spacing between characters. It can be negative or positive. + /// + /// ## C++ default parameters + /// * point_size: -1 + /// * color: Scalar::all(0) + /// * weight: QT_FONT_NORMAL + /// * style: QT_STYLE_NORMAL + /// * spacing: 0 + #[inline] + pub fn add_text_with_font(img: &impl core::MatTraitConst, text: &str, org: core::Point, name_font: &str, point_size: i32, color: core::Scalar, weight: i32, style: i32, spacing: i32) -> Result<()> { + extern_container_arg!(text); + extern_container_arg!(name_font); + return_send!(via ocvrs_return); + unsafe { sys::cv_addText_const_MatR_const_StringR_Point_const_StringR_int_Scalar_int_int_int(img.as_raw_Mat(), text.opencv_as_extern(), &org, name_font.opencv_as_extern(), point_size, &color, weight, style, spacing, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Attaches a button to the control panel. + /// + /// The function createButton attaches a button to the control panel. Each button is added to a + /// buttonbar to the right of the last button. A new buttonbar is created if nothing was attached to the + /// control panel before, or if the last element attached to the control panel was a trackbar or if the + /// QT_NEW_BUTTONBAR flag is added to the type. + /// + /// See below various examples of the cv::createButton function call: : + /// ```C++ + /// createButton("",callbackButton);//create a push button "button 0", that will call callbackButton. + /// createButton("button2",callbackButton,NULL,QT_CHECKBOX,0); + /// createButton("button3",callbackButton,&value); + /// createButton("button5",callbackButton1,NULL,QT_RADIOBOX); + /// createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON,1); + /// createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON|QT_NEW_BUTTONBAR);// create a push button in a new row + /// ``` + /// + /// + /// ## Parameters + /// * bar_name: Name of the button. + /// * on_change: Pointer to the function to be called every time the button changes its state. + /// This function should be prototyped as void Foo(int state,\*void); . *state* is the current state + /// of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button. + /// * userdata: Pointer passed to the callback function. + /// * type: Optional type of the button. Available types are: (cv::QtButtonTypes) + /// * initial_button_state: Default state of the button. Use for checkbox and radiobox. Its + /// value could be 0 or 1. (__Optional__) + /// + /// ## Note + /// This alternative version of [create_button] function uses the following default values for its arguments: + /// * typ: QT_PUSH_BUTTON + /// * initial_button_state: false + #[inline] + pub fn create_button_def(bar_name: &str, on_change: crate::highgui::ButtonCallback) -> Result { + extern_container_arg!(bar_name); + callback_arg!(on_change_trampoline(state: i32, userdata: *mut c_void) -> () => userdata in on_change(state: i32) -> ()); + userdata_arg!(userdata: *mut c_void => on_change); + return_send!(via ocvrs_return); + unsafe { sys::cv_createButton_const_StringR_ButtonCallback_voidX(bar_name.opencv_as_extern(), on_change_trampoline, userdata, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Attaches a button to the control panel. + /// + /// The function createButton attaches a button to the control panel. Each button is added to a + /// buttonbar to the right of the last button. A new buttonbar is created if nothing was attached to the + /// control panel before, or if the last element attached to the control panel was a trackbar or if the + /// QT_NEW_BUTTONBAR flag is added to the type. + /// + /// See below various examples of the cv::createButton function call: : + /// ```C++ + /// createButton("",callbackButton);//create a push button "button 0", that will call callbackButton. + /// createButton("button2",callbackButton,NULL,QT_CHECKBOX,0); + /// createButton("button3",callbackButton,&value); + /// createButton("button5",callbackButton1,NULL,QT_RADIOBOX); + /// createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON,1); + /// createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON|QT_NEW_BUTTONBAR);// create a push button in a new row + /// ``` + /// + /// + /// ## Parameters + /// * bar_name: Name of the button. + /// * on_change: Pointer to the function to be called every time the button changes its state. + /// This function should be prototyped as void Foo(int state,\*void); . *state* is the current state + /// of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button. + /// * userdata: Pointer passed to the callback function. + /// * type: Optional type of the button. Available types are: (cv::QtButtonTypes) + /// * initial_button_state: Default state of the button. Use for checkbox and radiobox. Its + /// value could be 0 or 1. (__Optional__) + /// + /// ## C++ default parameters + /// * typ: QT_PUSH_BUTTON + /// * initial_button_state: false + #[inline] + pub fn create_button(bar_name: &str, on_change: crate::highgui::ButtonCallback, typ: i32, initial_button_state: bool) -> Result { + extern_container_arg!(bar_name); + callback_arg!(on_change_trampoline(state: i32, userdata: *mut c_void) -> () => userdata in on_change(state: i32) -> ()); + userdata_arg!(userdata: *mut c_void => on_change); + return_send!(via ocvrs_return); + unsafe { sys::cv_createButton_const_StringR_ButtonCallback_voidX_int_bool(bar_name.opencv_as_extern(), on_change_trampoline, userdata, typ, initial_button_state, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates a trackbar and attaches it to the specified window. + /// + /// The function createTrackbar creates a trackbar (a slider or range control) with the specified name + /// and range, assigns a variable value to be a position synchronized with the trackbar and specifies + /// the callback function onChange to be called on the trackbar position change. The created trackbar is + /// displayed in the specified window winname. + /// + /// + /// Note: [__Qt Backend Only__] winname can be empty if the trackbar should be attached to the + /// control panel. + /// + /// Clicking the label of each trackbar enables editing the trackbar values manually. + /// + /// ## Parameters + /// * trackbarname: Name of the created trackbar. + /// * winname: Name of the window that will contain the trackbar. + /// * value: Pointer to the integer value that will be changed by the trackbar. + /// Pass `nullptr` if the value pointer is not used. In this case, manually handle + /// the trackbar position in the callback function. + /// * count: Maximum position of the trackbar. + /// * onChange: Pointer to the function to be called every time the slider changes position. + /// This function should have the prototype void Foo(int, void\*);, where the first parameter is + /// the trackbar position, and the second parameter is the user data (see the next parameter). + /// If the callback is a nullptr, no callbacks are called, but the trackbar's value will still be + /// updated automatically. + /// * userdata: Optional user data that is passed to the callback. + /// + /// Note: If the `value` pointer is `nullptr`, the trackbar position must be manually managed. + /// Call the callback function manually with the desired initial value to avoid runtime warnings. + /// ## See also + /// \ref tutorial_trackbar + #[inline] + pub fn create_trackbar(trackbarname: &str, winname: &str, value: Option<&mut i32>, count: i32, on_change: crate::highgui::TrackbarCallback) -> Result { + extern_container_arg!(trackbarname); + extern_container_arg!(winname); + callback_arg!(on_change_trampoline(pos: i32, userdata: *mut c_void) -> () => userdata in on_change(pos: i32) -> ()); + userdata_arg!(userdata: *mut c_void => on_change); + return_send!(via ocvrs_return); + unsafe { sys::cv_createTrackbar_const_StringR_const_StringR_intX_int_TrackbarCallback_voidX(trackbarname.opencv_as_extern(), winname.opencv_as_extern(), value.map_or(::core::ptr::null_mut(), |value| value), count, on_change_trampoline, userdata, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// HighGUI backend used. + /// + /// The function returns HighGUI backend name used: could be COCOA, GTK2/3, QT, WAYLAND or WIN32. + /// Returns empty string if there is no available UI backend. + #[inline] + pub fn current_ui_framework() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_currentUIFramework(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Destroys all of the HighGUI windows. + /// + /// The function destroyAllWindows destroys all of the opened HighGUI windows. + #[inline] + pub fn destroy_all_windows() -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_destroyAllWindows(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Destroys the specified window. + /// + /// The function destroyWindow destroys the window with the given name. + /// + /// ## Parameters + /// * winname: Name of the window to be destroyed. + #[inline] + pub fn destroy_window(winname: &str) -> Result<()> { + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_destroyWindow_const_StringR(winname.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Displays a text on a window image as an overlay for a specified duration. + /// + /// The function displayOverlay displays useful information/tips on top of the window for a certain + /// amount of time *delayms*. The function does not modify the image, displayed in the window, that is, + /// after the specified delay the original content of the window is restored. + /// + /// ## Parameters + /// * winname: Name of the window. + /// * text: Overlay text to write on a window image. + /// * delayms: The period (in milliseconds), during which the overlay text is displayed. If this + /// function is called before the previous overlay text timed out, the timer is restarted and the text + /// is updated. If this value is zero, the text never disappears. + /// + /// ## Note + /// This alternative version of [display_overlay] function uses the following default values for its arguments: + /// * delayms: 0 + #[inline] + pub fn display_overlay_def(winname: &str, text: &str) -> Result<()> { + extern_container_arg!(winname); + extern_container_arg!(text); + return_send!(via ocvrs_return); + unsafe { sys::cv_displayOverlay_const_StringR_const_StringR(winname.opencv_as_extern(), text.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Displays a text on a window image as an overlay for a specified duration. + /// + /// The function displayOverlay displays useful information/tips on top of the window for a certain + /// amount of time *delayms*. The function does not modify the image, displayed in the window, that is, + /// after the specified delay the original content of the window is restored. + /// + /// ## Parameters + /// * winname: Name of the window. + /// * text: Overlay text to write on a window image. + /// * delayms: The period (in milliseconds), during which the overlay text is displayed. If this + /// function is called before the previous overlay text timed out, the timer is restarted and the text + /// is updated. If this value is zero, the text never disappears. + /// + /// ## C++ default parameters + /// * delayms: 0 + #[inline] + pub fn display_overlay(winname: &str, text: &str, delayms: i32) -> Result<()> { + extern_container_arg!(winname); + extern_container_arg!(text); + return_send!(via ocvrs_return); + unsafe { sys::cv_displayOverlay_const_StringR_const_StringR_int(winname.opencv_as_extern(), text.opencv_as_extern(), delayms, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Displays a text on the window statusbar during the specified period of time. + /// + /// The function displayStatusBar displays useful information/tips on top of the window for a certain + /// amount of time *delayms* . This information is displayed on the window statusbar (the window must be + /// created with the CV_GUI_EXPANDED flags). + /// + /// ## Parameters + /// * winname: Name of the window. + /// * text: Text to write on the window statusbar. + /// * delayms: Duration (in milliseconds) to display the text. If this function is called before + /// the previous text timed out, the timer is restarted and the text is updated. If this value is + /// zero, the text never disappears. + /// + /// ## Note + /// This alternative version of [display_status_bar] function uses the following default values for its arguments: + /// * delayms: 0 + #[inline] + pub fn display_status_bar_def(winname: &str, text: &str) -> Result<()> { + extern_container_arg!(winname); + extern_container_arg!(text); + return_send!(via ocvrs_return); + unsafe { sys::cv_displayStatusBar_const_StringR_const_StringR(winname.opencv_as_extern(), text.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Displays a text on the window statusbar during the specified period of time. + /// + /// The function displayStatusBar displays useful information/tips on top of the window for a certain + /// amount of time *delayms* . This information is displayed on the window statusbar (the window must be + /// created with the CV_GUI_EXPANDED flags). + /// + /// ## Parameters + /// * winname: Name of the window. + /// * text: Text to write on the window statusbar. + /// * delayms: Duration (in milliseconds) to display the text. If this function is called before + /// the previous text timed out, the timer is restarted and the text is updated. If this value is + /// zero, the text never disappears. + /// + /// ## C++ default parameters + /// * delayms: 0 + #[inline] + pub fn display_status_bar(winname: &str, text: &str, delayms: i32) -> Result<()> { + extern_container_arg!(winname); + extern_container_arg!(text); + return_send!(via ocvrs_return); + unsafe { sys::cv_displayStatusBar_const_StringR_const_StringR_int(winname.opencv_as_extern(), text.opencv_as_extern(), delayms, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates the font to draw a text on an image. + /// + /// The function fontQt creates a cv::QtFont object. This cv::QtFont is not compatible with putText . + /// + /// A basic usage of this function is the following: : + /// ```C++ + /// QtFont font = fontQt("Times"); + /// addText( img1, "Hello World !", Point(50,50), font); + /// ``` + /// + /// + /// ## Parameters + /// * nameFont: Name of the font. The name should match the name of a system font (such as + /// *Times*). If the font is not found, a default one is used. + /// * pointSize: Size of the font. If not specified, equal zero or negative, the point size of the + /// font is set to a system-dependent default value. Generally, this is 12 points. + /// * color: Color of the font in BGRA where A = 255 is fully transparent. Use the macro CV_RGB + /// for simplicity. + /// * weight: Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control. + /// * style: Font style. Available operation flags are : cv::QtFontStyles + /// * spacing: Spacing between characters. It can be negative or positive. + /// + /// ## Note + /// This alternative version of [font_qt] function uses the following default values for its arguments: + /// * point_size: -1 + /// * color: Scalar::all(0) + /// * weight: QT_FONT_NORMAL + /// * style: QT_STYLE_NORMAL + /// * spacing: 0 + #[inline] + pub fn font_qt_def(name_font: &str) -> Result { + extern_container_arg!(name_font); + return_send!(via ocvrs_return); + unsafe { sys::cv_fontQt_const_StringR(name_font.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::highgui::QtFont::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates the font to draw a text on an image. + /// + /// The function fontQt creates a cv::QtFont object. This cv::QtFont is not compatible with putText . + /// + /// A basic usage of this function is the following: : + /// ```C++ + /// QtFont font = fontQt("Times"); + /// addText( img1, "Hello World !", Point(50,50), font); + /// ``` + /// + /// + /// ## Parameters + /// * nameFont: Name of the font. The name should match the name of a system font (such as + /// *Times*). If the font is not found, a default one is used. + /// * pointSize: Size of the font. If not specified, equal zero or negative, the point size of the + /// font is set to a system-dependent default value. Generally, this is 12 points. + /// * color: Color of the font in BGRA where A = 255 is fully transparent. Use the macro CV_RGB + /// for simplicity. + /// * weight: Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control. + /// * style: Font style. Available operation flags are : cv::QtFontStyles + /// * spacing: Spacing between characters. It can be negative or positive. + /// + /// ## C++ default parameters + /// * point_size: -1 + /// * color: Scalar::all(0) + /// * weight: QT_FONT_NORMAL + /// * style: QT_STYLE_NORMAL + /// * spacing: 0 + #[inline] + pub fn font_qt(name_font: &str, point_size: i32, color: core::Scalar, weight: i32, style: i32, spacing: i32) -> Result { + extern_container_arg!(name_font); + return_send!(via ocvrs_return); + unsafe { sys::cv_fontQt_const_StringR_int_Scalar_int_int_int(name_font.opencv_as_extern(), point_size, &color, weight, style, spacing, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::highgui::QtFont::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Gets the mouse-wheel motion delta, when handling mouse-wheel events cv::EVENT_MOUSEWHEEL and + /// cv::EVENT_MOUSEHWHEEL. + /// + /// For regular mice with a scroll-wheel, delta will be a multiple of 120. The value 120 corresponds to + /// a one notch rotation of the wheel or the threshold for action to be taken and one such action should + /// occur for each delta. Some high-precision mice with higher-resolution freely-rotating wheels may + /// generate smaller values. + /// + /// For cv::EVENT_MOUSEWHEEL positive and negative values mean forward and backward scrolling, + /// respectively. For cv::EVENT_MOUSEHWHEEL, where available, positive and negative values mean right and + /// left scrolling, respectively. + /// + /// + /// Note: Mouse-wheel events are currently supported only on Windows and Cocoa. + /// + /// ## Parameters + /// * flags: The mouse callback flags parameter. + #[inline] + pub fn get_mouse_wheel_delta(flags: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getMouseWheelDelta_int(flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the trackbar position. + /// + /// The function returns the current position of the specified trackbar. + /// + /// + /// Note: [__Qt Backend Only__] winname can be empty if the trackbar is attached to the control + /// panel. + /// + /// ## Parameters + /// * trackbarname: Name of the trackbar. + /// * winname: Name of the window that is the parent of the trackbar. + #[inline] + pub fn get_trackbar_pos(trackbarname: &str, winname: &str) -> Result { + extern_container_arg!(trackbarname); + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_getTrackbarPos_const_StringR_const_StringR(trackbarname.opencv_as_extern(), winname.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Provides rectangle of image in the window. + /// + /// The function getWindowImageRect returns the client screen coordinates, width and height of the image rendering area. + /// + /// ## Parameters + /// * winname: Name of the window. + /// ## See also + /// resizeWindow moveWindow + /// + /// + /// Note: [__Wayland Backend Only__] This function is not supported by the Wayland protocol limitation. + #[inline] + pub fn get_window_image_rect(winname: &str) -> Result { + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_getWindowImageRect_const_StringR(winname.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Provides parameters of a window. + /// + /// The function getWindowProperty returns properties of a window. + /// + /// ## Parameters + /// * winname: Name of the window. + /// * prop_id: Window property to retrieve. The following operation flags are available: (cv::WindowPropertyFlags) + /// ## See also + /// setWindowProperty + /// + /// + /// Note: [__Wayland Backend Only__] This function is not supported. + #[inline] + pub fn get_window_property(winname: &str, prop_id: i32) -> Result { + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_getWindowProperty_const_StringR_int(winname.opencv_as_extern(), prop_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Displays an image in the specified window. + /// + /// The function imshow displays an image in the specified window. If the window was created with the + /// cv::WINDOW_AUTOSIZE flag, the image is shown with its original size, however it is still limited by the screen resolution. + /// Otherwise, the image is scaled to fit the window. The function may scale the image, depending on its depth: + /// + /// * If the image is 8-bit unsigned, it is displayed as is. + /// * If the image is 16-bit unsigned, the pixels are divided by 256. That is, the + /// value range [0,255\*256] is mapped to [0,255]. + /// * If the image is 32-bit or 64-bit floating-point, the pixel values are multiplied by 255. That is, the + /// value range [0,1] is mapped to [0,255]. + /// * 32-bit integer images are not processed anymore due to ambiguouty of required transform. + /// Convert to 8-bit unsigned matrix using a custom preprocessing specific to image's context. + /// + /// If window was created with OpenGL support, cv::imshow also support ogl::Buffer , ogl::Texture2D and + /// cuda::GpuMat as input. + /// + /// If the window was not created before this function, it is assumed creating a window with cv::WINDOW_AUTOSIZE. + /// + /// If you need to show an image that is bigger than the screen resolution, you will need to call namedWindow("", WINDOW_NORMAL) before the imshow. + /// + /// + /// Note: This function should be followed by a call to cv::waitKey or cv::pollKey to perform GUI + /// housekeeping tasks that are necessary to actually show the given image and make the window respond + /// to mouse and keyboard events. Otherwise, it won't display the image and the window might lock up. + /// For example, **waitKey(0)** will display the window infinitely until any keypress (it is suitable + /// for image display). **waitKey(25)** will display a frame and wait approximately 25 ms for a key + /// press (suitable for displaying a video frame-by-frame). To remove the window, use cv::destroyWindow. + /// + /// + /// Note: [__Windows Backend Only__] Pressing Ctrl+C will copy the image to the clipboard. Pressing Ctrl+S will show a dialog to save the image. + /// + /// Note: [__Wayland Backend Only__] Supoorting format is extended. + /// * If the image is 8-bit signed, the pixels are biased by 128. That is, the + /// value range [-128,127] is mapped to [0,255]. + /// * If the image is 16-bit signed, the pixels are divided by 256 and biased by 128. That is, the + /// value range [-32768,32767] is mapped to [0,255]. + /// + /// ## Parameters + /// * winname: Name of the window. + /// * mat: Image to be shown. + #[inline] + pub fn imshow(winname: &str, mat: &impl ToInputArray) -> Result<()> { + extern_container_arg!(winname); + input_array_arg!(mat); + return_send!(via ocvrs_return); + unsafe { sys::cv_imshow_const_StringR_const__InputArrayR(winname.opencv_as_extern(), mat.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Loads parameters of the specified window. + /// + /// The function loadWindowParameters loads size, location, flags, trackbars value, zoom and panning + /// location of the window windowName. + /// + /// ## Parameters + /// * windowName: Name of the window. + #[inline] + pub fn load_window_parameters(window_name: &str) -> Result<()> { + extern_container_arg!(window_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_loadWindowParameters_const_StringR(window_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Moves the window to the specified position + /// + /// ## Parameters + /// * winname: Name of the window. + /// * x: The new x-coordinate of the window. + /// * y: The new y-coordinate of the window. + /// + /// + /// Note: [__Wayland Backend Only__] This function is not supported by the Wayland protocol limitation. + #[inline] + pub fn move_window(winname: &str, x: i32, y: i32) -> Result<()> { + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_moveWindow_const_StringR_int_int(winname.opencv_as_extern(), x, y, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates a window. + /// + /// The function namedWindow creates a window that can be used as a placeholder for images and + /// trackbars. Created windows are referred to by their names. + /// + /// If a window with the same name already exists, the function does nothing. + /// + /// You can call cv::destroyWindow or cv::destroyAllWindows to close the window and de-allocate any associated + /// memory usage. For a simple program, you do not really have to call these functions because all the + /// resources and windows of the application are closed automatically by the operating system upon exit. + /// + /// + /// Note: Qt backend supports additional flags: + /// * **WINDOW_NORMAL or WINDOW_AUTOSIZE:** WINDOW_NORMAL enables you to resize the + /// window, whereas WINDOW_AUTOSIZE adjusts automatically the window size to fit the + /// displayed image (see imshow ), and you cannot change the window size manually. + /// * **WINDOW_FREERATIO or WINDOW_KEEPRATIO:** WINDOW_FREERATIO adjusts the image + /// with no respect to its ratio, whereas WINDOW_KEEPRATIO keeps the image ratio. + /// * **WINDOW_GUI_NORMAL or WINDOW_GUI_EXPANDED:** WINDOW_GUI_NORMAL is the old way to draw the window + /// without statusbar and toolbar, whereas WINDOW_GUI_EXPANDED is a new enhanced GUI. + /// By default, flags == WINDOW_AUTOSIZE | WINDOW_KEEPRATIO | WINDOW_GUI_EXPANDED + /// + /// ## Parameters + /// * winname: Name of the window in the window caption that may be used as a window identifier. + /// * flags: Flags of the window. The supported flags are: (cv::WindowFlags) + /// + /// ## Note + /// This alternative version of [named_window] function uses the following default values for its arguments: + /// * flags: WINDOW_AUTOSIZE + #[inline] + pub fn named_window_def(winname: &str) -> Result<()> { + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_namedWindow_const_StringR(winname.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates a window. + /// + /// The function namedWindow creates a window that can be used as a placeholder for images and + /// trackbars. Created windows are referred to by their names. + /// + /// If a window with the same name already exists, the function does nothing. + /// + /// You can call cv::destroyWindow or cv::destroyAllWindows to close the window and de-allocate any associated + /// memory usage. For a simple program, you do not really have to call these functions because all the + /// resources and windows of the application are closed automatically by the operating system upon exit. + /// + /// + /// Note: Qt backend supports additional flags: + /// * **WINDOW_NORMAL or WINDOW_AUTOSIZE:** WINDOW_NORMAL enables you to resize the + /// window, whereas WINDOW_AUTOSIZE adjusts automatically the window size to fit the + /// displayed image (see imshow ), and you cannot change the window size manually. + /// * **WINDOW_FREERATIO or WINDOW_KEEPRATIO:** WINDOW_FREERATIO adjusts the image + /// with no respect to its ratio, whereas WINDOW_KEEPRATIO keeps the image ratio. + /// * **WINDOW_GUI_NORMAL or WINDOW_GUI_EXPANDED:** WINDOW_GUI_NORMAL is the old way to draw the window + /// without statusbar and toolbar, whereas WINDOW_GUI_EXPANDED is a new enhanced GUI. + /// By default, flags == WINDOW_AUTOSIZE | WINDOW_KEEPRATIO | WINDOW_GUI_EXPANDED + /// + /// ## Parameters + /// * winname: Name of the window in the window caption that may be used as a window identifier. + /// * flags: Flags of the window. The supported flags are: (cv::WindowFlags) + /// + /// ## C++ default parameters + /// * flags: WINDOW_AUTOSIZE + #[inline] + pub fn named_window(winname: &str, flags: i32) -> Result<()> { + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_namedWindow_const_StringR_int(winname.opencv_as_extern(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Polls for a pressed key. + /// + /// The function pollKey polls for a key event without waiting. It returns the code of the pressed key + /// or -1 if no key was pressed since the last invocation. To wait until a key was pressed, use #waitKey. + /// + /// + /// Note: The functions [wait_key] and [poll_key] are the only methods in HighGUI that can fetch and handle + /// GUI events, so one of them needs to be called periodically for normal event processing unless + /// HighGUI is used within an environment that takes care of event processing. + /// + /// + /// Note: The function only works if there is at least one HighGUI window created and the window is + /// active. If there are several HighGUI windows, any of them can be active. + #[inline] + pub fn poll_key() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_pollKey(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Resizes the window to the specified size + /// + /// + /// Note: The specified window size is for the image area. Toolbars are not counted. + /// Only windows created without cv::WINDOW_AUTOSIZE flag can be resized. + /// + /// ## Parameters + /// * winname: Window name. + /// * width: The new window width. + /// * height: The new window height. + /// + /// ## Overloaded parameters + /// + /// * winname: Window name. + /// * size: The new window size. + #[inline] + pub fn resize_window_size(winname: &str, size: core::Size) -> Result<()> { + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_resizeWindow_const_StringR_const_SizeR(winname.opencv_as_extern(), &size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Resizes the window to the specified size + /// + /// + /// Note: The specified window size is for the image area. Toolbars are not counted. + /// Only windows created without cv::WINDOW_AUTOSIZE flag can be resized. + /// + /// ## Parameters + /// * winname: Window name. + /// * width: The new window width. + /// * height: The new window height. + #[inline] + pub fn resize_window(winname: &str, width: i32, height: i32) -> Result<()> { + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_resizeWindow_const_StringR_int_int(winname.opencv_as_extern(), width, height, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Saves parameters of the specified window. + /// + /// The function saveWindowParameters saves size, location, flags, trackbars value, zoom and panning + /// location of the window windowName. + /// + /// ## Parameters + /// * windowName: Name of the window. + #[inline] + pub fn save_window_parameters(window_name: &str) -> Result<()> { + extern_container_arg!(window_name); + return_send!(via ocvrs_return); + unsafe { sys::cv_saveWindowParameters_const_StringR(window_name.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Allows users to select a ROI on the given image. + /// + /// The function creates a window and allows users to select a ROI using the mouse. + /// Controls: use `space` or `enter` to finish selection, use key `c` to cancel selection (function will return the zero cv::Rect). + /// + /// ## Parameters + /// * windowName: name of the window where selection process will be shown. + /// * img: image to select a ROI. + /// * showCrosshair: if true crosshair of selection rectangle will be shown. + /// * fromCenter: if true center of selection will match initial mouse position. In opposite case a corner of + /// selection rectangle will correspont to the initial mouse position. + /// * printNotice: if true a notice to select ROI or cancel selection will be printed in console. + /// ## Returns + /// selected ROI or empty rect if selection canceled. + /// + /// + /// Note: The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...). + /// After finish of work an empty callback will be set for the used window. + /// + /// ## Note + /// This alternative version of [select_roi] function uses the following default values for its arguments: + /// * show_crosshair: true + /// * from_center: false + /// * print_notice: true + #[inline] + pub fn select_roi_def(window_name: &str, img: &impl ToInputArray) -> Result { + extern_container_arg!(window_name); + input_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_selectROI_const_StringR_const__InputArrayR(window_name.opencv_as_extern(), img.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Allows users to select a ROI on the given image. + /// + /// The function creates a window and allows users to select a ROI using the mouse. + /// Controls: use `space` or `enter` to finish selection, use key `c` to cancel selection (function will return the zero cv::Rect). + /// + /// ## Parameters + /// * windowName: name of the window where selection process will be shown. + /// * img: image to select a ROI. + /// * showCrosshair: if true crosshair of selection rectangle will be shown. + /// * fromCenter: if true center of selection will match initial mouse position. In opposite case a corner of + /// selection rectangle will correspont to the initial mouse position. + /// * printNotice: if true a notice to select ROI or cancel selection will be printed in console. + /// ## Returns + /// selected ROI or empty rect if selection canceled. + /// + /// + /// Note: The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...). + /// After finish of work an empty callback will be set for the used window. + /// + /// ## C++ default parameters + /// * show_crosshair: true + /// * from_center: false + /// * print_notice: true + #[inline] + pub fn select_roi(window_name: &str, img: &impl ToInputArray, show_crosshair: bool, from_center: bool, print_notice: bool) -> Result { + extern_container_arg!(window_name); + input_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_selectROI_const_StringR_const__InputArrayR_bool_bool_bool(window_name.opencv_as_extern(), img.as_raw__InputArray(), show_crosshair, from_center, print_notice, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Allows users to select a ROI on the given image. + /// + /// The function creates a window and allows users to select a ROI using the mouse. + /// Controls: use `space` or `enter` to finish selection, use key `c` to cancel selection (function will return the zero cv::Rect). + /// + /// ## Parameters + /// * windowName: name of the window where selection process will be shown. + /// * img: image to select a ROI. + /// * showCrosshair: if true crosshair of selection rectangle will be shown. + /// * fromCenter: if true center of selection will match initial mouse position. In opposite case a corner of + /// selection rectangle will correspont to the initial mouse position. + /// * printNotice: if true a notice to select ROI or cancel selection will be printed in console. + /// ## Returns + /// selected ROI or empty rect if selection canceled. + /// + /// + /// Note: The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...). + /// After finish of work an empty callback will be set for the used window. + /// + /// ## Overloaded parameters + /// + /// + /// ## Note + /// This alternative version of [select_roi_1] function uses the following default values for its arguments: + /// * show_crosshair: true + /// * from_center: false + /// * print_notice: true + #[inline] + pub fn select_roi_1_def(img: &impl ToInputArray) -> Result { + input_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_selectROI_const__InputArrayR(img.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Allows users to select a ROI on the given image. + /// + /// The function creates a window and allows users to select a ROI using the mouse. + /// Controls: use `space` or `enter` to finish selection, use key `c` to cancel selection (function will return the zero cv::Rect). + /// + /// ## Parameters + /// * windowName: name of the window where selection process will be shown. + /// * img: image to select a ROI. + /// * showCrosshair: if true crosshair of selection rectangle will be shown. + /// * fromCenter: if true center of selection will match initial mouse position. In opposite case a corner of + /// selection rectangle will correspont to the initial mouse position. + /// * printNotice: if true a notice to select ROI or cancel selection will be printed in console. + /// ## Returns + /// selected ROI or empty rect if selection canceled. + /// + /// + /// Note: The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...). + /// After finish of work an empty callback will be set for the used window. + /// + /// ## Overloaded parameters + /// + /// ## C++ default parameters + /// * show_crosshair: true + /// * from_center: false + /// * print_notice: true + #[inline] + pub fn select_roi_1(img: &impl ToInputArray, show_crosshair: bool, from_center: bool, print_notice: bool) -> Result { + input_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_selectROI_const__InputArrayR_bool_bool_bool(img.as_raw__InputArray(), show_crosshair, from_center, print_notice, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Allows users to select multiple ROIs on the given image. + /// + /// The function creates a window and allows users to select multiple ROIs using the mouse. + /// Controls: use `space` or `enter` to finish current selection and start a new one, + /// use `esc` to terminate multiple ROI selection process. + /// + /// ## Parameters + /// * windowName: name of the window where selection process will be shown. + /// * img: image to select a ROI. + /// * boundingBoxes: selected ROIs. + /// * showCrosshair: if true crosshair of selection rectangle will be shown. + /// * fromCenter: if true center of selection will match initial mouse position. In opposite case a corner of + /// selection rectangle will correspont to the initial mouse position. + /// * printNotice: if true a notice to select ROI or cancel selection will be printed in console. + /// + /// + /// Note: The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...). + /// After finish of work an empty callback will be set for the used window. + /// + /// ## Note + /// This alternative version of [select_ro_is] function uses the following default values for its arguments: + /// * show_crosshair: true + /// * from_center: false + /// * print_notice: true + #[inline] + pub fn select_ro_is_def(window_name: &str, img: &impl ToInputArray, bounding_boxes: &mut core::Vector) -> Result<()> { + extern_container_arg!(window_name); + input_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_selectROIs_const_StringR_const__InputArrayR_vectorLRectGR(window_name.opencv_as_extern(), img.as_raw__InputArray(), bounding_boxes.as_raw_mut_VectorOfRect(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Allows users to select multiple ROIs on the given image. + /// + /// The function creates a window and allows users to select multiple ROIs using the mouse. + /// Controls: use `space` or `enter` to finish current selection and start a new one, + /// use `esc` to terminate multiple ROI selection process. + /// + /// ## Parameters + /// * windowName: name of the window where selection process will be shown. + /// * img: image to select a ROI. + /// * boundingBoxes: selected ROIs. + /// * showCrosshair: if true crosshair of selection rectangle will be shown. + /// * fromCenter: if true center of selection will match initial mouse position. In opposite case a corner of + /// selection rectangle will correspont to the initial mouse position. + /// * printNotice: if true a notice to select ROI or cancel selection will be printed in console. + /// + /// + /// Note: The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...). + /// After finish of work an empty callback will be set for the used window. + /// + /// ## C++ default parameters + /// * show_crosshair: true + /// * from_center: false + /// * print_notice: true + #[inline] + pub fn select_ro_is(window_name: &str, img: &impl ToInputArray, bounding_boxes: &mut core::Vector, show_crosshair: bool, from_center: bool, print_notice: bool) -> Result<()> { + extern_container_arg!(window_name); + input_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_selectROIs_const_StringR_const__InputArrayR_vectorLRectGR_bool_bool_bool(window_name.opencv_as_extern(), img.as_raw__InputArray(), bounding_boxes.as_raw_mut_VectorOfRect(), show_crosshair, from_center, print_notice, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// @example samples/cpp/create_mask.cpp + /// This program demonstrates using mouse events and how to make and use a mask image (black and white) . + /// + /// Sets mouse handler for the specified window + /// + /// ## Parameters + /// * winname: Name of the window. + /// * onMouse: Callback function for mouse events. See OpenCV samples on how to specify and use the callback. + /// * userdata: The optional parameter passed to the callback. + #[inline] + pub fn set_mouse_callback(winname: &str, on_mouse: crate::highgui::MouseCallback) -> Result<()> { + extern_container_arg!(winname); + callback_arg!(on_mouse_trampoline(event: i32, x: i32, y: i32, flags: i32, userdata: *mut c_void) -> () => userdata in on_mouse(event: i32, x: i32, y: i32, flags: i32) -> ()); + userdata_arg!(userdata: *mut c_void => on_mouse); + return_send!(via ocvrs_return); + unsafe { sys::cv_setMouseCallback_const_StringR_MouseCallback_voidX(winname.opencv_as_extern(), on_mouse_trampoline, userdata, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets the specified window as current OpenGL context. + /// + /// ## Parameters + /// * winname: Name of the window. + #[inline] + pub fn set_opengl_context(winname: &str) -> Result<()> { + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_setOpenGlContext_const_StringR(winname.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets a callback function to be called to draw on top of displayed image. + /// + /// The function setOpenGlDrawCallback can be used to draw 3D data on the window. See the example of + /// callback function below: + /// ```C++ + /// void on_opengl(void* param) + /// { + /// glLoadIdentity(); + /// + /// glTranslated(0.0, 0.0, -1.0); + /// + /// glRotatef( 55, 1, 0, 0 ); + /// glRotatef( 45, 0, 1, 0 ); + /// glRotatef( 0, 0, 0, 1 ); + /// + /// static const int coords[6][4][3] = { + /// { { +1, -1, -1 }, { -1, -1, -1 }, { -1, +1, -1 }, { +1, +1, -1 } }, + /// { { +1, +1, -1 }, { -1, +1, -1 }, { -1, +1, +1 }, { +1, +1, +1 } }, + /// { { +1, -1, +1 }, { +1, -1, -1 }, { +1, +1, -1 }, { +1, +1, +1 } }, + /// { { -1, -1, -1 }, { -1, -1, +1 }, { -1, +1, +1 }, { -1, +1, -1 } }, + /// { { +1, -1, +1 }, { -1, -1, +1 }, { -1, -1, -1 }, { +1, -1, -1 } }, + /// { { -1, -1, +1 }, { +1, -1, +1 }, { +1, +1, +1 }, { -1, +1, +1 } } + /// }; + /// + /// for (int i = 0; i < 6; ++i) { + /// glColor3ub( i*20, 100+i*10, i*42 ); + /// glBegin(GL_QUADS); + /// for (int j = 0; j < 4; ++j) { + /// glVertex3d(0.2 * coords[i][j][0], 0.2 * coords[i][j][1], 0.2 * coords[i][j][2]); + /// } + /// glEnd(); + /// } + /// } + /// ``` + /// + /// + /// ## Parameters + /// * winname: Name of the window. + /// * onOpenGlDraw: Pointer to the function to be called every frame. This function should be + /// prototyped as void Foo(void\*) . + /// * userdata: Pointer passed to the callback function.(__Optional__) + #[inline] + pub fn set_opengl_draw_callback(winname: &str, on_opengl_draw: crate::highgui::OpenGlDrawCallback) -> Result<()> { + extern_container_arg!(winname); + callback_arg!(on_opengl_draw_trampoline(userdata: *mut c_void) -> () => userdata in on_opengl_draw() -> ()); + userdata_arg!(userdata: *mut c_void => on_opengl_draw); + return_send!(via ocvrs_return); + unsafe { sys::cv_setOpenGlDrawCallback_const_StringR_OpenGlDrawCallback_voidX(winname.opencv_as_extern(), on_opengl_draw_trampoline, userdata, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets the trackbar maximum position. + /// + /// The function sets the maximum position of the specified trackbar in the specified window. + /// + /// + /// Note: [__Qt Backend Only__] winname can be empty if the trackbar is attached to the control + /// panel. + /// + /// ## Parameters + /// * trackbarname: Name of the trackbar. + /// * winname: Name of the window that is the parent of trackbar. + /// * maxval: New maximum position. + #[inline] + pub fn set_trackbar_max(trackbarname: &str, winname: &str, maxval: i32) -> Result<()> { + extern_container_arg!(trackbarname); + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_setTrackbarMax_const_StringR_const_StringR_int(trackbarname.opencv_as_extern(), winname.opencv_as_extern(), maxval, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets the trackbar minimum position. + /// + /// The function sets the minimum position of the specified trackbar in the specified window. + /// + /// + /// Note: [__Qt Backend Only__] winname can be empty if the trackbar is attached to the control + /// panel. + /// + /// ## Parameters + /// * trackbarname: Name of the trackbar. + /// * winname: Name of the window that is the parent of trackbar. + /// * minval: New minimum position. + #[inline] + pub fn set_trackbar_min(trackbarname: &str, winname: &str, minval: i32) -> Result<()> { + extern_container_arg!(trackbarname); + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_setTrackbarMin_const_StringR_const_StringR_int(trackbarname.opencv_as_extern(), winname.opencv_as_extern(), minval, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets the trackbar position. + /// + /// The function sets the position of the specified trackbar in the specified window. + /// + /// + /// Note: [__Qt Backend Only__] winname can be empty if the trackbar is attached to the control + /// panel. + /// + /// ## Parameters + /// * trackbarname: Name of the trackbar. + /// * winname: Name of the window that is the parent of trackbar. + /// * pos: New position. + #[inline] + pub fn set_trackbar_pos(trackbarname: &str, winname: &str, pos: i32) -> Result<()> { + extern_container_arg!(trackbarname); + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_setTrackbarPos_const_StringR_const_StringR_int(trackbarname.opencv_as_extern(), winname.opencv_as_extern(), pos, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Changes parameters of a window dynamically. + /// + /// The function setWindowProperty enables changing properties of a window. + /// + /// ## Parameters + /// * winname: Name of the window. + /// * prop_id: Window property to edit. The supported operation flags are: (cv::WindowPropertyFlags) + /// * prop_value: New value of the window property. The supported flags are: (cv::WindowFlags) + /// + /// + /// Note: [__Wayland Backend Only__] This function is not supported. + #[inline] + pub fn set_window_property(winname: &str, prop_id: i32, prop_value: f64) -> Result<()> { + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_setWindowProperty_const_StringR_int_double(winname.opencv_as_extern(), prop_id, prop_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Updates window title + /// ## Parameters + /// * winname: Name of the window. + /// * title: New title. + #[inline] + pub fn set_window_title(winname: &str, title: &str) -> Result<()> { + extern_container_arg!(winname); + extern_container_arg!(title); + return_send!(via ocvrs_return); + unsafe { sys::cv_setWindowTitle_const_StringR_const_StringR(winname.opencv_as_extern(), title.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn start_loop(pt2_func: Option i32>, argv: &mut [&str]) -> Result { + string_array_arg_mut!(argv); + return_send!(via ocvrs_return); + unsafe { sys::cv_startLoop_int__X__int__charXX__int_charXX(pt2_func, argv.len().try_into()?, argv.as_mut_ptr(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn start_window_thread() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_startWindowThread(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + pub fn stop_loop() -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_stopLoop(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Force window to redraw its context and call draw callback ( See cv::setOpenGlDrawCallback ). + /// + /// ## Parameters + /// * winname: Name of the window. + #[inline] + pub fn update_window(winname: &str) -> Result<()> { + extern_container_arg!(winname); + return_send!(via ocvrs_return); + unsafe { sys::cv_updateWindow_const_StringR(winname.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Waits for a pressed key. + /// + /// The function waitKey waits for a key event infinitely (when ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdelay%7D%5Cleq%200) ) or for delay + /// milliseconds, when it is positive. Since the OS has a minimum time between switching threads, the + /// function will not wait exactly delay ms, it will wait at least delay ms, depending on what else is + /// running on your computer at that time. It returns the code of the pressed key or -1 if no key was + /// pressed before the specified time had elapsed. To check for a key press but not wait for it, use + /// #pollKey. + /// + /// + /// Note: The functions [wait_key] and [poll_key] are the only methods in HighGUI that can fetch and handle + /// GUI events, so one of them needs to be called periodically for normal event processing unless + /// HighGUI is used within an environment that takes care of event processing. + /// + /// + /// Note: The function only works if there is at least one HighGUI window created and the window is + /// active. If there are several HighGUI windows, any of them can be active. + /// + /// ## Parameters + /// * delay: Delay in milliseconds. 0 is the special value that means "forever". + /// + /// ## Note + /// This alternative version of [wait_key] function uses the following default values for its arguments: + /// * delay: 0 + #[inline] + pub fn wait_key_def() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_waitKey(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Similar to #waitKey, but returns full key code. + /// + /// + /// Note: Key code is implementation specific and depends on used backend: QT/GTK/Win32/etc + /// + /// ## Note + /// This alternative version of [wait_key_ex] function uses the following default values for its arguments: + /// * delay: 0 + #[inline] + pub fn wait_key_ex_def() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_waitKeyEx(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Similar to #waitKey, but returns full key code. + /// + /// + /// Note: Key code is implementation specific and depends on used backend: QT/GTK/Win32/etc + /// + /// ## C++ default parameters + /// * delay: 0 + #[inline] + pub fn wait_key_ex(delay: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_waitKeyEx_int(delay, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Waits for a pressed key. + /// + /// The function waitKey waits for a key event infinitely (when ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdelay%7D%5Cleq%200) ) or for delay + /// milliseconds, when it is positive. Since the OS has a minimum time between switching threads, the + /// function will not wait exactly delay ms, it will wait at least delay ms, depending on what else is + /// running on your computer at that time. It returns the code of the pressed key or -1 if no key was + /// pressed before the specified time had elapsed. To check for a key press but not wait for it, use + /// #pollKey. + /// + /// + /// Note: The functions [wait_key] and [poll_key] are the only methods in HighGUI that can fetch and handle + /// GUI events, so one of them needs to be called periodically for normal event processing unless + /// HighGUI is used within an environment that takes care of event processing. + /// + /// + /// Note: The function only works if there is at least one HighGUI window created and the window is + /// active. If there are several HighGUI windows, any of them can be active. + /// + /// ## Parameters + /// * delay: Delay in milliseconds. 0 is the special value that means "forever". + /// + /// ## C++ default parameters + /// * delay: 0 + #[inline] + pub fn wait_key(delay: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_waitKey_int(delay, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// QtFont available only for Qt. See cv::fontQt + pub struct QtFont { + ptr: *mut c_void, + } + + opencv_type_boxed! { QtFont } + + impl Drop for QtFont { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_QtFont_delete(self.as_raw_mut_QtFont()) }; + } + } + + unsafe impl Send for QtFont {} + + impl QtFont { + /// Creates a default instance of the class by calling the default constructor + #[inline] + pub fn default() -> crate::highgui::QtFont { + let ret = unsafe { sys::cv_QtFont_defaultNew_const() }; + let ret = unsafe { crate::highgui::QtFont::opencv_from_extern(ret) }; + ret + } + + } + + /// Constant methods for [crate::highgui::QtFont] + pub trait QtFontTraitConst { + fn as_raw_QtFont(&self) -> *const c_void; + + /// Name of the font + #[inline] + fn name_font(&self) -> String { + let ret = unsafe { sys::cv_QtFont_propNameFont_const(self.as_raw_QtFont()) }; + let ret = unsafe { String::opencv_from_extern(ret) }; + ret + } + + /// Color of the font. Scalar(blue_component, green_component, red_component[, alpha_component]) + #[inline] + fn color(&self) -> core::Scalar { + return_send!(via ocvrs_return); + unsafe { sys::cv_QtFont_propColor_const(self.as_raw_QtFont(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + /// See cv::QtFontStyles + #[inline] + fn font_face(&self) -> i32 { + let ret = unsafe { sys::cv_QtFont_propFont_face_const(self.as_raw_QtFont()) }; + ret + } + + /// font data and metrics + #[inline] + fn ascii(&self) -> *const i32 { + let ret = unsafe { sys::cv_QtFont_propAscii_const(self.as_raw_QtFont()) }; + ret + } + + #[inline] + fn greek(&self) -> *const i32 { + let ret = unsafe { sys::cv_QtFont_propGreek_const(self.as_raw_QtFont()) }; + ret + } + + #[inline] + fn cyrillic(&self) -> *const i32 { + let ret = unsafe { sys::cv_QtFont_propCyrillic_const(self.as_raw_QtFont()) }; + ret + } + + #[inline] + fn hscale(&self) -> f32 { + let ret = unsafe { sys::cv_QtFont_propHscale_const(self.as_raw_QtFont()) }; + ret + } + + #[inline] + fn vscale(&self) -> f32 { + let ret = unsafe { sys::cv_QtFont_propVscale_const(self.as_raw_QtFont()) }; + ret + } + + /// slope coefficient: 0 - normal, >0 - italic + #[inline] + fn shear(&self) -> f32 { + let ret = unsafe { sys::cv_QtFont_propShear_const(self.as_raw_QtFont()) }; + ret + } + + /// See cv::QtFontWeights + #[inline] + fn thickness(&self) -> i32 { + let ret = unsafe { sys::cv_QtFont_propThickness_const(self.as_raw_QtFont()) }; + ret + } + + /// horizontal interval between letters + #[inline] + fn dx(&self) -> f32 { + let ret = unsafe { sys::cv_QtFont_propDx_const(self.as_raw_QtFont()) }; + ret + } + + /// PointSize + #[inline] + fn line_type(&self) -> i32 { + let ret = unsafe { sys::cv_QtFont_propLine_type_const(self.as_raw_QtFont()) }; + ret + } + + } + + /// Mutable methods for [crate::highgui::QtFont] + pub trait QtFontTrait: crate::highgui::QtFontTraitConst { + fn as_raw_mut_QtFont(&mut self) -> *mut c_void; + + /// Color of the font. Scalar(blue_component, green_component, red_component[, alpha_component]) + #[inline] + fn set_color(&mut self, val: core::Scalar) { + let ret = unsafe { sys::cv_QtFont_propColor_const_Scalar(self.as_raw_mut_QtFont(), &val) }; + ret + } + + /// See cv::QtFontStyles + #[inline] + fn set_font_face(&mut self, val: i32) { + let ret = unsafe { sys::cv_QtFont_propFont_face_const_int(self.as_raw_mut_QtFont(), val) }; + ret + } + + #[inline] + fn set_hscale(&mut self, val: f32) { + let ret = unsafe { sys::cv_QtFont_propHscale_const_float(self.as_raw_mut_QtFont(), val) }; + ret + } + + #[inline] + fn set_vscale(&mut self, val: f32) { + let ret = unsafe { sys::cv_QtFont_propVscale_const_float(self.as_raw_mut_QtFont(), val) }; + ret + } + + /// slope coefficient: 0 - normal, >0 - italic + #[inline] + fn set_shear(&mut self, val: f32) { + let ret = unsafe { sys::cv_QtFont_propShear_const_float(self.as_raw_mut_QtFont(), val) }; + ret + } + + /// See cv::QtFontWeights + #[inline] + fn set_thickness(&mut self, val: i32) { + let ret = unsafe { sys::cv_QtFont_propThickness_const_int(self.as_raw_mut_QtFont(), val) }; + ret + } + + /// horizontal interval between letters + #[inline] + fn set_dx(&mut self, val: f32) { + let ret = unsafe { sys::cv_QtFont_propDx_const_float(self.as_raw_mut_QtFont(), val) }; + ret + } + + /// PointSize + #[inline] + fn set_line_type(&mut self, val: i32) { + let ret = unsafe { sys::cv_QtFont_propLine_type_const_int(self.as_raw_mut_QtFont(), val) }; + ret + } + + } + + impl Default for QtFont { + #[inline] + /// Forwards to infallible Self::default() + fn default() -> Self { + Self::default() + } + } + + impl std::fmt::Debug for QtFont { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("QtFont") + .field("name_font", &crate::highgui::QtFontTraitConst::name_font(self)) + .field("color", &crate::highgui::QtFontTraitConst::color(self)) + .field("font_face", &crate::highgui::QtFontTraitConst::font_face(self)) + .field("ascii", &crate::highgui::QtFontTraitConst::ascii(self)) + .field("greek", &crate::highgui::QtFontTraitConst::greek(self)) + .field("cyrillic", &crate::highgui::QtFontTraitConst::cyrillic(self)) + .field("hscale", &crate::highgui::QtFontTraitConst::hscale(self)) + .field("vscale", &crate::highgui::QtFontTraitConst::vscale(self)) + .field("shear", &crate::highgui::QtFontTraitConst::shear(self)) + .field("thickness", &crate::highgui::QtFontTraitConst::thickness(self)) + .field("dx", &crate::highgui::QtFontTraitConst::dx(self)) + .field("line_type", &crate::highgui::QtFontTraitConst::line_type(self)) + .finish() + } + } + + impl crate::highgui::QtFontTraitConst for QtFont { + #[inline] fn as_raw_QtFont(&self) -> *const c_void { self.as_raw() } + } + + impl crate::highgui::QtFontTrait for QtFont { + #[inline] fn as_raw_mut_QtFont(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { QtFont, crate::highgui::QtFontTraitConst, as_raw_QtFont, crate::highgui::QtFontTrait, as_raw_mut_QtFont } + +} diff --git a/target/debug/build/opencv-4e105b5546afc119/out/opencv/hub.rs b/target/debug/build/opencv-4e105b5546afc119/out/opencv/hub.rs new file mode 100644 index 0000000..cdcc99d --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/opencv/hub.rs @@ -0,0 +1,25 @@ +include!(concat!(env!("OUT_DIR"), "/opencv/core.rs")); +include!(concat!(env!("OUT_DIR"), "/opencv/dnn.rs")); +include!(concat!(env!("OUT_DIR"), "/opencv/highgui.rs")); +include!(concat!(env!("OUT_DIR"), "/opencv/imgproc.rs")); +include!(concat!(env!("OUT_DIR"), "/opencv/videoio.rs")); +pub mod types { + include!(concat!(env!("OUT_DIR"), "/opencv/types.rs")); +} +#[doc(hidden)] +pub mod sys { + include!(concat!(env!("OUT_DIR"), "/opencv/sys.rs")); +} +pub mod hub_prelude { + pub use super::core::prelude::*; + pub use super::dnn::prelude::*; + pub use super::highgui::prelude::*; + pub use super::imgproc::prelude::*; + pub use super::videoio::prelude::*; +} + +mod ffi_exports { + use crate::mod_prelude_sys::*; + #[unsafe(no_mangle)] unsafe extern "C" fn ocvrs_create_string_0_98_1(s: *const c_char) -> *mut String { unsafe { crate::templ::ocvrs_create_string(s) } } + #[unsafe(no_mangle)] unsafe extern "C" fn ocvrs_create_byte_string_0_98_1(v: *const u8, len: size_t) -> *mut Vec { unsafe { crate::templ::ocvrs_create_byte_string(v, len) } } +} diff --git a/target/debug/build/opencv-4e105b5546afc119/out/opencv/imgproc.rs b/target/debug/build/opencv-4e105b5546afc119/out/opencv/imgproc.rs new file mode 100644 index 0000000..2e298cd --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/opencv/imgproc.rs @@ -0,0 +1,13614 @@ +pub mod imgproc { + //! # Image Processing + //! + //! This module offers a comprehensive suite of image processing functions, enabling tasks such as those listed above. + //! # Image Filtering + //! + //! Functions and classes described in this section are used to perform various linear or non-linear + //! filtering operations on 2D images (represented as Mat's). It means that for each pixel location + //! ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29) in the source image (normally, rectangular), its neighborhood is considered and used to + //! compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of + //! morphological operations, it is the minimum or maximum values, and so on. The computed response is + //! stored in the destination image at the same location ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29). It means that the output image + //! will be of the same size as the input image. Normally, the functions support multi-channel arrays, + //! in which case every channel is processed independently. Therefore, the output image will also have + //! the same number of channels as the input one. + //! + //! Another common feature of the functions and classes described in this section is that, unlike + //! simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For + //! example, if you want to smooth an image using a Gaussian ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%203) filter, then, when + //! processing the left-most pixels in each row, you need pixels to the left of them, that is, outside + //! of the image. You can let these pixels be the same as the left-most image pixels ("replicated + //! border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant + //! border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method. + //! For details, see [border_types] + //! + //! @anchor filter_depths + //! ### Depth combinations + //! Input depth (src.depth()) | Output depth (ddepth) + //! --------------------------|---------------------- + //! CV_8U | -1/CV_16S/CV_32F/CV_64F + //! CV_16U/CV_16S | -1/CV_32F/CV_64F + //! CV_32F | -1/CV_32F + //! CV_64F | -1/CV_64F + //! + //! + //! Note: when ddepth=-1, the output image will have the same depth as the source. + //! + //! + //! Note: if you need double floating-point accuracy and using single floating-point input data + //! (CV_32F input and CV_64F output depth combination), you can use [Mat].convertTo to convert + //! the input data to the desired precision. + //! + //! # Geometric Image Transformations + //! + //! The functions in this section perform various geometrical transformations of 2D images. They do not + //! change the image content but deform the pixel grid and map this deformed grid to the destination + //! image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from + //! destination to the source. That is, for each pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%29) of the destination image, the + //! functions compute coordinates of the corresponding "donor" pixel in the source image and copy the + //! pixel value: + //! + //! ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%3D%20%5Ctexttt%7Bsrc%7D%20%28f%5Fx%28x%2Cy%29%2C%20f%5Fy%28x%2Cy%29%29) + //! + //! In case when you specify the forward mapping ![inline formula](https://latex.codecogs.com/png.latex?%5Cleft%3Cg%5Fx%2C%20g%5Fy%5Cright%3E%3A%20%5Ctexttt%7Bsrc%7D%20%5Crightarrow%0A%20%20%20%20%5Ctexttt%7Bdst%7D), the OpenCV functions first compute the corresponding inverse mapping + //! ![inline formula](https://latex.codecogs.com/png.latex?%5Cleft%3Cf%5Fx%2C%20f%5Fy%5Cright%3E%3A%20%5Ctexttt%7Bdst%7D%20%5Crightarrow%20%5Ctexttt%7Bsrc%7D) and then use the above formula. + //! + //! The actual implementations of the geometrical transformations, from the most generic remap and to + //! the simplest and the fastest resize, need to solve two main problems with the above formula: + //! + //! - Extrapolation of non-existing pixels. Similarly to the filtering functions described in the + //! previous section, for some ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29), either one of ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx%28x%2Cy%29), or ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy%28x%2Cy%29), or both + //! of them may fall outside of the image. In this case, an extrapolation method needs to be used. + //! OpenCV provides the same selection of extrapolation methods as in the filtering functions. In + //! addition, it provides the method #BORDER_TRANSPARENT. This means that the corresponding pixels in + //! the destination image will not be modified at all. + //! + //! - Interpolation of pixel values. Usually ![inline formula](https://latex.codecogs.com/png.latex?f%5Fx%28x%2Cy%29) and ![inline formula](https://latex.codecogs.com/png.latex?f%5Fy%28x%2Cy%29) are floating-point + //! numbers. This means that ![inline formula](https://latex.codecogs.com/png.latex?%5Cleft%3Cf%5Fx%2C%20f%5Fy%5Cright%3E) can be either an affine or perspective + //! transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional + //! coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the + //! nearest integer coordinates and the corresponding pixel can be used. This is called a + //! nearest-neighbor interpolation. However, a better result can be achieved by using more + //! sophisticated [interpolation methods](https://en.wikipedia.org/wiki/Multivariate_interpolation) , + //! where a polynomial function is fit into some neighborhood of the computed pixel ![inline formula](https://latex.codecogs.com/png.latex?%28f%5Fx%28x%2Cy%29%2C%0A%20%20%20%20f%5Fy%28x%2Cy%29%29), and then the value of the polynomial at ![inline formula](https://latex.codecogs.com/png.latex?%28f%5Fx%28x%2Cy%29%2C%20f%5Fy%28x%2Cy%29%29) is taken as the + //! interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See + //! [resize] for details. + //! + //! + //! Note: The geometrical transformations do not work with `CV_8S` or `CV_32S` images. + //! + //! # Miscellaneous Image Transformations + //! # Drawing Functions + //! + //! Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be + //! rendered with antialiasing (implemented only for 8-bit images for now). All the functions include + //! the parameter color that uses an RGB value (that may be constructed with the Scalar constructor ) + //! for color images and brightness for grayscale images. For color images, the channel ordering is + //! normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a + //! color using the Scalar constructor, it should look like: + //! + //! ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BScalar%7D%20%28blue%20%5C%5F%20component%2C%20green%20%5C%5F%20component%2C%20red%20%5C%5F%20component%5B%2C%20alpha%20%5C%5F%20component%5D%29) + //! + //! If you are using your own image rendering and I/O functions, you can use any channel ordering. The + //! drawing functions process each channel independently and do not depend on the channel order or even + //! on the used color space. The whole image can be converted from BGR to RGB or to a different color + //! space using cvtColor . + //! + //! If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, + //! many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means + //! that the coordinates can be passed as fixed-point numbers encoded as integers. The number of + //! fractional bits is specified by the shift parameter and the real point coordinates are calculated as + //! ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BPoint%7D%28x%2Cy%29%5Crightarrow%5Ctexttt%7BPoint2f%7D%28x%2A2%5E%7B%2Dshift%7D%2Cy%2A2%5E%7B%2Dshift%7D%29) . This feature is + //! especially effective when rendering antialiased shapes. + //! + //! + //! Note: The functions do not support alpha-transparency when the target image is 4-channel. In this + //! case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint + //! semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main + //! image. + //! + //! # Color Space Conversions + //! # ColorMaps in OpenCV + //! + //! The human perception isn't built for observing fine changes in grayscale images. Human eyes are more + //! sensitive to observing changes between colors, so you often need to recolor your grayscale images to + //! get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your + //! computer vision application. + //! + //! In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample + //! code reads the path to an image from command line, applies a Jet colormap on it and shows the + //! result: + //! + //! @include snippets/imgproc_applyColorMap.cpp + //! ## See also + //! [colormap_types] + //! + //! # Planar Subdivision + //! + //! The Subdiv2D class described in this section is used to perform various planar subdivision on + //! a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles + //! using the Delaunay's algorithm, which corresponds to the dual graph of the Voronoi diagram. + //! In the figure below, the Delaunay's triangulation is marked with black lines and the Voronoi + //! diagram with red lines. + //! + //! ![Delaunay triangulation (black) and Voronoi (red)](https://docs.opencv.org/4.13.0/delaunay_voronoi.png) + //! + //! The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast + //! location of points on the plane, building special graphs (such as NNG,RNG), and so forth. + //! + //! # Histograms + //! # Structural Analysis and Shape Descriptors + //! # Motion Analysis and Object Tracking + //! # Feature Detection + //! # Object Detection + //! # Image Segmentation + //! # Hardware Acceleration Layer + //! # Functions + //! # Interface + use crate::mod_prelude::*; + use crate::{core, sys, types}; + pub mod prelude { + pub use super::{CLAHETrait, CLAHETraitConst, GeneralizedHoughBallardTrait, GeneralizedHoughBallardTraitConst, GeneralizedHoughGuilTrait, GeneralizedHoughGuilTraitConst, GeneralizedHoughTrait, GeneralizedHoughTraitConst, IntelligentScissorsMBTrait, IntelligentScissorsMBTraitConst, LineIteratorTrait, LineIteratorTraitConst, LineSegmentDetectorTrait, LineSegmentDetectorTraitConst, Subdiv2DTrait, Subdiv2DTraitConst}; + } + + /// the threshold value ![inline formula](https://latex.codecogs.com/png.latex?T%28x%2C%20y%29) is a weighted sum (cross-correlation with a Gaussian + /// window) of the ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BblockSize%7D%20%5Ctimes%20%5Ctexttt%7BblockSize%7D) neighborhood of ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%29) + /// minus C . The default sigma (standard deviation) is used for the specified blockSize . See + /// #getGaussianKernel + pub const ADAPTIVE_THRESH_GAUSSIAN_C: i32 = 1; + /// the threshold value ![inline formula](https://latex.codecogs.com/png.latex?T%28x%2Cy%29) is a mean of the ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BblockSize%7D%20%5Ctimes%0A%5Ctexttt%7BblockSize%7D) neighborhood of ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%29) minus C + pub const ADAPTIVE_THRESH_MEAN_C: i32 = 0; + /// Same as CCL_GRANA. It is preferable to use the flag with the name of the algorithm (CCL_BBDT) rather than the one with the name of the first author (CCL_GRANA). + pub const CCL_BBDT: i32 = 4; + /// Spaghetti [Bolelli2019](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Bolelli2019) algorithm for 8-way connectivity, Spaghetti4C [Bolelli2021](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Bolelli2021) algorithm for 4-way connectivity. The parallel implementation described in [Bolelli2017](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Bolelli2017) is available for both Spaghetti and Spaghetti4C. + pub const CCL_BOLELLI: i32 = 2; + /// Spaghetti [Bolelli2019](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Bolelli2019) algorithm for 8-way connectivity, Spaghetti4C [Bolelli2021](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Bolelli2021) algorithm for 4-way connectivity. + pub const CCL_DEFAULT: i32 = -1; + /// BBDT [Grana2010](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Grana2010) algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity. The parallel implementation described in [Bolelli2017](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Bolelli2017) is available for both BBDT and SAUF. + pub const CCL_GRANA: i32 = 1; + /// Same as CCL_WU. It is preferable to use the flag with the name of the algorithm (CCL_SAUF) rather than the one with the name of the first author (CCL_WU). + pub const CCL_SAUF: i32 = 3; + /// Same as CCL_BOLELLI. It is preferable to use the flag with the name of the algorithm (CCL_SPAGHETTI) rather than the one with the name of the first author (CCL_BOLELLI). + pub const CCL_SPAGHETTI: i32 = 5; + /// SAUF [Wu2009](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Wu2009) algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity. The parallel implementation described in [Bolelli2017](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Bolelli2017) is available for SAUF. + pub const CCL_WU: i32 = 0; + /// The total area (in pixels) of the connected component + pub const CC_STAT_AREA: i32 = 4; + /// The vertical size of the bounding box + pub const CC_STAT_HEIGHT: i32 = 3; + /// The leftmost (x) coordinate which is the inclusive start of the bounding + /// box in the horizontal direction. + pub const CC_STAT_LEFT: i32 = 0; + /// Max enumeration value. Used internally only for memory allocation + pub const CC_STAT_MAX: i32 = 5; + /// The topmost (y) coordinate which is the inclusive start of the bounding + /// box in the vertical direction. + pub const CC_STAT_TOP: i32 = 1; + /// The horizontal size of the bounding box + pub const CC_STAT_WIDTH: i32 = 2; + /// stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and + /// (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is, + /// max(abs(x1-x2),abs(y2-y1))==1. + pub const CHAIN_APPROX_NONE: i32 = 1; + /// compresses horizontal, vertical, and diagonal segments and leaves only their end points. + /// For example, an up-right rectangular contour is encoded with 4 points. + pub const CHAIN_APPROX_SIMPLE: i32 = 2; + /// applies one of the flavors of the Teh-Chin chain approximation algorithm [TehChin89](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_TehChin89) + pub const CHAIN_APPROX_TC89_KCOS: i32 = 4; + /// applies one of the flavors of the Teh-Chin chain approximation algorithm [TehChin89](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_TehChin89) + pub const CHAIN_APPROX_TC89_L1: i32 = 3; + /// ![autumn](https://docs.opencv.org/4.13.0/colorscale_autumn.jpg) + pub const COLORMAP_AUTUMN: i32 = 0; + /// ![bone](https://docs.opencv.org/4.13.0/colorscale_bone.jpg) + pub const COLORMAP_BONE: i32 = 1; + /// ![cividis](https://docs.opencv.org/4.13.0/colorscale_cividis.jpg) + pub const COLORMAP_CIVIDIS: i32 = 17; + /// ![cool](https://docs.opencv.org/4.13.0/colorscale_cool.jpg) + pub const COLORMAP_COOL: i32 = 8; + /// ![deepgreen](https://docs.opencv.org/4.13.0/colorscale_deepgreen.jpg) + pub const COLORMAP_DEEPGREEN: i32 = 21; + /// ![hot](https://docs.opencv.org/4.13.0/colorscale_hot.jpg) + pub const COLORMAP_HOT: i32 = 11; + /// ![HSV](https://docs.opencv.org/4.13.0/colorscale_hsv.jpg) + pub const COLORMAP_HSV: i32 = 9; + /// ![inferno](https://docs.opencv.org/4.13.0/colorscale_inferno.jpg) + pub const COLORMAP_INFERNO: i32 = 14; + /// ![jet](https://docs.opencv.org/4.13.0/colorscale_jet.jpg) + pub const COLORMAP_JET: i32 = 2; + /// ![magma](https://docs.opencv.org/4.13.0/colorscale_magma.jpg) + pub const COLORMAP_MAGMA: i32 = 13; + /// ![ocean](https://docs.opencv.org/4.13.0/colorscale_ocean.jpg) + pub const COLORMAP_OCEAN: i32 = 5; + /// ![parula](https://docs.opencv.org/4.13.0/colorscale_parula.jpg) + pub const COLORMAP_PARULA: i32 = 12; + /// ![pink](https://docs.opencv.org/4.13.0/colorscale_pink.jpg) + pub const COLORMAP_PINK: i32 = 10; + /// ![plasma](https://docs.opencv.org/4.13.0/colorscale_plasma.jpg) + pub const COLORMAP_PLASMA: i32 = 15; + /// ![rainbow](https://docs.opencv.org/4.13.0/colorscale_rainbow.jpg) + pub const COLORMAP_RAINBOW: i32 = 4; + /// ![spring](https://docs.opencv.org/4.13.0/colorscale_spring.jpg) + pub const COLORMAP_SPRING: i32 = 7; + /// ![summer](https://docs.opencv.org/4.13.0/colorscale_summer.jpg) + pub const COLORMAP_SUMMER: i32 = 6; + /// ![turbo](https://docs.opencv.org/4.13.0/colorscale_turbo.jpg) + pub const COLORMAP_TURBO: i32 = 20; + /// ![twilight](https://docs.opencv.org/4.13.0/colorscale_twilight.jpg) + pub const COLORMAP_TWILIGHT: i32 = 18; + /// ![twilight shifted](https://docs.opencv.org/4.13.0/colorscale_twilight_shifted.jpg) + pub const COLORMAP_TWILIGHT_SHIFTED: i32 = 19; + /// ![viridis](https://docs.opencv.org/4.13.0/colorscale_viridis.jpg) + pub const COLORMAP_VIRIDIS: i32 = 16; + /// ![winter](https://docs.opencv.org/4.13.0/colorscale_winter.jpg) + pub const COLORMAP_WINTER: i32 = 3; + /// [8U] convert between RGB/BGR and BGR555 (16-bit images) + pub const COLOR_BGR2BGR555: i32 = 22; + /// [8U] convert between RGB/BGR and BGR565 (16-bit images) + pub const COLOR_BGR2BGR565: i32 = 12; + /// [8U/16U/32F] add alpha channel to RGB or BGR image + pub const COLOR_BGR2BGRA: i32 = 0; + /// [8U/16U/32F] convert between RGB/BGR and grayscale, [color_convert_rgb_gray] "color conversions" + pub const COLOR_BGR2GRAY: i32 = 6; + /// [8U/32F] convert RGB/BGR to HLS (hue lightness saturation) with H range 0..180 if 8 bit image, [color_convert_rgb_hls] "color conversions" + pub const COLOR_BGR2HLS: i32 = 52; + /// [8U/32F] convert RGB/BGR to HLS (hue lightness saturation) with H range 0..255 if 8 bit image, [color_convert_rgb_hls] "color conversions" + pub const COLOR_BGR2HLS_FULL: i32 = 68; + /// [8U/32F] convert RGB/BGR to HSV (hue saturation value) with H range 0..180 if 8 bit image, [color_convert_rgb_hsv] "color conversions" + pub const COLOR_BGR2HSV: i32 = 40; + /// [8U/32F] convert RGB/BGR to HSV (hue saturation value) with H range 0..255 if 8 bit image, [color_convert_rgb_hsv] "color conversions" + pub const COLOR_BGR2HSV_FULL: i32 = 66; + /// [8U/32F] convert RGB/BGR to CIE Lab, [color_convert_rgb_lab] "color conversions" + pub const COLOR_BGR2Lab: i32 = 44; + /// [8U/32F] convert RGB/BGR to CIE Luv, [color_convert_rgb_luv] "color conversions" + pub const COLOR_BGR2Luv: i32 = 50; + /// [8U/16U/32F] + pub const COLOR_BGR2RGB: i32 = 4; + /// [8U/16U/32F] convert between RGB and BGR color spaces (with or without alpha channel) + pub const COLOR_BGR2RGBA: i32 = 2; + /// [8U/16U/32F] convert RGB/BGR to CIE XYZ, [color_convert_rgb_xyz] "color conversions" + pub const COLOR_BGR2XYZ: i32 = 32; + /// [8U/16U/32F] convert RGB/BGR to luma-chroma (aka YCC), [color_convert_rgb_ycrcb] "color conversions" + pub const COLOR_BGR2YCrCb: i32 = 36; + /// [8U/16U/32F] convert between RGB/BGR and YUV + pub const COLOR_BGR2YUV: i32 = 82; + /// [8U] convert between BGR and 4:2:0-subsampled YUV I420, three planes in one array: Y, U and V, see [color_convert_rgb_yuv_42x] + pub const COLOR_BGR2YUV_I420: i32 = 128; + /// synonym to I420 + pub const COLOR_BGR2YUV_IYUV: i32 = 128; + /// synonym to UYVY + pub const COLOR_BGR2YUV_UYNV: i32 = 144; + /// [8U] convert between BGR and YUV UYVU, YUV is 4:2:2 and interleaved as U/Y1/V/Y2, see [color_convert_rgb_yuv_42x] + pub const COLOR_BGR2YUV_UYVY: i32 = 144; + /// synonym to UYVY + pub const COLOR_BGR2YUV_Y422: i32 = 144; + /// synonym to YUY2 + pub const COLOR_BGR2YUV_YUNV: i32 = 148; + /// [8U] convert between BGR and YUV YUY2, YUV is 4:2:2 and interleaved as Y1/U/Y2/V, see [color_convert_rgb_yuv_42x] + pub const COLOR_BGR2YUV_YUY2: i32 = 148; + /// synonym to YUY2 + pub const COLOR_BGR2YUV_YUYV: i32 = 148; + /// [8U] convert between BGR and 4:2:0-subsampled YUV YV12, three planes in one array: Y, V and U, see [color_convert_rgb_yuv_42x] + pub const COLOR_BGR2YUV_YV12: i32 = 132; + /// [8U] convert between BGR and YUV YVYU, YUV is 4:2:2 and interleaved as Y1/V/Y2/U, see [color_convert_rgb_yuv_42x] + pub const COLOR_BGR2YUV_YVYU: i32 = 150; + /// [8U] + pub const COLOR_BGR5552BGR: i32 = 24; + /// [8U] + pub const COLOR_BGR5552BGRA: i32 = 28; + /// [8U] + pub const COLOR_BGR5552GRAY: i32 = 31; + /// [8U] + pub const COLOR_BGR5552RGB: i32 = 25; + /// [8U] + pub const COLOR_BGR5552RGBA: i32 = 29; + /// [8U] + pub const COLOR_BGR5652BGR: i32 = 14; + /// [8U] + pub const COLOR_BGR5652BGRA: i32 = 18; + /// [8U] + pub const COLOR_BGR5652GRAY: i32 = 21; + /// [8U] + pub const COLOR_BGR5652RGB: i32 = 15; + /// [8U] + pub const COLOR_BGR5652RGBA: i32 = 19; + /// [8U/16U/32F] remove alpha channel from RGB or BGR image + pub const COLOR_BGRA2BGR: i32 = 1; + /// [8U] + pub const COLOR_BGRA2BGR555: i32 = 26; + /// [8U] + pub const COLOR_BGRA2BGR565: i32 = 16; + /// [8U/16U/32F] + pub const COLOR_BGRA2GRAY: i32 = 10; + /// [8U/16U/32F] + pub const COLOR_BGRA2RGB: i32 = 3; + /// [8U/16U/32F] + pub const COLOR_BGRA2RGBA: i32 = 5; + /// [8U] convert between BGRA and 4:2:0-subsampled YUV I420, three planes in one array: Y, U and V, see [color_convert_rgb_yuv_42x] + pub const COLOR_BGRA2YUV_I420: i32 = 130; + /// synonym to I420 + pub const COLOR_BGRA2YUV_IYUV: i32 = 130; + /// synonym to UYVY + pub const COLOR_BGRA2YUV_UYNV: i32 = 146; + /// [8U] convert between BGRA and YUV UYVU, YUV is 4:2:2 and interleaved as U/Y1/V/Y2, see [color_convert_rgb_yuv_42x] + pub const COLOR_BGRA2YUV_UYVY: i32 = 146; + /// synonym to UYVY + pub const COLOR_BGRA2YUV_Y422: i32 = 146; + /// synonym to YUY2 + pub const COLOR_BGRA2YUV_YUNV: i32 = 152; + /// [8U] convert between BGRA and YUV YUY2, YUV is 4:2:2 and interleaved as Y1/U/Y2/V, see [color_convert_rgb_yuv_42x] + pub const COLOR_BGRA2YUV_YUY2: i32 = 152; + /// synonym to YUY2 + pub const COLOR_BGRA2YUV_YUYV: i32 = 152; + /// [8U] convert between BGRA and 4:2:0-subsampled YUV YV12, three planes in one array: Y, V and U, see [color_convert_rgb_yuv_42x] + pub const COLOR_BGRA2YUV_YV12: i32 = 134; + /// [8U] convert between BGRA and YUV YVYU, YUV is 4:2:2 and interleaved as Y1/V/Y2/U, see [color_convert_rgb_yuv_42x] + pub const COLOR_BGRA2YUV_YVYU: i32 = 154; + /// [8U/16U] equivalent to RGGB Bayer pattern + pub const COLOR_BayerBG2BGR: i32 = 46; + /// [8U/16U] equivalent to RGGB Bayer pattern + pub const COLOR_BayerBG2BGRA: i32 = 139; + /// [8U/16U] equivalent to RGGB Bayer pattern + pub const COLOR_BayerBG2BGR_EA: i32 = 135; + /// [8U] equivalent to RGGB Bayer pattern + pub const COLOR_BayerBG2BGR_VNG: i32 = 62; + /// [8U/16U] equivalent to RGGB Bayer pattern + pub const COLOR_BayerBG2GRAY: i32 = 86; + /// [8U/16U] equivalent to RGGB Bayer pattern + pub const COLOR_BayerBG2RGB: i32 = 48; + /// [8U/16U] equivalent to RGGB Bayer pattern + pub const COLOR_BayerBG2RGBA: i32 = 141; + /// [8U/16U] equivalent to RGGB Bayer pattern + pub const COLOR_BayerBG2RGB_EA: i32 = 137; + /// [8U] equivalent to RGGB Bayer pattern + pub const COLOR_BayerBG2RGB_VNG: i32 = 64; + /// [8U/16U] + pub const COLOR_BayerBGGR2BGR: i32 = 48; + /// [8U/16U] + pub const COLOR_BayerBGGR2BGRA: i32 = 141; + /// [8U/16U] + pub const COLOR_BayerBGGR2BGR_EA: i32 = 137; + /// [8U] + pub const COLOR_BayerBGGR2BGR_VNG: i32 = 64; + /// [8U/16U] + pub const COLOR_BayerBGGR2GRAY: i32 = 88; + /// [8U/16U] + pub const COLOR_BayerBGGR2RGB: i32 = 46; + /// [8U/16U] + pub const COLOR_BayerBGGR2RGBA: i32 = 139; + /// [8U/16U] + pub const COLOR_BayerBGGR2RGB_EA: i32 = 135; + /// [8U] + pub const COLOR_BayerBGGR2RGB_VNG: i32 = 62; + /// [8U/16U] equivalent to GRBG Bayer pattern + pub const COLOR_BayerGB2BGR: i32 = 47; + /// [8U/16U] equivalent to GRBG Bayer pattern + pub const COLOR_BayerGB2BGRA: i32 = 140; + /// [8U/16U] equivalent to GRBG Bayer pattern + pub const COLOR_BayerGB2BGR_EA: i32 = 136; + /// [8U] equivalent to GRBG Bayer pattern + pub const COLOR_BayerGB2BGR_VNG: i32 = 63; + /// [8U/16U] equivalent to GRBG Bayer pattern + pub const COLOR_BayerGB2GRAY: i32 = 87; + /// [8U/16U] equivalent to GRBG Bayer pattern + pub const COLOR_BayerGB2RGB: i32 = 49; + /// [8U/16U] equivalent to GRBG Bayer pattern + pub const COLOR_BayerGB2RGBA: i32 = 142; + /// [8U/16U] equivalent to GRBG Bayer pattern + pub const COLOR_BayerGB2RGB_EA: i32 = 138; + /// [8U] equivalent to GRBG Bayer pattern + pub const COLOR_BayerGB2RGB_VNG: i32 = 65; + /// [8U/16U] + pub const COLOR_BayerGBRG2BGR: i32 = 49; + /// [8U/16U] + pub const COLOR_BayerGBRG2BGRA: i32 = 142; + /// [8U/16U] + pub const COLOR_BayerGBRG2BGR_EA: i32 = 138; + /// [8U] + pub const COLOR_BayerGBRG2BGR_VNG: i32 = 65; + /// [8U/16U] + pub const COLOR_BayerGBRG2GRAY: i32 = 89; + /// [8U/16U] + pub const COLOR_BayerGBRG2RGB: i32 = 47; + /// [8U/16U] + pub const COLOR_BayerGBRG2RGBA: i32 = 140; + /// [8U/16U] + pub const COLOR_BayerGBRG2RGB_EA: i32 = 136; + /// [8U] + pub const COLOR_BayerGBRG2RGB_VNG: i32 = 63; + /// [8U/16U] equivalent to GBRG Bayer pattern + pub const COLOR_BayerGR2BGR: i32 = 49; + /// [8U/16U] equivalent to GBRG Bayer pattern + pub const COLOR_BayerGR2BGRA: i32 = 142; + /// [8U/16U] equivalent to GBRG Bayer pattern + pub const COLOR_BayerGR2BGR_EA: i32 = 138; + /// [8U] equivalent to GBRG Bayer pattern + pub const COLOR_BayerGR2BGR_VNG: i32 = 65; + /// [8U/16U] equivalent to GBRG Bayer pattern + pub const COLOR_BayerGR2GRAY: i32 = 89; + /// [8U/16U] equivalent to GBRG Bayer pattern + pub const COLOR_BayerGR2RGB: i32 = 47; + /// [8U/16U] equivalent to GBRG Bayer pattern + pub const COLOR_BayerGR2RGBA: i32 = 140; + /// [8U/16U] equivalent to GBRG Bayer pattern + pub const COLOR_BayerGR2RGB_EA: i32 = 136; + /// [8U] equivalent to GBRG Bayer pattern + pub const COLOR_BayerGR2RGB_VNG: i32 = 63; + /// [8U/16U] + pub const COLOR_BayerGRBG2BGR: i32 = 47; + /// [8U/16U] + pub const COLOR_BayerGRBG2BGRA: i32 = 140; + /// [8U/16U] + pub const COLOR_BayerGRBG2BGR_EA: i32 = 136; + /// [8U] + pub const COLOR_BayerGRBG2BGR_VNG: i32 = 63; + /// [8U/16U] + pub const COLOR_BayerGRBG2GRAY: i32 = 87; + /// [8U/16U] + pub const COLOR_BayerGRBG2RGB: i32 = 49; + /// [8U/16U] + pub const COLOR_BayerGRBG2RGBA: i32 = 142; + /// [8U/16U] + pub const COLOR_BayerGRBG2RGB_EA: i32 = 138; + /// [8U] + pub const COLOR_BayerGRBG2RGB_VNG: i32 = 65; + /// [8U/16U] equivalent to BGGR Bayer pattern + pub const COLOR_BayerRG2BGR: i32 = 48; + /// [8U/16U] equivalent to BGGR Bayer pattern + pub const COLOR_BayerRG2BGRA: i32 = 141; + /// [8U/16U] equivalent to BGGR Bayer pattern + pub const COLOR_BayerRG2BGR_EA: i32 = 137; + /// [8U] equivalent to BGGR Bayer pattern + pub const COLOR_BayerRG2BGR_VNG: i32 = 64; + /// [8U/16U] equivalent to BGGR Bayer pattern + pub const COLOR_BayerRG2GRAY: i32 = 88; + /// [8U/16U] equivalent to BGGR Bayer pattern + pub const COLOR_BayerRG2RGB: i32 = 46; + /// [8U/16U] equivalent to BGGR Bayer pattern + pub const COLOR_BayerRG2RGBA: i32 = 139; + /// [8U/16U] equivalent to BGGR Bayer pattern + pub const COLOR_BayerRG2RGB_EA: i32 = 135; + /// [8U] equivalent to BGGR Bayer pattern + pub const COLOR_BayerRG2RGB_VNG: i32 = 62; + /// [8U/16U] + pub const COLOR_BayerRGGB2BGR: i32 = 46; + /// [8U/16U] + pub const COLOR_BayerRGGB2BGRA: i32 = 139; + /// [8U/16U] + pub const COLOR_BayerRGGB2BGR_EA: i32 = 135; + /// [8U] + pub const COLOR_BayerRGGB2BGR_VNG: i32 = 62; + /// [8U/16U] + pub const COLOR_BayerRGGB2GRAY: i32 = 86; + /// [8U/16U] + pub const COLOR_BayerRGGB2RGB: i32 = 48; + /// [8U/16U] + pub const COLOR_BayerRGGB2RGBA: i32 = 141; + /// [8U/16U] + pub const COLOR_BayerRGGB2RGB_EA: i32 = 137; + /// [8U] + pub const COLOR_BayerRGGB2RGB_VNG: i32 = 64; + pub const COLOR_COLORCVT_MAX: i32 = 155; + /// [8U/16U/32F] + pub const COLOR_GRAY2BGR: i32 = 8; + /// [8U] convert between grayscale and BGR555 (16-bit images) + pub const COLOR_GRAY2BGR555: i32 = 30; + /// [8U] convert between grayscale to BGR565 (16-bit images) + pub const COLOR_GRAY2BGR565: i32 = 20; + /// [8U/16U/32F] + pub const COLOR_GRAY2BGRA: i32 = 9; + /// [8U/16U/32F] + pub const COLOR_GRAY2RGB: i32 = 8; + /// [8U/16U/32F] + pub const COLOR_GRAY2RGBA: i32 = 9; + /// [8U/32F] backward conversions HLS to RGB/BGR with H range 0..180 if 8 bit image + pub const COLOR_HLS2BGR: i32 = 60; + /// [8U/32F] backward conversions HLS to RGB/BGR with H range 0..255 if 8 bit image + pub const COLOR_HLS2BGR_FULL: i32 = 72; + /// [8U/32F] + pub const COLOR_HLS2RGB: i32 = 61; + /// [8U/32F] + pub const COLOR_HLS2RGB_FULL: i32 = 73; + /// [8U/32F] backward conversions HSV to RGB/BGR with H range 0..180 if 8 bit image + pub const COLOR_HSV2BGR: i32 = 54; + /// [8U/32F] backward conversions HSV to RGB/BGR with H range 0..255 if 8 bit image + pub const COLOR_HSV2BGR_FULL: i32 = 70; + /// [8U/32F] + pub const COLOR_HSV2RGB: i32 = 55; + /// [8U/32F] + pub const COLOR_HSV2RGB_FULL: i32 = 71; + /// [8U/32F] + pub const COLOR_LBGR2Lab: i32 = 74; + /// [8U/32F] + pub const COLOR_LBGR2Luv: i32 = 76; + /// [8U/32F] + pub const COLOR_LRGB2Lab: i32 = 75; + /// [8U/32F] + pub const COLOR_LRGB2Luv: i32 = 77; + /// [8U/32F] + pub const COLOR_Lab2BGR: i32 = 56; + /// [8U/32F] + pub const COLOR_Lab2LBGR: i32 = 78; + /// [8U/32F] + pub const COLOR_Lab2LRGB: i32 = 79; + /// [8U/32F] + pub const COLOR_Lab2RGB: i32 = 57; + /// [8U/32F] + pub const COLOR_Luv2BGR: i32 = 58; + /// [8U/32F] + pub const COLOR_Luv2LBGR: i32 = 80; + /// [8U/32F] + pub const COLOR_Luv2LRGB: i32 = 81; + /// [8U/32F] + pub const COLOR_Luv2RGB: i32 = 59; + /// [8U/16U/32F] + pub const COLOR_RGB2BGR: i32 = 4; + /// [8U] + pub const COLOR_RGB2BGR555: i32 = 23; + /// [8U] + pub const COLOR_RGB2BGR565: i32 = 13; + /// [8U/16U/32F] + pub const COLOR_RGB2BGRA: i32 = 2; + /// [8U/16U/32F] + pub const COLOR_RGB2GRAY: i32 = 7; + /// [8U/32F] + pub const COLOR_RGB2HLS: i32 = 53; + /// [8U/32F] + pub const COLOR_RGB2HLS_FULL: i32 = 69; + /// [8U/32F] + pub const COLOR_RGB2HSV: i32 = 41; + /// [8U/32F] + pub const COLOR_RGB2HSV_FULL: i32 = 67; + /// [8U/32F] + pub const COLOR_RGB2Lab: i32 = 45; + /// [8U/32F] + pub const COLOR_RGB2Luv: i32 = 51; + /// [8U/16U/32F] + pub const COLOR_RGB2RGBA: i32 = 0; + /// [8U/16U/32F] + pub const COLOR_RGB2XYZ: i32 = 33; + /// [8U/16U/32F] + pub const COLOR_RGB2YCrCb: i32 = 37; + /// [8U/16U/32F] + pub const COLOR_RGB2YUV: i32 = 83; + /// [8U] convert between RGB and 4:2:0-subsampled YUV I420, three planes in one array: Y, U and V, see [color_convert_rgb_yuv_42x] + pub const COLOR_RGB2YUV_I420: i32 = 127; + /// synonym to I420 + pub const COLOR_RGB2YUV_IYUV: i32 = 127; + /// synonym to UYVY + pub const COLOR_RGB2YUV_UYNV: i32 = 143; + /// [8U] convert between RGB and YUV UYVU, YUV is 4:2:2 and interleaved as U/Y1/V/Y2, see [color_convert_rgb_yuv_42x] + pub const COLOR_RGB2YUV_UYVY: i32 = 143; + /// synonym to UYVY + pub const COLOR_RGB2YUV_Y422: i32 = 143; + /// synonym to YUY2 + pub const COLOR_RGB2YUV_YUNV: i32 = 147; + /// [8U] convert between RGB and YUV YUY2, YUV is 4:2:2 and interleaved as Y1/U/Y2/V, see [color_convert_rgb_yuv_42x] + pub const COLOR_RGB2YUV_YUY2: i32 = 147; + /// synonym to YUY2 + pub const COLOR_RGB2YUV_YUYV: i32 = 147; + /// [8U] convert between RGB and 4:2:0-subsampled YUV YV12, three planes in one array: Y, V and U, see [color_convert_rgb_yuv_42x] + pub const COLOR_RGB2YUV_YV12: i32 = 131; + /// [8U] convert between RGB and YUV YVYU, YUV is 4:2:2 and interleaved as Y1/V/Y2/U, see [color_convert_rgb_yuv_42x] + pub const COLOR_RGB2YUV_YVYU: i32 = 149; + /// [8U/16U/32F] + pub const COLOR_RGBA2BGR: i32 = 3; + /// [8U] + pub const COLOR_RGBA2BGR555: i32 = 27; + /// [8U] + pub const COLOR_RGBA2BGR565: i32 = 17; + /// [8U/16U/32F] + pub const COLOR_RGBA2BGRA: i32 = 5; + /// [8U/16U/32F] + pub const COLOR_RGBA2GRAY: i32 = 11; + /// [8U/16U/32F] + pub const COLOR_RGBA2RGB: i32 = 1; + /// [8U] convert between RGBA and 4:2:0-subsampled YUV I420, three planes in one array: Y, U and V, see [color_convert_rgb_yuv_42x] + pub const COLOR_RGBA2YUV_I420: i32 = 129; + /// synonym to I420 + pub const COLOR_RGBA2YUV_IYUV: i32 = 129; + /// synonym to UYVY + pub const COLOR_RGBA2YUV_UYNV: i32 = 145; + /// [8U] convert between RGBA and YUV UYVU, YUV is 4:2:2 and interleaved as U/Y1/V/Y2, see [color_convert_rgb_yuv_42x] + pub const COLOR_RGBA2YUV_UYVY: i32 = 145; + /// synonym to UYVY + pub const COLOR_RGBA2YUV_Y422: i32 = 145; + /// synonym to YUY2 + pub const COLOR_RGBA2YUV_YUNV: i32 = 151; + /// [8U] convert between RGBA and YUV YUY2, YUV is 4:2:2 and interleaved as Y1/U/Y2/V, see [color_convert_rgb_yuv_42x] + pub const COLOR_RGBA2YUV_YUY2: i32 = 151; + /// synonym to YUY2 + pub const COLOR_RGBA2YUV_YUYV: i32 = 151; + /// [8U] convert between RGBA and 4:2:0-subsampled YUV YV12, three planes in one array: Y, V and U, see [color_convert_rgb_yuv_42x] + pub const COLOR_RGBA2YUV_YV12: i32 = 133; + /// [8U] convert between RGBA and YUV YVYU, YUV is 4:2:2 and interleaved as Y1/V/Y2/U, see [color_convert_rgb_yuv_42x] + pub const COLOR_RGBA2YUV_YVYU: i32 = 153; + /// [8U] + pub const COLOR_RGBA2mRGBA: i32 = 125; + /// [8U/16U/32F] + pub const COLOR_XYZ2BGR: i32 = 34; + /// [8U/16U/32F] + pub const COLOR_XYZ2RGB: i32 = 35; + /// [8U/16U/32F] + pub const COLOR_YCrCb2BGR: i32 = 38; + /// [8U/16U/32F] + pub const COLOR_YCrCb2RGB: i32 = 39; + /// [8U/16U/32F] + pub const COLOR_YUV2BGR: i32 = 84; + /// synonym to IYUV + pub const COLOR_YUV2BGRA_I420: i32 = 105; + /// [8U] convert between 4:2:0-subsampled YUV YV12 and BGRA, three planes in one array: Y, U and V, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGRA_IYUV: i32 = 105; + /// [8U] convert between 4:2:0-subsampled YUV NV12 and BGRA, two planes (in one or separate arrays): Y and U/V interleaved, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGRA_NV12: i32 = 95; + /// [8U] convert between 4:2:0-subsampled YUV NV21 and BGRA, two planes (in one or separate arrays): Y and V/U interleaved, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGRA_NV21: i32 = 97; + /// synonym to UYVY + pub const COLOR_YUV2BGRA_UYNV: i32 = 112; + /// [8U] convert between YUV UYVY and BGRA, YUV is 4:2:2-subsampled and interleaved as U/Y1/V/Y2, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGRA_UYVY: i32 = 112; + /// synonym to UYVY + pub const COLOR_YUV2BGRA_Y422: i32 = 112; + /// synonym to YUY2 + pub const COLOR_YUV2BGRA_YUNV: i32 = 120; + /// [8U] convert between YUV YUY2 and BGRA, YUV is 4:2:2-subsampled and interleaved as Y1/U/Y2/V, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGRA_YUY2: i32 = 120; + /// synonym to YUY2 + pub const COLOR_YUV2BGRA_YUYV: i32 = 120; + /// [8U] convert between 4:2:0-subsampled YUV YV12 and BGRA, three planes in one array: Y, V and U, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGRA_YV12: i32 = 103; + /// [8U] convert between YUV YVYU and BGRA, YUV is 4:2:2-subsampled and interleaved as Y1/V/Y2/U, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGRA_YVYU: i32 = 122; + /// synonym to IYUV + pub const COLOR_YUV2BGR_I420: i32 = 101; + /// [8U] convert between 4:2:0-subsampled YUV IYUV and BGR, three planes in one array: Y, U and V, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGR_IYUV: i32 = 101; + /// [8U] convert between 4:2:0-subsampled YUV NV12 and BGR, two planes (in one or separate arrays): Y and U/V interleaved, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGR_NV12: i32 = 91; + /// [8U] convert between 4:2:0-subsampled YUV NV21 and BGR, two planes (in one or separate arrays): Y and V/U interleaved, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGR_NV21: i32 = 93; + /// synonym to UYVY + pub const COLOR_YUV2BGR_UYNV: i32 = 108; + /// [8U] convert between YUV UYVY and BGR, YUV is 4:2:2-subsampled and interleaved as U/Y1/V/Y2, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGR_UYVY: i32 = 108; + /// synonym to UYVY + pub const COLOR_YUV2BGR_Y422: i32 = 108; + /// synonym to YUY2 + pub const COLOR_YUV2BGR_YUNV: i32 = 116; + /// [8U] convert between YUV YUY2 and BGR, YUV is 4:2:2-subsampled and interleaved as Y1/U/Y2/V, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGR_YUY2: i32 = 116; + /// synonym to YUY2 + pub const COLOR_YUV2BGR_YUYV: i32 = 116; + /// [8U] convert between 4:2:0-subsampled YUV YV12 and BGR, three planes in one array: Y, V and U, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGR_YV12: i32 = 99; + /// [8U] convert between YUV YVYU and BGR, YUV is 4:2:2-subsampled and interleaved as Y1/V/Y2/U, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2BGR_YVYU: i32 = 118; + /// [8U] extract Y channel from YUV 4:2:0 image + pub const COLOR_YUV2GRAY_420: i32 = 106; + /// synonym to COLOR_YUV2GRAY_420 + pub const COLOR_YUV2GRAY_I420: i32 = 106; + /// synonym to COLOR_YUV2GRAY_420 + pub const COLOR_YUV2GRAY_IYUV: i32 = 106; + /// synonym to COLOR_YUV2GRAY_420 + pub const COLOR_YUV2GRAY_NV12: i32 = 106; + /// synonym to COLOR_YUV2GRAY_420 + pub const COLOR_YUV2GRAY_NV21: i32 = 106; + /// synonym to COLOR_YUV2GRAY_UYVY + pub const COLOR_YUV2GRAY_UYNV: i32 = 123; + /// [8U] extract Y channel from YUV 4:2:2 image + pub const COLOR_YUV2GRAY_UYVY: i32 = 123; + /// synonym to COLOR_YUV2GRAY_UYVY + pub const COLOR_YUV2GRAY_Y422: i32 = 123; + /// synonym to COLOR_YUV2GRAY_YUY2 + pub const COLOR_YUV2GRAY_YUNV: i32 = 124; + /// [8U] extract Y channel from YUV 4:2:2 image + pub const COLOR_YUV2GRAY_YUY2: i32 = 124; + /// synonym to COLOR_YUV2GRAY_YUY2 + pub const COLOR_YUV2GRAY_YUYV: i32 = 124; + /// synonym to COLOR_YUV2GRAY_420 + pub const COLOR_YUV2GRAY_YV12: i32 = 106; + /// synonym to COLOR_YUV2GRAY_YUY2 + pub const COLOR_YUV2GRAY_YVYU: i32 = 124; + /// [8U/16U/32F] + pub const COLOR_YUV2RGB: i32 = 85; + /// synonym to IYUV + pub const COLOR_YUV2RGBA_I420: i32 = 104; + /// [8U] convert between 4:2:0-subsampled YUV YV12 and RGBA, three planes in one array: Y, U and V, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGBA_IYUV: i32 = 104; + /// [8U] convert between 4:2:0-subsampled YUV NV12 and RGBA, two planes (in one or separate arrays): Y and U/V interleaved, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGBA_NV12: i32 = 94; + /// [8U] convert between 4:2:0-subsampled YUV NV21 and RGBA, two planes (in one or separate arrays): Y and V/U interleaved, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGBA_NV21: i32 = 96; + /// synonym to UYVY + pub const COLOR_YUV2RGBA_UYNV: i32 = 111; + /// [8U] convert between YUV UYVY and RGBA, YUV is 4:2:2-subsampled and interleaved as U/Y1/V/Y2, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGBA_UYVY: i32 = 111; + /// synonym to UYVY + pub const COLOR_YUV2RGBA_Y422: i32 = 111; + /// synonym to YUY2 + pub const COLOR_YUV2RGBA_YUNV: i32 = 119; + /// [8U] convert between YUV YUY2 and RGBA, YUV is 4:2:2-subsampled and interleaved as Y1/U/Y2/V, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGBA_YUY2: i32 = 119; + /// synonym to YUY2 + pub const COLOR_YUV2RGBA_YUYV: i32 = 119; + /// [8U] convert between 4:2:0-subsampled YUV YV12 and RGBA, three planes in one array: Y, V and U, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGBA_YV12: i32 = 102; + /// [8U] convert between YUV YVYU and RGBA, YUV is 4:2:2-subsampled and interleaved as Y1/V/Y2/U, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGBA_YVYU: i32 = 121; + /// synonym to IYUV + pub const COLOR_YUV2RGB_I420: i32 = 100; + /// [8U] convert between 4:2:0-subsampled YUV IYUV and RGB, three planes in one array: Y, U and V, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGB_IYUV: i32 = 100; + /// [8U] convert between 4:2:0-subsampled YUV NV12 and RGB, two planes (in one or separate arrays): Y and U/V interleaved, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGB_NV12: i32 = 90; + /// [8U] convert between 4:2:0-subsampled YUV NV21 and RGB, two planes (in one or separate arrays): Y and V/U interleaved, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGB_NV21: i32 = 92; + /// synonym to UYVY + pub const COLOR_YUV2RGB_UYNV: i32 = 107; + /// [8U] convert between YUV UYVY and RGB, YUV is 4:2:2-subsampled and interleaved as U/Y1/V/Y2, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGB_UYVY: i32 = 107; + /// synonym to UYVY + pub const COLOR_YUV2RGB_Y422: i32 = 107; + /// synonym to YUY2 + pub const COLOR_YUV2RGB_YUNV: i32 = 115; + /// [8U] convert between YUV YUY2 and RGB, YUV is 4:2:2-subsampled and interleaved as Y1/U/Y2/V, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGB_YUY2: i32 = 115; + /// synonym to YUY2 + pub const COLOR_YUV2RGB_YUYV: i32 = 115; + /// [8U] convert between 4:2:0-subsampled YUV YV12 and RGB, three planes in one array: Y, V and U, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGB_YV12: i32 = 98; + /// [8U] convert between YUV YVYU and RGB, YUV is 4:2:2-subsampled and interleaved as Y1/V/Y2/U, see [color_convert_rgb_yuv_42x] + pub const COLOR_YUV2RGB_YVYU: i32 = 117; + /// synonym to YV12 + pub const COLOR_YUV420p2BGR: i32 = 99; + /// synonym to YV12 + pub const COLOR_YUV420p2BGRA: i32 = 103; + /// synonym to COLOR_YUV2GRAY_420 + pub const COLOR_YUV420p2GRAY: i32 = 106; + /// synonym to YV12 + pub const COLOR_YUV420p2RGB: i32 = 98; + /// synonym to YV12 + pub const COLOR_YUV420p2RGBA: i32 = 102; + /// synonym to NV21 + pub const COLOR_YUV420sp2BGR: i32 = 93; + /// synonym to NV21 + pub const COLOR_YUV420sp2BGRA: i32 = 97; + /// synonym to COLOR_YUV2GRAY_420 + pub const COLOR_YUV420sp2GRAY: i32 = 106; + /// synonym to NV21 + pub const COLOR_YUV420sp2RGB: i32 = 92; + /// synonym to NV21 + pub const COLOR_YUV420sp2RGBA: i32 = 96; + /// [8U] + pub const COLOR_mRGBA2RGBA: i32 = 126; + /// ![block formula](https://latex.codecogs.com/png.latex?I%5F1%28A%2CB%29%20%3D%20%20%5Csum%20%5F%7Bi%3D1%2E%2E%2E7%7D%20%20%5Cleft%20%7C%20%20%5Cfrac%7B1%7D%7Bm%5EA%5Fi%7D%20%2D%20%20%5Cfrac%7B1%7D%7Bm%5EB%5Fi%7D%20%5Cright%20%7C) + pub const CONTOURS_MATCH_I1: i32 = 1; + /// ![block formula](https://latex.codecogs.com/png.latex?I%5F2%28A%2CB%29%20%3D%20%20%5Csum%20%5F%7Bi%3D1%2E%2E%2E7%7D%20%20%5Cleft%20%7C%20m%5EA%5Fi%20%2D%20m%5EB%5Fi%20%20%5Cright%20%7C) + pub const CONTOURS_MATCH_I2: i32 = 2; + /// ![block formula](https://latex.codecogs.com/png.latex?I%5F3%28A%2CB%29%20%3D%20%20%5Cmax%20%5F%7Bi%3D1%2E%2E%2E7%7D%20%20%5Cfrac%7B%20%5Cleft%7C%20m%5EA%5Fi%20%2D%20m%5EB%5Fi%20%5Cright%7C%20%7D%7B%20%5Cleft%7C%20m%5EA%5Fi%20%5Cright%7C%20%7D) + pub const CONTOURS_MATCH_I3: i32 = 3; + /// distance = max(|x1-x2|,|y1-y2|) + pub const DIST_C: i32 = 3; + /// distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 + pub const DIST_FAIR: i32 = 5; + /// distance = |x| are supported + /// @ingroup imgproc_draw + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum HersheyFonts { + /// normal size sans-serif font + FONT_HERSHEY_SIMPLEX = 0, + /// small size sans-serif font + FONT_HERSHEY_PLAIN = 1, + /// normal size sans-serif font (more complex than FONT_HERSHEY_SIMPLEX) + FONT_HERSHEY_DUPLEX = 2, + /// normal size serif font + FONT_HERSHEY_COMPLEX = 3, + /// normal size serif font (more complex than FONT_HERSHEY_COMPLEX) + FONT_HERSHEY_TRIPLEX = 4, + /// smaller version of FONT_HERSHEY_COMPLEX + FONT_HERSHEY_COMPLEX_SMALL = 5, + /// hand-writing style font + FONT_HERSHEY_SCRIPT_SIMPLEX = 6, + /// more complex variant of FONT_HERSHEY_SCRIPT_SIMPLEX + FONT_HERSHEY_SCRIPT_COMPLEX = 7, + /// flag for italic font + FONT_ITALIC = 16, + } + + opencv_type_enum! { crate::imgproc::HersheyFonts { FONT_HERSHEY_SIMPLEX, FONT_HERSHEY_PLAIN, FONT_HERSHEY_DUPLEX, FONT_HERSHEY_COMPLEX, FONT_HERSHEY_TRIPLEX, FONT_HERSHEY_COMPLEX_SMALL, FONT_HERSHEY_SCRIPT_SIMPLEX, FONT_HERSHEY_SCRIPT_COMPLEX, FONT_ITALIC } } + + /// Histogram comparison methods + /// @ingroup imgproc_hist + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum HistCompMethods { + /// Correlation + /// ![block formula](https://latex.codecogs.com/png.latex?d%28H%5F1%2CH%5F2%29%20%3D%20%20%5Cfrac%7B%5Csum%5FI%20%28H%5F1%28I%29%20%2D%20%5Cbar%7BH%5F1%7D%29%20%28H%5F2%28I%29%20%2D%20%5Cbar%7BH%5F2%7D%29%7D%7B%5Csqrt%7B%5Csum%5FI%28H%5F1%28I%29%20%2D%20%5Cbar%7BH%5F1%7D%29%5E2%20%5Csum%5FI%28H%5F2%28I%29%20%2D%20%5Cbar%7BH%5F2%7D%29%5E2%7D%7D) + /// where + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbar%7BH%5Fk%7D%20%3D%20%20%5Cfrac%7B1%7D%7BN%7D%20%5Csum%20%5FJ%20H%5Fk%28J%29) + /// and ![inline formula](https://latex.codecogs.com/png.latex?N) is a total number of histogram bins. + HISTCMP_CORREL = 0, + /// Chi-Square + /// ![block formula](https://latex.codecogs.com/png.latex?d%28H%5F1%2CH%5F2%29%20%3D%20%20%5Csum%20%5FI%20%20%5Cfrac%7B%5Cleft%28H%5F1%28I%29%2DH%5F2%28I%29%5Cright%29%5E2%7D%7BH%5F1%28I%29%7D) + HISTCMP_CHISQR = 1, + /// Intersection + /// ![block formula](https://latex.codecogs.com/png.latex?d%28H%5F1%2CH%5F2%29%20%3D%20%20%5Csum%20%5FI%20%20%5Cmin%20%28H%5F1%28I%29%2C%20H%5F2%28I%29%29) + HISTCMP_INTERSECT = 2, + /// Bhattacharyya distance + /// (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.) + /// ![block formula](https://latex.codecogs.com/png.latex?d%28H%5F1%2CH%5F2%29%20%3D%20%20%5Csqrt%7B1%20%2D%20%5Cfrac%7B1%7D%7B%5Csqrt%7B%5Cbar%7BH%5F1%7D%20%5Cbar%7BH%5F2%7D%20N%5E2%7D%7D%20%5Csum%5FI%20%5Csqrt%7BH%5F1%28I%29%20%5Ccdot%20H%5F2%28I%29%7D%7D) + HISTCMP_BHATTACHARYYA = 3, + // Synonym for HISTCMP_BHATTACHARYYA + // Duplicate, use HISTCMP_BHATTACHARYYA instead + // HISTCMP_HELLINGER = 3, + /// Alternative Chi-Square + /// ![block formula](https://latex.codecogs.com/png.latex?d%28H%5F1%2CH%5F2%29%20%3D%20%202%20%2A%20%5Csum%20%5FI%20%20%5Cfrac%7B%5Cleft%28H%5F1%28I%29%2DH%5F2%28I%29%5Cright%29%5E2%7D%7BH%5F1%28I%29%2BH%5F2%28I%29%7D) + /// This alternative formula is regularly used for texture comparison. See e.g. [Puzicha1997](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Puzicha1997) + HISTCMP_CHISQR_ALT = 4, + /// Kullback-Leibler divergence + /// ![block formula](https://latex.codecogs.com/png.latex?d%28H%5F1%2CH%5F2%29%20%3D%20%5Csum%20%5FI%20H%5F1%28I%29%20%5Clog%20%5Cleft%28%5Cfrac%7BH%5F1%28I%29%7D%7BH%5F2%28I%29%7D%5Cright%29) + HISTCMP_KL_DIV = 5, + } + + opencv_type_enum! { crate::imgproc::HistCompMethods { HISTCMP_CORREL, HISTCMP_CHISQR, HISTCMP_INTERSECT, HISTCMP_BHATTACHARYYA, HISTCMP_CHISQR_ALT, HISTCMP_KL_DIV } } + + /// Variants of a Hough transform + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum HoughModes { + /// classical or standard Hough transform. Every line is represented by two floating-point + /// numbers ![inline formula](https://latex.codecogs.com/png.latex?%28%5Crho%2C%20%5Ctheta%29) , where ![inline formula](https://latex.codecogs.com/png.latex?%5Crho) is a distance between (0,0) point and the line, + /// and ![inline formula](https://latex.codecogs.com/png.latex?%5Ctheta) is the angle between x-axis and the normal to the line. Thus, the matrix must + /// be (the created sequence will be) of CV_32FC2 type + HOUGH_STANDARD = 0, + /// probabilistic Hough transform (more efficient in case if the picture contains a few long + /// linear segments). It returns line segments rather than the whole line. Each segment is + /// represented by starting and ending points, and the matrix must be (the created sequence will + /// be) of the CV_32SC4 type. + HOUGH_PROBABILISTIC = 1, + /// multi-scale variant of the classical Hough transform. The lines are encoded the same way as + /// HOUGH_STANDARD. + HOUGH_MULTI_SCALE = 2, + /// basically *21HT*, described in [Yuen90](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Yuen90) + HOUGH_GRADIENT = 3, + /// variation of HOUGH_GRADIENT to get better accuracy + HOUGH_GRADIENT_ALT = 4, + } + + opencv_type_enum! { crate::imgproc::HoughModes { HOUGH_STANDARD, HOUGH_PROBABILISTIC, HOUGH_MULTI_SCALE, HOUGH_GRADIENT, HOUGH_GRADIENT_ALT } } + + /// interpolation algorithm + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum InterpolationFlags { + /// nearest neighbor interpolation + INTER_NEAREST = 0, + /// bilinear interpolation + INTER_LINEAR = 1, + /// bicubic interpolation + INTER_CUBIC = 2, + /// resampling using pixel area relation. It may be a preferred method for image decimation, as + /// it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST + /// method. + INTER_AREA = 3, + /// Lanczos interpolation over 8x8 neighborhood + INTER_LANCZOS4 = 4, + /// Bit exact bilinear interpolation + INTER_LINEAR_EXACT = 5, + /// Bit exact nearest neighbor interpolation. This will produce same results as + /// the nearest neighbor method in PIL, scikit-image or Matlab. + INTER_NEAREST_EXACT = 6, + /// mask for interpolation codes + INTER_MAX = 7, + /// flag, fills all of the destination image pixels. If some of them correspond to outliers in the + /// source image, they are set to zero + WARP_FILL_OUTLIERS = 8, + /// flag, inverse transformation + /// + /// For example, [linear_polar] or [log_polar] transforms: + /// - flag is __not__ set: ![inline formula](https://latex.codecogs.com/png.latex?dst%28%20%5Crho%20%2C%20%5Cphi%20%29%20%3D%20src%28x%2Cy%29) + /// - flag is set: ![inline formula](https://latex.codecogs.com/png.latex?dst%28x%2Cy%29%20%3D%20src%28%20%5Crho%20%2C%20%5Cphi%20%29) + WARP_INVERSE_MAP = 16, + /// flag, inverse transformation + /// + /// For example, [linear_polar] or [log_polar] transforms: + /// - flag is __not__ set: ![inline formula](https://latex.codecogs.com/png.latex?dst%28%20%5Crho%20%2C%20%5Cphi%20%29%20%3D%20src%28x%2Cy%29) + /// - flag is set: ![inline formula](https://latex.codecogs.com/png.latex?dst%28x%2Cy%29%20%3D%20src%28%20%5Crho%20%2C%20%5Cphi%20%29) + WARP_RELATIVE_MAP = 32, + } + + opencv_type_enum! { crate::imgproc::InterpolationFlags { INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_AREA, INTER_LANCZOS4, INTER_LINEAR_EXACT, INTER_NEAREST_EXACT, INTER_MAX, WARP_FILL_OUTLIERS, WARP_INVERSE_MAP, WARP_RELATIVE_MAP } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum InterpolationMasks { + INTER_BITS = 5, + INTER_BITS2 = 10, + INTER_TAB_SIZE = 32, + INTER_TAB_SIZE2 = 1024, + } + + opencv_type_enum! { crate::imgproc::InterpolationMasks { INTER_BITS, INTER_BITS2, INTER_TAB_SIZE, INTER_TAB_SIZE2 } } + + /// Variants of Line Segment %Detector + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum LineSegmentDetectorModes { + /// No refinement applied + LSD_REFINE_NONE = 0, + /// Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations. + LSD_REFINE_STD = 1, + /// Advanced refinement. Number of false alarms is calculated, lines are + /// refined through increase of precision, decrement in size, etc. + LSD_REFINE_ADV = 2, + } + + opencv_type_enum! { crate::imgproc::LineSegmentDetectorModes { LSD_REFINE_NONE, LSD_REFINE_STD, LSD_REFINE_ADV } } + + /// types of line + /// @ingroup imgproc_draw + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum LineTypes { + FILLED = -1, + /// 4-connected line + LINE_4 = 4, + /// 8-connected line + LINE_8 = 8, + /// antialiased line + LINE_AA = 16, + } + + opencv_type_enum! { crate::imgproc::LineTypes { FILLED, LINE_4, LINE_8, LINE_AA } } + + /// Possible set of marker types used for the cv::drawMarker function + /// @ingroup imgproc_draw + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum MarkerTypes { + /// A crosshair marker shape + MARKER_CROSS = 0, + /// A 45 degree tilted crosshair marker shape + MARKER_TILTED_CROSS = 1, + /// A star marker shape, combination of cross and tilted cross + MARKER_STAR = 2, + /// A diamond marker shape + MARKER_DIAMOND = 3, + /// A square marker shape + MARKER_SQUARE = 4, + /// An upwards pointing triangle marker shape + MARKER_TRIANGLE_UP = 5, + /// A downwards pointing triangle marker shape + MARKER_TRIANGLE_DOWN = 6, + } + + opencv_type_enum! { crate::imgproc::MarkerTypes { MARKER_CROSS, MARKER_TILTED_CROSS, MARKER_STAR, MARKER_DIAMOND, MARKER_SQUARE, MARKER_TRIANGLE_UP, MARKER_TRIANGLE_DOWN } } + + /// shape of the structuring element + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum MorphShapes { + /// a rectangular structuring element: ![block formula](https://latex.codecogs.com/png.latex?E%5F%7Bij%7D%3D1) + MORPH_RECT = 0, + /// a cross-shaped structuring element: + /// ![block formula](https://latex.codecogs.com/png.latex?E%5F%7Bij%7D%20%3D%20%5Cbegin%7Bcases%7D%201%20%26%20%5Ctexttt%7Bif%20%7D%20%7Bi%3D%5Ctexttt%7Banchor%2Ey%20%7D%20%7Bor%20%7D%20%7Bj%3D%5Ctexttt%7Banchor%2Ex%7D%7D%7D%20%5C%5C0%20%26%20%5Ctexttt%7Botherwise%7D%20%5Cend%7Bcases%7D) + MORPH_CROSS = 1, + /// an elliptic structuring element, that is, a filled ellipse inscribed + /// into the rectangle Rect(0, 0, esize.width, esize.height) + MORPH_ELLIPSE = 2, + /// a diamond structuring element defined by Manhattan distance + MORPH_DIAMOND = 3, + } + + opencv_type_enum! { crate::imgproc::MorphShapes { MORPH_RECT, MORPH_CROSS, MORPH_ELLIPSE, MORPH_DIAMOND } } + + /// type of morphological operation + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum MorphTypes { + /// see #erode + MORPH_ERODE = 0, + /// see #dilate + MORPH_DILATE = 1, + /// an opening operation + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%5Cmathrm%7Bopen%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2C%20%5Ctexttt%7Belement%7D%20%29%3D%20%5Cmathrm%7Bdilate%7D%20%28%20%5Cmathrm%7Berode%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2C%20%5Ctexttt%7Belement%7D%20%29%29) + MORPH_OPEN = 2, + /// a closing operation + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%5Cmathrm%7Bclose%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2C%20%5Ctexttt%7Belement%7D%20%29%3D%20%5Cmathrm%7Berode%7D%20%28%20%5Cmathrm%7Bdilate%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2C%20%5Ctexttt%7Belement%7D%20%29%29) + MORPH_CLOSE = 3, + /// a morphological gradient + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%5Cmathrm%7Bmorph%5C%5Fgrad%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2C%20%5Ctexttt%7Belement%7D%20%29%3D%20%5Cmathrm%7Bdilate%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2C%20%5Ctexttt%7Belement%7D%20%29%2D%20%5Cmathrm%7Berode%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2C%20%5Ctexttt%7Belement%7D%20%29) + MORPH_GRADIENT = 4, + /// "top hat" + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%5Cmathrm%7Btophat%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2C%20%5Ctexttt%7Belement%7D%20%29%3D%20%5Ctexttt%7Bsrc%7D%20%2D%20%5Cmathrm%7Bopen%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2C%20%5Ctexttt%7Belement%7D%20%29) + MORPH_TOPHAT = 5, + /// "black hat" + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%5Cmathrm%7Bblackhat%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2C%20%5Ctexttt%7Belement%7D%20%29%3D%20%5Cmathrm%7Bclose%7D%20%28%20%5Ctexttt%7Bsrc%7D%20%2C%20%5Ctexttt%7Belement%7D%20%29%2D%20%5Ctexttt%7Bsrc%7D) + MORPH_BLACKHAT = 6, + /// "hit or miss" + /// .- Only supported for CV_8UC1 binary images. A tutorial can be found in the documentation + MORPH_HITMISS = 7, + } + + opencv_type_enum! { crate::imgproc::MorphTypes { MORPH_ERODE, MORPH_DILATE, MORPH_OPEN, MORPH_CLOSE, MORPH_GRADIENT, MORPH_TOPHAT, MORPH_BLACKHAT, MORPH_HITMISS } } + + /// types of intersection between rectangles + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum RectanglesIntersectTypes { + /// No intersection + INTERSECT_NONE = 0, + /// There is a partial intersection + INTERSECT_PARTIAL = 1, + /// One of the rectangle is fully enclosed in the other + INTERSECT_FULL = 2, + } + + opencv_type_enum! { crate::imgproc::RectanglesIntersectTypes { INTERSECT_NONE, INTERSECT_PARTIAL, INTERSECT_FULL } } + + /// mode of the contour retrieval algorithm + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum RetrievalModes { + /// retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for + /// all the contours. + RETR_EXTERNAL = 0, + /// retrieves all of the contours without establishing any hierarchical relationships. + RETR_LIST = 1, + /// retrieves all of the contours and organizes them into a two-level hierarchy. At the top + /// level, there are external boundaries of the components. At the second level, there are + /// boundaries of the holes. If there is another contour inside a hole of a connected component, it + /// is still put at the top level. + RETR_CCOMP = 2, + /// retrieves all of the contours and reconstructs a full hierarchy of nested contours. + RETR_TREE = 3, + RETR_FLOODFILL = 4, + } + + opencv_type_enum! { crate::imgproc::RetrievalModes { RETR_EXTERNAL, RETR_LIST, RETR_CCOMP, RETR_TREE, RETR_FLOODFILL } } + + /// Shape matching methods + /// + /// ![inline formula](https://latex.codecogs.com/png.latex?A) denotes object1,![inline formula](https://latex.codecogs.com/png.latex?B) denotes object2 + /// + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20m%5EA%5Fi%20%3D%20%20%5Cmathrm%7Bsign%7D%20%28h%5EA%5Fi%29%20%20%5Ccdot%20%5Clog%7Bh%5EA%5Fi%7D%20%5C%5C%20m%5EB%5Fi%20%3D%20%20%5Cmathrm%7Bsign%7D%20%28h%5EB%5Fi%29%20%20%5Ccdot%20%5Clog%7Bh%5EB%5Fi%7D%20%5Cend%7Barray%7D) + /// + /// and ![inline formula](https://latex.codecogs.com/png.latex?h%5EA%5Fi%2C%20h%5EB%5Fi) are the Hu moments of ![inline formula](https://latex.codecogs.com/png.latex?A) and ![inline formula](https://latex.codecogs.com/png.latex?B) , respectively. + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum ShapeMatchModes { + /// ![block formula](https://latex.codecogs.com/png.latex?I%5F1%28A%2CB%29%20%3D%20%20%5Csum%20%5F%7Bi%3D1%2E%2E%2E7%7D%20%20%5Cleft%20%7C%20%20%5Cfrac%7B1%7D%7Bm%5EA%5Fi%7D%20%2D%20%20%5Cfrac%7B1%7D%7Bm%5EB%5Fi%7D%20%5Cright%20%7C) + CONTOURS_MATCH_I1 = 1, + /// ![block formula](https://latex.codecogs.com/png.latex?I%5F2%28A%2CB%29%20%3D%20%20%5Csum%20%5F%7Bi%3D1%2E%2E%2E7%7D%20%20%5Cleft%20%7C%20m%5EA%5Fi%20%2D%20m%5EB%5Fi%20%20%5Cright%20%7C) + CONTOURS_MATCH_I2 = 2, + /// ![block formula](https://latex.codecogs.com/png.latex?I%5F3%28A%2CB%29%20%3D%20%20%5Cmax%20%5F%7Bi%3D1%2E%2E%2E7%7D%20%20%5Cfrac%7B%20%5Cleft%7C%20m%5EA%5Fi%20%2D%20m%5EB%5Fi%20%5Cright%7C%20%7D%7B%20%5Cleft%7C%20m%5EA%5Fi%20%5Cright%7C%20%7D) + CONTOURS_MATCH_I3 = 3, + } + + opencv_type_enum! { crate::imgproc::ShapeMatchModes { CONTOURS_MATCH_I1, CONTOURS_MATCH_I2, CONTOURS_MATCH_I3 } } + + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum SpecialFilter { + FILTER_SCHARR = -1, + } + + opencv_type_enum! { crate::imgproc::SpecialFilter { FILTER_SCHARR } } + + /// type of the template matching operation + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum TemplateMatchModes { + /// !< ![block formula](https://latex.codecogs.com/png.latex?R%28x%2Cy%29%3D%20%5Csum%20%5F%7Bx%27%2Cy%27%7D%20%28T%28x%27%2Cy%27%29%2DI%28x%2Bx%27%2Cy%2By%27%29%29%5E2) + /// with mask: + /// ![block formula](https://latex.codecogs.com/png.latex?R%28x%2Cy%29%3D%20%5Csum%20%5F%7Bx%27%2Cy%27%7D%20%5Cleft%28%20%28T%28x%27%2Cy%27%29%2DI%28x%2Bx%27%2Cy%2By%27%29%29%20%5Ccdot%0A%20%20%20M%28x%27%2Cy%27%29%20%5Cright%29%5E2) + TM_SQDIFF = 0, + /// !< ![block formula](https://latex.codecogs.com/png.latex?R%28x%2Cy%29%3D%20%5Cfrac%7B%5Csum%5F%7Bx%27%2Cy%27%7D%20%28T%28x%27%2Cy%27%29%2DI%28x%2Bx%27%2Cy%2By%27%29%29%5E2%7D%7B%5Csqrt%7B%5Csum%5F%7B%0A%20%20%20x%27%2Cy%27%7DT%28x%27%2Cy%27%29%5E2%20%5Ccdot%20%5Csum%5F%7Bx%27%2Cy%27%7D%20I%28x%2Bx%27%2Cy%2By%27%29%5E2%7D%7D) + /// with mask: + /// ![block formula](https://latex.codecogs.com/png.latex?R%28x%2Cy%29%3D%20%5Cfrac%7B%5Csum%20%5F%7Bx%27%2Cy%27%7D%20%5Cleft%28%20%28T%28x%27%2Cy%27%29%2DI%28x%2Bx%27%2Cy%2By%27%29%29%20%5Ccdot%0A%20%20%20M%28x%27%2Cy%27%29%20%5Cright%29%5E2%7D%7B%5Csqrt%7B%5Csum%5F%7Bx%27%2Cy%27%7D%20%5Cleft%28%20T%28x%27%2Cy%27%29%20%5Ccdot%0A%20%20%20M%28x%27%2Cy%27%29%20%5Cright%29%5E2%20%5Ccdot%20%5Csum%5F%7Bx%27%2Cy%27%7D%20%5Cleft%28%20I%28x%2Bx%27%2Cy%2By%27%29%20%5Ccdot%0A%20%20%20M%28x%27%2Cy%27%29%20%5Cright%29%5E2%7D%7D) + TM_SQDIFF_NORMED = 1, + /// !< ![block formula](https://latex.codecogs.com/png.latex?R%28x%2Cy%29%3D%20%5Csum%20%5F%7Bx%27%2Cy%27%7D%20%28T%28x%27%2Cy%27%29%20%5Ccdot%20I%28x%2Bx%27%2Cy%2By%27%29%29) + /// with mask: + /// ![block formula](https://latex.codecogs.com/png.latex?R%28x%2Cy%29%3D%20%5Csum%20%5F%7Bx%27%2Cy%27%7D%20%28T%28x%27%2Cy%27%29%20%5Ccdot%20I%28x%2Bx%27%2Cy%2By%27%29%20%5Ccdot%20M%28x%27%2Cy%27%29%0A%20%20%20%5E2%29) + TM_CCORR = 2, + /// !< ![block formula](https://latex.codecogs.com/png.latex?R%28x%2Cy%29%3D%20%5Cfrac%7B%5Csum%5F%7Bx%27%2Cy%27%7D%20%28T%28x%27%2Cy%27%29%20%5Ccdot%20I%28x%2Bx%27%2Cy%2By%27%29%29%7D%7B%5Csqrt%7B%0A%20%20%20%5Csum%5F%7Bx%27%2Cy%27%7DT%28x%27%2Cy%27%29%5E2%20%5Ccdot%20%5Csum%5F%7Bx%27%2Cy%27%7D%20I%28x%2Bx%27%2Cy%2By%27%29%5E2%7D%7D) + /// with mask: + /// ![block formula](https://latex.codecogs.com/png.latex?R%28x%2Cy%29%3D%20%5Cfrac%7B%5Csum%5F%7Bx%27%2Cy%27%7D%20%28T%28x%27%2Cy%27%29%20%5Ccdot%20I%28x%2Bx%27%2Cy%2By%27%29%20%5Ccdot%0A%20%20%20M%28x%27%2Cy%27%29%5E2%29%7D%7B%5Csqrt%7B%5Csum%5F%7Bx%27%2Cy%27%7D%20%5Cleft%28%20T%28x%27%2Cy%27%29%20%5Ccdot%20M%28x%27%2Cy%27%29%0A%20%20%20%5Cright%29%5E2%20%5Ccdot%20%5Csum%5F%7Bx%27%2Cy%27%7D%20%5Cleft%28%20I%28x%2Bx%27%2Cy%2By%27%29%20%5Ccdot%20M%28x%27%2Cy%27%29%0A%20%20%20%5Cright%29%5E2%7D%7D) + TM_CCORR_NORMED = 3, + /// !< ![block formula](https://latex.codecogs.com/png.latex?R%28x%2Cy%29%3D%20%5Csum%20%5F%7Bx%27%2Cy%27%7D%20%28T%27%28x%27%2Cy%27%29%20%5Ccdot%20I%27%28x%2Bx%27%2Cy%2By%27%29%29) + /// where + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20T%27%28x%27%2Cy%27%29%3DT%28x%27%2Cy%27%29%20%2D%201%2F%28w%20%5Ccdot%20h%29%20%5Ccdot%20%5Csum%20%5F%7B%0A%20%20%20x%27%27%2Cy%27%27%7D%20T%28x%27%27%2Cy%27%27%29%20%5C%5C%20I%27%28x%2Bx%27%2Cy%2By%27%29%3DI%28x%2Bx%27%2Cy%2By%27%29%20%2D%201%2F%28w%20%5Ccdot%20h%29%0A%20%20%20%5Ccdot%20%5Csum%20%5F%7Bx%27%27%2Cy%27%27%7D%20I%28x%2Bx%27%27%2Cy%2By%27%27%29%20%5Cend%7Barray%7D) + /// with mask: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20T%27%28x%27%2Cy%27%29%3DM%28x%27%2Cy%27%29%20%5Ccdot%20%5Cleft%28%20T%28x%27%2Cy%27%29%20%2D%0A%20%20%20%5Cfrac%7B1%7D%7B%5Csum%20%5F%7Bx%27%27%2Cy%27%27%7D%20M%28x%27%27%2Cy%27%27%29%7D%20%5Ccdot%20%5Csum%20%5F%7Bx%27%27%2Cy%27%27%7D%0A%20%20%20%28T%28x%27%27%2Cy%27%27%29%20%5Ccdot%20M%28x%27%27%2Cy%27%27%29%29%20%5Cright%29%20%5C%5C%20I%27%28x%2Bx%27%2Cy%2By%27%29%3DM%28x%27%2Cy%27%29%0A%20%20%20%5Ccdot%20%5Cleft%28%20I%28x%2Bx%27%2Cy%2By%27%29%20%2D%20%5Cfrac%7B1%7D%7B%5Csum%20%5F%7Bx%27%27%2Cy%27%27%7D%20M%28x%27%27%2Cy%27%27%29%7D%0A%20%20%20%5Ccdot%20%5Csum%20%5F%7Bx%27%27%2Cy%27%27%7D%20%28I%28x%2Bx%27%27%2Cy%2By%27%27%29%20%5Ccdot%20M%28x%27%27%2Cy%27%27%29%29%20%5Cright%29%0A%20%20%20%5Cend%7Barray%7D%20) + TM_CCOEFF = 4, + /// !< ![block formula](https://latex.codecogs.com/png.latex?R%28x%2Cy%29%3D%20%5Cfrac%7B%20%5Csum%5F%7Bx%27%2Cy%27%7D%20%28T%27%28x%27%2Cy%27%29%20%5Ccdot%20I%27%28x%2Bx%27%2Cy%2By%27%29%29%20%7D%7B%0A%5Csqrt%7B%5Csum%5F%7Bx%27%2Cy%27%7DT%27%28x%27%2Cy%27%29%5E2%20%5Ccdot%20%5Csum%5F%7Bx%27%2Cy%27%7D%20I%27%28x%2Bx%27%2Cy%2By%27%29%5E2%7D%0A%7D) + TM_CCOEFF_NORMED = 5, + } + + opencv_type_enum! { crate::imgproc::TemplateMatchModes { TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED } } + + /// type of the threshold operation + /// ![threshold types](https://docs.opencv.org/4.13.0/threshold.png) + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum ThresholdTypes { + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Cfork%7B%5Ctexttt%7Bmaxval%7D%7D%7Bif%20%5C%28%5Ctexttt%7Bsrc%7D%28x%2Cy%29%20%3E%20%5Ctexttt%7Bthresh%7D%5C%29%7D%7B0%7D%7Botherwise%7D) + THRESH_BINARY = 0, + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Cfork%7B0%7D%7Bif%20%5C%28%5Ctexttt%7Bsrc%7D%28x%2Cy%29%20%3E%20%5Ctexttt%7Bthresh%7D%5C%29%7D%7B%5Ctexttt%7Bmaxval%7D%7D%7Botherwise%7D) + THRESH_BINARY_INV = 1, + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Cfork%7B%5Ctexttt%7Bthreshold%7D%7D%7Bif%20%5C%28%5Ctexttt%7Bsrc%7D%28x%2Cy%29%20%3E%20%5Ctexttt%7Bthresh%7D%5C%29%7D%7B%5Ctexttt%7Bsrc%7D%28x%2Cy%29%7D%7Botherwise%7D) + THRESH_TRUNC = 2, + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Cfork%7B%5Ctexttt%7Bsrc%7D%28x%2Cy%29%7D%7Bif%20%5C%28%5Ctexttt%7Bsrc%7D%28x%2Cy%29%20%3E%20%5Ctexttt%7Bthresh%7D%5C%29%7D%7B0%7D%7Botherwise%7D) + THRESH_TOZERO = 3, + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Cfork%7B0%7D%7Bif%20%5C%28%5Ctexttt%7Bsrc%7D%28x%2Cy%29%20%3E%20%5Ctexttt%7Bthresh%7D%5C%29%7D%7B%5Ctexttt%7Bsrc%7D%28x%2Cy%29%7D%7Botherwise%7D) + THRESH_TOZERO_INV = 4, + THRESH_MASK = 7, + /// flag, use Otsu algorithm to choose the optimal threshold value + THRESH_OTSU = 8, + /// flag, use Triangle algorithm to choose the optimal threshold value + THRESH_TRIANGLE = 16, + /// flag, compute threshold only (useful for OTSU/TRIANGLE) but does not actually run thresholding + THRESH_DRYRUN = 128, + } + + opencv_type_enum! { crate::imgproc::ThresholdTypes { THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV, THRESH_MASK, THRESH_OTSU, THRESH_TRIANGLE, THRESH_DRYRUN } } + + /// \brief Specify the polar mapping mode + /// ## See also + /// warpPolar + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum WarpPolarMode { + /// Remaps an image to/from polar space. + WARP_POLAR_LINEAR = 0, + /// Remaps an image to/from semilog-polar space. + WARP_POLAR_LOG = 256, + } + + opencv_type_enum! { crate::imgproc::WarpPolarMode { WARP_POLAR_LINEAR, WARP_POLAR_LOG } } + + /// \overload + /// + /// Finds edges in an image using the Canny algorithm with custom image gradient. + /// + /// ## Parameters + /// * dx: 16-bit x derivative of input image (CV_16SC1 or CV_16SC3). + /// * dy: 16-bit y derivative of input image (same type as dx). + /// * edges: output edge map; single channels 8-bit image, which has the same size as image . + /// * threshold1: first threshold for the hysteresis procedure. + /// * threshold2: second threshold for the hysteresis procedure. + /// * L2gradient: a flag, indicating whether a more accurate ![inline formula](https://latex.codecogs.com/png.latex?L%5F2) norm + /// ![inline formula](https://latex.codecogs.com/png.latex?%3D%5Csqrt%7B%28dI%2Fdx%29%5E2%20%2B%20%28dI%2Fdy%29%5E2%7D) should be used to calculate the image gradient magnitude ( + /// L2gradient=true ), or whether the default ![inline formula](https://latex.codecogs.com/png.latex?L%5F1) norm ![inline formula](https://latex.codecogs.com/png.latex?%3D%7CdI%2Fdx%7C%2B%7CdI%2Fdy%7C) is enough ( + /// L2gradient=false ). + /// + /// ## Note + /// This alternative version of [canny_derivative] function uses the following default values for its arguments: + /// * l2gradient: false + #[inline] + pub fn canny_derivative_def(dx: &impl ToInputArray, dy: &impl ToInputArray, edges: &mut impl ToOutputArray, threshold1: f64, threshold2: f64) -> Result<()> { + input_array_arg!(dx); + input_array_arg!(dy); + output_array_arg!(edges); + return_send!(via ocvrs_return); + unsafe { sys::cv_Canny_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double_double(dx.as_raw__InputArray(), dy.as_raw__InputArray(), edges.as_raw__OutputArray(), threshold1, threshold2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// \overload + /// + /// Finds edges in an image using the Canny algorithm with custom image gradient. + /// + /// ## Parameters + /// * dx: 16-bit x derivative of input image (CV_16SC1 or CV_16SC3). + /// * dy: 16-bit y derivative of input image (same type as dx). + /// * edges: output edge map; single channels 8-bit image, which has the same size as image . + /// * threshold1: first threshold for the hysteresis procedure. + /// * threshold2: second threshold for the hysteresis procedure. + /// * L2gradient: a flag, indicating whether a more accurate ![inline formula](https://latex.codecogs.com/png.latex?L%5F2) norm + /// ![inline formula](https://latex.codecogs.com/png.latex?%3D%5Csqrt%7B%28dI%2Fdx%29%5E2%20%2B%20%28dI%2Fdy%29%5E2%7D) should be used to calculate the image gradient magnitude ( + /// L2gradient=true ), or whether the default ![inline formula](https://latex.codecogs.com/png.latex?L%5F1) norm ![inline formula](https://latex.codecogs.com/png.latex?%3D%7CdI%2Fdx%7C%2B%7CdI%2Fdy%7C) is enough ( + /// L2gradient=false ). + /// + /// ## C++ default parameters + /// * l2gradient: false + #[inline] + pub fn canny_derivative(dx: &impl ToInputArray, dy: &impl ToInputArray, edges: &mut impl ToOutputArray, threshold1: f64, threshold2: f64, l2gradient: bool) -> Result<()> { + input_array_arg!(dx); + input_array_arg!(dy); + output_array_arg!(edges); + return_send!(via ocvrs_return); + unsafe { sys::cv_Canny_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double_double_bool(dx.as_raw__InputArray(), dy.as_raw__InputArray(), edges.as_raw__OutputArray(), threshold1, threshold2, l2gradient, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds edges in an image using the Canny algorithm [Canny86](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Canny86) . + /// + /// The function finds edges in the input image and marks them in the output map edges using the + /// Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The + /// largest value is used to find initial segments of strong edges. See + /// + /// + /// ## Parameters + /// * image: 8-bit input image. + /// * edges: output edge map; single channels 8-bit image, which has the same size as image . + /// * threshold1: first threshold for the hysteresis procedure. + /// * threshold2: second threshold for the hysteresis procedure. + /// * apertureSize: aperture size for the Sobel operator. + /// * L2gradient: a flag, indicating whether a more accurate ![inline formula](https://latex.codecogs.com/png.latex?L%5F2) norm + /// ![inline formula](https://latex.codecogs.com/png.latex?%3D%5Csqrt%7B%28dI%2Fdx%29%5E2%20%2B%20%28dI%2Fdy%29%5E2%7D) should be used to calculate the image gradient magnitude ( + /// L2gradient=true ), or whether the default ![inline formula](https://latex.codecogs.com/png.latex?L%5F1) norm ![inline formula](https://latex.codecogs.com/png.latex?%3D%7CdI%2Fdx%7C%2B%7CdI%2Fdy%7C) is enough ( + /// L2gradient=false ). + /// + /// ## Note + /// This alternative version of [canny] function uses the following default values for its arguments: + /// * aperture_size: 3 + /// * l2gradient: false + #[inline] + pub fn canny_def(image: &impl ToInputArray, edges: &mut impl ToOutputArray, threshold1: f64, threshold2: f64) -> Result<()> { + input_array_arg!(image); + output_array_arg!(edges); + return_send!(via ocvrs_return); + unsafe { sys::cv_Canny_const__InputArrayR_const__OutputArrayR_double_double(image.as_raw__InputArray(), edges.as_raw__OutputArray(), threshold1, threshold2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds edges in an image using the Canny algorithm [Canny86](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Canny86) . + /// + /// The function finds edges in the input image and marks them in the output map edges using the + /// Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The + /// largest value is used to find initial segments of strong edges. See + /// + /// + /// ## Parameters + /// * image: 8-bit input image. + /// * edges: output edge map; single channels 8-bit image, which has the same size as image . + /// * threshold1: first threshold for the hysteresis procedure. + /// * threshold2: second threshold for the hysteresis procedure. + /// * apertureSize: aperture size for the Sobel operator. + /// * L2gradient: a flag, indicating whether a more accurate ![inline formula](https://latex.codecogs.com/png.latex?L%5F2) norm + /// ![inline formula](https://latex.codecogs.com/png.latex?%3D%5Csqrt%7B%28dI%2Fdx%29%5E2%20%2B%20%28dI%2Fdy%29%5E2%7D) should be used to calculate the image gradient magnitude ( + /// L2gradient=true ), or whether the default ![inline formula](https://latex.codecogs.com/png.latex?L%5F1) norm ![inline formula](https://latex.codecogs.com/png.latex?%3D%7CdI%2Fdx%7C%2B%7CdI%2Fdy%7C) is enough ( + /// L2gradient=false ). + /// + /// ## C++ default parameters + /// * aperture_size: 3 + /// * l2gradient: false + #[inline] + pub fn canny(image: &impl ToInputArray, edges: &mut impl ToOutputArray, threshold1: f64, threshold2: f64, aperture_size: i32, l2gradient: bool) -> Result<()> { + input_array_arg!(image); + output_array_arg!(edges); + return_send!(via ocvrs_return); + unsafe { sys::cv_Canny_const__InputArrayR_const__OutputArrayR_double_double_int_bool(image.as_raw__InputArray(), edges.as_raw__OutputArray(), threshold1, threshold2, aperture_size, l2gradient, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes the "minimal work" distance between two weighted point configurations. + /// + /// The function computes the earth mover distance and/or a lower boundary of the distance between the + /// two weighted point configurations. One of the applications described in [RubnerSept98](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_RubnerSept98), + /// [Rubner2000](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Rubner2000) is multi-dimensional histogram comparison for image retrieval. EMD is a transportation + /// problem that is solved using some modification of a simplex algorithm, thus the complexity is + /// exponential in the worst case, though, on average it is much faster. In the case of a real metric + /// the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used + /// to determine roughly whether the two signatures are far enough so that they cannot relate to the + /// same object. + /// + /// ## Parameters + /// * signature1: First signature, a ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsize1%7D%5Ctimes%20%5Ctexttt%7Bdims%7D%2B1) floating-point matrix. + /// Each row stores the point weight followed by the point coordinates. The matrix is allowed to have + /// a single column (weights only) if the user-defined cost matrix is used. The weights must be + /// non-negative and have at least one non-zero value. + /// * signature2: Second signature of the same format as signature1 , though the number of rows + /// may be different. The total weights may be different. In this case an extra "dummy" point is added + /// to either signature1 or signature2. The weights must be non-negative and have at least one non-zero + /// value. + /// * distType: Used metric. See #DistanceTypes. + /// * cost: User-defined ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsize1%7D%5Ctimes%20%5Ctexttt%7Bsize2%7D) cost matrix. Also, if a cost matrix + /// is used, lower boundary lowerBound cannot be calculated because it needs a metric function. + /// * lowerBound: Optional input/output parameter: lower boundary of a distance between the two + /// signatures that is a distance between mass centers. The lower boundary may not be calculated if + /// the user-defined cost matrix is used, the total weights of point configurations are not equal, or + /// if the signatures consist of weights only (the signature matrices have a single column). You + /// **must** initialize \*lowerBound . If the calculated distance between mass centers is greater or + /// equal to \*lowerBound (it means that the signatures are far enough), the function does not + /// calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on + /// return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound + /// should be set to 0. + /// * flow: Resultant ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsize1%7D%20%5Ctimes%20%5Ctexttt%7Bsize2%7D) flow matrix: ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bflow%7D%5F%7Bi%2Cj%7D) is + /// a flow from ![inline formula](https://latex.codecogs.com/png.latex?i) -th point of signature1 to ![inline formula](https://latex.codecogs.com/png.latex?j) -th point of signature2 . + /// + /// ## Note + /// This alternative version of [emd] function uses the following default values for its arguments: + /// * cost: noArray() + /// * lower_bound: 0 + /// * flow: noArray() + #[inline] + pub fn emd_def(signature1: &impl ToInputArray, signature2: &impl ToInputArray, dist_type: i32) -> Result { + input_array_arg!(signature1); + input_array_arg!(signature2); + return_send!(via ocvrs_return); + unsafe { sys::cv_EMD_const__InputArrayR_const__InputArrayR_int(signature1.as_raw__InputArray(), signature2.as_raw__InputArray(), dist_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Computes the "minimal work" distance between two weighted point configurations. + /// + /// The function computes the earth mover distance and/or a lower boundary of the distance between the + /// two weighted point configurations. One of the applications described in [RubnerSept98](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_RubnerSept98), + /// [Rubner2000](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Rubner2000) is multi-dimensional histogram comparison for image retrieval. EMD is a transportation + /// problem that is solved using some modification of a simplex algorithm, thus the complexity is + /// exponential in the worst case, though, on average it is much faster. In the case of a real metric + /// the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used + /// to determine roughly whether the two signatures are far enough so that they cannot relate to the + /// same object. + /// + /// ## Parameters + /// * signature1: First signature, a ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsize1%7D%5Ctimes%20%5Ctexttt%7Bdims%7D%2B1) floating-point matrix. + /// Each row stores the point weight followed by the point coordinates. The matrix is allowed to have + /// a single column (weights only) if the user-defined cost matrix is used. The weights must be + /// non-negative and have at least one non-zero value. + /// * signature2: Second signature of the same format as signature1 , though the number of rows + /// may be different. The total weights may be different. In this case an extra "dummy" point is added + /// to either signature1 or signature2. The weights must be non-negative and have at least one non-zero + /// value. + /// * distType: Used metric. See #DistanceTypes. + /// * cost: User-defined ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsize1%7D%5Ctimes%20%5Ctexttt%7Bsize2%7D) cost matrix. Also, if a cost matrix + /// is used, lower boundary lowerBound cannot be calculated because it needs a metric function. + /// * lowerBound: Optional input/output parameter: lower boundary of a distance between the two + /// signatures that is a distance between mass centers. The lower boundary may not be calculated if + /// the user-defined cost matrix is used, the total weights of point configurations are not equal, or + /// if the signatures consist of weights only (the signature matrices have a single column). You + /// **must** initialize \*lowerBound . If the calculated distance between mass centers is greater or + /// equal to \*lowerBound (it means that the signatures are far enough), the function does not + /// calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on + /// return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound + /// should be set to 0. + /// * flow: Resultant ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsize1%7D%20%5Ctimes%20%5Ctexttt%7Bsize2%7D) flow matrix: ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bflow%7D%5F%7Bi%2Cj%7D) is + /// a flow from ![inline formula](https://latex.codecogs.com/png.latex?i) -th point of signature1 to ![inline formula](https://latex.codecogs.com/png.latex?j) -th point of signature2 . + /// + /// ## C++ default parameters + /// * cost: noArray() + /// * lower_bound: 0 + /// * flow: noArray() + #[inline] + pub fn emd(signature1: &impl ToInputArray, signature2: &impl ToInputArray, dist_type: i32, cost: &impl ToInputArray, lower_bound: Option<&mut f32>, flow: &mut impl ToOutputArray) -> Result { + input_array_arg!(signature1); + input_array_arg!(signature2); + input_array_arg!(cost); + output_array_arg!(flow); + return_send!(via ocvrs_return); + unsafe { sys::cv_EMD_const__InputArrayR_const__InputArrayR_int_const__InputArrayR_floatX_const__OutputArrayR(signature1.as_raw__InputArray(), signature2.as_raw__InputArray(), dist_type, cost.as_raw__InputArray(), lower_bound.map_or(::core::ptr::null_mut(), |lower_bound| lower_bound), flow.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Blurs an image using a Gaussian filter. + /// + /// The function convolves the source image with the specified Gaussian kernel. In-place filtering is + /// supported. + /// + /// ## Parameters + /// * src: input image; the image can have any number of channels, which are processed + /// independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. + /// * dst: output image of the same size and type as src. + /// * ksize: Gaussian kernel size. ksize.width and ksize.height can differ but they both must be + /// positive and odd. Or, they can be zero's and then they are computed from sigma. + /// * sigmaX: Gaussian kernel standard deviation in X direction. + /// * sigmaY: Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be + /// equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, + /// respectively (see [get_gaussian_kernel] for details); to fully control the result regardless of + /// possible future modifications of all this semantics, it is recommended to specify all of ksize, + /// sigmaX, and sigmaY. + /// * borderType: pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// * hint: Implementation modfication flags. See [algorithm_hint] + /// ## See also + /// sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur + /// + /// ## Note + /// This alternative version of [gaussian_blur] function uses the following default values for its arguments: + /// * sigma_y: 0 + /// * border_type: BORDER_DEFAULT + /// * hint: cv::ALGO_HINT_DEFAULT + #[inline] + pub fn gaussian_blur_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ksize: core::Size, sigma_x: f64) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_GaussianBlur_const__InputArrayR_const__OutputArrayR_Size_double(src.as_raw__InputArray(), dst.as_raw__OutputArray(), &ksize, sigma_x, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Blurs an image using a Gaussian filter. + /// + /// The function convolves the source image with the specified Gaussian kernel. In-place filtering is + /// supported. + /// + /// ## Parameters + /// * src: input image; the image can have any number of channels, which are processed + /// independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. + /// * dst: output image of the same size and type as src. + /// * ksize: Gaussian kernel size. ksize.width and ksize.height can differ but they both must be + /// positive and odd. Or, they can be zero's and then they are computed from sigma. + /// * sigmaX: Gaussian kernel standard deviation in X direction. + /// * sigmaY: Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be + /// equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, + /// respectively (see [get_gaussian_kernel] for details); to fully control the result regardless of + /// possible future modifications of all this semantics, it is recommended to specify all of ksize, + /// sigmaX, and sigmaY. + /// * borderType: pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// * hint: Implementation modfication flags. See [algorithm_hint] + /// ## See also + /// sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur + /// + /// ## C++ default parameters + /// * sigma_y: 0 + /// * border_type: BORDER_DEFAULT + /// * hint: cv::ALGO_HINT_DEFAULT + #[inline] + pub fn gaussian_blur(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ksize: core::Size, sigma_x: f64, sigma_y: f64, border_type: i32, hint: core::AlgorithmHint) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_GaussianBlur_const__InputArrayR_const__OutputArrayR_Size_double_double_int_AlgorithmHint(src.as_raw__InputArray(), dst.as_raw__OutputArray(), &ksize, sigma_x, sigma_y, border_type, hint, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds circles in a grayscale image using the Hough transform. + /// + /// The function finds circles in a grayscale image using a modification of the Hough transform. + /// + /// Example: : + /// @include snippets/imgproc_HoughLinesCircles.cpp + /// + /// + /// Note: Usually the function detects the centers of circles well. However, it may fail to find correct + /// radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if + /// you know it. Or, in the case of [HOUGH_GRADIENT] method you may set maxRadius to a negative number + /// to return centers only without radius search, and find the correct radius using an additional procedure. + /// + /// It also helps to smooth image a bit unless it's already soft. For example, + /// GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help. + /// + /// ## Parameters + /// * image: 8-bit, single-channel, grayscale input image. + /// * circles: Output vector of found circles. Each vector is encoded as 3 or 4 element + /// floating-point vector ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%2C%20radius%29) or ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%2C%20radius%2C%20votes%29) . + /// * method: Detection method, see #HoughModes. The available methods are [HOUGH_GRADIENT] and #HOUGH_GRADIENT_ALT. + /// * dp: Inverse ratio of the accumulator resolution to the image resolution. For example, if + /// dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has + /// half as big width and height. For [HOUGH_GRADIENT_ALT] the recommended value is dp=1.5, + /// unless some small very circles need to be detected. + /// * minDist: Minimum distance between the centers of the detected circles. If the parameter is + /// too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is + /// too large, some circles may be missed. + /// * param1: First method-specific parameter. In case of [HOUGH_GRADIENT] and #HOUGH_GRADIENT_ALT, + /// it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). + /// Note that [HOUGH_GRADIENT_ALT] uses [scharr] algorithm to compute image derivatives, so the threshold value + /// should normally be higher, such as 300 or normally exposed and contrasty images. + /// * param2: Second method-specific parameter. In case of #HOUGH_GRADIENT, it is the + /// accumulator threshold for the circle centers at the detection stage. The smaller it is, the more + /// false circles may be detected. Circles, corresponding to the larger accumulator values, will be + /// returned first. In the case of [HOUGH_GRADIENT_ALT] algorithm, this is the circle "perfectness" measure. + /// The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. + /// If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. + /// But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. + /// * minRadius: Minimum circle radius. + /// * maxRadius: Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, [HOUGH_GRADIENT] returns + /// centers without finding the radius. [HOUGH_GRADIENT_ALT] always computes circle radiuses. + /// ## See also + /// fitEllipse, minEnclosingCircle + /// + /// ## Note + /// This alternative version of [hough_circles] function uses the following default values for its arguments: + /// * param1: 100 + /// * param2: 100 + /// * min_radius: 0 + /// * max_radius: 0 + #[inline] + pub fn hough_circles_def(image: &impl ToInputArray, circles: &mut impl ToOutputArray, method: i32, dp: f64, min_dist: f64) -> Result<()> { + input_array_arg!(image); + output_array_arg!(circles); + return_send!(via ocvrs_return); + unsafe { sys::cv_HoughCircles_const__InputArrayR_const__OutputArrayR_int_double_double(image.as_raw__InputArray(), circles.as_raw__OutputArray(), method, dp, min_dist, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds circles in a grayscale image using the Hough transform. + /// + /// The function finds circles in a grayscale image using a modification of the Hough transform. + /// + /// Example: : + /// @include snippets/imgproc_HoughLinesCircles.cpp + /// + /// + /// Note: Usually the function detects the centers of circles well. However, it may fail to find correct + /// radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if + /// you know it. Or, in the case of [HOUGH_GRADIENT] method you may set maxRadius to a negative number + /// to return centers only without radius search, and find the correct radius using an additional procedure. + /// + /// It also helps to smooth image a bit unless it's already soft. For example, + /// GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help. + /// + /// ## Parameters + /// * image: 8-bit, single-channel, grayscale input image. + /// * circles: Output vector of found circles. Each vector is encoded as 3 or 4 element + /// floating-point vector ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%2C%20radius%29) or ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%2C%20radius%2C%20votes%29) . + /// * method: Detection method, see #HoughModes. The available methods are [HOUGH_GRADIENT] and #HOUGH_GRADIENT_ALT. + /// * dp: Inverse ratio of the accumulator resolution to the image resolution. For example, if + /// dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has + /// half as big width and height. For [HOUGH_GRADIENT_ALT] the recommended value is dp=1.5, + /// unless some small very circles need to be detected. + /// * minDist: Minimum distance between the centers of the detected circles. If the parameter is + /// too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is + /// too large, some circles may be missed. + /// * param1: First method-specific parameter. In case of [HOUGH_GRADIENT] and #HOUGH_GRADIENT_ALT, + /// it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). + /// Note that [HOUGH_GRADIENT_ALT] uses [scharr] algorithm to compute image derivatives, so the threshold value + /// should normally be higher, such as 300 or normally exposed and contrasty images. + /// * param2: Second method-specific parameter. In case of #HOUGH_GRADIENT, it is the + /// accumulator threshold for the circle centers at the detection stage. The smaller it is, the more + /// false circles may be detected. Circles, corresponding to the larger accumulator values, will be + /// returned first. In the case of [HOUGH_GRADIENT_ALT] algorithm, this is the circle "perfectness" measure. + /// The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. + /// If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. + /// But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles. + /// * minRadius: Minimum circle radius. + /// * maxRadius: Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, [HOUGH_GRADIENT] returns + /// centers without finding the radius. [HOUGH_GRADIENT_ALT] always computes circle radiuses. + /// ## See also + /// fitEllipse, minEnclosingCircle + /// + /// ## C++ default parameters + /// * param1: 100 + /// * param2: 100 + /// * min_radius: 0 + /// * max_radius: 0 + #[inline] + pub fn hough_circles(image: &impl ToInputArray, circles: &mut impl ToOutputArray, method: i32, dp: f64, min_dist: f64, param1: f64, param2: f64, min_radius: i32, max_radius: i32) -> Result<()> { + input_array_arg!(image); + output_array_arg!(circles); + return_send!(via ocvrs_return); + unsafe { sys::cv_HoughCircles_const__InputArrayR_const__OutputArrayR_int_double_double_double_double_int_int(image.as_raw__InputArray(), circles.as_raw__OutputArray(), method, dp, min_dist, param1, param2, min_radius, max_radius, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds line segments in a binary image using the probabilistic Hough transform. + /// + /// The function implements the probabilistic Hough transform algorithm for line detection, described + /// in [Matas00](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Matas00) + /// + /// See the line detection example below: + /// @include snippets/imgproc_HoughLinesP.cpp + /// This is a sample picture the function parameters have been tuned for: + /// + /// ![image](https://docs.opencv.org/4.13.0/building.jpg) + /// + /// And this is the output of the above program in case of the probabilistic Hough transform: + /// + /// ![image](https://docs.opencv.org/4.13.0/houghp.png) + /// + /// ## Parameters + /// * image: 8-bit, single-channel binary source image. The image may be modified by the function. + /// * lines: Output vector of lines. Each line is represented by a 4-element vector + /// ![inline formula](https://latex.codecogs.com/png.latex?%28x%5F1%2C%20y%5F1%2C%20x%5F2%2C%20y%5F2%29) , where ![inline formula](https://latex.codecogs.com/png.latex?%28x%5F1%2Cy%5F1%29) and ![inline formula](https://latex.codecogs.com/png.latex?%28x%5F2%2C%20y%5F2%29) are the ending points of each detected + /// line segment. + /// * rho: Distance resolution of the accumulator in pixels. + /// * theta: Angle resolution of the accumulator in radians. + /// * threshold: %Accumulator threshold parameter. Only those lines are returned that get enough + /// votes ( ![inline formula](https://latex.codecogs.com/png.latex?%3E%5Ctexttt%7Bthreshold%7D) ). + /// * minLineLength: Minimum line length. Line segments shorter than that are rejected. + /// * maxLineGap: Maximum allowed gap between points on the same line to link them. + /// ## See also + /// LineSegmentDetector + /// + /// ## Note + /// This alternative version of [hough_lines_p] function uses the following default values for its arguments: + /// * min_line_length: 0 + /// * max_line_gap: 0 + #[inline] + pub fn hough_lines_p_def(image: &impl ToInputArray, lines: &mut impl ToOutputArray, rho: f64, theta: f64, threshold: i32) -> Result<()> { + input_array_arg!(image); + output_array_arg!(lines); + return_send!(via ocvrs_return); + unsafe { sys::cv_HoughLinesP_const__InputArrayR_const__OutputArrayR_double_double_int(image.as_raw__InputArray(), lines.as_raw__OutputArray(), rho, theta, threshold, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds line segments in a binary image using the probabilistic Hough transform. + /// + /// The function implements the probabilistic Hough transform algorithm for line detection, described + /// in [Matas00](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Matas00) + /// + /// See the line detection example below: + /// @include snippets/imgproc_HoughLinesP.cpp + /// This is a sample picture the function parameters have been tuned for: + /// + /// ![image](https://docs.opencv.org/4.13.0/building.jpg) + /// + /// And this is the output of the above program in case of the probabilistic Hough transform: + /// + /// ![image](https://docs.opencv.org/4.13.0/houghp.png) + /// + /// ## Parameters + /// * image: 8-bit, single-channel binary source image. The image may be modified by the function. + /// * lines: Output vector of lines. Each line is represented by a 4-element vector + /// ![inline formula](https://latex.codecogs.com/png.latex?%28x%5F1%2C%20y%5F1%2C%20x%5F2%2C%20y%5F2%29) , where ![inline formula](https://latex.codecogs.com/png.latex?%28x%5F1%2Cy%5F1%29) and ![inline formula](https://latex.codecogs.com/png.latex?%28x%5F2%2C%20y%5F2%29) are the ending points of each detected + /// line segment. + /// * rho: Distance resolution of the accumulator in pixels. + /// * theta: Angle resolution of the accumulator in radians. + /// * threshold: %Accumulator threshold parameter. Only those lines are returned that get enough + /// votes ( ![inline formula](https://latex.codecogs.com/png.latex?%3E%5Ctexttt%7Bthreshold%7D) ). + /// * minLineLength: Minimum line length. Line segments shorter than that are rejected. + /// * maxLineGap: Maximum allowed gap between points on the same line to link them. + /// ## See also + /// LineSegmentDetector + /// + /// ## C++ default parameters + /// * min_line_length: 0 + /// * max_line_gap: 0 + #[inline] + pub fn hough_lines_p(image: &impl ToInputArray, lines: &mut impl ToOutputArray, rho: f64, theta: f64, threshold: i32, min_line_length: f64, max_line_gap: f64) -> Result<()> { + input_array_arg!(image); + output_array_arg!(lines); + return_send!(via ocvrs_return); + unsafe { sys::cv_HoughLinesP_const__InputArrayR_const__OutputArrayR_double_double_int_double_double(image.as_raw__InputArray(), lines.as_raw__OutputArray(), rho, theta, threshold, min_line_length, max_line_gap, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds lines in a set of points using the standard Hough transform. + /// + /// The function finds lines in a set of points using a modification of the Hough transform. + /// @include snippets/imgproc_HoughLinesPointSet.cpp + /// ## Parameters + /// * point: Input vector of points. Each vector must be encoded as a Point vector ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29). Type must be CV_32FC2 or CV_32SC2. + /// * lines: Output vector of found lines. Each vector is encoded as a vector ![inline formula](https://latex.codecogs.com/png.latex?%28votes%2C%20rho%2C%20theta%29). + /// The larger the value of 'votes', the higher the reliability of the Hough line. + /// * lines_max: Max count of Hough lines. + /// * threshold: %Accumulator threshold parameter. Only those lines are returned that get enough + /// votes ( ![inline formula](https://latex.codecogs.com/png.latex?%3E%5Ctexttt%7Bthreshold%7D) ). + /// * min_rho: Minimum value for ![inline formula](https://latex.codecogs.com/png.latex?%5Crho) for the accumulator (Note: ![inline formula](https://latex.codecogs.com/png.latex?%5Crho) can be negative. The absolute value ![inline formula](https://latex.codecogs.com/png.latex?%7C%5Crho%7C) is the distance of a line to the origin.). + /// * max_rho: Maximum value for ![inline formula](https://latex.codecogs.com/png.latex?%5Crho) for the accumulator. + /// * rho_step: Distance resolution of the accumulator. + /// * min_theta: Minimum angle value of the accumulator in radians. + /// * max_theta: Upper bound for the angle value of the accumulator in radians. The actual maximum + /// angle may be slightly less than max_theta, depending on the parameters min_theta and theta_step. + /// * theta_step: Angle resolution of the accumulator in radians. + #[inline] + pub fn hough_lines_point_set(point: &impl ToInputArray, lines: &mut impl ToOutputArray, lines_max: i32, threshold: i32, min_rho: f64, max_rho: f64, rho_step: f64, min_theta: f64, max_theta: f64, theta_step: f64) -> Result<()> { + input_array_arg!(point); + output_array_arg!(lines); + return_send!(via ocvrs_return); + unsafe { sys::cv_HoughLinesPointSet_const__InputArrayR_const__OutputArrayR_int_int_double_double_double_double_double_double(point.as_raw__InputArray(), lines.as_raw__OutputArray(), lines_max, threshold, min_rho, max_rho, rho_step, min_theta, max_theta, theta_step, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds lines in a binary image using the standard Hough transform. + /// + /// The function implements the standard or standard multi-scale Hough transform algorithm for line + /// detection. See for a good explanation of Hough + /// transform. + /// + /// ## Parameters + /// * image: 8-bit, single-channel binary source image. The image may be modified by the function. + /// * lines: Output vector of lines. Each line is represented by a 2 or 3 element vector + /// ![inline formula](https://latex.codecogs.com/png.latex?%28%5Crho%2C%20%5Ctheta%29) or ![inline formula](https://latex.codecogs.com/png.latex?%28%5Crho%2C%20%5Ctheta%2C%20%5Ctextrm%7Bvotes%7D%29), where ![inline formula](https://latex.codecogs.com/png.latex?%5Crho) is the distance from + /// the coordinate origin ![inline formula](https://latex.codecogs.com/png.latex?%280%2C0%29) (top-left corner of the image), ![inline formula](https://latex.codecogs.com/png.latex?%5Ctheta) is the line rotation + /// angle in radians ( ![inline formula](https://latex.codecogs.com/png.latex?0%20%5Csim%20%5Ctextrm%7Bvertical%20line%7D%2C%20%5Cpi%2F2%20%5Csim%20%5Ctextrm%7Bhorizontal%20line%7D) ), and + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctextrm%7Bvotes%7D) is the value of accumulator. + /// * rho: Distance resolution of the accumulator in pixels. + /// * theta: Angle resolution of the accumulator in radians. + /// * threshold: %Accumulator threshold parameter. Only those lines are returned that get enough + /// votes ( ![inline formula](https://latex.codecogs.com/png.latex?%3E%5Ctexttt%7Bthreshold%7D) ). + /// * srn: For the multi-scale Hough transform, it is a divisor for the distance resolution rho. + /// The coarse accumulator distance resolution is rho and the accurate accumulator resolution is + /// rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these + /// parameters should be positive. + /// * stn: For the multi-scale Hough transform, it is a divisor for the distance resolution theta. + /// * min_theta: For standard and multi-scale Hough transform, minimum angle to check for lines. + /// Must fall between 0 and max_theta. + /// * max_theta: For standard and multi-scale Hough transform, an upper bound for the angle. + /// Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly + /// less than max_theta, depending on the parameters min_theta and theta. + /// * use_edgeval: True if you want to use weighted Hough transform. + /// + /// ## Note + /// This alternative version of [hough_lines] function uses the following default values for its arguments: + /// * srn: 0 + /// * stn: 0 + /// * min_theta: 0 + /// * max_theta: CV_PI + /// * use_edgeval: false + #[inline] + pub fn hough_lines_def(image: &impl ToInputArray, lines: &mut impl ToOutputArray, rho: f64, theta: f64, threshold: i32) -> Result<()> { + input_array_arg!(image); + output_array_arg!(lines); + return_send!(via ocvrs_return); + unsafe { sys::cv_HoughLines_const__InputArrayR_const__OutputArrayR_double_double_int(image.as_raw__InputArray(), lines.as_raw__OutputArray(), rho, theta, threshold, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds lines in a binary image using the standard Hough transform. + /// + /// The function implements the standard or standard multi-scale Hough transform algorithm for line + /// detection. See for a good explanation of Hough + /// transform. + /// + /// ## Parameters + /// * image: 8-bit, single-channel binary source image. The image may be modified by the function. + /// * lines: Output vector of lines. Each line is represented by a 2 or 3 element vector + /// ![inline formula](https://latex.codecogs.com/png.latex?%28%5Crho%2C%20%5Ctheta%29) or ![inline formula](https://latex.codecogs.com/png.latex?%28%5Crho%2C%20%5Ctheta%2C%20%5Ctextrm%7Bvotes%7D%29), where ![inline formula](https://latex.codecogs.com/png.latex?%5Crho) is the distance from + /// the coordinate origin ![inline formula](https://latex.codecogs.com/png.latex?%280%2C0%29) (top-left corner of the image), ![inline formula](https://latex.codecogs.com/png.latex?%5Ctheta) is the line rotation + /// angle in radians ( ![inline formula](https://latex.codecogs.com/png.latex?0%20%5Csim%20%5Ctextrm%7Bvertical%20line%7D%2C%20%5Cpi%2F2%20%5Csim%20%5Ctextrm%7Bhorizontal%20line%7D) ), and + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctextrm%7Bvotes%7D) is the value of accumulator. + /// * rho: Distance resolution of the accumulator in pixels. + /// * theta: Angle resolution of the accumulator in radians. + /// * threshold: %Accumulator threshold parameter. Only those lines are returned that get enough + /// votes ( ![inline formula](https://latex.codecogs.com/png.latex?%3E%5Ctexttt%7Bthreshold%7D) ). + /// * srn: For the multi-scale Hough transform, it is a divisor for the distance resolution rho. + /// The coarse accumulator distance resolution is rho and the accurate accumulator resolution is + /// rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these + /// parameters should be positive. + /// * stn: For the multi-scale Hough transform, it is a divisor for the distance resolution theta. + /// * min_theta: For standard and multi-scale Hough transform, minimum angle to check for lines. + /// Must fall between 0 and max_theta. + /// * max_theta: For standard and multi-scale Hough transform, an upper bound for the angle. + /// Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly + /// less than max_theta, depending on the parameters min_theta and theta. + /// * use_edgeval: True if you want to use weighted Hough transform. + /// + /// ## C++ default parameters + /// * srn: 0 + /// * stn: 0 + /// * min_theta: 0 + /// * max_theta: CV_PI + /// * use_edgeval: false + #[inline] + pub fn hough_lines(image: &impl ToInputArray, lines: &mut impl ToOutputArray, rho: f64, theta: f64, threshold: i32, srn: f64, stn: f64, min_theta: f64, max_theta: f64, use_edgeval: bool) -> Result<()> { + input_array_arg!(image); + output_array_arg!(lines); + return_send!(via ocvrs_return); + unsafe { sys::cv_HoughLines_const__InputArrayR_const__OutputArrayR_double_double_int_double_double_double_double_bool(image.as_raw__InputArray(), lines.as_raw__OutputArray(), rho, theta, threshold, srn, stn, min_theta, max_theta, use_edgeval, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates seven Hu invariants. + /// + /// The function calculates seven Hu invariants (introduced in [Hu62](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Hu62); see also + /// ) defined as: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20hu%5B0%5D%3D%20%5Ceta%20%5F%7B20%7D%2B%20%5Ceta%20%5F%7B02%7D%20%5C%5C%20hu%5B1%5D%3D%28%20%5Ceta%20%5F%7B20%7D%2D%20%5Ceta%20%5F%7B02%7D%29%5E%7B2%7D%2B4%20%5Ceta%20%5F%7B11%7D%5E%7B2%7D%20%5C%5C%20hu%5B2%5D%3D%28%20%5Ceta%20%5F%7B30%7D%2D3%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2B%20%283%20%5Ceta%20%5F%7B21%7D%2D%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%20%5C%5C%20hu%5B3%5D%3D%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2B%20%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%20%5C%5C%20hu%5B4%5D%3D%28%20%5Ceta%20%5F%7B30%7D%2D3%20%5Ceta%20%5F%7B12%7D%29%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5B%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2D3%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%5D%2B%283%20%5Ceta%20%5F%7B21%7D%2D%20%5Ceta%20%5F%7B03%7D%29%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5B3%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2D%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%5D%20%5C%5C%20hu%5B5%5D%3D%28%20%5Ceta%20%5F%7B20%7D%2D%20%5Ceta%20%5F%7B02%7D%29%5B%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2D%20%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%5D%2B4%20%5Ceta%20%5F%7B11%7D%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%20%5C%5C%20hu%5B6%5D%3D%283%20%5Ceta%20%5F%7B21%7D%2D%20%5Ceta%20%5F%7B03%7D%29%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5B3%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2D%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%5D%2D%28%20%5Ceta%20%5F%7B30%7D%2D3%20%5Ceta%20%5F%7B12%7D%29%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5B3%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2D%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%5D%20%5C%5C%20%5Cend%7Barray%7D) + /// + /// where ![inline formula](https://latex.codecogs.com/png.latex?%5Ceta%5F%7Bji%7D) stands for ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BMoments%3A%3Anu%7D%5F%7Bji%7D) . + /// + /// These values are proved to be invariants to the image scale, rotation, and reflection except the + /// seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of + /// infinite image resolution. In case of raster images, the computed Hu invariants for the original and + /// transformed images are a bit different. + /// + /// ## Parameters + /// * moments: Input moments computed with moments . + /// * hu: Output Hu invariants. + /// ## See also + /// matchShapes + /// + /// ## Overloaded parameters + #[inline] + pub fn hu_moments_1(m: core::Moments, hu: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(hu); + return_send!(via ocvrs_return); + unsafe { sys::cv_HuMoments_const_MomentsR_const__OutputArrayR(&m, hu.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates seven Hu invariants. + /// + /// The function calculates seven Hu invariants (introduced in [Hu62](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Hu62); see also + /// ) defined as: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20hu%5B0%5D%3D%20%5Ceta%20%5F%7B20%7D%2B%20%5Ceta%20%5F%7B02%7D%20%5C%5C%20hu%5B1%5D%3D%28%20%5Ceta%20%5F%7B20%7D%2D%20%5Ceta%20%5F%7B02%7D%29%5E%7B2%7D%2B4%20%5Ceta%20%5F%7B11%7D%5E%7B2%7D%20%5C%5C%20hu%5B2%5D%3D%28%20%5Ceta%20%5F%7B30%7D%2D3%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2B%20%283%20%5Ceta%20%5F%7B21%7D%2D%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%20%5C%5C%20hu%5B3%5D%3D%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2B%20%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%20%5C%5C%20hu%5B4%5D%3D%28%20%5Ceta%20%5F%7B30%7D%2D3%20%5Ceta%20%5F%7B12%7D%29%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5B%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2D3%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%5D%2B%283%20%5Ceta%20%5F%7B21%7D%2D%20%5Ceta%20%5F%7B03%7D%29%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5B3%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2D%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%5D%20%5C%5C%20hu%5B5%5D%3D%28%20%5Ceta%20%5F%7B20%7D%2D%20%5Ceta%20%5F%7B02%7D%29%5B%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2D%20%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%5D%2B4%20%5Ceta%20%5F%7B11%7D%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%20%5C%5C%20hu%5B6%5D%3D%283%20%5Ceta%20%5F%7B21%7D%2D%20%5Ceta%20%5F%7B03%7D%29%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5B3%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2D%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%5D%2D%28%20%5Ceta%20%5F%7B30%7D%2D3%20%5Ceta%20%5F%7B12%7D%29%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5B3%28%20%5Ceta%20%5F%7B30%7D%2B%20%5Ceta%20%5F%7B12%7D%29%5E%7B2%7D%2D%28%20%5Ceta%20%5F%7B21%7D%2B%20%5Ceta%20%5F%7B03%7D%29%5E%7B2%7D%5D%20%5C%5C%20%5Cend%7Barray%7D) + /// + /// where ![inline formula](https://latex.codecogs.com/png.latex?%5Ceta%5F%7Bji%7D) stands for ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BMoments%3A%3Anu%7D%5F%7Bji%7D) . + /// + /// These values are proved to be invariants to the image scale, rotation, and reflection except the + /// seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of + /// infinite image resolution. In case of raster images, the computed Hu invariants for the original and + /// transformed images are a bit different. + /// + /// ## Parameters + /// * moments: Input moments computed with moments . + /// * hu: Output Hu invariants. + /// ## See also + /// matchShapes + #[inline] + pub fn hu_moments(moments: core::Moments, hu: &mut [f64; 7]) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_HuMoments_const_MomentsR_doubleXX(&moments, hu, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the Laplacian of an image. + /// + /// The function calculates the Laplacian of the source image by adding up the second x and y + /// derivatives calculated using the Sobel operator: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%20%5CDelta%20%5Ctexttt%7Bsrc%7D%20%3D%20%20%5Cfrac%7B%5Cpartial%5E2%20%5Ctexttt%7Bsrc%7D%7D%7B%5Cpartial%20x%5E2%7D%20%2B%20%20%5Cfrac%7B%5Cpartial%5E2%20%5Ctexttt%7Bsrc%7D%7D%7B%5Cpartial%20y%5E2%7D) + /// + /// This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image + /// with the following ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%203) aperture: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%200%20%26%201%20%26%200%5C%5C%201%20%26%20%2D4%20%26%201%5C%5C%200%20%26%201%20%26%200%20%5Cend%7Bbmatrix%7D) + /// + /// ## Parameters + /// * src: Source image. + /// * dst: Destination image of the same size and the same number of channels as src . + /// * ddepth: Desired depth of the destination image, see [filter_depths] "combinations". + /// * ksize: Aperture size used to compute the second-derivative filters. See [get_deriv_kernels] for + /// details. The size must be positive and odd. + /// * scale: Optional scale factor for the computed Laplacian values. By default, no scaling is + /// applied. See [get_deriv_kernels] for details. + /// * delta: Optional delta value that is added to the results prior to storing them in dst . + /// * borderType: Pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// Sobel, Scharr + /// + /// ## Note + /// This alternative version of [laplacian] function uses the following default values for its arguments: + /// * ksize: 1 + /// * scale: 1 + /// * delta: 0 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn laplacian_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_Laplacian_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the Laplacian of an image. + /// + /// The function calculates the Laplacian of the source image by adding up the second x and y + /// derivatives calculated using the Sobel operator: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%20%5CDelta%20%5Ctexttt%7Bsrc%7D%20%3D%20%20%5Cfrac%7B%5Cpartial%5E2%20%5Ctexttt%7Bsrc%7D%7D%7B%5Cpartial%20x%5E2%7D%20%2B%20%20%5Cfrac%7B%5Cpartial%5E2%20%5Ctexttt%7Bsrc%7D%7D%7B%5Cpartial%20y%5E2%7D) + /// + /// This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image + /// with the following ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%203) aperture: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%200%20%26%201%20%26%200%5C%5C%201%20%26%20%2D4%20%26%201%5C%5C%200%20%26%201%20%26%200%20%5Cend%7Bbmatrix%7D) + /// + /// ## Parameters + /// * src: Source image. + /// * dst: Destination image of the same size and the same number of channels as src . + /// * ddepth: Desired depth of the destination image, see [filter_depths] "combinations". + /// * ksize: Aperture size used to compute the second-derivative filters. See [get_deriv_kernels] for + /// details. The size must be positive and odd. + /// * scale: Optional scale factor for the computed Laplacian values. By default, no scaling is + /// applied. See [get_deriv_kernels] for details. + /// * delta: Optional delta value that is added to the results prior to storing them in dst . + /// * borderType: Pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// Sobel, Scharr + /// + /// ## C++ default parameters + /// * ksize: 1 + /// * scale: 1 + /// * delta: 0 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn laplacian(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32, ksize: i32, scale: f64, delta: f64, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_Laplacian_const__InputArrayR_const__OutputArrayR_int_int_double_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, ksize, scale, delta, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the first x- or y- image derivative using Scharr operator. + /// + /// The function computes the first x- or y- spatial image derivative using the Scharr operator. The + /// call + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BScharr%28src%2C%20dst%2C%20ddepth%2C%20dx%2C%20dy%2C%20scale%2C%20delta%2C%20borderType%29%7D) + /// + /// is equivalent to + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BSobel%28src%2C%20dst%2C%20ddepth%2C%20dx%2C%20dy%2C%20FILTER%5FSCHARR%2C%20scale%2C%20delta%2C%20borderType%29%7D%20%2E) + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image of the same size and the same number of channels as src. + /// * ddepth: output image depth, see [filter_depths] "combinations" + /// * dx: order of the derivative x. + /// * dy: order of the derivative y. + /// * scale: optional scale factor for the computed derivative values; by default, no scaling is + /// applied (see [get_deriv_kernels] for details). + /// * delta: optional delta value that is added to the results prior to storing them in dst. + /// * borderType: pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// cartToPolar + /// + /// ## Note + /// This alternative version of [scharr] function uses the following default values for its arguments: + /// * scale: 1 + /// * delta: 0 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn scharr_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32, dx: i32, dy: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_Scharr_const__InputArrayR_const__OutputArrayR_int_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, dx, dy, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the first x- or y- image derivative using Scharr operator. + /// + /// The function computes the first x- or y- spatial image derivative using the Scharr operator. The + /// call + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BScharr%28src%2C%20dst%2C%20ddepth%2C%20dx%2C%20dy%2C%20scale%2C%20delta%2C%20borderType%29%7D) + /// + /// is equivalent to + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BSobel%28src%2C%20dst%2C%20ddepth%2C%20dx%2C%20dy%2C%20FILTER%5FSCHARR%2C%20scale%2C%20delta%2C%20borderType%29%7D%20%2E) + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image of the same size and the same number of channels as src. + /// * ddepth: output image depth, see [filter_depths] "combinations" + /// * dx: order of the derivative x. + /// * dy: order of the derivative y. + /// * scale: optional scale factor for the computed derivative values; by default, no scaling is + /// applied (see [get_deriv_kernels] for details). + /// * delta: optional delta value that is added to the results prior to storing them in dst. + /// * borderType: pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// cartToPolar + /// + /// ## C++ default parameters + /// * scale: 1 + /// * delta: 0 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn scharr(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32, dx: i32, dy: i32, scale: f64, delta: f64, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_Scharr_const__InputArrayR_const__OutputArrayR_int_int_int_double_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, dx, dy, scale, delta, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. + /// + /// In all cases except one, the ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bksize%7D%20%5Ctimes%20%5Ctexttt%7Bksize%7D) separable kernel is used to + /// calculate the derivative. When ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bksize%20%3D%201%7D), the ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%201) or ![inline formula](https://latex.codecogs.com/png.latex?1%20%5Ctimes%203) + /// kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first + /// or the second x- or y- derivatives. + /// + /// There is also the special value `ksize = [FILTER_SCHARR] (-1)` that corresponds to the ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes3) Scharr + /// filter that may give more accurate results than the ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes3) Sobel. The Scharr aperture is + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20%2D3%20%26%200%20%26%203%5C%5C%20%2D10%20%26%200%20%26%2010%5C%5C%20%2D3%20%26%200%20%26%203%20%5Cend%7Bbmatrix%7D) + /// + /// for the x-derivative, or transposed for the y-derivative. + /// + /// The function calculates an image derivative by convolving the image with the appropriate kernel: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%20%5Cfrac%7B%5Cpartial%5E%7Bxorder%2Byorder%7D%20%5Ctexttt%7Bsrc%7D%7D%7B%5Cpartial%20x%5E%7Bxorder%7D%20%5Cpartial%20y%5E%7Byorder%7D%7D) + /// + /// The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less + /// resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) + /// or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first + /// case corresponds to a kernel of: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20%2D1%20%26%200%20%26%201%5C%5C%20%2D2%20%26%200%20%26%202%5C%5C%20%2D1%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) + /// + /// The second case corresponds to a kernel of: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20%2D1%20%26%20%2D2%20%26%20%2D1%5C%5C%200%20%26%200%20%26%200%5C%5C%201%20%26%202%20%26%201%20%5Cend%7Bbmatrix%7D) + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image of the same size and the same number of channels as src . + /// * ddepth: output image depth, see [filter_depths] "combinations"; in the case of + /// 8-bit input images it will result in truncated derivatives. + /// * dx: order of the derivative x. + /// * dy: order of the derivative y. + /// * ksize: size of the extended Sobel kernel; it must be 1, 3, 5, or 7. + /// * scale: optional scale factor for the computed derivative values; by default, no scaling is + /// applied (see [get_deriv_kernels] for details). + /// * delta: optional delta value that is added to the results prior to storing them in dst. + /// * borderType: pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar + /// + /// ## Note + /// This alternative version of [sobel] function uses the following default values for its arguments: + /// * ksize: 3 + /// * scale: 1 + /// * delta: 0 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn sobel_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32, dx: i32, dy: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_Sobel_const__InputArrayR_const__OutputArrayR_int_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, dx, dy, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. + /// + /// In all cases except one, the ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bksize%7D%20%5Ctimes%20%5Ctexttt%7Bksize%7D) separable kernel is used to + /// calculate the derivative. When ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bksize%20%3D%201%7D), the ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%201) or ![inline formula](https://latex.codecogs.com/png.latex?1%20%5Ctimes%203) + /// kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first + /// or the second x- or y- derivatives. + /// + /// There is also the special value `ksize = [FILTER_SCHARR] (-1)` that corresponds to the ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes3) Scharr + /// filter that may give more accurate results than the ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes3) Sobel. The Scharr aperture is + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20%2D3%20%26%200%20%26%203%5C%5C%20%2D10%20%26%200%20%26%2010%5C%5C%20%2D3%20%26%200%20%26%203%20%5Cend%7Bbmatrix%7D) + /// + /// for the x-derivative, or transposed for the y-derivative. + /// + /// The function calculates an image derivative by convolving the image with the appropriate kernel: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%20%5Cfrac%7B%5Cpartial%5E%7Bxorder%2Byorder%7D%20%5Ctexttt%7Bsrc%7D%7D%7B%5Cpartial%20x%5E%7Bxorder%7D%20%5Cpartial%20y%5E%7Byorder%7D%7D) + /// + /// The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less + /// resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) + /// or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first + /// case corresponds to a kernel of: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20%2D1%20%26%200%20%26%201%5C%5C%20%2D2%20%26%200%20%26%202%5C%5C%20%2D1%20%26%200%20%26%201%20%5Cend%7Bbmatrix%7D) + /// + /// The second case corresponds to a kernel of: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20%2D1%20%26%20%2D2%20%26%20%2D1%5C%5C%200%20%26%200%20%26%200%5C%5C%201%20%26%202%20%26%201%20%5Cend%7Bbmatrix%7D) + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image of the same size and the same number of channels as src . + /// * ddepth: output image depth, see [filter_depths] "combinations"; in the case of + /// 8-bit input images it will result in truncated derivatives. + /// * dx: order of the derivative x. + /// * dy: order of the derivative y. + /// * ksize: size of the extended Sobel kernel; it must be 1, 3, 5, or 7. + /// * scale: optional scale factor for the computed derivative values; by default, no scaling is + /// applied (see [get_deriv_kernels] for details). + /// * delta: optional delta value that is added to the results prior to storing them in dst. + /// * borderType: pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar + /// + /// ## C++ default parameters + /// * ksize: 3 + /// * scale: 1 + /// * delta: 0 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn sobel(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32, dx: i32, dy: i32, ksize: i32, scale: f64, delta: f64, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_Sobel_const__InputArrayR_const__OutputArrayR_int_int_int_int_double_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, dx, dy, ksize, scale, delta, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Adds the per-element product of two input images to the accumulator image. + /// + /// The function adds the product of two images or their selected regions to the accumulator dst : + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%20%5Cleftarrow%20%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%2B%20%20%5Ctexttt%7Bsrc1%7D%20%28x%2Cy%29%20%20%5Ccdot%20%5Ctexttt%7Bsrc2%7D%20%28x%2Cy%29%20%20%5Cquad%20%5Ctext%7Bif%7D%20%5Cquad%20%5Ctexttt%7Bmask%7D%20%28x%2Cy%29%20%20%5Cne%200) + /// + /// The function supports multi-channel images. Each channel is processed independently. + /// + /// ## Parameters + /// * src1: First input image, 1- or 3-channel, 8-bit or 32-bit floating point. + /// * src2: Second input image of the same type and the same size as src1 . + /// * dst: %Accumulator image with the same number of channels as input images, 32-bit or 64-bit + /// floating-point. + /// * mask: Optional operation mask. + /// ## See also + /// accumulate, accumulateSquare, accumulateWeighted + /// + /// ## Note + /// This alternative version of [accumulate_product] function uses the following default values for its arguments: + /// * mask: noArray() + #[inline] + pub fn accumulate_product_def(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToInputOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + input_output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_accumulateProduct_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Adds the per-element product of two input images to the accumulator image. + /// + /// The function adds the product of two images or their selected regions to the accumulator dst : + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%20%5Cleftarrow%20%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%2B%20%20%5Ctexttt%7Bsrc1%7D%20%28x%2Cy%29%20%20%5Ccdot%20%5Ctexttt%7Bsrc2%7D%20%28x%2Cy%29%20%20%5Cquad%20%5Ctext%7Bif%7D%20%5Cquad%20%5Ctexttt%7Bmask%7D%20%28x%2Cy%29%20%20%5Cne%200) + /// + /// The function supports multi-channel images. Each channel is processed independently. + /// + /// ## Parameters + /// * src1: First input image, 1- or 3-channel, 8-bit or 32-bit floating point. + /// * src2: Second input image of the same type and the same size as src1 . + /// * dst: %Accumulator image with the same number of channels as input images, 32-bit or 64-bit + /// floating-point. + /// * mask: Optional operation mask. + /// ## See also + /// accumulate, accumulateSquare, accumulateWeighted + /// + /// ## C++ default parameters + /// * mask: noArray() + #[inline] + pub fn accumulate_product(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToInputOutputArray, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + input_output_array_arg!(dst); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_accumulateProduct_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__InputOutputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Adds the square of a source image to the accumulator image. + /// + /// The function adds the input image src or its selected region, raised to a power of 2, to the + /// accumulator dst : + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%20%5Cleftarrow%20%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%2B%20%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5E2%20%20%5Cquad%20%5Ctext%7Bif%7D%20%5Cquad%20%5Ctexttt%7Bmask%7D%20%28x%2Cy%29%20%20%5Cne%200) + /// + /// The function supports multi-channel images. Each channel is processed independently. + /// + /// ## Parameters + /// * src: Input image as 1- or 3-channel, 8-bit or 32-bit floating point. + /// * dst: %Accumulator image with the same number of channels as input image, 32-bit or 64-bit + /// floating-point. + /// * mask: Optional operation mask. + /// ## See also + /// accumulateSquare, accumulateProduct, accumulateWeighted + /// + /// ## Note + /// This alternative version of [accumulate_square] function uses the following default values for its arguments: + /// * mask: noArray() + #[inline] + pub fn accumulate_square_def(src: &impl ToInputArray, dst: &mut impl ToInputOutputArray) -> Result<()> { + input_array_arg!(src); + input_output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_accumulateSquare_const__InputArrayR_const__InputOutputArrayR(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Adds the square of a source image to the accumulator image. + /// + /// The function adds the input image src or its selected region, raised to a power of 2, to the + /// accumulator dst : + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%20%5Cleftarrow%20%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%2B%20%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5E2%20%20%5Cquad%20%5Ctext%7Bif%7D%20%5Cquad%20%5Ctexttt%7Bmask%7D%20%28x%2Cy%29%20%20%5Cne%200) + /// + /// The function supports multi-channel images. Each channel is processed independently. + /// + /// ## Parameters + /// * src: Input image as 1- or 3-channel, 8-bit or 32-bit floating point. + /// * dst: %Accumulator image with the same number of channels as input image, 32-bit or 64-bit + /// floating-point. + /// * mask: Optional operation mask. + /// ## See also + /// accumulateSquare, accumulateProduct, accumulateWeighted + /// + /// ## C++ default parameters + /// * mask: noArray() + #[inline] + pub fn accumulate_square(src: &impl ToInputArray, dst: &mut impl ToInputOutputArray, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + input_output_array_arg!(dst); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_accumulateSquare_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Updates a running average. + /// + /// The function calculates the weighted sum of the input image src and the accumulator dst so that dst + /// becomes a running average of a frame sequence: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%20%5Cleftarrow%20%281%2D%20%5Ctexttt%7Balpha%7D%20%29%20%20%5Ccdot%20%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%2B%20%20%5Ctexttt%7Balpha%7D%20%5Ccdot%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%20%20%5Cquad%20%5Ctext%7Bif%7D%20%5Cquad%20%5Ctexttt%7Bmask%7D%20%28x%2Cy%29%20%20%5Cne%200) + /// + /// That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images). + /// The function supports multi-channel images. Each channel is processed independently. + /// + /// ## Parameters + /// * src: Input image as 1- or 3-channel, 8-bit or 32-bit floating point. + /// * dst: %Accumulator image with the same number of channels as input image, 32-bit or 64-bit + /// floating-point. + /// * alpha: Weight of the input image. + /// * mask: Optional operation mask. + /// ## See also + /// accumulate, accumulateSquare, accumulateProduct + /// + /// ## Note + /// This alternative version of [accumulate_weighted] function uses the following default values for its arguments: + /// * mask: noArray() + #[inline] + pub fn accumulate_weighted_def(src: &impl ToInputArray, dst: &mut impl ToInputOutputArray, alpha: f64) -> Result<()> { + input_array_arg!(src); + input_output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_accumulateWeighted_const__InputArrayR_const__InputOutputArrayR_double(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), alpha, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Updates a running average. + /// + /// The function calculates the weighted sum of the input image src and the accumulator dst so that dst + /// becomes a running average of a frame sequence: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%20%5Cleftarrow%20%281%2D%20%5Ctexttt%7Balpha%7D%20%29%20%20%5Ccdot%20%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%2B%20%20%5Ctexttt%7Balpha%7D%20%5Ccdot%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%20%20%5Cquad%20%5Ctext%7Bif%7D%20%5Cquad%20%5Ctexttt%7Bmask%7D%20%28x%2Cy%29%20%20%5Cne%200) + /// + /// That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images). + /// The function supports multi-channel images. Each channel is processed independently. + /// + /// ## Parameters + /// * src: Input image as 1- or 3-channel, 8-bit or 32-bit floating point. + /// * dst: %Accumulator image with the same number of channels as input image, 32-bit or 64-bit + /// floating-point. + /// * alpha: Weight of the input image. + /// * mask: Optional operation mask. + /// ## See also + /// accumulate, accumulateSquare, accumulateProduct + /// + /// ## C++ default parameters + /// * mask: noArray() + #[inline] + pub fn accumulate_weighted(src: &impl ToInputArray, dst: &mut impl ToInputOutputArray, alpha: f64, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + input_output_array_arg!(dst); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_accumulateWeighted_const__InputArrayR_const__InputOutputArrayR_double_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), alpha, mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Adds an image to the accumulator image. + /// + /// The function adds src or some of its elements to dst : + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%20%5Cleftarrow%20%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%2B%20%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%20%20%5Cquad%20%5Ctext%7Bif%7D%20%5Cquad%20%5Ctexttt%7Bmask%7D%20%28x%2Cy%29%20%20%5Cne%200) + /// + /// The function supports multi-channel images. Each channel is processed independently. + /// + /// The function cv::accumulate can be used, for example, to collect statistics of a scene background + /// viewed by a still camera and for the further foreground-background segmentation. + /// + /// ## Parameters + /// * src: Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer. + /// * dst: %Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F. + /// * mask: Optional operation mask. + /// ## See also + /// accumulateSquare, accumulateProduct, accumulateWeighted + /// + /// ## Note + /// This alternative version of [accumulate] function uses the following default values for its arguments: + /// * mask: noArray() + #[inline] + pub fn accumulate_def(src: &impl ToInputArray, dst: &mut impl ToInputOutputArray) -> Result<()> { + input_array_arg!(src); + input_output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_accumulate_const__InputArrayR_const__InputOutputArrayR(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Adds an image to the accumulator image. + /// + /// The function adds src or some of its elements to dst : + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%20%5Cleftarrow%20%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%2B%20%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%20%20%5Cquad%20%5Ctext%7Bif%7D%20%5Cquad%20%5Ctexttt%7Bmask%7D%20%28x%2Cy%29%20%20%5Cne%200) + /// + /// The function supports multi-channel images. Each channel is processed independently. + /// + /// The function cv::accumulate can be used, for example, to collect statistics of a scene background + /// viewed by a still camera and for the further foreground-background segmentation. + /// + /// ## Parameters + /// * src: Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer. + /// * dst: %Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F. + /// * mask: Optional operation mask. + /// ## See also + /// accumulateSquare, accumulateProduct, accumulateWeighted + /// + /// ## C++ default parameters + /// * mask: noArray() + #[inline] + pub fn accumulate(src: &impl ToInputArray, dst: &mut impl ToInputOutputArray, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + input_output_array_arg!(dst); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_accumulate_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies an adaptive threshold to an array. + /// + /// The function transforms a grayscale image to a binary image according to the formulae: + /// * **THRESH_BINARY** + /// ![block formula](https://latex.codecogs.com/png.latex?dst%28x%2Cy%29%20%3D%20%20%5Cfork%7B%5Ctexttt%7BmaxValue%7D%7D%7Bif%20%5C%28src%28x%2Cy%29%20%3E%20T%28x%2Cy%29%5C%29%7D%7B0%7D%7Botherwise%7D) + /// * **THRESH_BINARY_INV** + /// ![block formula](https://latex.codecogs.com/png.latex?dst%28x%2Cy%29%20%3D%20%20%5Cfork%7B0%7D%7Bif%20%5C%28src%28x%2Cy%29%20%3E%20T%28x%2Cy%29%5C%29%7D%7B%5Ctexttt%7BmaxValue%7D%7D%7Botherwise%7D) + /// where ![inline formula](https://latex.codecogs.com/png.latex?T%28x%2Cy%29) is a threshold calculated individually for each pixel (see adaptiveMethod parameter). + /// + /// The function can process the image in-place. + /// + /// ## Parameters + /// * src: Source 8-bit single-channel image. + /// * dst: Destination image of the same size and the same type as src. + /// * maxValue: Non-zero value assigned to the pixels for which the condition is satisfied + /// * adaptiveMethod: Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes. + /// The [BORDER_REPLICATE] | [BORDER_ISOLATED] is used to process boundaries. + /// * thresholdType: Thresholding type that must be either [THRESH_BINARY] or #THRESH_BINARY_INV, + /// see #ThresholdTypes. + /// * blockSize: Size of a pixel neighborhood that is used to calculate a threshold value for the + /// pixel: 3, 5, 7, and so on. + /// * C: Constant subtracted from the mean or weighted mean (see the details below). Normally, it + /// is positive but may be zero or negative as well. + /// ## See also + /// threshold, blur, GaussianBlur + #[inline] + pub fn adaptive_threshold(src: &impl ToInputArray, dst: &mut impl ToOutputArray, max_value: f64, adaptive_method: i32, threshold_type: i32, block_size: i32, c: f64) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_adaptiveThreshold_const__InputArrayR_const__OutputArrayR_double_int_int_int_double(src.as_raw__InputArray(), dst.as_raw__OutputArray(), max_value, adaptive_method, threshold_type, block_size, c, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies a user colormap on a given image. + /// + /// ## Parameters + /// * src: The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. If CV_8UC3, then the CV_8UC1 image is generated internally using cv::COLOR_BGR2GRAY. + /// * dst: The result is the colormapped source image of the same number of channels as userColor. Note: Mat::create is called on dst. + /// * userColor: The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256 + #[inline] + pub fn apply_color_map_user(src: &impl ToInputArray, dst: &mut impl ToOutputArray, user_color: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(user_color); + return_send!(via ocvrs_return); + unsafe { sys::cv_applyColorMap_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), user_color.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies a GNU Octave/MATLAB equivalent colormap on a given image. + /// + /// ## Parameters + /// * src: The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. If CV_8UC3, then the CV_8UC1 image is generated internally using cv::COLOR_BGR2GRAY. + /// * dst: The result is the colormapped source image. Note: Mat::create is called on dst. + /// * colormap: The colormap to apply, see #ColormapTypes + #[inline] + pub fn apply_color_map(src: &impl ToInputArray, dst: &mut impl ToOutputArray, colormap: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_applyColorMap_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), colormap, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Approximates a polygonal curve(s) with the specified precision. + /// + /// The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less + /// vertices so that the distance between them is less or equal to the specified precision. It uses the + /// Douglas-Peucker algorithm + /// + /// ## Parameters + /// * curve: Input vector of a 2D point stored in std::vector or Mat + /// * approxCurve: Result of the approximation. The type should match the type of the input curve. + /// * epsilon: Parameter specifying the approximation accuracy. This is the maximum distance + /// between the original curve and its approximation. + /// * closed: If true, the approximated curve is closed (its first and last vertices are + /// connected). Otherwise, it is not closed. + #[inline] + pub fn approx_poly_dp(curve: &impl ToInputArray, approx_curve: &mut impl ToOutputArray, epsilon: f64, closed: bool) -> Result<()> { + input_array_arg!(curve); + output_array_arg!(approx_curve); + return_send!(via ocvrs_return); + unsafe { sys::cv_approxPolyDP_const__InputArrayR_const__OutputArrayR_double_bool(curve.as_raw__InputArray(), approx_curve.as_raw__OutputArray(), epsilon, closed, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Approximates a polygon with a convex hull with a specified accuracy and number of sides. + /// + /// The cv::approxPolyN function approximates a polygon with a convex hull + /// so that the difference between the contour area of the original contour and the new polygon is minimal. + /// It uses a greedy algorithm for contracting two vertices into one in such a way that the additional area is minimal. + /// Straight lines formed by each edge of the convex contour are drawn and the areas of the resulting triangles are considered. + /// Each vertex will lie either on the original contour or outside it. + /// + /// The algorithm based on the paper [LowIlie2003](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_LowIlie2003) . + /// + /// ## Parameters + /// * curve: Input vector of a 2D points stored in std::vector or Mat, points must be float or integer. + /// * approxCurve: Result of the approximation. The type is vector of a 2D point (Point2f or Point) in std::vector or Mat. + /// * nsides: The parameter defines the number of sides of the result polygon. + /// * epsilon_percentage: defines the percentage of the maximum of additional area. + /// If it equals -1, it is not used. Otherwise algorithm stops if additional area is greater than contourArea(_curve) * percentage. + /// If additional area exceeds the limit, algorithm returns as many vertices as there were at the moment the limit was exceeded. + /// * ensure_convex: If it is true, algorithm creates a convex hull of input contour. Otherwise input vector should be convex. + /// + /// ## Note + /// This alternative version of [approx_poly_n] function uses the following default values for its arguments: + /// * epsilon_percentage: -1.0 + /// * ensure_convex: true + #[inline] + pub fn approx_poly_n_def(curve: &impl ToInputArray, approx_curve: &mut impl ToOutputArray, nsides: i32) -> Result<()> { + input_array_arg!(curve); + output_array_arg!(approx_curve); + return_send!(via ocvrs_return); + unsafe { sys::cv_approxPolyN_const__InputArrayR_const__OutputArrayR_int(curve.as_raw__InputArray(), approx_curve.as_raw__OutputArray(), nsides, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Approximates a polygon with a convex hull with a specified accuracy and number of sides. + /// + /// The cv::approxPolyN function approximates a polygon with a convex hull + /// so that the difference between the contour area of the original contour and the new polygon is minimal. + /// It uses a greedy algorithm for contracting two vertices into one in such a way that the additional area is minimal. + /// Straight lines formed by each edge of the convex contour are drawn and the areas of the resulting triangles are considered. + /// Each vertex will lie either on the original contour or outside it. + /// + /// The algorithm based on the paper [LowIlie2003](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_LowIlie2003) . + /// + /// ## Parameters + /// * curve: Input vector of a 2D points stored in std::vector or Mat, points must be float or integer. + /// * approxCurve: Result of the approximation. The type is vector of a 2D point (Point2f or Point) in std::vector or Mat. + /// * nsides: The parameter defines the number of sides of the result polygon. + /// * epsilon_percentage: defines the percentage of the maximum of additional area. + /// If it equals -1, it is not used. Otherwise algorithm stops if additional area is greater than contourArea(_curve) * percentage. + /// If additional area exceeds the limit, algorithm returns as many vertices as there were at the moment the limit was exceeded. + /// * ensure_convex: If it is true, algorithm creates a convex hull of input contour. Otherwise input vector should be convex. + /// + /// ## C++ default parameters + /// * epsilon_percentage: -1.0 + /// * ensure_convex: true + #[inline] + pub fn approx_poly_n(curve: &impl ToInputArray, approx_curve: &mut impl ToOutputArray, nsides: i32, epsilon_percentage: f32, ensure_convex: bool) -> Result<()> { + input_array_arg!(curve); + output_array_arg!(approx_curve); + return_send!(via ocvrs_return); + unsafe { sys::cv_approxPolyN_const__InputArrayR_const__OutputArrayR_int_float_bool(curve.as_raw__InputArray(), approx_curve.as_raw__OutputArray(), nsides, epsilon_percentage, ensure_convex, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates a contour perimeter or a curve length. + /// + /// The function computes a curve length or a closed contour perimeter. + /// + /// ## Parameters + /// * curve: Input vector of 2D points, stored in std::vector or Mat. + /// * closed: Flag indicating whether the curve is closed or not. + #[inline] + pub fn arc_length(curve: &impl ToInputArray, closed: bool) -> Result { + input_array_arg!(curve); + return_send!(via ocvrs_return); + unsafe { sys::cv_arcLength_const__InputArrayR_bool(curve.as_raw__InputArray(), closed, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws an arrow segment pointing from the first point to the second one. + /// + /// The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line. + /// + /// ## Parameters + /// * img: Image. + /// * pt1: The point the arrow starts from. + /// * pt2: The point the arrow points to. + /// * color: Line color. + /// * thickness: Line thickness. + /// * line_type: Type of the line. See [line_types] + /// * shift: Number of fractional bits in the point coordinates. + /// * tipLength: The length of the arrow tip in relation to the arrow length + /// + /// ## Note + /// This alternative version of [arrowed_line] function uses the following default values for its arguments: + /// * thickness: 1 + /// * line_type: 8 + /// * shift: 0 + /// * tip_length: 0.1 + #[inline] + pub fn arrowed_line_def(img: &mut impl ToInputOutputArray, pt1: core::Point, pt2: core::Point, color: core::Scalar) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_arrowedLine_const__InputOutputArrayR_Point_Point_const_ScalarR(img.as_raw__InputOutputArray(), &pt1, &pt2, &color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws an arrow segment pointing from the first point to the second one. + /// + /// The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line. + /// + /// ## Parameters + /// * img: Image. + /// * pt1: The point the arrow starts from. + /// * pt2: The point the arrow points to. + /// * color: Line color. + /// * thickness: Line thickness. + /// * line_type: Type of the line. See [line_types] + /// * shift: Number of fractional bits in the point coordinates. + /// * tipLength: The length of the arrow tip in relation to the arrow length + /// + /// ## C++ default parameters + /// * thickness: 1 + /// * line_type: 8 + /// * shift: 0 + /// * tip_length: 0.1 + #[inline] + pub fn arrowed_line(img: &mut impl ToInputOutputArray, pt1: core::Point, pt2: core::Point, color: core::Scalar, thickness: i32, line_type: i32, shift: i32, tip_length: f64) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_arrowedLine_const__InputOutputArrayR_Point_Point_const_ScalarR_int_int_int_double(img.as_raw__InputOutputArray(), &pt1, &pt2, &color, thickness, line_type, shift, tip_length, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies the bilateral filter to an image. + /// + /// The function applies bilateral filtering to the input image, as described in + /// + /// bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is + /// very slow compared to most filters. + /// + /// _Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\< + /// 10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very + /// strong effect, making the image look "cartoonish". + /// + /// _Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time + /// applications, and perhaps d=9 for offline applications that need heavy noise filtering. + /// + /// This filter does not work inplace. + /// ## Parameters + /// * src: Source 8-bit or floating-point, 1-channel or 3-channel image. + /// * dst: Destination image of the same size and type as src . + /// * d: Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, + /// it is computed from sigmaSpace. + /// * sigmaColor: Filter sigma in the color space. A larger value of the parameter means that + /// farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting + /// in larger areas of semi-equal color. + /// * sigmaSpace: Filter sigma in the coordinate space. A larger value of the parameter means that + /// farther pixels will influence each other as long as their colors are close enough (see sigmaColor + /// ). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is + /// proportional to sigmaSpace. + /// * borderType: border mode used to extrapolate pixels outside of the image, see [border_types] + /// + /// ## Note + /// This alternative version of [bilateral_filter] function uses the following default values for its arguments: + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn bilateral_filter_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, d: i32, sigma_color: f64, sigma_space: f64) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_bilateralFilter_const__InputArrayR_const__OutputArrayR_int_double_double(src.as_raw__InputArray(), dst.as_raw__OutputArray(), d, sigma_color, sigma_space, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies the bilateral filter to an image. + /// + /// The function applies bilateral filtering to the input image, as described in + /// + /// bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is + /// very slow compared to most filters. + /// + /// _Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\< + /// 10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very + /// strong effect, making the image look "cartoonish". + /// + /// _Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time + /// applications, and perhaps d=9 for offline applications that need heavy noise filtering. + /// + /// This filter does not work inplace. + /// ## Parameters + /// * src: Source 8-bit or floating-point, 1-channel or 3-channel image. + /// * dst: Destination image of the same size and type as src . + /// * d: Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, + /// it is computed from sigmaSpace. + /// * sigmaColor: Filter sigma in the color space. A larger value of the parameter means that + /// farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting + /// in larger areas of semi-equal color. + /// * sigmaSpace: Filter sigma in the coordinate space. A larger value of the parameter means that + /// farther pixels will influence each other as long as their colors are close enough (see sigmaColor + /// ). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is + /// proportional to sigmaSpace. + /// * borderType: border mode used to extrapolate pixels outside of the image, see #BorderTypes + /// + /// ## C++ default parameters + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn bilateral_filter(src: &impl ToInputArray, dst: &mut impl ToOutputArray, d: i32, sigma_color: f64, sigma_space: f64, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_bilateralFilter_const__InputArrayR_const__OutputArrayR_int_double_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), d, sigma_color, sigma_space, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs linear blending of two images: + /// ![block formula](https://latex.codecogs.com/png.latex?%20%5Ctexttt%7Bdst%7D%28i%2Cj%29%20%3D%20%5Ctexttt%7Bweights1%7D%28i%2Cj%29%2A%5Ctexttt%7Bsrc1%7D%28i%2Cj%29%20%2B%20%5Ctexttt%7Bweights2%7D%28i%2Cj%29%2A%5Ctexttt%7Bsrc2%7D%28i%2Cj%29%20) + /// ## Parameters + /// * src1: It has a type of CV_8UC(n) or CV_32FC(n), where n is a positive integer. + /// * src2: It has the same type and size as src1. + /// * weights1: It has a type of CV_32FC1 and the same size with src1. + /// * weights2: It has a type of CV_32FC1 and the same size with src1. + /// * dst: It is created if it does not have the same size and type with src1. + #[inline] + pub fn blend_linear(src1: &impl ToInputArray, src2: &impl ToInputArray, weights1: &impl ToInputArray, weights2: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + input_array_arg!(weights1); + input_array_arg!(weights2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_blendLinear_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), weights1.as_raw__InputArray(), weights2.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Blurs an image using the normalized box filter. + /// + /// The function smooths an image using the kernel: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BK%7D%20%3D%20%20%5Cfrac%7B1%7D%7B%5Ctexttt%7Bksize%2Ewidth%2Aksize%2Eheight%7D%7D%20%5Cbegin%7Bbmatrix%7D%201%20%26%201%20%26%201%20%26%20%20%5Ccdots%20%26%201%20%26%201%20%20%5C%5C%201%20%26%201%20%26%201%20%26%20%20%5Ccdots%20%26%201%20%26%201%20%20%5C%5C%20%5Cdots%20%5C%5C%201%20%26%201%20%26%201%20%26%20%20%5Ccdots%20%26%201%20%26%201%20%20%5C%5C%20%5Cend%7Bbmatrix%7D) + /// + /// The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), ksize, + /// anchor, true, borderType)`. + /// + /// ## Parameters + /// * src: input image; it can have any number of channels, which are processed independently, but + /// the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. + /// * dst: output image of the same size and type as src. + /// * ksize: blurring kernel size. + /// * anchor: anchor point; default value Point(-1,-1) means that the anchor is at the kernel + /// center. + /// * borderType: border mode used to extrapolate pixels outside of the image, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// boxFilter, bilateralFilter, GaussianBlur, medianBlur + /// + /// ## Note + /// This alternative version of [blur] function uses the following default values for its arguments: + /// * anchor: Point(-1,-1) + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn blur_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ksize: core::Size) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_blur_const__InputArrayR_const__OutputArrayR_Size(src.as_raw__InputArray(), dst.as_raw__OutputArray(), &ksize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Blurs an image using the normalized box filter. + /// + /// The function smooths an image using the kernel: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BK%7D%20%3D%20%20%5Cfrac%7B1%7D%7B%5Ctexttt%7Bksize%2Ewidth%2Aksize%2Eheight%7D%7D%20%5Cbegin%7Bbmatrix%7D%201%20%26%201%20%26%201%20%26%20%20%5Ccdots%20%26%201%20%26%201%20%20%5C%5C%201%20%26%201%20%26%201%20%26%20%20%5Ccdots%20%26%201%20%26%201%20%20%5C%5C%20%5Cdots%20%5C%5C%201%20%26%201%20%26%201%20%26%20%20%5Ccdots%20%26%201%20%26%201%20%20%5C%5C%20%5Cend%7Bbmatrix%7D) + /// + /// The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), ksize, + /// anchor, true, borderType)`. + /// + /// ## Parameters + /// * src: input image; it can have any number of channels, which are processed independently, but + /// the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. + /// * dst: output image of the same size and type as src. + /// * ksize: blurring kernel size. + /// * anchor: anchor point; default value Point(-1,-1) means that the anchor is at the kernel + /// center. + /// * borderType: border mode used to extrapolate pixels outside of the image, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// boxFilter, bilateralFilter, GaussianBlur, medianBlur + /// + /// ## C++ default parameters + /// * anchor: Point(-1,-1) + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn blur(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ksize: core::Size, anchor: core::Point, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_blur_const__InputArrayR_const__OutputArrayR_Size_Point_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), &ksize, &anchor, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image. + /// + /// The function calculates and returns the minimal up-right bounding rectangle for the specified point set or + /// non-zero pixels of gray-scale image. + /// + /// ## Parameters + /// * array: Input gray-scale image or 2D point set, stored in std::vector or Mat. + #[inline] + pub fn bounding_rect(array: &impl ToInputArray) -> Result { + input_array_arg!(array); + return_send!(via ocvrs_return); + unsafe { sys::cv_boundingRect_const__InputArrayR(array.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Blurs an image using the box filter. + /// + /// The function smooths an image using the kernel: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BK%7D%20%3D%20%20%5Calpha%20%5Cbegin%7Bbmatrix%7D%201%20%26%201%20%26%201%20%26%20%20%5Ccdots%20%26%201%20%26%201%20%20%5C%5C%201%20%26%201%20%26%201%20%26%20%20%5Ccdots%20%26%201%20%26%201%20%20%5C%5C%20%5Cdots%20%5C%5C%201%20%26%201%20%26%201%20%26%20%20%5Ccdots%20%26%201%20%26%201%20%5Cend%7Bbmatrix%7D) + /// + /// where + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Calpha%20%3D%20%5Cbegin%7Bcases%7D%20%5Cfrac%7B1%7D%7B%5Ctexttt%7Bksize%2Ewidth%2Aksize%2Eheight%7D%7D%20%26%20%5Ctexttt%7Bwhen%20%7D%20%5Ctexttt%7Bnormalize%3Dtrue%7D%20%20%5C%5C1%20%26%20%5Ctexttt%7Botherwise%7D%5Cend%7Bcases%7D) + /// + /// Unnormalized box filter is useful for computing various integral characteristics over each pixel + /// neighborhood, such as covariance matrices of image derivatives (used in dense optical flow + /// algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral. + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image of the same size and type as src. + /// * ddepth: the output image depth (-1 to use src.depth()). + /// * ksize: blurring kernel size. + /// * anchor: anchor point; default value Point(-1,-1) means that the anchor is at the kernel + /// center. + /// * normalize: flag, specifying whether the kernel is normalized by its area or not. + /// * borderType: border mode used to extrapolate pixels outside of the image, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// blur, bilateralFilter, GaussianBlur, medianBlur, integral + /// + /// ## Note + /// This alternative version of [box_filter] function uses the following default values for its arguments: + /// * anchor: Point(-1,-1) + /// * normalize: true + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn box_filter_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32, ksize: core::Size) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_boxFilter_const__InputArrayR_const__OutputArrayR_int_Size(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, &ksize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Blurs an image using the box filter. + /// + /// The function smooths an image using the kernel: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BK%7D%20%3D%20%20%5Calpha%20%5Cbegin%7Bbmatrix%7D%201%20%26%201%20%26%201%20%26%20%20%5Ccdots%20%26%201%20%26%201%20%20%5C%5C%201%20%26%201%20%26%201%20%26%20%20%5Ccdots%20%26%201%20%26%201%20%20%5C%5C%20%5Cdots%20%5C%5C%201%20%26%201%20%26%201%20%26%20%20%5Ccdots%20%26%201%20%26%201%20%5Cend%7Bbmatrix%7D) + /// + /// where + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Calpha%20%3D%20%5Cbegin%7Bcases%7D%20%5Cfrac%7B1%7D%7B%5Ctexttt%7Bksize%2Ewidth%2Aksize%2Eheight%7D%7D%20%26%20%5Ctexttt%7Bwhen%20%7D%20%5Ctexttt%7Bnormalize%3Dtrue%7D%20%20%5C%5C1%20%26%20%5Ctexttt%7Botherwise%7D%5Cend%7Bcases%7D) + /// + /// Unnormalized box filter is useful for computing various integral characteristics over each pixel + /// neighborhood, such as covariance matrices of image derivatives (used in dense optical flow + /// algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral. + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image of the same size and type as src. + /// * ddepth: the output image depth (-1 to use src.depth()). + /// * ksize: blurring kernel size. + /// * anchor: anchor point; default value Point(-1,-1) means that the anchor is at the kernel + /// center. + /// * normalize: flag, specifying whether the kernel is normalized by its area or not. + /// * borderType: border mode used to extrapolate pixels outside of the image, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// blur, bilateralFilter, GaussianBlur, medianBlur, integral + /// + /// ## C++ default parameters + /// * anchor: Point(-1,-1) + /// * normalize: true + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn box_filter(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32, ksize: core::Size, anchor: core::Point, normalize: bool, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_boxFilter_const__InputArrayR_const__OutputArrayR_int_Size_Point_bool_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, &ksize, &anchor, normalize, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle. + /// + /// The function finds the four vertices of a rotated rectangle. The four vertices are returned + /// in clockwise order starting from the point with greatest ![inline formula](https://latex.codecogs.com/png.latex?y). If two points have the + /// same ![inline formula](https://latex.codecogs.com/png.latex?y) coordinate the rightmost is the starting point. This function is useful to draw the + /// rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please + /// visit the [tutorial_bounding_rotated_ellipses] "tutorial on Creating Bounding rotated boxes and ellipses + /// for contours" for more information. + /// + /// ## Parameters + /// * box: The input rotated rectangle. It may be the output of [minAreaRect]. + /// * points: The output array of four vertices of rectangles. + #[inline] + pub fn box_points(box_: core::RotatedRect, points: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(points); + return_send!(via ocvrs_return); + unsafe { sys::cv_boxPoints_RotatedRect_const__OutputArrayR(&box_, points.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Constructs the Gaussian pyramid for an image. + /// + /// The function constructs a vector of images and builds the Gaussian pyramid by recursively applying + /// pyrDown to the previously built pyramid layers, starting from `dst[0]==src`. + /// + /// ## Parameters + /// * src: Source image. Check pyrDown for the list of supported types. + /// * dst: Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the + /// same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on. + /// * maxlevel: 0-based index of the last (the smallest) pyramid layer. It must be non-negative. + /// * borderType: Pixel extrapolation method, see [border_types] ([BORDER_CONSTANT] isn't supported) + /// + /// ## Note + /// This alternative version of [build_pyramid] function uses the following default values for its arguments: + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn build_pyramid_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, maxlevel: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_buildPyramid_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), maxlevel, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Constructs the Gaussian pyramid for an image. + /// + /// The function constructs a vector of images and builds the Gaussian pyramid by recursively applying + /// pyrDown to the previously built pyramid layers, starting from `dst[0]==src`. + /// + /// ## Parameters + /// * src: Source image. Check pyrDown for the list of supported types. + /// * dst: Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the + /// same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on. + /// * maxlevel: 0-based index of the last (the smallest) pyramid layer. It must be non-negative. + /// * borderType: Pixel extrapolation method, see [border_types] ([BORDER_CONSTANT] isn't supported) + /// + /// ## C++ default parameters + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn build_pyramid(src: &impl ToInputArray, dst: &mut impl ToOutputArray, maxlevel: i32, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_buildPyramid_const__InputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), maxlevel, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the back projection of a histogram. + /// + /// The function cv::calcBackProject calculates the back project of the histogram. That is, similarly to + /// [calc_hist] , at each location (x, y) the function collects the values from the selected channels + /// in the input images and finds the corresponding histogram bin. But instead of incrementing it, the + /// function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of + /// statistics, the function computes probability of each element value in respect with the empirical + /// probability distribution represented by the histogram. See how, for example, you can find and track + /// a bright-colored object in a scene: + /// + /// - Before tracking, show the object to the camera so that it covers almost the whole frame. + /// Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant + /// colors in the object. + /// + /// - When tracking, calculate a back projection of a hue plane of each input video frame using that + /// pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make + /// sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels. + /// + /// - Find connected components in the resulting picture and choose, for example, the largest + /// component. + /// + /// This is an approximate algorithm of the CamShift color object tracker. + /// + /// ## Parameters + /// * images: Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same + /// size. Each of them can have an arbitrary number of channels. + /// * nimages: Number of source images. + /// * channels: The list of channels used to compute the back projection. The number of channels + /// must match the histogram dimensionality. The first array channels are numerated from 0 to + /// images[0].channels()-1 , the second array channels are counted from images[0].channels() to + /// images[0].channels() + images[1].channels()-1, and so on. + /// * hist: Input histogram that can be dense or sparse. + /// * backProject: Destination back projection array that is a single-channel array of the same + /// size and depth as images[0] . + /// * ranges: Array of arrays of the histogram bin boundaries in each dimension. See [calc_hist] . + /// * scale: Optional scale factor for the output back projection. + /// * uniform: Flag indicating whether the histogram is uniform or not (see #calcHist). + /// ## See also + /// calcHist, compareHist + /// + /// ## Overloaded parameters + #[inline] + pub fn calc_back_project(images: &impl ToInputArray, channels: &core::Vector, hist: &impl ToInputArray, dst: &mut impl ToOutputArray, ranges: &core::Vector, scale: f64) -> Result<()> { + input_array_arg!(images); + input_array_arg!(hist); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_calcBackProject_const__InputArrayR_const_vectorLintGR_const__InputArrayR_const__OutputArrayR_const_vectorLfloatGR_double(images.as_raw__InputArray(), channels.as_raw_VectorOfi32(), hist.as_raw__InputArray(), dst.as_raw__OutputArray(), ranges.as_raw_VectorOff32(), scale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates a histogram of a set of arrays. + /// + /// The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used + /// to increment a histogram bin are taken from the corresponding input arrays at the same location. The + /// sample below shows how to compute a 2D Hue-Saturation histogram for a color image. : + /// @include snippets/imgproc_calcHist.cpp + /// + /// ## Parameters + /// * images: Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same + /// size. Each of them can have an arbitrary number of channels. + /// * nimages: Number of source images. + /// * channels: List of the dims channels used to compute the histogram. The first array channels + /// are numerated from 0 to images[0].channels()-1 , the second array channels are counted from + /// images[0].channels() to images[0].channels() + images[1].channels()-1, and so on. + /// * mask: Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size + /// as images[i] . The non-zero mask elements mark the array elements counted in the histogram. + /// * hist: Output histogram, which is a dense or sparse dims -dimensional array. + /// * dims: Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS + /// (equal to 32 in the current OpenCV version). + /// * histSize: Array of histogram sizes in each dimension. + /// * ranges: Array of the dims arrays of the histogram bin boundaries in each dimension. When the + /// histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower + /// (inclusive) boundary ![inline formula](https://latex.codecogs.com/png.latex?L%5F0) of the 0-th histogram bin and the upper (exclusive) boundary + /// ![inline formula](https://latex.codecogs.com/png.latex?U%5F%7B%5Ctexttt%7BhistSize%7D%5Bi%5D%2D1%7D) for the last histogram bin histSize[i]-1 . That is, in case of a + /// uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform ( + /// uniform=false ), then each of ranges[i] contains histSize[i]+1 elements: + /// ![inline formula](https://latex.codecogs.com/png.latex?L%5F0%2C%20U%5F0%3DL%5F1%2C%20U%5F1%3DL%5F2%2C%20%2E%2E%2E%2C%20U%5F%7B%5Ctexttt%7BhistSize%5Bi%5D%7D%2D2%7D%3DL%5F%7B%5Ctexttt%7BhistSize%5Bi%5D%7D%2D1%7D%2C%20U%5F%7B%5Ctexttt%7BhistSize%5Bi%5D%7D%2D1%7D) + /// . The array elements, that are not between ![inline formula](https://latex.codecogs.com/png.latex?L%5F0) and ![inline formula](https://latex.codecogs.com/png.latex?U%5F%7B%5Ctexttt%7BhistSize%5Bi%5D%7D%2D1%7D) , are not + /// counted in the histogram. + /// * uniform: Flag indicating whether the histogram is uniform or not (see above). + /// * accumulate: Accumulation flag. If it is set, the histogram is not cleared in the beginning + /// when it is allocated. This feature enables you to compute a single histogram from several sets of + /// arrays, or to update the histogram in time. + /// + /// ## Overloaded parameters + /// + /// + /// this variant supports only uniform histograms. + /// + /// ranges argument is either empty vector or a flattened vector of histSize.size()*2 elements + /// (histSize.size() element pairs). The first and second elements of each pair specify the lower and + /// upper boundaries. + /// + /// ## Note + /// This alternative version of [calc_hist] function uses the following default values for its arguments: + /// * accumulate: false + #[inline] + pub fn calc_hist_def(images: &impl ToInputArray, channels: &core::Vector, mask: &impl ToInputArray, hist: &mut impl ToOutputArray, hist_size: &core::Vector, ranges: &core::Vector) -> Result<()> { + input_array_arg!(images); + input_array_arg!(mask); + output_array_arg!(hist); + return_send!(via ocvrs_return); + unsafe { sys::cv_calcHist_const__InputArrayR_const_vectorLintGR_const__InputArrayR_const__OutputArrayR_const_vectorLintGR_const_vectorLfloatGR(images.as_raw__InputArray(), channels.as_raw_VectorOfi32(), mask.as_raw__InputArray(), hist.as_raw__OutputArray(), hist_size.as_raw_VectorOfi32(), ranges.as_raw_VectorOff32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates a histogram of a set of arrays. + /// + /// The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used + /// to increment a histogram bin are taken from the corresponding input arrays at the same location. The + /// sample below shows how to compute a 2D Hue-Saturation histogram for a color image. : + /// @include snippets/imgproc_calcHist.cpp + /// + /// ## Parameters + /// * images: Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same + /// size. Each of them can have an arbitrary number of channels. + /// * nimages: Number of source images. + /// * channels: List of the dims channels used to compute the histogram. The first array channels + /// are numerated from 0 to images[0].channels()-1 , the second array channels are counted from + /// images[0].channels() to images[0].channels() + images[1].channels()-1, and so on. + /// * mask: Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size + /// as images[i] . The non-zero mask elements mark the array elements counted in the histogram. + /// * hist: Output histogram, which is a dense or sparse dims -dimensional array. + /// * dims: Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS + /// (equal to 32 in the current OpenCV version). + /// * histSize: Array of histogram sizes in each dimension. + /// * ranges: Array of the dims arrays of the histogram bin boundaries in each dimension. When the + /// histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower + /// (inclusive) boundary ![inline formula](https://latex.codecogs.com/png.latex?L%5F0) of the 0-th histogram bin and the upper (exclusive) boundary + /// ![inline formula](https://latex.codecogs.com/png.latex?U%5F%7B%5Ctexttt%7BhistSize%7D%5Bi%5D%2D1%7D) for the last histogram bin histSize[i]-1 . That is, in case of a + /// uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform ( + /// uniform=false ), then each of ranges[i] contains histSize[i]+1 elements: + /// ![inline formula](https://latex.codecogs.com/png.latex?L%5F0%2C%20U%5F0%3DL%5F1%2C%20U%5F1%3DL%5F2%2C%20%2E%2E%2E%2C%20U%5F%7B%5Ctexttt%7BhistSize%5Bi%5D%7D%2D2%7D%3DL%5F%7B%5Ctexttt%7BhistSize%5Bi%5D%7D%2D1%7D%2C%20U%5F%7B%5Ctexttt%7BhistSize%5Bi%5D%7D%2D1%7D) + /// . The array elements, that are not between ![inline formula](https://latex.codecogs.com/png.latex?L%5F0) and ![inline formula](https://latex.codecogs.com/png.latex?U%5F%7B%5Ctexttt%7BhistSize%5Bi%5D%7D%2D1%7D) , are not + /// counted in the histogram. + /// * uniform: Flag indicating whether the histogram is uniform or not (see above). + /// * accumulate: Accumulation flag. If it is set, the histogram is not cleared in the beginning + /// when it is allocated. This feature enables you to compute a single histogram from several sets of + /// arrays, or to update the histogram in time. + /// + /// ## Overloaded parameters + /// + /// + /// this variant supports only uniform histograms. + /// + /// ranges argument is either empty vector or a flattened vector of histSize.size()*2 elements + /// (histSize.size() element pairs). The first and second elements of each pair specify the lower and + /// upper boundaries. + /// + /// ## C++ default parameters + /// * accumulate: false + #[inline] + pub fn calc_hist(images: &impl ToInputArray, channels: &core::Vector, mask: &impl ToInputArray, hist: &mut impl ToOutputArray, hist_size: &core::Vector, ranges: &core::Vector, accumulate: bool) -> Result<()> { + input_array_arg!(images); + input_array_arg!(mask); + output_array_arg!(hist); + return_send!(via ocvrs_return); + unsafe { sys::cv_calcHist_const__InputArrayR_const_vectorLintGR_const__InputArrayR_const__OutputArrayR_const_vectorLintGR_const_vectorLfloatGR_bool(images.as_raw__InputArray(), channels.as_raw_VectorOfi32(), mask.as_raw__InputArray(), hist.as_raw__OutputArray(), hist_size.as_raw_VectorOfi32(), ranges.as_raw_VectorOff32(), accumulate, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a circle. + /// + /// The function cv::circle draws a simple or filled circle with a given center and radius. + /// ## Parameters + /// * img: Image where the circle is drawn. + /// * center: Center of the circle. + /// * radius: Radius of the circle. + /// * color: Circle color. + /// * thickness: Thickness of the circle outline, if positive. Negative values, like #FILLED, + /// mean that a filled circle is to be drawn. + /// * lineType: Type of the circle boundary. See [line_types] + /// * shift: Number of fractional bits in the coordinates of the center and in the radius value. + /// + /// ## Note + /// This alternative version of [circle] function uses the following default values for its arguments: + /// * thickness: 1 + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn circle_def(img: &mut impl ToInputOutputArray, center: core::Point, radius: i32, color: core::Scalar) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_circle_const__InputOutputArrayR_Point_int_const_ScalarR(img.as_raw__InputOutputArray(), ¢er, radius, &color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a circle. + /// + /// The function cv::circle draws a simple or filled circle with a given center and radius. + /// ## Parameters + /// * img: Image where the circle is drawn. + /// * center: Center of the circle. + /// * radius: Radius of the circle. + /// * color: Circle color. + /// * thickness: Thickness of the circle outline, if positive. Negative values, like #FILLED, + /// mean that a filled circle is to be drawn. + /// * lineType: Type of the circle boundary. See [line_types] + /// * shift: Number of fractional bits in the coordinates of the center and in the radius value. + /// + /// ## C++ default parameters + /// * thickness: 1 + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn circle(img: &mut impl ToInputOutputArray, center: core::Point, radius: i32, color: core::Scalar, thickness: i32, line_type: i32, shift: i32) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_circle_const__InputOutputArrayR_Point_int_const_ScalarR_int_int_int(img.as_raw__InputOutputArray(), ¢er, radius, &color, thickness, line_type, shift, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Clips the line against the image rectangle. + /// + /// The function cv::clipLine calculates a part of the line segment that is entirely within the specified + /// rectangle. It returns false if the line segment is completely outside the rectangle. Otherwise, + /// it returns true . + /// ## Parameters + /// * imgSize: Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . + /// * pt1: First line point. + /// * pt2: Second line point. + /// + /// ## Overloaded parameters + /// + /// * imgRect: Image rectangle. + /// * pt1: First line point. + /// * pt2: Second line point. + #[inline] + pub fn clip_line(img_rect: core::Rect, pt1: &mut core::Point, pt2: &mut core::Point) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_clipLine_Rect_PointR_PointR(&img_rect, pt1, pt2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Clips the line against the image rectangle. + /// + /// The function cv::clipLine calculates a part of the line segment that is entirely within the specified + /// rectangle. It returns false if the line segment is completely outside the rectangle. Otherwise, + /// it returns true . + /// ## Parameters + /// * imgSize: Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . + /// * pt1: First line point. + /// * pt2: Second line point. + /// + /// ## Overloaded parameters + /// + /// * imgSize: Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . + /// * pt1: First line point. + /// * pt2: Second line point. + #[inline] + pub fn clip_line_size_i64(img_size: core::Size2l, pt1: &mut core::Point2l, pt2: &mut core::Point2l) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_clipLine_Size2l_Point2lR_Point2lR(&img_size, pt1, pt2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Clips the line against the image rectangle. + /// + /// The function cv::clipLine calculates a part of the line segment that is entirely within the specified + /// rectangle. It returns false if the line segment is completely outside the rectangle. Otherwise, + /// it returns true . + /// ## Parameters + /// * imgSize: Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . + /// * pt1: First line point. + /// * pt2: Second line point. + #[inline] + pub fn clip_line_size(img_size: core::Size, pt1: &mut core::Point, pt2: &mut core::Point) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_clipLine_Size_PointR_PointR(&img_size, pt1, pt2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Compares two histograms. + /// + /// The function cv::compareHist compares two dense or two sparse histograms using the specified method. + /// + /// The function returns ![inline formula](https://latex.codecogs.com/png.latex?d%28H%5F1%2C%20H%5F2%29) . + /// + /// While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable + /// for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling + /// problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms + /// or more general sparse configurations of weighted points, consider using the [EMD] function. + /// + /// ## Parameters + /// * H1: First compared histogram. + /// * H2: Second compared histogram of the same size as H1 . + /// * method: Comparison method, see [hist_comp_methods] + /// + /// ## Overloaded parameters + #[inline] + pub fn compare_hist_1(h1: &impl core::SparseMatTraitConst, h2: &impl core::SparseMatTraitConst, method: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_compareHist_const_SparseMatR_const_SparseMatR_int(h1.as_raw_SparseMat(), h2.as_raw_SparseMat(), method, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Compares two histograms. + /// + /// The function cv::compareHist compares two dense or two sparse histograms using the specified method. + /// + /// The function returns ![inline formula](https://latex.codecogs.com/png.latex?d%28H%5F1%2C%20H%5F2%29) . + /// + /// While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable + /// for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling + /// problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms + /// or more general sparse configurations of weighted points, consider using the [EMD] function. + /// + /// ## Parameters + /// * H1: First compared histogram. + /// * H2: Second compared histogram of the same size as H1 . + /// * method: Comparison method, see #HistCompMethods + #[inline] + pub fn compare_hist(h1: &impl ToInputArray, h2: &impl ToInputArray, method: i32) -> Result { + input_array_arg!(h1); + input_array_arg!(h2); + return_send!(via ocvrs_return); + unsafe { sys::cv_compareHist_const__InputArrayR_const__InputArrayR_int(h1.as_raw__InputArray(), h2.as_raw__InputArray(), method, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// computes the connected components labeled image of boolean image and also produces a statistics output for each label + /// + /// image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 + /// represents the background label. ltype specifies the output label image type, an important + /// consideration based on the total number of labels or alternatively the total number of pixels in + /// the source image. ccltype specifies the connected components labeling algorithm to use, currently + /// Bolelli (Spaghetti) [Bolelli2019](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Bolelli2019), Grana (BBDT) [Grana2010](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Grana2010) and Wu's (SAUF) [Wu2009](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Wu2009) algorithms + /// are supported, see the [connected_components_algorithms_types] for details. Note that SAUF algorithm forces + /// a row major ordering of labels while Spaghetti and BBDT do not. + /// This function uses parallel version of the algorithms (statistics included) if at least one allowed + /// parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. + /// + /// ## Parameters + /// * image: the 8-bit single-channel image to be labeled + /// * labels: destination labeled image + /// * stats: statistics output for each label, including the background label. + /// Statistics are accessed via stats(label, COLUMN) where COLUMN is one of + /// #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. + /// * centroids: centroid output for each label, including the background label. Centroids are + /// accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. + /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively + /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. + /// * ccltype: connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes). + /// + /// ## Overloaded parameters + /// + /// * image: the 8-bit single-channel image to be labeled + /// * labels: destination labeled image + /// * stats: statistics output for each label, including the background label. + /// Statistics are accessed via stats(label, COLUMN) where COLUMN is one of + /// #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. + /// * centroids: centroid output for each label, including the background label. Centroids are + /// accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. + /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively + /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. + /// + /// ## Note + /// This alternative version of [connected_components_with_stats] function uses the following default values for its arguments: + /// * connectivity: 8 + /// * ltype: CV_32S + #[inline] + pub fn connected_components_with_stats_def(image: &impl ToInputArray, labels: &mut impl ToOutputArray, stats: &mut impl ToOutputArray, centroids: &mut impl ToOutputArray) -> Result { + input_array_arg!(image); + output_array_arg!(labels); + output_array_arg!(stats); + output_array_arg!(centroids); + return_send!(via ocvrs_return); + unsafe { sys::cv_connectedComponentsWithStats_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(image.as_raw__InputArray(), labels.as_raw__OutputArray(), stats.as_raw__OutputArray(), centroids.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// computes the connected components labeled image of boolean image and also produces a statistics output for each label + /// + /// image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 + /// represents the background label. ltype specifies the output label image type, an important + /// consideration based on the total number of labels or alternatively the total number of pixels in + /// the source image. ccltype specifies the connected components labeling algorithm to use, currently + /// Bolelli (Spaghetti) [Bolelli2019](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Bolelli2019), Grana (BBDT) [Grana2010](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Grana2010) and Wu's (SAUF) [Wu2009](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Wu2009) algorithms + /// are supported, see the [connected_components_algorithms_types] for details. Note that SAUF algorithm forces + /// a row major ordering of labels while Spaghetti and BBDT do not. + /// This function uses parallel version of the algorithms (statistics included) if at least one allowed + /// parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. + /// + /// ## Parameters + /// * image: the 8-bit single-channel image to be labeled + /// * labels: destination labeled image + /// * stats: statistics output for each label, including the background label. + /// Statistics are accessed via stats(label, COLUMN) where COLUMN is one of + /// #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. + /// * centroids: centroid output for each label, including the background label. Centroids are + /// accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. + /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively + /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. + /// * ccltype: connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes). + /// + /// ## Overloaded parameters + /// + /// * image: the 8-bit single-channel image to be labeled + /// * labels: destination labeled image + /// * stats: statistics output for each label, including the background label. + /// Statistics are accessed via stats(label, COLUMN) where COLUMN is one of + /// #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. + /// * centroids: centroid output for each label, including the background label. Centroids are + /// accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. + /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively + /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. + /// + /// ## C++ default parameters + /// * connectivity: 8 + /// * ltype: CV_32S + #[inline] + pub fn connected_components_with_stats(image: &impl ToInputArray, labels: &mut impl ToOutputArray, stats: &mut impl ToOutputArray, centroids: &mut impl ToOutputArray, connectivity: i32, ltype: i32) -> Result { + input_array_arg!(image); + output_array_arg!(labels); + output_array_arg!(stats); + output_array_arg!(centroids); + return_send!(via ocvrs_return); + unsafe { sys::cv_connectedComponentsWithStats_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(image.as_raw__InputArray(), labels.as_raw__OutputArray(), stats.as_raw__OutputArray(), centroids.as_raw__OutputArray(), connectivity, ltype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// computes the connected components labeled image of boolean image and also produces a statistics output for each label + /// + /// image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 + /// represents the background label. ltype specifies the output label image type, an important + /// consideration based on the total number of labels or alternatively the total number of pixels in + /// the source image. ccltype specifies the connected components labeling algorithm to use, currently + /// Bolelli (Spaghetti) [Bolelli2019](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Bolelli2019), Grana (BBDT) [Grana2010](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Grana2010) and Wu's (SAUF) [Wu2009](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Wu2009) algorithms + /// are supported, see the [connected_components_algorithms_types] for details. Note that SAUF algorithm forces + /// a row major ordering of labels while Spaghetti and BBDT do not. + /// This function uses parallel version of the algorithms (statistics included) if at least one allowed + /// parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. + /// + /// ## Parameters + /// * image: the 8-bit single-channel image to be labeled + /// * labels: destination labeled image + /// * stats: statistics output for each label, including the background label. + /// Statistics are accessed via stats(label, COLUMN) where COLUMN is one of + /// #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. + /// * centroids: centroid output for each label, including the background label. Centroids are + /// accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. + /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively + /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. + /// * ccltype: connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes). + #[inline] + pub fn connected_components_with_stats_with_algorithm(image: &impl ToInputArray, labels: &mut impl ToOutputArray, stats: &mut impl ToOutputArray, centroids: &mut impl ToOutputArray, connectivity: i32, ltype: i32, ccltype: i32) -> Result { + input_array_arg!(image); + output_array_arg!(labels); + output_array_arg!(stats); + output_array_arg!(centroids); + return_send!(via ocvrs_return); + unsafe { sys::cv_connectedComponentsWithStats_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_int_int(image.as_raw__InputArray(), labels.as_raw__OutputArray(), stats.as_raw__OutputArray(), centroids.as_raw__OutputArray(), connectivity, ltype, ccltype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// computes the connected components labeled image of boolean image + /// + /// image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 + /// represents the background label. ltype specifies the output label image type, an important + /// consideration based on the total number of labels or alternatively the total number of pixels in + /// the source image. ccltype specifies the connected components labeling algorithm to use, currently + /// Bolelli (Spaghetti) [Bolelli2019](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Bolelli2019), Grana (BBDT) [Grana2010](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Grana2010) and Wu's (SAUF) [Wu2009](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Wu2009) algorithms + /// are supported, see the [connected_components_algorithms_types] for details. Note that SAUF algorithm forces + /// a row major ordering of labels while Spaghetti and BBDT do not. + /// This function uses parallel version of the algorithms if at least one allowed + /// parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. + /// + /// ## Parameters + /// * image: the 8-bit single-channel image to be labeled + /// * labels: destination labeled image + /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively + /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. + /// * ccltype: connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes). + /// + /// ## Overloaded parameters + /// + /// + /// * image: the 8-bit single-channel image to be labeled + /// * labels: destination labeled image + /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively + /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. + /// + /// ## Note + /// This alternative version of [connected_components] function uses the following default values for its arguments: + /// * connectivity: 8 + /// * ltype: CV_32S + #[inline] + pub fn connected_components_def(image: &impl ToInputArray, labels: &mut impl ToOutputArray) -> Result { + input_array_arg!(image); + output_array_arg!(labels); + return_send!(via ocvrs_return); + unsafe { sys::cv_connectedComponents_const__InputArrayR_const__OutputArrayR(image.as_raw__InputArray(), labels.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// computes the connected components labeled image of boolean image + /// + /// image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 + /// represents the background label. ltype specifies the output label image type, an important + /// consideration based on the total number of labels or alternatively the total number of pixels in + /// the source image. ccltype specifies the connected components labeling algorithm to use, currently + /// Bolelli (Spaghetti) [Bolelli2019](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Bolelli2019), Grana (BBDT) [Grana2010](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Grana2010) and Wu's (SAUF) [Wu2009](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Wu2009) algorithms + /// are supported, see the [connected_components_algorithms_types] for details. Note that SAUF algorithm forces + /// a row major ordering of labels while Spaghetti and BBDT do not. + /// This function uses parallel version of the algorithms if at least one allowed + /// parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. + /// + /// ## Parameters + /// * image: the 8-bit single-channel image to be labeled + /// * labels: destination labeled image + /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively + /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. + /// * ccltype: connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes). + /// + /// ## Overloaded parameters + /// + /// + /// * image: the 8-bit single-channel image to be labeled + /// * labels: destination labeled image + /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively + /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. + /// + /// ## C++ default parameters + /// * connectivity: 8 + /// * ltype: CV_32S + #[inline] + pub fn connected_components(image: &impl ToInputArray, labels: &mut impl ToOutputArray, connectivity: i32, ltype: i32) -> Result { + input_array_arg!(image); + output_array_arg!(labels); + return_send!(via ocvrs_return); + unsafe { sys::cv_connectedComponents_const__InputArrayR_const__OutputArrayR_int_int(image.as_raw__InputArray(), labels.as_raw__OutputArray(), connectivity, ltype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// computes the connected components labeled image of boolean image + /// + /// image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 + /// represents the background label. ltype specifies the output label image type, an important + /// consideration based on the total number of labels or alternatively the total number of pixels in + /// the source image. ccltype specifies the connected components labeling algorithm to use, currently + /// Bolelli (Spaghetti) [Bolelli2019](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Bolelli2019), Grana (BBDT) [Grana2010](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Grana2010) and Wu's (SAUF) [Wu2009](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Wu2009) algorithms + /// are supported, see the [connected_components_algorithms_types] for details. Note that SAUF algorithm forces + /// a row major ordering of labels while Spaghetti and BBDT do not. + /// This function uses parallel version of the algorithms if at least one allowed + /// parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. + /// + /// ## Parameters + /// * image: the 8-bit single-channel image to be labeled + /// * labels: destination labeled image + /// * connectivity: 8 or 4 for 8-way or 4-way connectivity respectively + /// * ltype: output image label type. Currently CV_32S and CV_16U are supported. + /// * ccltype: connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes). + #[inline] + pub fn connected_components_with_algorithm(image: &impl ToInputArray, labels: &mut impl ToOutputArray, connectivity: i32, ltype: i32, ccltype: i32) -> Result { + input_array_arg!(image); + output_array_arg!(labels); + return_send!(via ocvrs_return); + unsafe { sys::cv_connectedComponents_const__InputArrayR_const__OutputArrayR_int_int_int(image.as_raw__InputArray(), labels.as_raw__OutputArray(), connectivity, ltype, ccltype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates a contour area. + /// + /// The function computes a contour area. Similarly to moments , the area is computed using the Green + /// formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using + /// [draw_contours] or [fill_poly] , can be different. Also, the function will most certainly give a wrong + /// results for contours with self-intersections. + /// + /// Example: + /// ```C++ + /// vector contour; + /// contour.push_back(Point2f(0, 0)); + /// contour.push_back(Point2f(10, 0)); + /// contour.push_back(Point2f(10, 10)); + /// contour.push_back(Point2f(5, 4)); + /// + /// double area0 = contourArea(contour); + /// vector approx; + /// approxPolyDP(contour, approx, 5, true); + /// double area1 = contourArea(approx); + /// + /// cout << "area0 =" << area0 << endl << + /// "area1 =" << area1 << endl << + /// "approx poly vertices" << approx.size() << endl; + /// ``` + /// + /// ## Parameters + /// * contour: Input vector of 2D points (contour vertices), stored in std::vector or Mat. + /// * oriented: Oriented area flag. If it is true, the function returns a signed area value, + /// depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can + /// determine orientation of a contour by taking the sign of an area. By default, the parameter is + /// false, which means that the absolute value is returned. + /// + /// ## Note + /// This alternative version of [contour_area] function uses the following default values for its arguments: + /// * oriented: false + #[inline] + pub fn contour_area_def(contour: &impl ToInputArray) -> Result { + input_array_arg!(contour); + return_send!(via ocvrs_return); + unsafe { sys::cv_contourArea_const__InputArrayR(contour.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates a contour area. + /// + /// The function computes a contour area. Similarly to moments , the area is computed using the Green + /// formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using + /// [draw_contours] or [fill_poly] , can be different. Also, the function will most certainly give a wrong + /// results for contours with self-intersections. + /// + /// Example: + /// ```C++ + /// vector contour; + /// contour.push_back(Point2f(0, 0)); + /// contour.push_back(Point2f(10, 0)); + /// contour.push_back(Point2f(10, 10)); + /// contour.push_back(Point2f(5, 4)); + /// + /// double area0 = contourArea(contour); + /// vector approx; + /// approxPolyDP(contour, approx, 5, true); + /// double area1 = contourArea(approx); + /// + /// cout << "area0 =" << area0 << endl << + /// "area1 =" << area1 << endl << + /// "approx poly vertices" << approx.size() << endl; + /// ``` + /// + /// ## Parameters + /// * contour: Input vector of 2D points (contour vertices), stored in std::vector or Mat. + /// * oriented: Oriented area flag. If it is true, the function returns a signed area value, + /// depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can + /// determine orientation of a contour by taking the sign of an area. By default, the parameter is + /// false, which means that the absolute value is returned. + /// + /// ## C++ default parameters + /// * oriented: false + #[inline] + pub fn contour_area(contour: &impl ToInputArray, oriented: bool) -> Result { + input_array_arg!(contour); + return_send!(via ocvrs_return); + unsafe { sys::cv_contourArea_const__InputArrayR_bool(contour.as_raw__InputArray(), oriented, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Converts image transformation maps from one representation to another. + /// + /// The function converts a pair of maps for remap from one representation to another. The following + /// options ( (map1.type(), map2.type()) ![inline formula](https://latex.codecogs.com/png.latex?%5Crightarrow) (dstmap1.type(), dstmap2.type()) ) are + /// supported: + /// + /// - ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7B%28CV%5F32FC1%2C%20CV%5F32FC1%29%7D%20%5Crightarrow%20%5Ctexttt%7B%28CV%5F16SC2%2C%20CV%5F16UC1%29%7D). This is the + /// most frequently used conversion operation, in which the original floating-point maps (see #remap) + /// are converted to a more compact and much faster fixed-point representation. The first output array + /// contains the rounded coordinates and the second array (created only when nninterpolation=false ) + /// contains indices in the interpolation tables. + /// + /// - ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7B%28CV%5F32FC2%29%7D%20%5Crightarrow%20%5Ctexttt%7B%28CV%5F16SC2%2C%20CV%5F16UC1%29%7D). The same as above but + /// the original maps are stored in one 2-channel matrix. + /// + /// - Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same + /// as the originals. + /// + /// ## Parameters + /// * map1: The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 . + /// * map2: The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), + /// respectively. + /// * dstmap1: The first output map that has the type dstmap1type and the same size as src . + /// * dstmap2: The second output map. + /// * dstmap1type: Type of the first output map that should be CV_16SC2, CV_32FC1, or + /// CV_32FC2 . + /// * nninterpolation: Flag indicating whether the fixed-point maps are used for the + /// nearest-neighbor or for a more complex interpolation. + /// ## See also + /// remap, undistort, initUndistortRectifyMap + /// + /// ## Note + /// This alternative version of [convert_maps] function uses the following default values for its arguments: + /// * nninterpolation: false + #[inline] + pub fn convert_maps_def(map1: &impl ToInputArray, map2: &impl ToInputArray, dstmap1: &mut impl ToOutputArray, dstmap2: &mut impl ToOutputArray, dstmap1type: i32) -> Result<()> { + input_array_arg!(map1); + input_array_arg!(map2); + output_array_arg!(dstmap1); + output_array_arg!(dstmap2); + return_send!(via ocvrs_return); + unsafe { sys::cv_convertMaps_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int(map1.as_raw__InputArray(), map2.as_raw__InputArray(), dstmap1.as_raw__OutputArray(), dstmap2.as_raw__OutputArray(), dstmap1type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Converts image transformation maps from one representation to another. + /// + /// The function converts a pair of maps for remap from one representation to another. The following + /// options ( (map1.type(), map2.type()) ![inline formula](https://latex.codecogs.com/png.latex?%5Crightarrow) (dstmap1.type(), dstmap2.type()) ) are + /// supported: + /// + /// - ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7B%28CV%5F32FC1%2C%20CV%5F32FC1%29%7D%20%5Crightarrow%20%5Ctexttt%7B%28CV%5F16SC2%2C%20CV%5F16UC1%29%7D). This is the + /// most frequently used conversion operation, in which the original floating-point maps (see #remap) + /// are converted to a more compact and much faster fixed-point representation. The first output array + /// contains the rounded coordinates and the second array (created only when nninterpolation=false ) + /// contains indices in the interpolation tables. + /// + /// - ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7B%28CV%5F32FC2%29%7D%20%5Crightarrow%20%5Ctexttt%7B%28CV%5F16SC2%2C%20CV%5F16UC1%29%7D). The same as above but + /// the original maps are stored in one 2-channel matrix. + /// + /// - Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same + /// as the originals. + /// + /// ## Parameters + /// * map1: The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 . + /// * map2: The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), + /// respectively. + /// * dstmap1: The first output map that has the type dstmap1type and the same size as src . + /// * dstmap2: The second output map. + /// * dstmap1type: Type of the first output map that should be CV_16SC2, CV_32FC1, or + /// CV_32FC2 . + /// * nninterpolation: Flag indicating whether the fixed-point maps are used for the + /// nearest-neighbor or for a more complex interpolation. + /// ## See also + /// remap, undistort, initUndistortRectifyMap + /// + /// ## C++ default parameters + /// * nninterpolation: false + #[inline] + pub fn convert_maps(map1: &impl ToInputArray, map2: &impl ToInputArray, dstmap1: &mut impl ToOutputArray, dstmap2: &mut impl ToOutputArray, dstmap1type: i32, nninterpolation: bool) -> Result<()> { + input_array_arg!(map1); + input_array_arg!(map2); + output_array_arg!(dstmap1); + output_array_arg!(dstmap2); + return_send!(via ocvrs_return); + unsafe { sys::cv_convertMaps_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_bool(map1.as_raw__InputArray(), map2.as_raw__InputArray(), dstmap1.as_raw__OutputArray(), dstmap2.as_raw__OutputArray(), dstmap1type, nninterpolation, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the convex hull of a point set. + /// + /// The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm [Sklansky82](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Sklansky82) + /// that has *O(N logN)* complexity in the current implementation. + /// + /// ## Parameters + /// * points: Input 2D point set, stored in std::vector or Mat. + /// * hull: Output convex hull. It is either an integer vector of indices or vector of points. In + /// the first case, the hull elements are 0-based indices of the convex hull points in the original + /// array (since the set of convex hull points is a subset of the original point set). In the second + /// case, hull elements are the convex hull points themselves. + /// * clockwise: Orientation flag. If it is true, the output convex hull is oriented clockwise. + /// Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing + /// to the right, and its Y axis pointing upwards. + /// * returnPoints: Operation flag. In case of a matrix, when the flag is true, the function + /// returns convex hull points. Otherwise, it returns indices of the convex hull points. When the + /// output array is std::vector, the flag is ignored, and the output depends on the type of the + /// vector: std::vector\ implies returnPoints=false, std::vector\ implies + /// returnPoints=true. + /// + /// + /// Note: `points` and `hull` should be different arrays, inplace processing isn't supported. + /// + /// Check [tutorial_hull] "the corresponding tutorial" for more details. + /// + /// useful links: + /// + /// + /// + /// ## Note + /// This alternative version of [convex_hull] function uses the following default values for its arguments: + /// * clockwise: false + /// * return_points: true + #[inline] + pub fn convex_hull_def(points: &impl ToInputArray, hull: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(points); + output_array_arg!(hull); + return_send!(via ocvrs_return); + unsafe { sys::cv_convexHull_const__InputArrayR_const__OutputArrayR(points.as_raw__InputArray(), hull.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the convex hull of a point set. + /// + /// The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm [Sklansky82](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Sklansky82) + /// that has *O(N logN)* complexity in the current implementation. + /// + /// ## Parameters + /// * points: Input 2D point set, stored in std::vector or Mat. + /// * hull: Output convex hull. It is either an integer vector of indices or vector of points. In + /// the first case, the hull elements are 0-based indices of the convex hull points in the original + /// array (since the set of convex hull points is a subset of the original point set). In the second + /// case, hull elements are the convex hull points themselves. + /// * clockwise: Orientation flag. If it is true, the output convex hull is oriented clockwise. + /// Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing + /// to the right, and its Y axis pointing upwards. + /// * returnPoints: Operation flag. In case of a matrix, when the flag is true, the function + /// returns convex hull points. Otherwise, it returns indices of the convex hull points. When the + /// output array is std::vector, the flag is ignored, and the output depends on the type of the + /// vector: std::vector\ implies returnPoints=false, std::vector\ implies + /// returnPoints=true. + /// + /// + /// Note: `points` and `hull` should be different arrays, inplace processing isn't supported. + /// + /// Check [tutorial_hull] "the corresponding tutorial" for more details. + /// + /// useful links: + /// + /// + /// + /// ## C++ default parameters + /// * clockwise: false + /// * return_points: true + #[inline] + pub fn convex_hull(points: &impl ToInputArray, hull: &mut impl ToOutputArray, clockwise: bool, return_points: bool) -> Result<()> { + input_array_arg!(points); + output_array_arg!(hull); + return_send!(via ocvrs_return); + unsafe { sys::cv_convexHull_const__InputArrayR_const__OutputArrayR_bool_bool(points.as_raw__InputArray(), hull.as_raw__OutputArray(), clockwise, return_points, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the convexity defects of a contour. + /// + /// The figure below displays convexity defects of a hand contour: + /// + /// ![image](https://docs.opencv.org/4.13.0/defects.png) + /// + /// ## Parameters + /// * contour: Input contour. + /// * convexhull: Convex hull obtained using convexHull that should contain indices of the contour + /// points that make the hull. + /// * convexityDefects: The output vector of convexity defects. In C++ and the new Python/Java + /// interface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i): + /// (start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices + /// in the original contour of the convexity defect beginning, end and the farthest point, and + /// fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the + /// farthest contour point and the hull. That is, to get the floating-point value of the depth will be + /// fixpt_depth/256.0. + #[inline] + pub fn convexity_defects(contour: &impl ToInputArray, convexhull: &impl ToInputArray, convexity_defects: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(contour); + input_array_arg!(convexhull); + output_array_arg!(convexity_defects); + return_send!(via ocvrs_return); + unsafe { sys::cv_convexityDefects_const__InputArrayR_const__InputArrayR_const__OutputArrayR(contour.as_raw__InputArray(), convexhull.as_raw__InputArray(), convexity_defects.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates eigenvalues and eigenvectors of image blocks for corner detection. + /// + /// For every pixel ![inline formula](https://latex.codecogs.com/png.latex?p) , the function cornerEigenValsAndVecs considers a blockSize ![inline formula](https://latex.codecogs.com/png.latex?%5Ctimes) blockSize + /// neighborhood ![inline formula](https://latex.codecogs.com/png.latex?S%28p%29) . It calculates the covariation matrix of derivatives over the neighborhood as: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?M%20%3D%20%20%5Cbegin%7Bbmatrix%7D%20%5Csum%20%5F%7BS%28p%29%7D%28dI%2Fdx%29%5E2%20%26%20%20%5Csum%20%5F%7BS%28p%29%7DdI%2Fdx%20dI%2Fdy%20%20%5C%5C%20%5Csum%20%5F%7BS%28p%29%7DdI%2Fdx%20dI%2Fdy%20%26%20%20%5Csum%20%5F%7BS%28p%29%7D%28dI%2Fdy%29%5E2%20%5Cend%7Bbmatrix%7D) + /// + /// where the derivatives are computed using the Sobel operator. + /// + /// After that, it finds eigenvectors and eigenvalues of ![inline formula](https://latex.codecogs.com/png.latex?M) and stores them in the destination image as + /// ![inline formula](https://latex.codecogs.com/png.latex?%28%5Clambda%5F1%2C%20%5Clambda%5F2%2C%20x%5F1%2C%20y%5F1%2C%20x%5F2%2C%20y%5F2%29) where + /// + /// * ![inline formula](https://latex.codecogs.com/png.latex?%5Clambda%5F1%2C%20%5Clambda%5F2) are the non-sorted eigenvalues of ![inline formula](https://latex.codecogs.com/png.latex?M) + /// * ![inline formula](https://latex.codecogs.com/png.latex?x%5F1%2C%20y%5F1) are the eigenvectors corresponding to ![inline formula](https://latex.codecogs.com/png.latex?%5Clambda%5F1) + /// * ![inline formula](https://latex.codecogs.com/png.latex?x%5F2%2C%20y%5F2) are the eigenvectors corresponding to ![inline formula](https://latex.codecogs.com/png.latex?%5Clambda%5F2) + /// + /// The output of the function can be used for robust edge or corner detection. + /// + /// ## Parameters + /// * src: Input single-channel 8-bit or floating-point image. + /// * dst: Image to store the results. It has the same size as src and the type CV_32FC(6) . + /// * blockSize: Neighborhood size (see details below). + /// * ksize: Aperture parameter for the Sobel operator. + /// * borderType: Pixel extrapolation method. See #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// cornerMinEigenVal, cornerHarris, preCornerDetect + /// + /// ## Note + /// This alternative version of [corner_eigen_vals_and_vecs] function uses the following default values for its arguments: + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn corner_eigen_vals_and_vecs_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, block_size: i32, ksize: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_cornerEigenValsAndVecs_const__InputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), block_size, ksize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates eigenvalues and eigenvectors of image blocks for corner detection. + /// + /// For every pixel ![inline formula](https://latex.codecogs.com/png.latex?p) , the function cornerEigenValsAndVecs considers a blockSize ![inline formula](https://latex.codecogs.com/png.latex?%5Ctimes) blockSize + /// neighborhood ![inline formula](https://latex.codecogs.com/png.latex?S%28p%29) . It calculates the covariation matrix of derivatives over the neighborhood as: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?M%20%3D%20%20%5Cbegin%7Bbmatrix%7D%20%5Csum%20%5F%7BS%28p%29%7D%28dI%2Fdx%29%5E2%20%26%20%20%5Csum%20%5F%7BS%28p%29%7DdI%2Fdx%20dI%2Fdy%20%20%5C%5C%20%5Csum%20%5F%7BS%28p%29%7DdI%2Fdx%20dI%2Fdy%20%26%20%20%5Csum%20%5F%7BS%28p%29%7D%28dI%2Fdy%29%5E2%20%5Cend%7Bbmatrix%7D) + /// + /// where the derivatives are computed using the Sobel operator. + /// + /// After that, it finds eigenvectors and eigenvalues of ![inline formula](https://latex.codecogs.com/png.latex?M) and stores them in the destination image as + /// ![inline formula](https://latex.codecogs.com/png.latex?%28%5Clambda%5F1%2C%20%5Clambda%5F2%2C%20x%5F1%2C%20y%5F1%2C%20x%5F2%2C%20y%5F2%29) where + /// + /// * ![inline formula](https://latex.codecogs.com/png.latex?%5Clambda%5F1%2C%20%5Clambda%5F2) are the non-sorted eigenvalues of ![inline formula](https://latex.codecogs.com/png.latex?M) + /// * ![inline formula](https://latex.codecogs.com/png.latex?x%5F1%2C%20y%5F1) are the eigenvectors corresponding to ![inline formula](https://latex.codecogs.com/png.latex?%5Clambda%5F1) + /// * ![inline formula](https://latex.codecogs.com/png.latex?x%5F2%2C%20y%5F2) are the eigenvectors corresponding to ![inline formula](https://latex.codecogs.com/png.latex?%5Clambda%5F2) + /// + /// The output of the function can be used for robust edge or corner detection. + /// + /// ## Parameters + /// * src: Input single-channel 8-bit or floating-point image. + /// * dst: Image to store the results. It has the same size as src and the type CV_32FC(6) . + /// * blockSize: Neighborhood size (see details below). + /// * ksize: Aperture parameter for the Sobel operator. + /// * borderType: Pixel extrapolation method. See #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// cornerMinEigenVal, cornerHarris, preCornerDetect + /// + /// ## C++ default parameters + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn corner_eigen_vals_and_vecs(src: &impl ToInputArray, dst: &mut impl ToOutputArray, block_size: i32, ksize: i32, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_cornerEigenValsAndVecs_const__InputArrayR_const__OutputArrayR_int_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), block_size, ksize, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Harris corner detector. + /// + /// The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and + /// cornerEigenValsAndVecs , for each pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%29) it calculates a ![inline formula](https://latex.codecogs.com/png.latex?2%5Ctimes2) gradient covariance + /// matrix ![inline formula](https://latex.codecogs.com/png.latex?M%5E%7B%28x%2Cy%29%7D) over a ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BblockSize%7D%20%5Ctimes%20%5Ctexttt%7BblockSize%7D) neighborhood. Then, it + /// computes the following characteristic: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Cmathrm%7Bdet%7D%20M%5E%7B%28x%2Cy%29%7D%20%2D%20k%20%20%5Ccdot%20%5Cleft%20%28%20%5Cmathrm%7Btr%7D%20M%5E%7B%28x%2Cy%29%7D%20%5Cright%20%29%5E2) + /// + /// Corners in the image can be found as the local maxima of this response map. + /// + /// ## Parameters + /// * src: Input single-channel 8-bit or floating-point image. + /// * dst: Image to store the Harris detector responses. It has the type CV_32FC1 and the same + /// size as src . + /// * blockSize: Neighborhood size (see the details on [corner_eigen_vals_and_vecs] ). + /// * ksize: Aperture parameter for the Sobel operator. + /// * k: Harris detector free parameter. See the formula above. + /// * borderType: Pixel extrapolation method. See #BorderTypes. [BORDER_WRAP] is not supported. + /// + /// ## Note + /// This alternative version of [corner_harris] function uses the following default values for its arguments: + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn corner_harris_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, block_size: i32, ksize: i32, k: f64) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_cornerHarris_const__InputArrayR_const__OutputArrayR_int_int_double(src.as_raw__InputArray(), dst.as_raw__OutputArray(), block_size, ksize, k, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Harris corner detector. + /// + /// The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and + /// cornerEigenValsAndVecs , for each pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%29) it calculates a ![inline formula](https://latex.codecogs.com/png.latex?2%5Ctimes2) gradient covariance + /// matrix ![inline formula](https://latex.codecogs.com/png.latex?M%5E%7B%28x%2Cy%29%7D) over a ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BblockSize%7D%20%5Ctimes%20%5Ctexttt%7BblockSize%7D) neighborhood. Then, it + /// computes the following characteristic: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Cmathrm%7Bdet%7D%20M%5E%7B%28x%2Cy%29%7D%20%2D%20k%20%20%5Ccdot%20%5Cleft%20%28%20%5Cmathrm%7Btr%7D%20M%5E%7B%28x%2Cy%29%7D%20%5Cright%20%29%5E2) + /// + /// Corners in the image can be found as the local maxima of this response map. + /// + /// ## Parameters + /// * src: Input single-channel 8-bit or floating-point image. + /// * dst: Image to store the Harris detector responses. It has the type CV_32FC1 and the same + /// size as src . + /// * blockSize: Neighborhood size (see the details on [corner_eigen_vals_and_vecs] ). + /// * ksize: Aperture parameter for the Sobel operator. + /// * k: Harris detector free parameter. See the formula above. + /// * borderType: Pixel extrapolation method. See #BorderTypes. [BORDER_WRAP] is not supported. + /// + /// ## C++ default parameters + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn corner_harris(src: &impl ToInputArray, dst: &mut impl ToOutputArray, block_size: i32, ksize: i32, k: f64, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_cornerHarris_const__InputArrayR_const__OutputArrayR_int_int_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), block_size, ksize, k, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the minimal eigenvalue of gradient matrices for corner detection. + /// + /// The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal + /// eigenvalue of the covariance matrix of derivatives, that is, ![inline formula](https://latex.codecogs.com/png.latex?%5Cmin%28%5Clambda%5F1%2C%20%5Clambda%5F2%29) in terms + /// of the formulae in the cornerEigenValsAndVecs description. + /// + /// ## Parameters + /// * src: Input single-channel 8-bit or floating-point image. + /// * dst: Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as + /// src . + /// * blockSize: Neighborhood size (see the details on [corner_eigen_vals_and_vecs] ). + /// * ksize: Aperture parameter for the Sobel operator. + /// * borderType: Pixel extrapolation method. See #BorderTypes. [BORDER_WRAP] is not supported. + /// + /// ## Note + /// This alternative version of [corner_min_eigen_val] function uses the following default values for its arguments: + /// * ksize: 3 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn corner_min_eigen_val_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, block_size: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_cornerMinEigenVal_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), block_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the minimal eigenvalue of gradient matrices for corner detection. + /// + /// The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal + /// eigenvalue of the covariance matrix of derivatives, that is, ![inline formula](https://latex.codecogs.com/png.latex?%5Cmin%28%5Clambda%5F1%2C%20%5Clambda%5F2%29) in terms + /// of the formulae in the cornerEigenValsAndVecs description. + /// + /// ## Parameters + /// * src: Input single-channel 8-bit or floating-point image. + /// * dst: Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as + /// src . + /// * blockSize: Neighborhood size (see the details on [corner_eigen_vals_and_vecs] ). + /// * ksize: Aperture parameter for the Sobel operator. + /// * borderType: Pixel extrapolation method. See #BorderTypes. [BORDER_WRAP] is not supported. + /// + /// ## C++ default parameters + /// * ksize: 3 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn corner_min_eigen_val(src: &impl ToInputArray, dst: &mut impl ToOutputArray, block_size: i32, ksize: i32, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_cornerMinEigenVal_const__InputArrayR_const__OutputArrayR_int_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), block_size, ksize, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Refines the corner locations. + /// + /// The function iterates to find the sub-pixel accurate location of corners or radial saddle + /// points as described in [forstner1987fast](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_forstner1987fast), and as shown on the figure below. + /// + /// ![image](https://docs.opencv.org/4.13.0/cornersubpix.png) + /// + /// Sub-pixel accurate corner locator is based on the observation that every vector from the center ![inline formula](https://latex.codecogs.com/png.latex?q) + /// to a point ![inline formula](https://latex.codecogs.com/png.latex?p) located within a neighborhood of ![inline formula](https://latex.codecogs.com/png.latex?q) is orthogonal to the image gradient at ![inline formula](https://latex.codecogs.com/png.latex?p) + /// subject to image and measurement noise. Consider the expression: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cepsilon%20%5Fi%20%3D%20%7BDI%5F%7Bp%5Fi%7D%7D%5ET%20%20%5Ccdot%20%28q%20%2D%20p%5Fi%29) + /// + /// where ![inline formula](https://latex.codecogs.com/png.latex?%7BDI%5F%7Bp%5Fi%7D%7D) is an image gradient at one of the points ![inline formula](https://latex.codecogs.com/png.latex?p%5Fi) in a neighborhood of ![inline formula](https://latex.codecogs.com/png.latex?q) . The + /// value of ![inline formula](https://latex.codecogs.com/png.latex?q) is to be found so that ![inline formula](https://latex.codecogs.com/png.latex?%5Cepsilon%5Fi) is minimized. A system of equations may be set up + /// with ![inline formula](https://latex.codecogs.com/png.latex?%5Cepsilon%5Fi) set to zero: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5Fi%28DI%5F%7Bp%5Fi%7D%20%20%5Ccdot%20%7BDI%5F%7Bp%5Fi%7D%7D%5ET%29%20%5Ccdot%20q%20%2D%20%20%5Csum%20%5Fi%28DI%5F%7Bp%5Fi%7D%20%20%5Ccdot%20%7BDI%5F%7Bp%5Fi%7D%7D%5ET%20%20%5Ccdot%20p%5Fi%29) + /// + /// where the gradients are summed within a neighborhood ("search window") of ![inline formula](https://latex.codecogs.com/png.latex?q) . Calling the first + /// gradient term ![inline formula](https://latex.codecogs.com/png.latex?G) and the second gradient term ![inline formula](https://latex.codecogs.com/png.latex?b) gives: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?q%20%3D%20G%5E%7B%2D1%7D%20%20%5Ccdot%20b) + /// + /// The algorithm sets the center of the neighborhood window at this new center ![inline formula](https://latex.codecogs.com/png.latex?q) and then iterates + /// until the center stays within a set threshold. + /// + /// ## Parameters + /// * image: Input single-channel, 8-bit or float image. + /// * corners: Initial coordinates of the input corners and refined coordinates provided for + /// output. + /// * winSize: Half of the side length of the search window. For example, if winSize=Size(5,5) , + /// then a ![inline formula](https://latex.codecogs.com/png.latex?%285%2A2%2B1%29%20%5Ctimes%20%285%2A2%2B1%29%20%3D%2011%20%5Ctimes%2011) search window is used. + /// * zeroZone: Half of the size of the dead region in the middle of the search zone over which + /// the summation in the formula below is not done. It is used sometimes to avoid possible + /// singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such + /// a size. + /// * criteria: Criteria for termination of the iterative process of corner refinement. That is, + /// the process of corner position refinement stops either after criteria.maxCount iterations or when + /// the corner position moves by less than criteria.epsilon on some iteration. + #[inline] + pub fn corner_sub_pix(image: &impl ToInputArray, corners: &mut impl ToInputOutputArray, win_size: core::Size, zero_zone: core::Size, criteria: core::TermCriteria) -> Result<()> { + input_array_arg!(image); + input_output_array_arg!(corners); + return_send!(via ocvrs_return); + unsafe { sys::cv_cornerSubPix_const__InputArrayR_const__InputOutputArrayR_Size_Size_TermCriteria(image.as_raw__InputArray(), corners.as_raw__InputOutputArray(), &win_size, &zero_zone, &criteria, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates a smart pointer to a cv::CLAHE class and initializes it. + /// + /// ## Parameters + /// * clipLimit: Threshold for contrast limiting. + /// * tileGridSize: Size of grid for histogram equalization. Input image will be divided into + /// equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column. + /// + /// ## Note + /// This alternative version of [create_clahe] function uses the following default values for its arguments: + /// * clip_limit: 40.0 + /// * tile_grid_size: Size(8,8) + #[inline] + pub fn create_clahe_def() -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_createCLAHE(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a smart pointer to a cv::CLAHE class and initializes it. + /// + /// ## Parameters + /// * clipLimit: Threshold for contrast limiting. + /// * tileGridSize: Size of grid for histogram equalization. Input image will be divided into + /// equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column. + /// + /// ## C++ default parameters + /// * clip_limit: 40.0 + /// * tile_grid_size: Size(8,8) + #[inline] + pub fn create_clahe(clip_limit: f64, tile_grid_size: core::Size) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_createCLAHE_double_Size(clip_limit, &tile_grid_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it. + #[inline] + pub fn create_generalized_hough_ballard() -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_createGeneralizedHoughBallard(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it. + #[inline] + pub fn create_generalized_hough_guil() -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_createGeneralizedHoughGuil(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// This function computes a Hanning window coefficients in two dimensions. + /// + /// See () and () + /// for more information. + /// + /// An example is shown below: + /// ```C++ + /// // create hanning window of size 100x100 and type CV_32F + /// Mat hann; + /// createHanningWindow(hann, Size(100, 100), CV_32F); + /// ``` + /// + /// ## Parameters + /// * dst: Destination array to place Hann coefficients in + /// * winSize: The window size specifications (both width and height must be > 1) + /// * type: Created array type + #[inline] + pub fn create_hanning_window(dst: &mut impl ToOutputArray, win_size: core::Size, typ: i32) -> Result<()> { + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_createHanningWindow_const__OutputArrayR_Size_int(dst.as_raw__OutputArray(), &win_size, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Creates a smart pointer to a LineSegmentDetector object and initializes it. + /// + /// The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want + /// to edit those, as to tailor it for their own application. + /// + /// ## Parameters + /// * refine: The way found lines will be refined, see [line_segment_detector_modes] + /// * scale: The scale of the image that will be used to find the lines. Range (0..1]. + /// * sigma_scale: Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. + /// * quant: Bound to the quantization error on the gradient norm. + /// * ang_th: Gradient angle tolerance in degrees. + /// * log_eps: Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement is chosen. + /// * density_th: Minimal density of aligned region points in the enclosing rectangle. + /// * n_bins: Number of bins in pseudo-ordering of gradient modulus. + /// + /// ## Note + /// This alternative version of [create_line_segment_detector] function uses the following default values for its arguments: + /// * refine: LSD_REFINE_STD + /// * scale: 0.8 + /// * sigma_scale: 0.6 + /// * quant: 2.0 + /// * ang_th: 22.5 + /// * log_eps: 0 + /// * density_th: 0.7 + /// * n_bins: 1024 + #[inline] + pub fn create_line_segment_detector_def() -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_createLineSegmentDetector(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Creates a smart pointer to a LineSegmentDetector object and initializes it. + /// + /// The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want + /// to edit those, as to tailor it for their own application. + /// + /// ## Parameters + /// * refine: The way found lines will be refined, see [line_segment_detector_modes] + /// * scale: The scale of the image that will be used to find the lines. Range (0..1]. + /// * sigma_scale: Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale. + /// * quant: Bound to the quantization error on the gradient norm. + /// * ang_th: Gradient angle tolerance in degrees. + /// * log_eps: Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement is chosen. + /// * density_th: Minimal density of aligned region points in the enclosing rectangle. + /// * n_bins: Number of bins in pseudo-ordering of gradient modulus. + /// + /// ## C++ default parameters + /// * refine: LSD_REFINE_STD + /// * scale: 0.8 + /// * sigma_scale: 0.6 + /// * quant: 2.0 + /// * ang_th: 22.5 + /// * log_eps: 0 + /// * density_th: 0.7 + /// * n_bins: 1024 + #[inline] + pub fn create_line_segment_detector(refine: i32, scale: f64, sigma_scale: f64, quant: f64, ang_th: f64, log_eps: f64, density_th: f64, n_bins: i32) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_createLineSegmentDetector_int_double_double_double_double_double_double_int(refine, scale, sigma_scale, quant, ang_th, log_eps, density_th, n_bins, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Ptr::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Converts an image from one color space to another where the source image is + /// stored in two planes. + /// + /// This function only supports YUV420 to RGB conversion as of now. + /// + /// ## Parameters + /// * src1: 8-bit image (#CV_8U) of the Y plane. + /// * src2: image containing interleaved U/V plane. + /// * dst: output image. + /// * code: Specifies the type of conversion. It can take any of the following values: + /// - [COLOR_YUV2BGR_NV12] + /// - [COLOR_YUV2RGB_NV12] + /// - [COLOR_YUV2BGRA_NV12] + /// - [COLOR_YUV2RGBA_NV12] + /// - [COLOR_YUV2BGR_NV21] + /// - [COLOR_YUV2RGB_NV21] + /// - [COLOR_YUV2BGRA_NV21] + /// - [COLOR_YUV2RGBA_NV21] + /// * hint: Implementation modfication flags. See [algorithm_hint] + /// + /// ## Note + /// This alternative version of [cvt_color_two_plane] function uses the following default values for its arguments: + /// * hint: cv::ALGO_HINT_DEFAULT + #[inline] + pub fn cvt_color_two_plane_def(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray, code: i32) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_cvtColorTwoPlane_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), code, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Converts an image from one color space to another where the source image is + /// stored in two planes. + /// + /// This function only supports YUV420 to RGB conversion as of now. + /// + /// ## Parameters + /// * src1: 8-bit image (#CV_8U) of the Y plane. + /// * src2: image containing interleaved U/V plane. + /// * dst: output image. + /// * code: Specifies the type of conversion. It can take any of the following values: + /// - [COLOR_YUV2BGR_NV12] + /// - [COLOR_YUV2RGB_NV12] + /// - [COLOR_YUV2BGRA_NV12] + /// - [COLOR_YUV2RGBA_NV12] + /// - [COLOR_YUV2BGR_NV21] + /// - [COLOR_YUV2RGB_NV21] + /// - [COLOR_YUV2BGRA_NV21] + /// - [COLOR_YUV2RGBA_NV21] + /// * hint: Implementation modfication flags. See #AlgorithmHint + /// + /// ## C++ default parameters + /// * hint: cv::ALGO_HINT_DEFAULT + #[inline] + pub fn cvt_color_two_plane(src1: &impl ToInputArray, src2: &impl ToInputArray, dst: &mut impl ToOutputArray, code: i32, hint: core::AlgorithmHint) -> Result<()> { + input_array_arg!(src1); + input_array_arg!(src2); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_cvtColorTwoPlane_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_AlgorithmHint(src1.as_raw__InputArray(), src2.as_raw__InputArray(), dst.as_raw__OutputArray(), code, hint, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Converts an image from one color space to another. + /// + /// The function converts an input image from one color space to another. In case of a transformation + /// to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note + /// that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the + /// bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue + /// component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and + /// sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. + /// + /// The conventional ranges for R, G, and B channel values are: + /// * 0 to 255 for CV_8U images + /// * 0 to 65535 for CV_16U images + /// * 0 to 1 for CV_32F images + /// + /// In case of linear transformations, the range does not matter. But in case of a non-linear + /// transformation, an input RGB image should be normalized to the proper value range to get the correct + /// results, for example, for RGB ![inline formula](https://latex.codecogs.com/png.latex?%5Crightarrow) L\*u\*v\* transformation. For example, if you have a + /// 32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will + /// have the 0..255 value range instead of 0..1 assumed by the function. So, before calling [cvt_color] , + /// you need first to scale the image down: + /// ```C++ + /// img *= 1./255; + /// cvtColor(img, img, COLOR_BGR2Luv); + /// ``` + /// + /// If you use [cvt_color] with 8-bit images, the conversion will have some information lost. For many + /// applications, this will not be noticeable but it is recommended to use 32-bit images in applications + /// that need the full range of colors or that convert an image before an operation and then convert + /// back. + /// + /// If conversion adds the alpha channel, its value will set to the maximum of corresponding channel + /// range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F. + /// + /// ## Parameters + /// * src: input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision + /// floating-point. + /// * dst: output image of the same size and depth as src. + /// * code: color space conversion code (see #ColorConversionCodes). + /// * dstCn: number of channels in the destination image; if the parameter is 0, the number of the + /// channels is derived automatically from src and code. + /// * hint: Implementation modfication flags. See [algorithm_hint] + /// + /// + /// Note: The source image (src) must be of an appropriate type for the desired color conversion. see ColorConversionCodes + /// ## See also + /// [imgproc_color_conversions] + /// + /// ## Note + /// This alternative version of [cvt_color] function uses the following default values for its arguments: + /// * dst_cn: 0 + /// * hint: cv::ALGO_HINT_DEFAULT + #[inline] + pub fn cvt_color_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, code: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_cvtColor_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), code, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Converts an image from one color space to another. + /// + /// The function converts an input image from one color space to another. In case of a transformation + /// to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note + /// that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the + /// bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue + /// component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and + /// sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. + /// + /// The conventional ranges for R, G, and B channel values are: + /// * 0 to 255 for CV_8U images + /// * 0 to 65535 for CV_16U images + /// * 0 to 1 for CV_32F images + /// + /// In case of linear transformations, the range does not matter. But in case of a non-linear + /// transformation, an input RGB image should be normalized to the proper value range to get the correct + /// results, for example, for RGB ![inline formula](https://latex.codecogs.com/png.latex?%5Crightarrow) L\*u\*v\* transformation. For example, if you have a + /// 32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will + /// have the 0..255 value range instead of 0..1 assumed by the function. So, before calling [cvt_color] , + /// you need first to scale the image down: + /// ```C++ + /// img *= 1./255; + /// cvtColor(img, img, COLOR_BGR2Luv); + /// ``` + /// + /// If you use [cvt_color] with 8-bit images, the conversion will have some information lost. For many + /// applications, this will not be noticeable but it is recommended to use 32-bit images in applications + /// that need the full range of colors or that convert an image before an operation and then convert + /// back. + /// + /// If conversion adds the alpha channel, its value will set to the maximum of corresponding channel + /// range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F. + /// + /// ## Parameters + /// * src: input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision + /// floating-point. + /// * dst: output image of the same size and depth as src. + /// * code: color space conversion code (see #ColorConversionCodes). + /// * dstCn: number of channels in the destination image; if the parameter is 0, the number of the + /// channels is derived automatically from src and code. + /// * hint: Implementation modfication flags. See [algorithm_hint] + /// + /// + /// Note: The source image (src) must be of an appropriate type for the desired color conversion. see ColorConversionCodes + /// ## See also + /// [imgproc_color_conversions] + /// + /// ## C++ default parameters + /// * dst_cn: 0 + /// * hint: cv::ALGO_HINT_DEFAULT + #[inline] + pub fn cvt_color(src: &impl ToInputArray, dst: &mut impl ToOutputArray, code: i32, dst_cn: i32, hint: core::AlgorithmHint) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_cvtColor_const__InputArrayR_const__OutputArrayR_int_int_AlgorithmHint(src.as_raw__InputArray(), dst.as_raw__OutputArray(), code, dst_cn, hint, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// main function for all demosaicing processes + /// + /// ## Parameters + /// * src: input image: 8-bit unsigned or 16-bit unsigned. + /// * dst: output image of the same size and depth as src. + /// * code: Color space conversion code (see the description below). + /// * dstCn: number of channels in the destination image; if the parameter is 0, the number of the + /// channels is derived automatically from src and code. + /// + /// The function can do the following transformations: + /// + /// * Demosaicing using bilinear interpolation + /// + /// [color_bayer_bg2_bgr] , [color_bayer_gb2_bgr] , [color_bayer_rg2_bgr] , [color_bayer_gr2_bgr] + /// + /// [color_bayer_bg2_gray] , [color_bayer_gb2_gray] , [color_bayer_rg2_gray] , [color_bayer_gr2_gray] + /// + /// * Demosaicing using Variable Number of Gradients. + /// + /// [color_bayer_bg2_bgr_vng] , [color_bayer_gb2_bgr_vng] , [color_bayer_rg2_bgr_vng] , [color_bayer_gr2_bgr_vng] + /// + /// * Edge-Aware Demosaicing. + /// + /// [color_bayer_bg2_bgr_ea] , [color_bayer_gb2_bgr_ea] , [color_bayer_rg2_bgr_ea] , [color_bayer_gr2_bgr_ea] + /// + /// * Demosaicing with alpha channel + /// + /// [color_bayer_bg2_bgra] , [color_bayer_gb2_bgra] , [color_bayer_rg2_bgra] , [color_bayer_gr2_bgra] + /// + /// + /// Note: The source image (src) must be of an appropriate type for the desired color conversion. see ColorConversionCodes + /// ## See also + /// cvtColor + /// + /// ## Note + /// This alternative version of [demosaicing] function uses the following default values for its arguments: + /// * dst_cn: 0 + #[inline] + pub fn demosaicing_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, code: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_demosaicing_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), code, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// main function for all demosaicing processes + /// + /// ## Parameters + /// * src: input image: 8-bit unsigned or 16-bit unsigned. + /// * dst: output image of the same size and depth as src. + /// * code: Color space conversion code (see the description below). + /// * dstCn: number of channels in the destination image; if the parameter is 0, the number of the + /// channels is derived automatically from src and code. + /// + /// The function can do the following transformations: + /// + /// * Demosaicing using bilinear interpolation + /// + /// [color_bayer_bg2_bgr] , [color_bayer_gb2_bgr] , [color_bayer_rg2_bgr] , [color_bayer_gr2_bgr] + /// + /// [color_bayer_bg2_gray] , [color_bayer_gb2_gray] , [color_bayer_rg2_gray] , [color_bayer_gr2_gray] + /// + /// * Demosaicing using Variable Number of Gradients. + /// + /// [color_bayer_bg2_bgr_vng] , [color_bayer_gb2_bgr_vng] , [color_bayer_rg2_bgr_vng] , [color_bayer_gr2_bgr_vng] + /// + /// * Edge-Aware Demosaicing. + /// + /// [color_bayer_bg2_bgr_ea] , [color_bayer_gb2_bgr_ea] , [color_bayer_rg2_bgr_ea] , [color_bayer_gr2_bgr_ea] + /// + /// * Demosaicing with alpha channel + /// + /// [color_bayer_bg2_bgra] , [color_bayer_gb2_bgra] , [color_bayer_rg2_bgra] , [color_bayer_gr2_bgra] + /// + /// + /// Note: The source image (src) must be of an appropriate type for the desired color conversion. see ColorConversionCodes + /// ## See also + /// cvtColor + /// + /// ## C++ default parameters + /// * dst_cn: 0 + #[inline] + pub fn demosaicing(src: &impl ToInputArray, dst: &mut impl ToOutputArray, code: i32, dst_cn: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_demosaicing_const__InputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), code, dst_cn, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Dilates an image by using a specific structuring element. + /// + /// The function dilates the source image using the specified structuring element that determines the + /// shape of a pixel neighborhood over which the maximum is taken: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Cmax%20%5F%7B%28x%27%2Cy%27%29%3A%20%20%5C%2C%20%5Ctexttt%7Belement%7D%20%28x%27%2Cy%27%29%20%5Cne0%20%7D%20%5Ctexttt%7Bsrc%7D%20%28x%2Bx%27%2Cy%2By%27%29) + /// + /// The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In + /// case of multi-channel images, each channel is processed independently. + /// + /// ## Parameters + /// * src: input image; the number of channels can be arbitrary, but the depth should be one of + /// CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. + /// * dst: output image of the same size and type as src. + /// * kernel: structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular + /// structuring element is used. Kernel can be created using [get_structuring_element] + /// * anchor: position of the anchor within the element; default value (-1, -1) means that the + /// anchor is at the element center. + /// * iterations: number of times dilation is applied. + /// * borderType: pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not suported. + /// * borderValue: border value in case of a constant border + /// ## See also + /// erode, morphologyEx, getStructuringElement + /// + /// ## Note + /// This alternative version of [dilate] function uses the following default values for its arguments: + /// * anchor: Point(-1,-1) + /// * iterations: 1 + /// * border_type: BORDER_CONSTANT + /// * border_value: morphologyDefaultBorderValue() + #[inline] + pub fn dilate_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, kernel: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(kernel); + return_send!(via ocvrs_return); + unsafe { sys::cv_dilate_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), kernel.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Dilates an image by using a specific structuring element. + /// + /// The function dilates the source image using the specified structuring element that determines the + /// shape of a pixel neighborhood over which the maximum is taken: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Cmax%20%5F%7B%28x%27%2Cy%27%29%3A%20%20%5C%2C%20%5Ctexttt%7Belement%7D%20%28x%27%2Cy%27%29%20%5Cne0%20%7D%20%5Ctexttt%7Bsrc%7D%20%28x%2Bx%27%2Cy%2By%27%29) + /// + /// The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In + /// case of multi-channel images, each channel is processed independently. + /// + /// ## Parameters + /// * src: input image; the number of channels can be arbitrary, but the depth should be one of + /// CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. + /// * dst: output image of the same size and type as src. + /// * kernel: structuring element used for dilation; if element=Mat(), a 3 x 3 rectangular + /// structuring element is used. Kernel can be created using [get_structuring_element] + /// * anchor: position of the anchor within the element; default value (-1, -1) means that the + /// anchor is at the element center. + /// * iterations: number of times dilation is applied. + /// * borderType: pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not suported. + /// * borderValue: border value in case of a constant border + /// ## See also + /// erode, morphologyEx, getStructuringElement + /// + /// ## C++ default parameters + /// * anchor: Point(-1,-1) + /// * iterations: 1 + /// * border_type: BORDER_CONSTANT + /// * border_value: morphologyDefaultBorderValue() + #[inline] + pub fn dilate(src: &impl ToInputArray, dst: &mut impl ToOutputArray, kernel: &impl ToInputArray, anchor: core::Point, iterations: i32, border_type: i32, border_value: core::Scalar) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(kernel); + return_send!(via ocvrs_return); + unsafe { sys::cv_dilate_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Point_int_int_const_ScalarR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), kernel.as_raw__InputArray(), &anchor, iterations, border_type, &border_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the distance to the closest zero pixel for each pixel of the source image. + /// + /// The function cv::distanceTransform calculates the approximate or precise distance from every binary + /// image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. + /// + /// When maskSize == [DIST_MASK_PRECISE] and distanceType == [DIST_L2] , the function runs the + /// algorithm described in [Felzenszwalb04](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Felzenszwalb04) . This algorithm is parallelized with the TBB library. + /// + /// In other cases, the algorithm [Borgefors86](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Borgefors86) is used. This means that for a pixel the function + /// finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, + /// diagonal, or knight's move (the latest is available for a ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) mask). The overall + /// distance is calculated as a sum of these basic distances. Since the distance function should be + /// symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all + /// the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the + /// same cost (denoted as `c`). For the [DIST_C] and [DIST_L1] types, the distance is calculated + /// precisely, whereas for [DIST_L2] (Euclidean distance) the distance can be calculated only with a + /// relative error (a ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) mask gives more accurate results). For `a`,`b`, and `c`, OpenCV + /// uses the values suggested in the original paper: + /// - DIST_L1: `a = 1, b = 2` + /// - DIST_L2: + /// - `3 x 3`: `a=0.955, b=1.3693` + /// - `5 x 5`: `a=1, b=1.4, c=2.1969` + /// - DIST_C: `a = 1, b = 1` + /// + /// Typically, for a fast, coarse distance estimation #DIST_L2, a ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes%203) mask is used. For a + /// more accurate distance estimation #DIST_L2, a ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) mask or the precise algorithm is used. + /// Note that both the precise and the approximate algorithms are linear on the number of pixels. + /// + /// This variant of the function does not only compute the minimum distance for each pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%29) + /// but also identifies the nearest connected component consisting of zero pixels + /// (labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the + /// component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function + /// automatically finds connected components of zero pixels in the input image and marks them with + /// distinct labels. When labelType==#DIST_LABEL_PIXEL, the function scans through the input image and + /// marks all the zero pixels with distinct labels. + /// + /// In this mode, the complexity is still linear. That is, the function provides a very fast way to + /// compute the Voronoi diagram for a binary image. Currently, the second variant can use only the + /// approximate distance transform algorithm, i.e. maskSize=[DIST_MASK_PRECISE] is not supported + /// yet. + /// + /// ## Parameters + /// * src: 8-bit, single-channel (binary) source image. + /// * dst: Output image with calculated distances. It is a 8-bit or 32-bit floating-point, + /// single-channel image of the same size as src. + /// * labels: Output 2D array of labels (the discrete Voronoi diagram). It has the type + /// CV_32SC1 and the same size as src. + /// * distanceType: Type of distance, see [distance_types] + /// * maskSize: Size of the distance transform mask, see #DistanceTransformMasks. + /// [DIST_MASK_PRECISE] is not supported by this variant. In case of the [DIST_L1] or [DIST_C] distance type, + /// the parameter is forced to 3 because a ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes%203) mask gives the same result as ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%0A5) or any larger aperture. + /// * labelType: Type of the label array to build, see #DistanceTransformLabelTypes. + /// + /// ## Note + /// This alternative version of [distance_transform_with_labels] function uses the following default values for its arguments: + /// * label_type: DIST_LABEL_CCOMP + #[inline] + pub fn distance_transform_with_labels_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, labels: &mut impl ToOutputArray, distance_type: i32, mask_size: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + output_array_arg!(labels); + return_send!(via ocvrs_return); + unsafe { sys::cv_distanceTransform_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), labels.as_raw__OutputArray(), distance_type, mask_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the distance to the closest zero pixel for each pixel of the source image. + /// + /// The function cv::distanceTransform calculates the approximate or precise distance from every binary + /// image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. + /// + /// When maskSize == [DIST_MASK_PRECISE] and distanceType == [DIST_L2] , the function runs the + /// algorithm described in [Felzenszwalb04](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Felzenszwalb04) . This algorithm is parallelized with the TBB library. + /// + /// In other cases, the algorithm [Borgefors86](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Borgefors86) is used. This means that for a pixel the function + /// finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, + /// diagonal, or knight's move (the latest is available for a ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) mask). The overall + /// distance is calculated as a sum of these basic distances. Since the distance function should be + /// symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all + /// the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the + /// same cost (denoted as `c`). For the [DIST_C] and [DIST_L1] types, the distance is calculated + /// precisely, whereas for [DIST_L2] (Euclidean distance) the distance can be calculated only with a + /// relative error (a ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) mask gives more accurate results). For `a`,`b`, and `c`, OpenCV + /// uses the values suggested in the original paper: + /// - DIST_L1: `a = 1, b = 2` + /// - DIST_L2: + /// - `3 x 3`: `a=0.955, b=1.3693` + /// - `5 x 5`: `a=1, b=1.4, c=2.1969` + /// - DIST_C: `a = 1, b = 1` + /// + /// Typically, for a fast, coarse distance estimation #DIST_L2, a ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes%203) mask is used. For a + /// more accurate distance estimation #DIST_L2, a ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) mask or the precise algorithm is used. + /// Note that both the precise and the approximate algorithms are linear on the number of pixels. + /// + /// This variant of the function does not only compute the minimum distance for each pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%29) + /// but also identifies the nearest connected component consisting of zero pixels + /// (labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the + /// component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function + /// automatically finds connected components of zero pixels in the input image and marks them with + /// distinct labels. When labelType==#DIST_LABEL_PIXEL, the function scans through the input image and + /// marks all the zero pixels with distinct labels. + /// + /// In this mode, the complexity is still linear. That is, the function provides a very fast way to + /// compute the Voronoi diagram for a binary image. Currently, the second variant can use only the + /// approximate distance transform algorithm, i.e. maskSize=[DIST_MASK_PRECISE] is not supported + /// yet. + /// + /// ## Parameters + /// * src: 8-bit, single-channel (binary) source image. + /// * dst: Output image with calculated distances. It is a 8-bit or 32-bit floating-point, + /// single-channel image of the same size as src. + /// * labels: Output 2D array of labels (the discrete Voronoi diagram). It has the type + /// CV_32SC1 and the same size as src. + /// * distanceType: Type of distance, see [distance_types] + /// * maskSize: Size of the distance transform mask, see #DistanceTransformMasks. + /// [DIST_MASK_PRECISE] is not supported by this variant. In case of the [DIST_L1] or [DIST_C] distance type, + /// the parameter is forced to 3 because a ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes%203) mask gives the same result as ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%0A5) or any larger aperture. + /// * labelType: Type of the label array to build, see #DistanceTransformLabelTypes. + /// + /// ## C++ default parameters + /// * label_type: DIST_LABEL_CCOMP + #[inline] + pub fn distance_transform_with_labels(src: &impl ToInputArray, dst: &mut impl ToOutputArray, labels: &mut impl ToOutputArray, distance_type: i32, mask_size: i32, label_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + output_array_arg!(labels); + return_send!(via ocvrs_return); + unsafe { sys::cv_distanceTransform_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), labels.as_raw__OutputArray(), distance_type, mask_size, label_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the distance to the closest zero pixel for each pixel of the source image. + /// + /// The function cv::distanceTransform calculates the approximate or precise distance from every binary + /// image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. + /// + /// When maskSize == [DIST_MASK_PRECISE] and distanceType == [DIST_L2] , the function runs the + /// algorithm described in [Felzenszwalb04](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Felzenszwalb04) . This algorithm is parallelized with the TBB library. + /// + /// In other cases, the algorithm [Borgefors86](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Borgefors86) is used. This means that for a pixel the function + /// finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, + /// diagonal, or knight's move (the latest is available for a ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) mask). The overall + /// distance is calculated as a sum of these basic distances. Since the distance function should be + /// symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all + /// the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the + /// same cost (denoted as `c`). For the [DIST_C] and [DIST_L1] types, the distance is calculated + /// precisely, whereas for [DIST_L2] (Euclidean distance) the distance can be calculated only with a + /// relative error (a ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) mask gives more accurate results). For `a`,`b`, and `c`, OpenCV + /// uses the values suggested in the original paper: + /// - DIST_L1: `a = 1, b = 2` + /// - DIST_L2: + /// - `3 x 3`: `a=0.955, b=1.3693` + /// - `5 x 5`: `a=1, b=1.4, c=2.1969` + /// - DIST_C: `a = 1, b = 1` + /// + /// Typically, for a fast, coarse distance estimation #DIST_L2, a ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes%203) mask is used. For a + /// more accurate distance estimation #DIST_L2, a ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) mask or the precise algorithm is used. + /// Note that both the precise and the approximate algorithms are linear on the number of pixels. + /// + /// This variant of the function does not only compute the minimum distance for each pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%29) + /// but also identifies the nearest connected component consisting of zero pixels + /// (labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the + /// component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function + /// automatically finds connected components of zero pixels in the input image and marks them with + /// distinct labels. When labelType==#DIST_LABEL_PIXEL, the function scans through the input image and + /// marks all the zero pixels with distinct labels. + /// + /// In this mode, the complexity is still linear. That is, the function provides a very fast way to + /// compute the Voronoi diagram for a binary image. Currently, the second variant can use only the + /// approximate distance transform algorithm, i.e. maskSize=[DIST_MASK_PRECISE] is not supported + /// yet. + /// + /// ## Parameters + /// * src: 8-bit, single-channel (binary) source image. + /// * dst: Output image with calculated distances. It is a 8-bit or 32-bit floating-point, + /// single-channel image of the same size as src. + /// * labels: Output 2D array of labels (the discrete Voronoi diagram). It has the type + /// CV_32SC1 and the same size as src. + /// * distanceType: Type of distance, see [distance_types] + /// * maskSize: Size of the distance transform mask, see #DistanceTransformMasks. + /// [DIST_MASK_PRECISE] is not supported by this variant. In case of the [DIST_L1] or [DIST_C] distance type, + /// the parameter is forced to 3 because a ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes%203) mask gives the same result as ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%0A5) or any larger aperture. + /// * labelType: Type of the label array to build, see #DistanceTransformLabelTypes. + /// + /// ## Overloaded parameters + /// + /// * src: 8-bit, single-channel (binary) source image. + /// * dst: Output image with calculated distances. It is a 8-bit or 32-bit floating-point, + /// single-channel image of the same size as src . + /// * distanceType: Type of distance, see [distance_types] + /// * maskSize: Size of the distance transform mask, see #DistanceTransformMasks. In case of the + /// [DIST_L1] or [DIST_C] distance type, the parameter is forced to 3 because a ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes%203) mask gives + /// the same result as ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) or any larger aperture. + /// * dstType: Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for + /// the first variant of the function and distanceType == #DIST_L1. + /// + /// ## Note + /// This alternative version of [distance_transform] function uses the following default values for its arguments: + /// * dst_type: CV_32F + #[inline] + pub fn distance_transform_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, distance_type: i32, mask_size: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_distanceTransform_const__InputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), distance_type, mask_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the distance to the closest zero pixel for each pixel of the source image. + /// + /// The function cv::distanceTransform calculates the approximate or precise distance from every binary + /// image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. + /// + /// When maskSize == [DIST_MASK_PRECISE] and distanceType == [DIST_L2] , the function runs the + /// algorithm described in [Felzenszwalb04](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Felzenszwalb04) . This algorithm is parallelized with the TBB library. + /// + /// In other cases, the algorithm [Borgefors86](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Borgefors86) is used. This means that for a pixel the function + /// finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, + /// diagonal, or knight's move (the latest is available for a ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) mask). The overall + /// distance is calculated as a sum of these basic distances. Since the distance function should be + /// symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all + /// the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the + /// same cost (denoted as `c`). For the [DIST_C] and [DIST_L1] types, the distance is calculated + /// precisely, whereas for [DIST_L2] (Euclidean distance) the distance can be calculated only with a + /// relative error (a ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) mask gives more accurate results). For `a`,`b`, and `c`, OpenCV + /// uses the values suggested in the original paper: + /// - DIST_L1: `a = 1, b = 2` + /// - DIST_L2: + /// - `3 x 3`: `a=0.955, b=1.3693` + /// - `5 x 5`: `a=1, b=1.4, c=2.1969` + /// - DIST_C: `a = 1, b = 1` + /// + /// Typically, for a fast, coarse distance estimation #DIST_L2, a ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes%203) mask is used. For a + /// more accurate distance estimation #DIST_L2, a ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) mask or the precise algorithm is used. + /// Note that both the precise and the approximate algorithms are linear on the number of pixels. + /// + /// This variant of the function does not only compute the minimum distance for each pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%29) + /// but also identifies the nearest connected component consisting of zero pixels + /// (labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the + /// component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function + /// automatically finds connected components of zero pixels in the input image and marks them with + /// distinct labels. When labelType==#DIST_LABEL_PIXEL, the function scans through the input image and + /// marks all the zero pixels with distinct labels. + /// + /// In this mode, the complexity is still linear. That is, the function provides a very fast way to + /// compute the Voronoi diagram for a binary image. Currently, the second variant can use only the + /// approximate distance transform algorithm, i.e. maskSize=[DIST_MASK_PRECISE] is not supported + /// yet. + /// + /// ## Parameters + /// * src: 8-bit, single-channel (binary) source image. + /// * dst: Output image with calculated distances. It is a 8-bit or 32-bit floating-point, + /// single-channel image of the same size as src. + /// * labels: Output 2D array of labels (the discrete Voronoi diagram). It has the type + /// CV_32SC1 and the same size as src. + /// * distanceType: Type of distance, see [distance_types] + /// * maskSize: Size of the distance transform mask, see #DistanceTransformMasks. + /// [DIST_MASK_PRECISE] is not supported by this variant. In case of the [DIST_L1] or [DIST_C] distance type, + /// the parameter is forced to 3 because a ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes%203) mask gives the same result as ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%0A5) or any larger aperture. + /// * labelType: Type of the label array to build, see #DistanceTransformLabelTypes. + /// + /// ## Overloaded parameters + /// + /// * src: 8-bit, single-channel (binary) source image. + /// * dst: Output image with calculated distances. It is a 8-bit or 32-bit floating-point, + /// single-channel image of the same size as src . + /// * distanceType: Type of distance, see [distance_types] + /// * maskSize: Size of the distance transform mask, see #DistanceTransformMasks. In case of the + /// [DIST_L1] or [DIST_C] distance type, the parameter is forced to 3 because a ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes%203) mask gives + /// the same result as ![inline formula](https://latex.codecogs.com/png.latex?5%5Ctimes%205) or any larger aperture. + /// * dstType: Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for + /// the first variant of the function and distanceType == #DIST_L1. + /// + /// ## C++ default parameters + /// * dst_type: CV_32F + #[inline] + pub fn distance_transform(src: &impl ToInputArray, dst: &mut impl ToOutputArray, distance_type: i32, mask_size: i32, dst_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_distanceTransform_const__InputArrayR_const__OutputArrayR_int_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), distance_type, mask_size, dst_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum. + /// + /// The function cv::divSpectrums performs the per-element division of the first array by the second array. + /// The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform. + /// + /// ## Parameters + /// * a: first input array. + /// * b: second input array of the same size and type as src1 . + /// * c: output array of the same size and type as src1 . + /// * flags: operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that + /// each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value. + /// * conjB: optional flag that conjugates the second input array before the multiplication (true) + /// or not (false). + /// + /// ## Note + /// This alternative version of [div_spectrums] function uses the following default values for its arguments: + /// * conj_b: false + #[inline] + pub fn div_spectrums_def(a: &impl ToInputArray, b: &impl ToInputArray, c: &mut impl ToOutputArray, flags: i32) -> Result<()> { + input_array_arg!(a); + input_array_arg!(b); + output_array_arg!(c); + return_send!(via ocvrs_return); + unsafe { sys::cv_divSpectrums_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(a.as_raw__InputArray(), b.as_raw__InputArray(), c.as_raw__OutputArray(), flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum. + /// + /// The function cv::divSpectrums performs the per-element division of the first array by the second array. + /// The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform. + /// + /// ## Parameters + /// * a: first input array. + /// * b: second input array of the same size and type as src1 . + /// * c: output array of the same size and type as src1 . + /// * flags: operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that + /// each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value. + /// * conjB: optional flag that conjugates the second input array before the multiplication (true) + /// or not (false). + /// + /// ## C++ default parameters + /// * conj_b: false + #[inline] + pub fn div_spectrums(a: &impl ToInputArray, b: &impl ToInputArray, c: &mut impl ToOutputArray, flags: i32, conj_b: bool) -> Result<()> { + input_array_arg!(a); + input_array_arg!(b); + output_array_arg!(c); + return_send!(via ocvrs_return); + unsafe { sys::cv_divSpectrums_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_bool(a.as_raw__InputArray(), b.as_raw__InputArray(), c.as_raw__OutputArray(), flags, conj_b, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws contours outlines or filled contours. + /// + /// The function draws contour outlines in the image if ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bthickness%7D%20%5Cge%200) or fills the area + /// bounded by the contours if ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bthickness%7D%3C0) . The example below shows how to retrieve + /// connected components from the binary image and label them: : + /// @include snippets/imgproc_drawContours.cpp + /// + /// ## Parameters + /// * image: Destination image. + /// * contours: All the input contours. Each contour is stored as a point vector. + /// * contourIdx: Parameter indicating a contour to draw. If it is negative, all the contours are drawn. + /// * color: Color of the contours. + /// * thickness: Thickness of lines the contours are drawn with. If it is negative (for example, + /// thickness=[FILLED] ), the contour interiors are drawn. + /// * lineType: Line connectivity. See [line_types] + /// * hierarchy: Optional information about hierarchy. It is only needed if you want to draw only + /// some of the contours (see maxLevel ). + /// * maxLevel: Maximal level for drawn contours. If it is 0, only the specified contour is drawn. + /// If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function + /// draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This + /// parameter is only taken into account when there is hierarchy available. + /// * offset: Optional contour shift parameter. Shift all the drawn contours by the specified + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Boffset%7D%3D%28dx%2Cdy%29) . + /// + /// Note: When thickness=#FILLED, the function is designed to handle connected components with holes correctly + /// even when no hierarchy data is provided. This is done by analyzing all the outlines together + /// using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved + /// contours. In order to solve this problem, you need to call [draw_contours] separately for each sub-group + /// of contours, or iterate over the collection using contourIdx parameter. + /// + /// ## Note + /// This alternative version of [draw_contours] function uses the following default values for its arguments: + /// * thickness: 1 + /// * line_type: LINE_8 + /// * hierarchy: noArray() + /// * max_level: INT_MAX + /// * offset: Point() + #[inline] + pub fn draw_contours_def(image: &mut impl ToInputOutputArray, contours: &impl ToInputArray, contour_idx: i32, color: core::Scalar) -> Result<()> { + input_output_array_arg!(image); + input_array_arg!(contours); + return_send!(via ocvrs_return); + unsafe { sys::cv_drawContours_const__InputOutputArrayR_const__InputArrayR_int_const_ScalarR(image.as_raw__InputOutputArray(), contours.as_raw__InputArray(), contour_idx, &color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws contours outlines or filled contours. + /// + /// The function draws contour outlines in the image if ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bthickness%7D%20%5Cge%200) or fills the area + /// bounded by the contours if ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bthickness%7D%3C0) . The example below shows how to retrieve + /// connected components from the binary image and label them: : + /// @include snippets/imgproc_drawContours.cpp + /// + /// ## Parameters + /// * image: Destination image. + /// * contours: All the input contours. Each contour is stored as a point vector. + /// * contourIdx: Parameter indicating a contour to draw. If it is negative, all the contours are drawn. + /// * color: Color of the contours. + /// * thickness: Thickness of lines the contours are drawn with. If it is negative (for example, + /// thickness=[FILLED] ), the contour interiors are drawn. + /// * lineType: Line connectivity. See [line_types] + /// * hierarchy: Optional information about hierarchy. It is only needed if you want to draw only + /// some of the contours (see maxLevel ). + /// * maxLevel: Maximal level for drawn contours. If it is 0, only the specified contour is drawn. + /// If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function + /// draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This + /// parameter is only taken into account when there is hierarchy available. + /// * offset: Optional contour shift parameter. Shift all the drawn contours by the specified + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Boffset%7D%3D%28dx%2Cdy%29) . + /// + /// Note: When thickness=#FILLED, the function is designed to handle connected components with holes correctly + /// even when no hierarchy data is provided. This is done by analyzing all the outlines together + /// using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved + /// contours. In order to solve this problem, you need to call [draw_contours] separately for each sub-group + /// of contours, or iterate over the collection using contourIdx parameter. + /// + /// ## C++ default parameters + /// * thickness: 1 + /// * line_type: LINE_8 + /// * hierarchy: noArray() + /// * max_level: INT_MAX + /// * offset: Point() + #[inline] + pub fn draw_contours(image: &mut impl ToInputOutputArray, contours: &impl ToInputArray, contour_idx: i32, color: core::Scalar, thickness: i32, line_type: i32, hierarchy: &impl ToInputArray, max_level: i32, offset: core::Point) -> Result<()> { + input_output_array_arg!(image); + input_array_arg!(contours); + input_array_arg!(hierarchy); + return_send!(via ocvrs_return); + unsafe { sys::cv_drawContours_const__InputOutputArrayR_const__InputArrayR_int_const_ScalarR_int_int_const__InputArrayR_int_Point(image.as_raw__InputOutputArray(), contours.as_raw__InputArray(), contour_idx, &color, thickness, line_type, hierarchy.as_raw__InputArray(), max_level, &offset, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a marker on a predefined position in an image. + /// + /// The function cv::drawMarker draws a marker on a given position in the image. For the moment several + /// marker types are supported, see [marker_types] for more information. + /// + /// ## Parameters + /// * img: Image. + /// * position: The point where the crosshair is positioned. + /// * color: Line color. + /// * markerType: The specific type of marker you want to use, see [marker_types] + /// * thickness: Line thickness. + /// * line_type: Type of the line, See [line_types] + /// * markerSize: The length of the marker axis [default = 20 pixels] + /// + /// ## Note + /// This alternative version of [draw_marker] function uses the following default values for its arguments: + /// * marker_type: MARKER_CROSS + /// * marker_size: 20 + /// * thickness: 1 + /// * line_type: 8 + #[inline] + pub fn draw_marker_def(img: &mut impl ToInputOutputArray, position: core::Point, color: core::Scalar) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_drawMarker_const__InputOutputArrayR_Point_const_ScalarR(img.as_raw__InputOutputArray(), &position, &color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a marker on a predefined position in an image. + /// + /// The function cv::drawMarker draws a marker on a given position in the image. For the moment several + /// marker types are supported, see [marker_types] for more information. + /// + /// ## Parameters + /// * img: Image. + /// * position: The point where the crosshair is positioned. + /// * color: Line color. + /// * markerType: The specific type of marker you want to use, see [marker_types] + /// * thickness: Line thickness. + /// * line_type: Type of the line, See [line_types] + /// * markerSize: The length of the marker axis [default = 20 pixels] + /// + /// ## C++ default parameters + /// * marker_type: MARKER_CROSS + /// * marker_size: 20 + /// * thickness: 1 + /// * line_type: 8 + #[inline] + pub fn draw_marker(img: &mut impl ToInputOutputArray, position: core::Point, color: core::Scalar, marker_type: i32, marker_size: i32, thickness: i32, line_type: i32) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_drawMarker_const__InputOutputArrayR_Point_const_ScalarR_int_int_int_int(img.as_raw__InputOutputArray(), &position, &color, marker_type, marker_size, thickness, line_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Approximates an elliptic arc with a polyline. + /// + /// The function ellipse2Poly computes the vertices of a polyline that approximates the specified + /// elliptic arc. It is used by #ellipse. If `arcStart` is greater than `arcEnd`, they are swapped. + /// + /// ## Parameters + /// * center: Center of the arc. + /// * axes: Half of the size of the ellipse main axes. See [ellipse] for details. + /// * angle: Rotation angle of the ellipse in degrees. See [ellipse] for details. + /// * arcStart: Starting angle of the elliptic arc in degrees. + /// * arcEnd: Ending angle of the elliptic arc in degrees. + /// * delta: Angle between the subsequent polyline vertices. It defines the approximation + /// accuracy. + /// * pts: Output vector of polyline vertices. + /// + /// ## Overloaded parameters + /// + /// * center: Center of the arc. + /// * axes: Half of the size of the ellipse main axes. See [ellipse] for details. + /// * angle: Rotation angle of the ellipse in degrees. See [ellipse] for details. + /// * arcStart: Starting angle of the elliptic arc in degrees. + /// * arcEnd: Ending angle of the elliptic arc in degrees. + /// * delta: Angle between the subsequent polyline vertices. It defines the approximation accuracy. + /// * pts: Output vector of polyline vertices. + #[inline] + pub fn ellipse_2_poly_f64(center: core::Point2d, axes: core::Size2d, angle: i32, arc_start: i32, arc_end: i32, delta: i32, pts: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ellipse2Poly_Point2d_Size2d_int_int_int_int_vectorLPoint2dGR(¢er, &axes, angle, arc_start, arc_end, delta, pts.as_raw_mut_VectorOfPoint2d(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Approximates an elliptic arc with a polyline. + /// + /// The function ellipse2Poly computes the vertices of a polyline that approximates the specified + /// elliptic arc. It is used by #ellipse. If `arcStart` is greater than `arcEnd`, they are swapped. + /// + /// ## Parameters + /// * center: Center of the arc. + /// * axes: Half of the size of the ellipse main axes. See [ellipse] for details. + /// * angle: Rotation angle of the ellipse in degrees. See [ellipse] for details. + /// * arcStart: Starting angle of the elliptic arc in degrees. + /// * arcEnd: Ending angle of the elliptic arc in degrees. + /// * delta: Angle between the subsequent polyline vertices. It defines the approximation + /// accuracy. + /// * pts: Output vector of polyline vertices. + #[inline] + pub fn ellipse_2_poly(center: core::Point, axes: core::Size, angle: i32, arc_start: i32, arc_end: i32, delta: i32, pts: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_ellipse2Poly_Point_Size_int_int_int_int_vectorLPointGR(¢er, &axes, angle, arc_start, arc_end, delta, pts.as_raw_mut_VectorOfPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a simple or thick elliptic arc or fills an ellipse sector. + /// + /// The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic + /// arc, or a filled ellipse sector. The drawing code uses general parametric form. + /// A piecewise-linear curve is used to approximate the elliptic arc + /// boundary. If you need more control of the ellipse rendering, you can retrieve the curve using + /// [ellipse2_poly] and then render it with [polylines] or fill it with #fillPoly. If you use the first + /// variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and + /// `endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains + /// the meaning of the parameters to draw the blue arc. + /// + /// ![Parameters of Elliptic Arc](https://docs.opencv.org/4.13.0/ellipse.svg) + /// + /// ## Parameters + /// * img: Image. + /// * center: Center of the ellipse. + /// * axes: Half of the size of the ellipse main axes. + /// * angle: Ellipse rotation angle in degrees. + /// * startAngle: Starting angle of the elliptic arc in degrees. + /// * endAngle: Ending angle of the elliptic arc in degrees. + /// * color: Ellipse color. + /// * thickness: Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that + /// a filled ellipse sector is to be drawn. + /// * lineType: Type of the ellipse boundary. See [line_types] + /// * shift: Number of fractional bits in the coordinates of the center and values of axes. + /// + /// ## Note + /// This alternative version of [ellipse] function uses the following default values for its arguments: + /// * thickness: 1 + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn ellipse_def(img: &mut impl ToInputOutputArray, center: core::Point, axes: core::Size, angle: f64, start_angle: f64, end_angle: f64, color: core::Scalar) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_ellipse_const__InputOutputArrayR_Point_Size_double_double_double_const_ScalarR(img.as_raw__InputOutputArray(), ¢er, &axes, angle, start_angle, end_angle, &color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a simple or thick elliptic arc or fills an ellipse sector. + /// + /// The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic + /// arc, or a filled ellipse sector. The drawing code uses general parametric form. + /// A piecewise-linear curve is used to approximate the elliptic arc + /// boundary. If you need more control of the ellipse rendering, you can retrieve the curve using + /// [ellipse2_poly] and then render it with [polylines] or fill it with #fillPoly. If you use the first + /// variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and + /// `endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains + /// the meaning of the parameters to draw the blue arc. + /// + /// ![Parameters of Elliptic Arc](https://docs.opencv.org/4.13.0/ellipse.svg) + /// + /// ## Parameters + /// * img: Image. + /// * center: Center of the ellipse. + /// * axes: Half of the size of the ellipse main axes. + /// * angle: Ellipse rotation angle in degrees. + /// * startAngle: Starting angle of the elliptic arc in degrees. + /// * endAngle: Ending angle of the elliptic arc in degrees. + /// * color: Ellipse color. + /// * thickness: Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that + /// a filled ellipse sector is to be drawn. + /// * lineType: Type of the ellipse boundary. See [line_types] + /// * shift: Number of fractional bits in the coordinates of the center and values of axes. + /// + /// ## C++ default parameters + /// * thickness: 1 + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn ellipse(img: &mut impl ToInputOutputArray, center: core::Point, axes: core::Size, angle: f64, start_angle: f64, end_angle: f64, color: core::Scalar, thickness: i32, line_type: i32, shift: i32) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_ellipse_const__InputOutputArrayR_Point_Size_double_double_double_const_ScalarR_int_int_int(img.as_raw__InputOutputArray(), ¢er, &axes, angle, start_angle, end_angle, &color, thickness, line_type, shift, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a simple or thick elliptic arc or fills an ellipse sector. + /// + /// The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic + /// arc, or a filled ellipse sector. The drawing code uses general parametric form. + /// A piecewise-linear curve is used to approximate the elliptic arc + /// boundary. If you need more control of the ellipse rendering, you can retrieve the curve using + /// [ellipse2_poly] and then render it with [polylines] or fill it with #fillPoly. If you use the first + /// variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and + /// `endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains + /// the meaning of the parameters to draw the blue arc. + /// + /// ![Parameters of Elliptic Arc](https://docs.opencv.org/4.13.0/ellipse.svg) + /// + /// ## Parameters + /// * img: Image. + /// * center: Center of the ellipse. + /// * axes: Half of the size of the ellipse main axes. + /// * angle: Ellipse rotation angle in degrees. + /// * startAngle: Starting angle of the elliptic arc in degrees. + /// * endAngle: Ending angle of the elliptic arc in degrees. + /// * color: Ellipse color. + /// * thickness: Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that + /// a filled ellipse sector is to be drawn. + /// * lineType: Type of the ellipse boundary. See [line_types] + /// * shift: Number of fractional bits in the coordinates of the center and values of axes. + /// + /// ## Overloaded parameters + /// + /// * img: Image. + /// * box: Alternative ellipse representation via RotatedRect. This means that the function draws + /// an ellipse inscribed in the rotated rectangle. + /// * color: Ellipse color. + /// * thickness: Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that + /// a filled ellipse sector is to be drawn. + /// * lineType: Type of the ellipse boundary. See [line_types] + /// + /// ## Note + /// This alternative version of [ellipse_rotated_rect] function uses the following default values for its arguments: + /// * thickness: 1 + /// * line_type: LINE_8 + #[inline] + pub fn ellipse_rotated_rect_def(img: &mut impl ToInputOutputArray, box_: core::RotatedRect, color: core::Scalar) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_ellipse_const__InputOutputArrayR_const_RotatedRectR_const_ScalarR(img.as_raw__InputOutputArray(), &box_, &color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a simple or thick elliptic arc or fills an ellipse sector. + /// + /// The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic + /// arc, or a filled ellipse sector. The drawing code uses general parametric form. + /// A piecewise-linear curve is used to approximate the elliptic arc + /// boundary. If you need more control of the ellipse rendering, you can retrieve the curve using + /// [ellipse2_poly] and then render it with [polylines] or fill it with #fillPoly. If you use the first + /// variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and + /// `endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains + /// the meaning of the parameters to draw the blue arc. + /// + /// ![Parameters of Elliptic Arc](https://docs.opencv.org/4.13.0/ellipse.svg) + /// + /// ## Parameters + /// * img: Image. + /// * center: Center of the ellipse. + /// * axes: Half of the size of the ellipse main axes. + /// * angle: Ellipse rotation angle in degrees. + /// * startAngle: Starting angle of the elliptic arc in degrees. + /// * endAngle: Ending angle of the elliptic arc in degrees. + /// * color: Ellipse color. + /// * thickness: Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that + /// a filled ellipse sector is to be drawn. + /// * lineType: Type of the ellipse boundary. See [line_types] + /// * shift: Number of fractional bits in the coordinates of the center and values of axes. + /// + /// ## Overloaded parameters + /// + /// * img: Image. + /// * box: Alternative ellipse representation via RotatedRect. This means that the function draws + /// an ellipse inscribed in the rotated rectangle. + /// * color: Ellipse color. + /// * thickness: Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that + /// a filled ellipse sector is to be drawn. + /// * lineType: Type of the ellipse boundary. See #LineTypes + /// + /// ## C++ default parameters + /// * thickness: 1 + /// * line_type: LINE_8 + #[inline] + pub fn ellipse_rotated_rect(img: &mut impl ToInputOutputArray, box_: core::RotatedRect, color: core::Scalar, thickness: i32, line_type: i32) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_ellipse_const__InputOutputArrayR_const_RotatedRectR_const_ScalarR_int_int(img.as_raw__InputOutputArray(), &box_, &color, thickness, line_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Equalizes the histogram of a grayscale image. + /// + /// The function equalizes the histogram of the input image using the following algorithm: + /// + /// - Calculate the histogram ![inline formula](https://latex.codecogs.com/png.latex?H) for src . + /// - Normalize the histogram so that the sum of histogram bins is 255. + /// - Compute the integral of the histogram: + /// ![block formula](https://latex.codecogs.com/png.latex?H%27%5Fi%20%3D%20%20%5Csum%20%5F%7B0%20%20%5Cle%20j%20%3C%20i%7D%20H%28j%29) + /// - Transform the image using ![inline formula](https://latex.codecogs.com/png.latex?H%27) as a look-up table: ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%28x%2Cy%29%20%3D%20H%27%28%5Ctexttt%7Bsrc%7D%28x%2Cy%29%29) + /// + /// The algorithm normalizes the brightness and increases the contrast of the image. + /// + /// ## Parameters + /// * src: Source 8-bit single channel image. + /// * dst: Destination image of the same size and type as src . + #[inline] + pub fn equalize_hist(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_equalizeHist_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Erodes an image by using a specific structuring element. + /// + /// The function erodes the source image using the specified structuring element that determines the + /// shape of a pixel neighborhood over which the minimum is taken: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Cmin%20%5F%7B%28x%27%2Cy%27%29%3A%20%20%5C%2C%20%5Ctexttt%7Belement%7D%20%28x%27%2Cy%27%29%20%5Cne0%20%7D%20%5Ctexttt%7Bsrc%7D%20%28x%2Bx%27%2Cy%2By%27%29) + /// + /// The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In + /// case of multi-channel images, each channel is processed independently. + /// + /// ## Parameters + /// * src: input image; the number of channels can be arbitrary, but the depth should be one of + /// CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. + /// * dst: output image of the same size and type as src. + /// * kernel: structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular + /// structuring element is used. Kernel can be created using #getStructuringElement. + /// * anchor: position of the anchor within the element; default value (-1, -1) means that the + /// anchor is at the element center. + /// * iterations: number of times erosion is applied. + /// * borderType: pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// * borderValue: border value in case of a constant border + /// ## See also + /// dilate, morphologyEx, getStructuringElement + /// + /// ## Note + /// This alternative version of [erode] function uses the following default values for its arguments: + /// * anchor: Point(-1,-1) + /// * iterations: 1 + /// * border_type: BORDER_CONSTANT + /// * border_value: morphologyDefaultBorderValue() + #[inline] + pub fn erode_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, kernel: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(kernel); + return_send!(via ocvrs_return); + unsafe { sys::cv_erode_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), kernel.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Erodes an image by using a specific structuring element. + /// + /// The function erodes the source image using the specified structuring element that determines the + /// shape of a pixel neighborhood over which the minimum is taken: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Cmin%20%5F%7B%28x%27%2Cy%27%29%3A%20%20%5C%2C%20%5Ctexttt%7Belement%7D%20%28x%27%2Cy%27%29%20%5Cne0%20%7D%20%5Ctexttt%7Bsrc%7D%20%28x%2Bx%27%2Cy%2By%27%29) + /// + /// The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In + /// case of multi-channel images, each channel is processed independently. + /// + /// ## Parameters + /// * src: input image; the number of channels can be arbitrary, but the depth should be one of + /// CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. + /// * dst: output image of the same size and type as src. + /// * kernel: structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular + /// structuring element is used. Kernel can be created using #getStructuringElement. + /// * anchor: position of the anchor within the element; default value (-1, -1) means that the + /// anchor is at the element center. + /// * iterations: number of times erosion is applied. + /// * borderType: pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// * borderValue: border value in case of a constant border + /// ## See also + /// dilate, morphologyEx, getStructuringElement + /// + /// ## C++ default parameters + /// * anchor: Point(-1,-1) + /// * iterations: 1 + /// * border_type: BORDER_CONSTANT + /// * border_value: morphologyDefaultBorderValue() + #[inline] + pub fn erode(src: &impl ToInputArray, dst: &mut impl ToOutputArray, kernel: &impl ToInputArray, anchor: core::Point, iterations: i32, border_type: i32, border_value: core::Scalar) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(kernel); + return_send!(via ocvrs_return); + unsafe { sys::cv_erode_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Point_int_int_const_ScalarR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), kernel.as_raw__InputArray(), &anchor, iterations, border_type, &border_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fills a convex polygon. + /// + /// The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the + /// function [fill_poly] . It can fill not only convex polygons but any monotonic polygon without + /// self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) + /// twice at the most (though, its top-most and/or the bottom edge could be horizontal). + /// + /// ## Parameters + /// * img: Image. + /// * points: Polygon vertices. + /// * color: Polygon color. + /// * lineType: Type of the polygon boundaries. See [line_types] + /// * shift: Number of fractional bits in the vertex coordinates. + /// + /// ## Note + /// This alternative version of [fill_convex_poly] function uses the following default values for its arguments: + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn fill_convex_poly_def(img: &mut impl ToInputOutputArray, points: &impl ToInputArray, color: core::Scalar) -> Result<()> { + input_output_array_arg!(img); + input_array_arg!(points); + return_send!(via ocvrs_return); + unsafe { sys::cv_fillConvexPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR(img.as_raw__InputOutputArray(), points.as_raw__InputArray(), &color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fills a convex polygon. + /// + /// The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the + /// function [fill_poly] . It can fill not only convex polygons but any monotonic polygon without + /// self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) + /// twice at the most (though, its top-most and/or the bottom edge could be horizontal). + /// + /// ## Parameters + /// * img: Image. + /// * points: Polygon vertices. + /// * color: Polygon color. + /// * lineType: Type of the polygon boundaries. See [line_types] + /// * shift: Number of fractional bits in the vertex coordinates. + /// + /// ## C++ default parameters + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn fill_convex_poly(img: &mut impl ToInputOutputArray, points: &impl ToInputArray, color: core::Scalar, line_type: i32, shift: i32) -> Result<()> { + input_output_array_arg!(img); + input_array_arg!(points); + return_send!(via ocvrs_return); + unsafe { sys::cv_fillConvexPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR_int_int(img.as_raw__InputOutputArray(), points.as_raw__InputArray(), &color, line_type, shift, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fills the area bounded by one or more polygons. + /// + /// The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill + /// complex areas, for example, areas with holes, contours with self-intersections (some of their + /// parts), and so forth. + /// + /// ## Parameters + /// * img: Image. + /// * pts: Array of polygons where each polygon is represented as an array of points. + /// * color: Polygon color. + /// * lineType: Type of the polygon boundaries. See [line_types] + /// * shift: Number of fractional bits in the vertex coordinates. + /// * offset: Optional offset of all points of the contours. + /// + /// ## Note + /// This alternative version of [fill_poly] function uses the following default values for its arguments: + /// * line_type: LINE_8 + /// * shift: 0 + /// * offset: Point() + #[inline] + pub fn fill_poly_def(img: &mut impl ToInputOutputArray, pts: &impl ToInputArray, color: core::Scalar) -> Result<()> { + input_output_array_arg!(img); + input_array_arg!(pts); + return_send!(via ocvrs_return); + unsafe { sys::cv_fillPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR(img.as_raw__InputOutputArray(), pts.as_raw__InputArray(), &color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fills the area bounded by one or more polygons. + /// + /// The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill + /// complex areas, for example, areas with holes, contours with self-intersections (some of their + /// parts), and so forth. + /// + /// ## Parameters + /// * img: Image. + /// * pts: Array of polygons where each polygon is represented as an array of points. + /// * color: Polygon color. + /// * lineType: Type of the polygon boundaries. See [line_types] + /// * shift: Number of fractional bits in the vertex coordinates. + /// * offset: Optional offset of all points of the contours. + /// + /// ## C++ default parameters + /// * line_type: LINE_8 + /// * shift: 0 + /// * offset: Point() + #[inline] + pub fn fill_poly(img: &mut impl ToInputOutputArray, pts: &impl ToInputArray, color: core::Scalar, line_type: i32, shift: i32, offset: core::Point) -> Result<()> { + input_output_array_arg!(img); + input_array_arg!(pts); + return_send!(via ocvrs_return); + unsafe { sys::cv_fillPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR_int_int_Point(img.as_raw__InputOutputArray(), pts.as_raw__InputArray(), &color, line_type, shift, &offset, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Convolves an image with the kernel. + /// + /// The function applies an arbitrary linear filter to an image. In-place operation is supported. When + /// the aperture is partially outside the image, the function interpolates outlier pixel values + /// according to the specified border mode. + /// + /// The function does actually compute correlation, not the convolution: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Csum%20%5F%7B%20%5Csubstack%7B0%5Cleq%20x%27%20%3C%20%5Ctexttt%7Bkernel%2Ecols%7D%5C%5C%7B0%5Cleq%20y%27%20%3C%20%5Ctexttt%7Bkernel%2Erows%7D%7D%7D%7D%20%20%5Ctexttt%7Bkernel%7D%20%28x%27%2Cy%27%29%2A%20%5Ctexttt%7Bsrc%7D%20%28x%2Bx%27%2D%20%5Ctexttt%7Banchor%2Ex%7D%20%2Cy%2By%27%2D%20%5Ctexttt%7Banchor%2Ey%7D%20%29) + /// + /// That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip + /// the kernel using [flip] and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - + /// anchor.y - 1)`. + /// + /// The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or + /// larger) and the direct algorithm for small kernels. + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image of the same size and the same number of channels as src. + /// * ddepth: desired depth of the destination image, see [filter_depths] "combinations" + /// * kernel: convolution kernel (or rather a correlation kernel), a single-channel floating point + /// matrix; if you want to apply different kernels to different channels, split the image into + /// separate color planes using split and process them individually. + /// * anchor: anchor of the kernel that indicates the relative position of a filtered point within + /// the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor + /// is at the kernel center. + /// * delta: optional value added to the filtered pixels before storing them in dst. + /// * borderType: pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// sepFilter2D, dft, matchTemplate + /// + /// ## Note + /// This alternative version of [filter_2d] function uses the following default values for its arguments: + /// * anchor: Point(-1,-1) + /// * delta: 0 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn filter_2d_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32, kernel: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(kernel); + return_send!(via ocvrs_return); + unsafe { sys::cv_filter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, kernel.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Convolves an image with the kernel. + /// + /// The function applies an arbitrary linear filter to an image. In-place operation is supported. When + /// the aperture is partially outside the image, the function interpolates outlier pixel values + /// according to the specified border mode. + /// + /// The function does actually compute correlation, not the convolution: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Csum%20%5F%7B%20%5Csubstack%7B0%5Cleq%20x%27%20%3C%20%5Ctexttt%7Bkernel%2Ecols%7D%5C%5C%7B0%5Cleq%20y%27%20%3C%20%5Ctexttt%7Bkernel%2Erows%7D%7D%7D%7D%20%20%5Ctexttt%7Bkernel%7D%20%28x%27%2Cy%27%29%2A%20%5Ctexttt%7Bsrc%7D%20%28x%2Bx%27%2D%20%5Ctexttt%7Banchor%2Ex%7D%20%2Cy%2By%27%2D%20%5Ctexttt%7Banchor%2Ey%7D%20%29) + /// + /// That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip + /// the kernel using [flip] and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - + /// anchor.y - 1)`. + /// + /// The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or + /// larger) and the direct algorithm for small kernels. + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image of the same size and the same number of channels as src. + /// * ddepth: desired depth of the destination image, see [filter_depths] "combinations" + /// * kernel: convolution kernel (or rather a correlation kernel), a single-channel floating point + /// matrix; if you want to apply different kernels to different channels, split the image into + /// separate color planes using split and process them individually. + /// * anchor: anchor of the kernel that indicates the relative position of a filtered point within + /// the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor + /// is at the kernel center. + /// * delta: optional value added to the filtered pixels before storing them in dst. + /// * borderType: pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// sepFilter2D, dft, matchTemplate + /// + /// ## C++ default parameters + /// * anchor: Point(-1,-1) + /// * delta: 0 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn filter_2d(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32, kernel: &impl ToInputArray, anchor: core::Point, delta: f64, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(kernel); + return_send!(via ocvrs_return); + unsafe { sys::cv_filter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_Point_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, kernel.as_raw__InputArray(), &anchor, delta, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Find contours using link runs algorithm + /// + /// This function implements an algorithm different from cv::findContours: + /// - doesn't allocate temporary image internally, thus it has reduced memory consumption + /// - supports CV_8UC1 images only + /// - outputs 2-level hierarhy only (RETR_CCOMP mode) + /// - doesn't support approximation change other than CHAIN_APPROX_SIMPLE + /// In all other aspects this function is compatible with cv::findContours. + /// + /// ## Overloaded parameters + #[inline] + pub fn find_contours_link_runs_1(image: &impl ToInputArray, contours: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(image); + output_array_arg!(contours); + return_send!(via ocvrs_return); + unsafe { sys::cv_findContoursLinkRuns_const__InputArrayR_const__OutputArrayR(image.as_raw__InputArray(), contours.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Find contours using link runs algorithm + /// + /// This function implements an algorithm different from cv::findContours: + /// - doesn't allocate temporary image internally, thus it has reduced memory consumption + /// - supports CV_8UC1 images only + /// - outputs 2-level hierarhy only (RETR_CCOMP mode) + /// - doesn't support approximation change other than CHAIN_APPROX_SIMPLE + /// In all other aspects this function is compatible with cv::findContours. + #[inline] + pub fn find_contours_link_runs(image: &impl ToInputArray, contours: &mut impl ToOutputArray, hierarchy: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(image); + output_array_arg!(contours); + output_array_arg!(hierarchy); + return_send!(via ocvrs_return); + unsafe { sys::cv_findContoursLinkRuns_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(image.as_raw__InputArray(), contours.as_raw__OutputArray(), hierarchy.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds contours in a binary image. + /// + /// The function retrieves contours from the binary image using the algorithm [Suzuki85](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Suzuki85) . The contours + /// are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the + /// OpenCV sample directory. + /// + /// Note: Since opencv 3.2 source image is not modified by this function. + /// + /// ## Parameters + /// * image: Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero + /// pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, [threshold] , + /// #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. + /// If mode equals to [RETR_CCOMP] or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). + /// * contours: Detected contours. Each contour is stored as a vector of points (e.g. + /// std::vector >). + /// * hierarchy: Optional output vector (e.g. std::vector), containing information about the image topology. It has + /// as many elements as the number of contours. For each i-th contour contours[i], the elements + /// hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices + /// in contours of the next and previous contours at the same hierarchical level, the first child + /// contour and the parent contour, respectively. If for the contour i there are no next, previous, + /// parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. + /// + /// Note: In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour. + /// * mode: Contour retrieval mode, see [retrieval_modes] + /// * method: Contour approximation method, see [contour_approximation_modes] + /// * offset: Optional offset by which every contour point is shifted. This is useful if the + /// contours are extracted from the image ROI and then they should be analyzed in the whole image + /// context. + /// + /// ## Note + /// This alternative version of [find_contours_with_hierarchy] function uses the following default values for its arguments: + /// * offset: Point() + #[inline] + pub fn find_contours_with_hierarchy_def(image: &impl ToInputArray, contours: &mut impl ToOutputArray, hierarchy: &mut impl ToOutputArray, mode: i32, method: i32) -> Result<()> { + input_array_arg!(image); + output_array_arg!(contours); + output_array_arg!(hierarchy); + return_send!(via ocvrs_return); + unsafe { sys::cv_findContours_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(image.as_raw__InputArray(), contours.as_raw__OutputArray(), hierarchy.as_raw__OutputArray(), mode, method, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds contours in a binary image. + /// + /// The function retrieves contours from the binary image using the algorithm [Suzuki85](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Suzuki85) . The contours + /// are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the + /// OpenCV sample directory. + /// + /// Note: Since opencv 3.2 source image is not modified by this function. + /// + /// ## Parameters + /// * image: Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero + /// pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, [threshold] , + /// #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. + /// If mode equals to [RETR_CCOMP] or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). + /// * contours: Detected contours. Each contour is stored as a vector of points (e.g. + /// std::vector >). + /// * hierarchy: Optional output vector (e.g. std::vector), containing information about the image topology. It has + /// as many elements as the number of contours. For each i-th contour contours[i], the elements + /// hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices + /// in contours of the next and previous contours at the same hierarchical level, the first child + /// contour and the parent contour, respectively. If for the contour i there are no next, previous, + /// parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. + /// + /// Note: In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour. + /// * mode: Contour retrieval mode, see [retrieval_modes] + /// * method: Contour approximation method, see [contour_approximation_modes] + /// * offset: Optional offset by which every contour point is shifted. This is useful if the + /// contours are extracted from the image ROI and then they should be analyzed in the whole image + /// context. + /// + /// ## C++ default parameters + /// * offset: Point() + #[inline] + pub fn find_contours_with_hierarchy(image: &impl ToInputArray, contours: &mut impl ToOutputArray, hierarchy: &mut impl ToOutputArray, mode: i32, method: i32, offset: core::Point) -> Result<()> { + input_array_arg!(image); + output_array_arg!(contours); + output_array_arg!(hierarchy); + return_send!(via ocvrs_return); + unsafe { sys::cv_findContours_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int_Point(image.as_raw__InputArray(), contours.as_raw__OutputArray(), hierarchy.as_raw__OutputArray(), mode, method, &offset, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds contours in a binary image. + /// + /// The function retrieves contours from the binary image using the algorithm [Suzuki85](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Suzuki85) . The contours + /// are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the + /// OpenCV sample directory. + /// + /// Note: Since opencv 3.2 source image is not modified by this function. + /// + /// ## Parameters + /// * image: Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero + /// pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, [threshold] , + /// #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. + /// If mode equals to [RETR_CCOMP] or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). + /// * contours: Detected contours. Each contour is stored as a vector of points (e.g. + /// std::vector >). + /// * hierarchy: Optional output vector (e.g. std::vector), containing information about the image topology. It has + /// as many elements as the number of contours. For each i-th contour contours[i], the elements + /// hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices + /// in contours of the next and previous contours at the same hierarchical level, the first child + /// contour and the parent contour, respectively. If for the contour i there are no next, previous, + /// parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. + /// + /// Note: In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour. + /// * mode: Contour retrieval mode, see [retrieval_modes] + /// * method: Contour approximation method, see [contour_approximation_modes] + /// * offset: Optional offset by which every contour point is shifted. This is useful if the + /// contours are extracted from the image ROI and then they should be analyzed in the whole image + /// context. + /// + /// ## Overloaded parameters + /// + /// + /// ## Note + /// This alternative version of [find_contours] function uses the following default values for its arguments: + /// * offset: Point() + #[inline] + pub fn find_contours_def(image: &impl ToInputArray, contours: &mut impl ToOutputArray, mode: i32, method: i32) -> Result<()> { + input_array_arg!(image); + output_array_arg!(contours); + return_send!(via ocvrs_return); + unsafe { sys::cv_findContours_const__InputArrayR_const__OutputArrayR_int_int(image.as_raw__InputArray(), contours.as_raw__OutputArray(), mode, method, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds contours in a binary image. + /// + /// The function retrieves contours from the binary image using the algorithm [Suzuki85](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Suzuki85) . The contours + /// are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the + /// OpenCV sample directory. + /// + /// Note: Since opencv 3.2 source image is not modified by this function. + /// + /// ## Parameters + /// * image: Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero + /// pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, [threshold] , + /// #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. + /// If mode equals to [RETR_CCOMP] or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). + /// * contours: Detected contours. Each contour is stored as a vector of points (e.g. + /// std::vector >). + /// * hierarchy: Optional output vector (e.g. std::vector), containing information about the image topology. It has + /// as many elements as the number of contours. For each i-th contour contours[i], the elements + /// hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices + /// in contours of the next and previous contours at the same hierarchical level, the first child + /// contour and the parent contour, respectively. If for the contour i there are no next, previous, + /// parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. + /// + /// Note: In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour. + /// * mode: Contour retrieval mode, see [retrieval_modes] + /// * method: Contour approximation method, see [contour_approximation_modes] + /// * offset: Optional offset by which every contour point is shifted. This is useful if the + /// contours are extracted from the image ROI and then they should be analyzed in the whole image + /// context. + /// + /// ## Overloaded parameters + /// + /// ## C++ default parameters + /// * offset: Point() + #[inline] + pub fn find_contours(image: &impl ToInputArray, contours: &mut impl ToOutputArray, mode: i32, method: i32, offset: core::Point) -> Result<()> { + input_array_arg!(image); + output_array_arg!(contours); + return_send!(via ocvrs_return); + unsafe { sys::cv_findContours_const__InputArrayR_const__OutputArrayR_int_int_Point(image.as_raw__InputArray(), contours.as_raw__OutputArray(), mode, method, &offset, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fits an ellipse around a set of 2D points. + /// + /// The function calculates the ellipse that fits a set of 2D points. + /// It returns the rotated rectangle in which the ellipse is inscribed. + /// The Approximate Mean Square (AMS) proposed by [Taubin1991](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Taubin1991) is used. + /// + /// For an ellipse, this basis set is ![inline formula](https://latex.codecogs.com/png.latex?%20%5Cchi%3D%20%5Cleft%28x%5E2%2C%20x%20y%2C%20y%5E2%2C%20x%2C%20y%2C%201%5Cright%29%20), + /// which is a set of six free coefficients ![inline formula](https://latex.codecogs.com/png.latex?%20A%5ET%3D%5Cleft%5C%7BA%5F%7B%5Ctext%7Bxx%7D%7D%2CA%5F%7B%5Ctext%7Bxy%7D%7D%2CA%5F%7B%5Ctext%7Byy%7D%7D%2CA%5Fx%2CA%5Fy%2CA%5F0%5Cright%5C%7D%20). + /// However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths ![inline formula](https://latex.codecogs.com/png.latex?%20%28a%2Cb%29%20), + /// the position ![inline formula](https://latex.codecogs.com/png.latex?%20%28x%5F0%2Cy%5F0%29%20), and the orientation ![inline formula](https://latex.codecogs.com/png.latex?%20%5Ctheta%20). This is because the basis set includes lines, + /// quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. + /// If the fit is found to be a parabolic or hyperbolic function then the standard [fit_ellipse] method is used. + /// The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves + /// by imposing the condition that ![inline formula](https://latex.codecogs.com/png.latex?%20A%5ET%20%28%20D%5Fx%5ET%20D%5Fx%20%20%2B%20%20%20D%5Fy%5ET%20D%5Fy%29%20A%20%3D%201%20) where + /// the matrices ![inline formula](https://latex.codecogs.com/png.latex?%20Dx%20) and ![inline formula](https://latex.codecogs.com/png.latex?%20Dy%20) are the partial derivatives of the design matrix ![inline formula](https://latex.codecogs.com/png.latex?%20D%20) with + /// respect to x and y. The matrices are formed row by row applying the following to + /// each of the points in the set: + /// \f{align*}{ + /// D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} & + /// D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} & + /// D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\} + /// \f} + /// The AMS method minimizes the cost function + /// \f{equation*}{ + /// \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } + /// \f} + /// + /// The minimum cost is found by solving the generalized eigenvalue problem. + /// + /// \f{equation*}{ + /// D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A + /// \f} + /// + /// ## Parameters + /// * points: Input 2D point set, stored in std::vector\<\> or Mat + /// + /// + /// Note: Input point types are [Point2i] or [Point2f] and at least 5 points are required. + /// + /// Note: [getClosestEllipsePoints] function can be used to compute the ellipse fitting error. + #[inline] + pub fn fit_ellipse_ams(points: &impl ToInputArray) -> Result { + input_array_arg!(points); + return_send!(via ocvrs_return); + unsafe { sys::cv_fitEllipseAMS_const__InputArrayR(points.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fits an ellipse around a set of 2D points. + /// + /// The function calculates the ellipse that fits a set of 2D points. + /// It returns the rotated rectangle in which the ellipse is inscribed. + /// The Direct least square (Direct) method by [oy1998NumericallySD](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_oy1998NumericallySD) is used. + /// + /// For an ellipse, this basis set is ![inline formula](https://latex.codecogs.com/png.latex?%20%5Cchi%3D%20%5Cleft%28x%5E2%2C%20x%20y%2C%20y%5E2%2C%20x%2C%20y%2C%201%5Cright%29%20), + /// which is a set of six free coefficients ![inline formula](https://latex.codecogs.com/png.latex?%20A%5ET%3D%5Cleft%5C%7BA%5F%7B%5Ctext%7Bxx%7D%7D%2CA%5F%7B%5Ctext%7Bxy%7D%7D%2CA%5F%7B%5Ctext%7Byy%7D%7D%2CA%5Fx%2CA%5Fy%2CA%5F0%5Cright%5C%7D%20). + /// However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths ![inline formula](https://latex.codecogs.com/png.latex?%20%28a%2Cb%29%20), + /// the position ![inline formula](https://latex.codecogs.com/png.latex?%20%28x%5F0%2Cy%5F0%29%20), and the orientation ![inline formula](https://latex.codecogs.com/png.latex?%20%5Ctheta%20). This is because the basis set includes lines, + /// quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. + /// The Direct method confines the fit to ellipses by ensuring that ![inline formula](https://latex.codecogs.com/png.latex?%204%20A%5F%7Bxx%7D%20A%5F%7Byy%7D%2D%20A%5F%7Bxy%7D%5E2%20%3E%200%20). + /// The condition imposed is that ![inline formula](https://latex.codecogs.com/png.latex?%204%20A%5F%7Bxx%7D%20A%5F%7Byy%7D%2D%20A%5F%7Bxy%7D%5E2%3D1%20) which satisfies the inequality + /// and as the coefficients can be arbitrarily scaled is not overly restrictive. + /// + /// \f{equation*}{ + /// \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} + /// 0 & 0 & 2 & 0 & 0 & 0 \\ + /// 0 & -1 & 0 & 0 & 0 & 0 \\ + /// 2 & 0 & 0 & 0 & 0 & 0 \\ + /// 0 & 0 & 0 & 0 & 0 & 0 \\ + /// 0 & 0 & 0 & 0 & 0 & 0 \\ + /// 0 & 0 & 0 & 0 & 0 & 0 + /// \end{matrix} \right) + /// \f} + /// + /// The minimum cost is found by solving the generalized eigenvalue problem. + /// + /// \f{equation*}{ + /// D^T D A = \lambda \left( C\right) A + /// \f} + /// + /// The system produces only one positive eigenvalue ![inline formula](https://latex.codecogs.com/png.latex?%20%5Clambda) which is chosen as the solution + /// with its eigenvector ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7Bu%7D). These are used to find the coefficients + /// + /// \f{equation*}{ + /// A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} + /// \f} + /// The scaling factor guarantees that ![inline formula](https://latex.codecogs.com/png.latex?A%5ET%20C%20A%20%3D1). + /// + /// ## Parameters + /// * points: Input 2D point set, stored in std::vector\<\> or Mat + /// + /// + /// Note: Input point types are [Point2i] or [Point2f] and at least 5 points are required. + /// + /// Note: [getClosestEllipsePoints] function can be used to compute the ellipse fitting error. + #[inline] + pub fn fit_ellipse_direct(points: &impl ToInputArray) -> Result { + input_array_arg!(points); + return_send!(via ocvrs_return); + unsafe { sys::cv_fitEllipseDirect_const__InputArrayR(points.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fits an ellipse around a set of 2D points. + /// + /// The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of + /// all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by [Fitzgibbon95](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Fitzgibbon95) + /// is used. Developer should keep in mind that it is possible that the returned + /// ellipse/rotatedRect data contains negative indices, due to the data points being close to the + /// border of the containing Mat element. + /// + /// ## Parameters + /// * points: Input 2D point set, stored in std::vector\<\> or Mat + /// + /// + /// Note: Input point types are [Point2i] or [Point2f] and at least 5 points are required. + /// + /// Note: [getClosestEllipsePoints] function can be used to compute the ellipse fitting error. + #[inline] + pub fn fit_ellipse(points: &impl ToInputArray) -> Result { + input_array_arg!(points); + return_send!(via ocvrs_return); + unsafe { sys::cv_fitEllipse_const__InputArrayR(points.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fits a line to a 2D or 3D point set. + /// + /// The function fitLine fits a line to a 2D or 3D point set by minimizing ![inline formula](https://latex.codecogs.com/png.latex?%5Csum%5Fi%20%5Crho%28r%5Fi%29) where + /// ![inline formula](https://latex.codecogs.com/png.latex?r%5Fi) is a distance between the ![inline formula](https://latex.codecogs.com/png.latex?i%5E%7Bth%7D) point, the line and ![inline formula](https://latex.codecogs.com/png.latex?%5Crho%28r%29) is a distance function, one + /// of the following: + /// * DIST_L2 + /// ![block formula](https://latex.codecogs.com/png.latex?%5Crho%20%28r%29%20%3D%20r%5E2%2F2%20%20%5Cquad%20%5Ctext%7B%28the%20simplest%20and%20the%20fastest%20least%2Dsquares%20method%29%7D) + /// - DIST_L1 + /// ![block formula](https://latex.codecogs.com/png.latex?%5Crho%20%28r%29%20%3D%20r) + /// - DIST_L12 + /// ![block formula](https://latex.codecogs.com/png.latex?%5Crho%20%28r%29%20%3D%202%20%20%5Ccdot%20%28%20%5Csqrt%7B1%20%2B%20%5Cfrac%7Br%5E2%7D%7B2%7D%7D%20%2D%201%29) + /// - DIST_FAIR + /// ![block formula](https://latex.codecogs.com/png.latex?%5Crho%20%5Cleft%20%28r%20%5Cright%20%29%20%3D%20C%5E2%20%20%5Ccdot%20%5Cleft%20%28%20%20%5Cfrac%7Br%7D%7BC%7D%20%2D%20%20%5Clog%7B%5Cleft%281%20%2B%20%5Cfrac%7Br%7D%7BC%7D%5Cright%29%7D%20%5Cright%20%29%20%20%5Cquad%20%5Ctext%7Bwhere%7D%20%5Cquad%20C%3D1%2E3998) + /// - DIST_WELSCH + /// ![block formula](https://latex.codecogs.com/png.latex?%5Crho%20%5Cleft%20%28r%20%5Cright%20%29%20%3D%20%20%5Cfrac%7BC%5E2%7D%7B2%7D%20%5Ccdot%20%5Cleft%20%28%201%20%2D%20%20%5Cexp%7B%5Cleft%28%2D%5Cleft%28%5Cfrac%7Br%7D%7BC%7D%5Cright%29%5E2%5Cright%29%7D%20%5Cright%20%29%20%20%5Cquad%20%5Ctext%7Bwhere%7D%20%5Cquad%20C%3D2%2E9846) + /// - DIST_HUBER + /// ![block formula](https://latex.codecogs.com/png.latex?%5Crho%20%28r%29%20%3D%20%20%5Cleft%5C%7B%20%5Cbegin%7Barray%7D%7Bl%20l%7D%20r%5E2%2F2%20%26%20%5Cmbox%7Bif%20%5C%28r%20%3C%20C%5C%29%7D%5C%5C%20C%20%5Ccdot%20%28r%2DC%2F2%29%20%26%20%5Cmbox%7Botherwise%7D%5C%5C%20%5Cend%7Barray%7D%20%5Cright%2E%20%5Cquad%20%5Ctext%7Bwhere%7D%20%5Cquad%20C%3D1%2E345) + /// + /// The algorithm is based on the M-estimator ( ) technique + /// that iteratively fits the line using the weighted least-squares algorithm. After each iteration the + /// weights ![inline formula](https://latex.codecogs.com/png.latex?w%5Fi) are adjusted to be inversely proportional to ![inline formula](https://latex.codecogs.com/png.latex?%5Crho%28r%5Fi%29) . + /// + /// ## Parameters + /// * points: Input vector of 2D or 3D points, stored in std::vector\<\> or Mat. + /// * line: Output line parameters. In case of 2D fitting, it should be a vector of 4 elements + /// (like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and + /// (x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like + /// Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line + /// and (x0, y0, z0) is a point on the line. + /// * distType: Distance used by the M-estimator, see [distance_types] + /// * param: Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value + /// is chosen. + /// * reps: Sufficient accuracy for the radius (distance between the coordinate origin and the line). + /// * aeps: Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps. + #[inline] + pub fn fit_line(points: &impl ToInputArray, line: &mut impl ToOutputArray, dist_type: i32, param: f64, reps: f64, aeps: f64) -> Result<()> { + input_array_arg!(points); + output_array_arg!(line); + return_send!(via ocvrs_return); + unsafe { sys::cv_fitLine_const__InputArrayR_const__OutputArrayR_int_double_double_double(points.as_raw__InputArray(), line.as_raw__OutputArray(), dist_type, param, reps, aeps, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fills a connected component with the given color. + /// + /// The function cv::floodFill fills a connected component starting from the seed point with the specified + /// color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The + /// pixel at ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29) is considered to belong to the repainted domain if: + /// + /// - in case of a grayscale image and floating range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%2D%20%5Ctexttt%7BloDiff%7D%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%20%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%2B%20%5Ctexttt%7BupDiff%7D) + /// + /// + /// - in case of a grayscale image and fixed range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%2D%20%5Ctexttt%7BloDiff%7D%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%20%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%2B%20%5Ctexttt%7BupDiff%7D) + /// + /// + /// - in case of a color image and floating range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fr%2D%20%5Ctexttt%7BloDiff%7D%20%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fr%2B%20%5Ctexttt%7BupDiff%7D%20%5Fr%2C) + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fg%2D%20%5Ctexttt%7BloDiff%7D%20%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fg%2B%20%5Ctexttt%7BupDiff%7D%20%5Fg) + /// and + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fb%2D%20%5Ctexttt%7BloDiff%7D%20%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fb%2B%20%5Ctexttt%7BupDiff%7D%20%5Fb) + /// + /// + /// - in case of a color image and fixed range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fr%2D%20%5Ctexttt%7BloDiff%7D%20%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fr%2B%20%5Ctexttt%7BupDiff%7D%20%5Fr%2C) + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fg%2D%20%5Ctexttt%7BloDiff%7D%20%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fg%2B%20%5Ctexttt%7BupDiff%7D%20%5Fg) + /// and + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fb%2D%20%5Ctexttt%7BloDiff%7D%20%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fb%2B%20%5Ctexttt%7BupDiff%7D%20%5Fb) + /// + /// + /// where ![inline formula](https://latex.codecogs.com/png.latex?src%28x%27%2Cy%27%29) is the value of one of pixel neighbors that is already known to belong to the + /// component. That is, to be added to the connected component, a color/brightness of the pixel should + /// be close enough to: + /// - Color/brightness of one of its neighbors that already belong to the connected component in case + /// of a floating range. + /// - Color/brightness of the seed point in case of a fixed range. + /// + /// Use these functions to either mark a connected component with the specified color in-place, or build + /// a mask and then extract the contour, or copy the region to another image, and so on. + /// + /// ## Parameters + /// * image: Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the + /// function unless the [FLOODFILL_MASK_ONLY] flag is set in the second variant of the function. See + /// the details below. + /// * mask: Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels + /// taller than image. If an empty Mat is passed it will be created automatically. Since this is both an + /// input and output parameter, you must take responsibility of initializing it. + /// Flood-filling cannot go across non-zero pixels in the input mask. For example, + /// an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the + /// mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags + /// as described below. Additionally, the function fills the border of the mask with ones to simplify + /// internal processing. It is therefore possible to use the same mask in multiple calls to the function + /// to make sure the filled areas do not overlap. + /// * seedPoint: Starting point. + /// * newVal: New value of the repainted domain pixels. + /// * loDiff: Maximal lower brightness/color difference between the currently observed pixel and + /// one of its neighbors belonging to the component, or a seed pixel being added to the component. + /// * upDiff: Maximal upper brightness/color difference between the currently observed pixel and + /// one of its neighbors belonging to the component, or a seed pixel being added to the component. + /// * rect: Optional output parameter set by the function to the minimum bounding rectangle of the + /// repainted domain. + /// * flags: Operation flags. The first 8 bits contain a connectivity value. The default value of + /// 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A + /// connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) + /// will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill + /// the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest + /// neighbours and fill the mask with a value of 255. The following additional options occupy higher + /// bits and therefore may be further combined with the connectivity and mask fill values using + /// bit-wise or (|), see #FloodFillFlags. + /// + /// + /// Note: Since the mask is larger than the filled image, a pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%29) in image corresponds to the + /// pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2B1%2C%20y%2B1%29) in the mask . + /// ## See also + /// findContours + /// + /// ## Overloaded parameters + /// + /// + /// variant without `mask` parameter + /// + /// ## Note + /// This alternative version of [flood_fill] function uses the following default values for its arguments: + /// * rect: 0 + /// * lo_diff: Scalar() + /// * up_diff: Scalar() + /// * flags: 4 + #[inline] + pub fn flood_fill_def(image: &mut impl ToInputOutputArray, seed_point: core::Point, new_val: core::Scalar) -> Result { + input_output_array_arg!(image); + return_send!(via ocvrs_return); + unsafe { sys::cv_floodFill_const__InputOutputArrayR_Point_Scalar(image.as_raw__InputOutputArray(), &seed_point, &new_val, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fills a connected component with the given color. + /// + /// The function cv::floodFill fills a connected component starting from the seed point with the specified + /// color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The + /// pixel at ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29) is considered to belong to the repainted domain if: + /// + /// - in case of a grayscale image and floating range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%2D%20%5Ctexttt%7BloDiff%7D%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%20%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%2B%20%5Ctexttt%7BupDiff%7D) + /// + /// + /// - in case of a grayscale image and fixed range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%2D%20%5Ctexttt%7BloDiff%7D%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%20%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%2B%20%5Ctexttt%7BupDiff%7D) + /// + /// + /// - in case of a color image and floating range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fr%2D%20%5Ctexttt%7BloDiff%7D%20%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fr%2B%20%5Ctexttt%7BupDiff%7D%20%5Fr%2C) + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fg%2D%20%5Ctexttt%7BloDiff%7D%20%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fg%2B%20%5Ctexttt%7BupDiff%7D%20%5Fg) + /// and + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fb%2D%20%5Ctexttt%7BloDiff%7D%20%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fb%2B%20%5Ctexttt%7BupDiff%7D%20%5Fb) + /// + /// + /// - in case of a color image and fixed range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fr%2D%20%5Ctexttt%7BloDiff%7D%20%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fr%2B%20%5Ctexttt%7BupDiff%7D%20%5Fr%2C) + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fg%2D%20%5Ctexttt%7BloDiff%7D%20%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fg%2B%20%5Ctexttt%7BupDiff%7D%20%5Fg) + /// and + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fb%2D%20%5Ctexttt%7BloDiff%7D%20%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fb%2B%20%5Ctexttt%7BupDiff%7D%20%5Fb) + /// + /// + /// where ![inline formula](https://latex.codecogs.com/png.latex?src%28x%27%2Cy%27%29) is the value of one of pixel neighbors that is already known to belong to the + /// component. That is, to be added to the connected component, a color/brightness of the pixel should + /// be close enough to: + /// - Color/brightness of one of its neighbors that already belong to the connected component in case + /// of a floating range. + /// - Color/brightness of the seed point in case of a fixed range. + /// + /// Use these functions to either mark a connected component with the specified color in-place, or build + /// a mask and then extract the contour, or copy the region to another image, and so on. + /// + /// ## Parameters + /// * image: Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the + /// function unless the [FLOODFILL_MASK_ONLY] flag is set in the second variant of the function. See + /// the details below. + /// * mask: Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels + /// taller than image. If an empty Mat is passed it will be created automatically. Since this is both an + /// input and output parameter, you must take responsibility of initializing it. + /// Flood-filling cannot go across non-zero pixels in the input mask. For example, + /// an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the + /// mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags + /// as described below. Additionally, the function fills the border of the mask with ones to simplify + /// internal processing. It is therefore possible to use the same mask in multiple calls to the function + /// to make sure the filled areas do not overlap. + /// * seedPoint: Starting point. + /// * newVal: New value of the repainted domain pixels. + /// * loDiff: Maximal lower brightness/color difference between the currently observed pixel and + /// one of its neighbors belonging to the component, or a seed pixel being added to the component. + /// * upDiff: Maximal upper brightness/color difference between the currently observed pixel and + /// one of its neighbors belonging to the component, or a seed pixel being added to the component. + /// * rect: Optional output parameter set by the function to the minimum bounding rectangle of the + /// repainted domain. + /// * flags: Operation flags. The first 8 bits contain a connectivity value. The default value of + /// 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A + /// connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) + /// will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill + /// the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest + /// neighbours and fill the mask with a value of 255. The following additional options occupy higher + /// bits and therefore may be further combined with the connectivity and mask fill values using + /// bit-wise or (|), see #FloodFillFlags. + /// + /// + /// Note: Since the mask is larger than the filled image, a pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%29) in image corresponds to the + /// pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2B1%2C%20y%2B1%29) in the mask . + /// ## See also + /// findContours + /// + /// ## Overloaded parameters + /// + /// + /// variant without `mask` parameter + /// + /// ## C++ default parameters + /// * rect: 0 + /// * lo_diff: Scalar() + /// * up_diff: Scalar() + /// * flags: 4 + #[inline] + pub fn flood_fill(image: &mut impl ToInputOutputArray, seed_point: core::Point, new_val: core::Scalar, rect: &mut core::Rect, lo_diff: core::Scalar, up_diff: core::Scalar, flags: i32) -> Result { + input_output_array_arg!(image); + return_send!(via ocvrs_return); + unsafe { sys::cv_floodFill_const__InputOutputArrayR_Point_Scalar_RectX_Scalar_Scalar_int(image.as_raw__InputOutputArray(), &seed_point, &new_val, rect, &lo_diff, &up_diff, flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fills a connected component with the given color. + /// + /// The function cv::floodFill fills a connected component starting from the seed point with the specified + /// color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The + /// pixel at ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29) is considered to belong to the repainted domain if: + /// + /// - in case of a grayscale image and floating range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%2D%20%5Ctexttt%7BloDiff%7D%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%20%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%2B%20%5Ctexttt%7BupDiff%7D) + /// + /// + /// - in case of a grayscale image and fixed range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%2D%20%5Ctexttt%7BloDiff%7D%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%20%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%2B%20%5Ctexttt%7BupDiff%7D) + /// + /// + /// - in case of a color image and floating range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fr%2D%20%5Ctexttt%7BloDiff%7D%20%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fr%2B%20%5Ctexttt%7BupDiff%7D%20%5Fr%2C) + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fg%2D%20%5Ctexttt%7BloDiff%7D%20%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fg%2B%20%5Ctexttt%7BupDiff%7D%20%5Fg) + /// and + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fb%2D%20%5Ctexttt%7BloDiff%7D%20%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fb%2B%20%5Ctexttt%7BupDiff%7D%20%5Fb) + /// + /// + /// - in case of a color image and fixed range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fr%2D%20%5Ctexttt%7BloDiff%7D%20%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fr%2B%20%5Ctexttt%7BupDiff%7D%20%5Fr%2C) + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fg%2D%20%5Ctexttt%7BloDiff%7D%20%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fg%2B%20%5Ctexttt%7BupDiff%7D%20%5Fg) + /// and + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fb%2D%20%5Ctexttt%7BloDiff%7D%20%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fb%2B%20%5Ctexttt%7BupDiff%7D%20%5Fb) + /// + /// + /// where ![inline formula](https://latex.codecogs.com/png.latex?src%28x%27%2Cy%27%29) is the value of one of pixel neighbors that is already known to belong to the + /// component. That is, to be added to the connected component, a color/brightness of the pixel should + /// be close enough to: + /// - Color/brightness of one of its neighbors that already belong to the connected component in case + /// of a floating range. + /// - Color/brightness of the seed point in case of a fixed range. + /// + /// Use these functions to either mark a connected component with the specified color in-place, or build + /// a mask and then extract the contour, or copy the region to another image, and so on. + /// + /// ## Parameters + /// * image: Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the + /// function unless the [FLOODFILL_MASK_ONLY] flag is set in the second variant of the function. See + /// the details below. + /// * mask: Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels + /// taller than image. If an empty Mat is passed it will be created automatically. Since this is both an + /// input and output parameter, you must take responsibility of initializing it. + /// Flood-filling cannot go across non-zero pixels in the input mask. For example, + /// an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the + /// mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags + /// as described below. Additionally, the function fills the border of the mask with ones to simplify + /// internal processing. It is therefore possible to use the same mask in multiple calls to the function + /// to make sure the filled areas do not overlap. + /// * seedPoint: Starting point. + /// * newVal: New value of the repainted domain pixels. + /// * loDiff: Maximal lower brightness/color difference between the currently observed pixel and + /// one of its neighbors belonging to the component, or a seed pixel being added to the component. + /// * upDiff: Maximal upper brightness/color difference between the currently observed pixel and + /// one of its neighbors belonging to the component, or a seed pixel being added to the component. + /// * rect: Optional output parameter set by the function to the minimum bounding rectangle of the + /// repainted domain. + /// * flags: Operation flags. The first 8 bits contain a connectivity value. The default value of + /// 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A + /// connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) + /// will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill + /// the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest + /// neighbours and fill the mask with a value of 255. The following additional options occupy higher + /// bits and therefore may be further combined with the connectivity and mask fill values using + /// bit-wise or (|), see #FloodFillFlags. + /// + /// + /// Note: Since the mask is larger than the filled image, a pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%29) in image corresponds to the + /// pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2B1%2C%20y%2B1%29) in the mask . + /// ## See also + /// findContours + /// + /// ## Note + /// This alternative version of [flood_fill_mask] function uses the following default values for its arguments: + /// * rect: 0 + /// * lo_diff: Scalar() + /// * up_diff: Scalar() + /// * flags: 4 + #[inline] + pub fn flood_fill_mask_def(image: &mut impl ToInputOutputArray, mask: &mut impl ToInputOutputArray, seed_point: core::Point, new_val: core::Scalar) -> Result { + input_output_array_arg!(image); + input_output_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_floodFill_const__InputOutputArrayR_const__InputOutputArrayR_Point_Scalar(image.as_raw__InputOutputArray(), mask.as_raw__InputOutputArray(), &seed_point, &new_val, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Fills a connected component with the given color. + /// + /// The function cv::floodFill fills a connected component starting from the seed point with the specified + /// color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The + /// pixel at ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29) is considered to belong to the repainted domain if: + /// + /// - in case of a grayscale image and floating range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%2D%20%5Ctexttt%7BloDiff%7D%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%20%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%2B%20%5Ctexttt%7BupDiff%7D) + /// + /// + /// - in case of a grayscale image and fixed range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%2D%20%5Ctexttt%7BloDiff%7D%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%20%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%2B%20%5Ctexttt%7BupDiff%7D) + /// + /// + /// - in case of a color image and floating range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fr%2D%20%5Ctexttt%7BloDiff%7D%20%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fr%2B%20%5Ctexttt%7BupDiff%7D%20%5Fr%2C) + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fg%2D%20%5Ctexttt%7BloDiff%7D%20%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fg%2B%20%5Ctexttt%7BupDiff%7D%20%5Fg) + /// and + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fb%2D%20%5Ctexttt%7BloDiff%7D%20%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%27%2Cy%27%29%5Fb%2B%20%5Ctexttt%7BupDiff%7D%20%5Fb) + /// + /// + /// - in case of a color image and fixed range + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fr%2D%20%5Ctexttt%7BloDiff%7D%20%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fr%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fr%2B%20%5Ctexttt%7BupDiff%7D%20%5Fr%2C) + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fg%2D%20%5Ctexttt%7BloDiff%7D%20%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fg%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fg%2B%20%5Ctexttt%7BupDiff%7D%20%5Fg) + /// and + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fb%2D%20%5Ctexttt%7BloDiff%7D%20%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28x%2Cy%29%5Fb%20%5Cleq%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BseedPoint%7D%20%2Ex%2C%20%5Ctexttt%7BseedPoint%7D%20%2Ey%29%5Fb%2B%20%5Ctexttt%7BupDiff%7D%20%5Fb) + /// + /// + /// where ![inline formula](https://latex.codecogs.com/png.latex?src%28x%27%2Cy%27%29) is the value of one of pixel neighbors that is already known to belong to the + /// component. That is, to be added to the connected component, a color/brightness of the pixel should + /// be close enough to: + /// - Color/brightness of one of its neighbors that already belong to the connected component in case + /// of a floating range. + /// - Color/brightness of the seed point in case of a fixed range. + /// + /// Use these functions to either mark a connected component with the specified color in-place, or build + /// a mask and then extract the contour, or copy the region to another image, and so on. + /// + /// ## Parameters + /// * image: Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the + /// function unless the [FLOODFILL_MASK_ONLY] flag is set in the second variant of the function. See + /// the details below. + /// * mask: Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels + /// taller than image. If an empty Mat is passed it will be created automatically. Since this is both an + /// input and output parameter, you must take responsibility of initializing it. + /// Flood-filling cannot go across non-zero pixels in the input mask. For example, + /// an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the + /// mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags + /// as described below. Additionally, the function fills the border of the mask with ones to simplify + /// internal processing. It is therefore possible to use the same mask in multiple calls to the function + /// to make sure the filled areas do not overlap. + /// * seedPoint: Starting point. + /// * newVal: New value of the repainted domain pixels. + /// * loDiff: Maximal lower brightness/color difference between the currently observed pixel and + /// one of its neighbors belonging to the component, or a seed pixel being added to the component. + /// * upDiff: Maximal upper brightness/color difference between the currently observed pixel and + /// one of its neighbors belonging to the component, or a seed pixel being added to the component. + /// * rect: Optional output parameter set by the function to the minimum bounding rectangle of the + /// repainted domain. + /// * flags: Operation flags. The first 8 bits contain a connectivity value. The default value of + /// 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A + /// connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) + /// will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill + /// the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest + /// neighbours and fill the mask with a value of 255. The following additional options occupy higher + /// bits and therefore may be further combined with the connectivity and mask fill values using + /// bit-wise or (|), see #FloodFillFlags. + /// + /// + /// Note: Since the mask is larger than the filled image, a pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2C%20y%29) in image corresponds to the + /// pixel ![inline formula](https://latex.codecogs.com/png.latex?%28x%2B1%2C%20y%2B1%29) in the mask . + /// ## See also + /// findContours + /// + /// ## C++ default parameters + /// * rect: 0 + /// * lo_diff: Scalar() + /// * up_diff: Scalar() + /// * flags: 4 + #[inline] + pub fn flood_fill_mask(image: &mut impl ToInputOutputArray, mask: &mut impl ToInputOutputArray, seed_point: core::Point, new_val: core::Scalar, rect: &mut core::Rect, lo_diff: core::Scalar, up_diff: core::Scalar, flags: i32) -> Result { + input_output_array_arg!(image); + input_output_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_floodFill_const__InputOutputArrayR_const__InputOutputArrayR_Point_Scalar_RectX_Scalar_Scalar_int(image.as_raw__InputOutputArray(), mask.as_raw__InputOutputArray(), &seed_point, &new_val, rect, &lo_diff, &up_diff, flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates an affine transform from three pairs of the corresponding points. + /// + /// The function calculates the ![inline formula](https://latex.codecogs.com/png.latex?2%20%5Ctimes%203) matrix of an affine transform so that: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20x%27%5Fi%20%5C%5C%20y%27%5Fi%20%5Cend%7Bbmatrix%7D%20%3D%20%5Ctexttt%7Bmap%5Fmatrix%7D%20%5Ccdot%20%5Cbegin%7Bbmatrix%7D%20x%5Fi%20%5C%5C%20y%5Fi%20%5C%5C%201%20%5Cend%7Bbmatrix%7D) + /// + /// where + /// + /// ![block formula](https://latex.codecogs.com/png.latex?dst%28i%29%3D%28x%27%5Fi%2Cy%27%5Fi%29%2C%20src%28i%29%3D%28x%5Fi%2C%20y%5Fi%29%2C%20i%3D0%2C1%2C2) + /// + /// ## Parameters + /// * src: Coordinates of triangle vertices in the source image. + /// * dst: Coordinates of the corresponding triangle vertices in the destination image. + /// ## See also + /// warpAffine, transform + #[inline] + pub fn get_affine_transform_slice(src: &[core::Point2f; 3], dst: &[core::Point2f; 3]) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getAffineTransform_const_Point2fXX_const_Point2fXX(src, dst, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + #[inline] + pub fn get_affine_transform(src: &impl ToInputArray, dst: &impl ToInputArray) -> Result { + input_array_arg!(src); + input_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_getAffineTransform_const__InputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Compute for each 2d point the nearest 2d point located on a given ellipse. + /// + /// The function computes the nearest 2d location on a given ellipse for a vector of 2d points and is based on [Chatfield2017](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Chatfield2017) code. + /// This function can be used to compute for instance the ellipse fitting error. + /// + /// ## Parameters + /// * ellipse_params: Ellipse parameters + /// * points: Input 2d points + /// * closest_pts: For each 2d point, their corresponding closest 2d point located on a given ellipse + /// + /// + /// Note: Input point types are [Point2i] or [Point2f] + /// ## See also + /// fitEllipse, fitEllipseAMS, fitEllipseDirect + #[inline] + pub fn get_closest_ellipse_points(ellipse_params: core::RotatedRect, points: &impl ToInputArray, closest_pts: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(points); + output_array_arg!(closest_pts); + return_send!(via ocvrs_return); + unsafe { sys::cv_getClosestEllipsePoints_const_RotatedRectR_const__InputArrayR_const__OutputArrayR(&ellipse_params, points.as_raw__InputArray(), closest_pts.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns filter coefficients for computing spatial image derivatives. + /// + /// The function computes and returns the filter coefficients for spatial image derivatives. When + /// `ksize=FILTER_SCHARR`, the Scharr ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%203) kernels are generated (see #Scharr). Otherwise, Sobel + /// kernels are generated (see #Sobel). The filters are normally passed to [sep_filter_2d] or to + /// + /// ## Parameters + /// * kx: Output matrix of row filter coefficients. It has the type ktype . + /// * ky: Output matrix of column filter coefficients. It has the type ktype . + /// * dx: Derivative order in respect of x. + /// * dy: Derivative order in respect of y. + /// * ksize: Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7. + /// * normalize: Flag indicating whether to normalize (scale down) the filter coefficients or not. + /// Theoretically, the coefficients should have the denominator ![inline formula](https://latex.codecogs.com/png.latex?%3D2%5E%7Bksize%2A2%2Ddx%2Ddy%2D2%7D). If you are + /// going to filter floating-point images, you are likely to use the normalized kernels. But if you + /// compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve + /// all the fractional bits, you may want to set normalize=false . + /// * ktype: Type of filter coefficients. It can be CV_32f or CV_64F . + /// + /// ## Note + /// This alternative version of [get_deriv_kernels] function uses the following default values for its arguments: + /// * normalize: false + /// * ktype: CV_32F + #[inline] + pub fn get_deriv_kernels_def(kx: &mut impl ToOutputArray, ky: &mut impl ToOutputArray, dx: i32, dy: i32, ksize: i32) -> Result<()> { + output_array_arg!(kx); + output_array_arg!(ky); + return_send!(via ocvrs_return); + unsafe { sys::cv_getDerivKernels_const__OutputArrayR_const__OutputArrayR_int_int_int(kx.as_raw__OutputArray(), ky.as_raw__OutputArray(), dx, dy, ksize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns filter coefficients for computing spatial image derivatives. + /// + /// The function computes and returns the filter coefficients for spatial image derivatives. When + /// `ksize=FILTER_SCHARR`, the Scharr ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%203) kernels are generated (see #Scharr). Otherwise, Sobel + /// kernels are generated (see #Sobel). The filters are normally passed to [sep_filter_2d] or to + /// + /// ## Parameters + /// * kx: Output matrix of row filter coefficients. It has the type ktype . + /// * ky: Output matrix of column filter coefficients. It has the type ktype . + /// * dx: Derivative order in respect of x. + /// * dy: Derivative order in respect of y. + /// * ksize: Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7. + /// * normalize: Flag indicating whether to normalize (scale down) the filter coefficients or not. + /// Theoretically, the coefficients should have the denominator ![inline formula](https://latex.codecogs.com/png.latex?%3D2%5E%7Bksize%2A2%2Ddx%2Ddy%2D2%7D). If you are + /// going to filter floating-point images, you are likely to use the normalized kernels. But if you + /// compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve + /// all the fractional bits, you may want to set normalize=false . + /// * ktype: Type of filter coefficients. It can be CV_32f or CV_64F . + /// + /// ## C++ default parameters + /// * normalize: false + /// * ktype: CV_32F + #[inline] + pub fn get_deriv_kernels(kx: &mut impl ToOutputArray, ky: &mut impl ToOutputArray, dx: i32, dy: i32, ksize: i32, normalize: bool, ktype: i32) -> Result<()> { + output_array_arg!(kx); + output_array_arg!(ky); + return_send!(via ocvrs_return); + unsafe { sys::cv_getDerivKernels_const__OutputArrayR_const__OutputArrayR_int_int_int_bool_int(kx.as_raw__OutputArray(), ky.as_raw__OutputArray(), dx, dy, ksize, normalize, ktype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the font-specific size to use to achieve a given height in pixels. + /// + /// ## Parameters + /// * fontFace: Font to use, see cv::HersheyFonts. + /// * pixelHeight: Pixel height to compute the fontScale for + /// * thickness: Thickness of lines used to render the text.See putText for details. + /// ## Returns + /// The fontSize to use for cv::putText + /// ## See also + /// cv::putText + /// + /// ## Note + /// This alternative version of [get_font_scale_from_height] function uses the following default values for its arguments: + /// * thickness: 1 + #[inline] + pub fn get_font_scale_from_height_def(font_face: i32, pixel_height: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getFontScaleFromHeight_const_int_const_int(font_face, pixel_height, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the font-specific size to use to achieve a given height in pixels. + /// + /// ## Parameters + /// * fontFace: Font to use, see cv::HersheyFonts. + /// * pixelHeight: Pixel height to compute the fontScale for + /// * thickness: Thickness of lines used to render the text.See putText for details. + /// ## Returns + /// The fontSize to use for cv::putText + /// ## See also + /// cv::putText + /// + /// ## C++ default parameters + /// * thickness: 1 + #[inline] + pub fn get_font_scale_from_height(font_face: i32, pixel_height: i32, thickness: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getFontScaleFromHeight_const_int_const_int_const_int(font_face, pixel_height, thickness, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns Gabor filter coefficients. + /// + /// For more details about gabor filter equations and parameters, see: [Gabor + /// Filter](https://en.wikipedia.org/wiki/Gabor_filter). + /// + /// ## Parameters + /// * ksize: Size of the filter returned. + /// * sigma: Standard deviation of the gaussian envelope. + /// * theta: Orientation of the normal to the parallel stripes of a Gabor function. + /// * lambd: Wavelength of the sinusoidal factor. + /// * gamma: Spatial aspect ratio. + /// * psi: Phase offset. + /// * ktype: Type of filter coefficients. It can be CV_32F or CV_64F . + /// + /// ## Note + /// This alternative version of [get_gabor_kernel] function uses the following default values for its arguments: + /// * psi: CV_PI*0.5 + /// * ktype: CV_64F + #[inline] + pub fn get_gabor_kernel_def(ksize: core::Size, sigma: f64, theta: f64, lambd: f64, gamma: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getGaborKernel_Size_double_double_double_double(&ksize, sigma, theta, lambd, gamma, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns Gabor filter coefficients. + /// + /// For more details about gabor filter equations and parameters, see: [Gabor + /// Filter](https://en.wikipedia.org/wiki/Gabor_filter). + /// + /// ## Parameters + /// * ksize: Size of the filter returned. + /// * sigma: Standard deviation of the gaussian envelope. + /// * theta: Orientation of the normal to the parallel stripes of a Gabor function. + /// * lambd: Wavelength of the sinusoidal factor. + /// * gamma: Spatial aspect ratio. + /// * psi: Phase offset. + /// * ktype: Type of filter coefficients. It can be CV_32F or CV_64F . + /// + /// ## C++ default parameters + /// * psi: CV_PI*0.5 + /// * ktype: CV_64F + #[inline] + pub fn get_gabor_kernel(ksize: core::Size, sigma: f64, theta: f64, lambd: f64, gamma: f64, psi: f64, ktype: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getGaborKernel_Size_double_double_double_double_double_int(&ksize, sigma, theta, lambd, gamma, psi, ktype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns Gaussian filter coefficients. + /// + /// The function computes and returns the ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bksize%7D%20%5Ctimes%201) matrix of Gaussian filter + /// coefficients: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?G%5Fi%3D%20%5Calpha%20%2Ae%5E%7B%2D%28i%2D%28%20%5Ctexttt%7Bksize%7D%20%2D1%29%2F2%29%5E2%2F%282%2A%20%5Ctexttt%7Bsigma%7D%5E2%29%7D%2C) + /// + /// where ![inline formula](https://latex.codecogs.com/png.latex?i%3D0%2E%2E%5Ctexttt%7Bksize%7D%2D1) and ![inline formula](https://latex.codecogs.com/png.latex?%5Calpha) is the scale factor chosen so that ![inline formula](https://latex.codecogs.com/png.latex?%5Csum%5Fi%20G%5Fi%3D1). + /// + /// Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize + /// smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. + /// You may also use the higher-level GaussianBlur. + /// ## Parameters + /// * ksize: Aperture size. It should be odd ( ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bksize%7D%20%5Cmod%202%20%3D%201) ) and positive. + /// * sigma: Gaussian standard deviation. If it is non-positive, it is computed from ksize as + /// `sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`. + /// * ktype: Type of filter coefficients. It can be CV_32F or CV_64F . + /// ## See also + /// sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur + /// + /// ## Note + /// This alternative version of [get_gaussian_kernel] function uses the following default values for its arguments: + /// * ktype: CV_64F + #[inline] + pub fn get_gaussian_kernel_def(ksize: i32, sigma: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getGaussianKernel_int_double(ksize, sigma, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns Gaussian filter coefficients. + /// + /// The function computes and returns the ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bksize%7D%20%5Ctimes%201) matrix of Gaussian filter + /// coefficients: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?G%5Fi%3D%20%5Calpha%20%2Ae%5E%7B%2D%28i%2D%28%20%5Ctexttt%7Bksize%7D%20%2D1%29%2F2%29%5E2%2F%282%2A%20%5Ctexttt%7Bsigma%7D%5E2%29%7D%2C) + /// + /// where ![inline formula](https://latex.codecogs.com/png.latex?i%3D0%2E%2E%5Ctexttt%7Bksize%7D%2D1) and ![inline formula](https://latex.codecogs.com/png.latex?%5Calpha) is the scale factor chosen so that ![inline formula](https://latex.codecogs.com/png.latex?%5Csum%5Fi%20G%5Fi%3D1). + /// + /// Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize + /// smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. + /// You may also use the higher-level GaussianBlur. + /// ## Parameters + /// * ksize: Aperture size. It should be odd ( ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bksize%7D%20%5Cmod%202%20%3D%201) ) and positive. + /// * sigma: Gaussian standard deviation. If it is non-positive, it is computed from ksize as + /// `sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`. + /// * ktype: Type of filter coefficients. It can be CV_32F or CV_64F . + /// ## See also + /// sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur + /// + /// ## C++ default parameters + /// * ktype: CV_64F + #[inline] + pub fn get_gaussian_kernel(ksize: i32, sigma: f64, ktype: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getGaussianKernel_int_double_int(ksize, sigma, ktype, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Calculates a perspective transform from four pairs of the corresponding points. + /// + /// The function calculates the ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%203) matrix of a perspective transform so that: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20t%5Fi%20x%27%5Fi%20%5C%5C%20t%5Fi%20y%27%5Fi%20%5C%5C%20t%5Fi%20%5Cend%7Bbmatrix%7D%20%3D%20%5Ctexttt%7Bmap%5Fmatrix%7D%20%5Ccdot%20%5Cbegin%7Bbmatrix%7D%20x%5Fi%20%5C%5C%20y%5Fi%20%5C%5C%201%20%5Cend%7Bbmatrix%7D) + /// + /// where + /// + /// ![block formula](https://latex.codecogs.com/png.latex?dst%28i%29%3D%28x%27%5Fi%2Cy%27%5Fi%29%2C%20src%28i%29%3D%28x%5Fi%2C%20y%5Fi%29%2C%20i%3D0%2C1%2C2%2C3) + /// + /// ## Parameters + /// * src: Coordinates of quadrangle vertices in the source image. + /// * dst: Coordinates of the corresponding quadrangle vertices in the destination image. + /// * solveMethod: method passed to cv::solve (#DecompTypes) + /// ## See also + /// findHomography, warpPerspective, perspectiveTransform + /// + /// ## Overloaded parameters + /// + /// + /// ## Note + /// This alternative version of [get_perspective_transform_slice] function uses the following default values for its arguments: + /// * solve_method: DECOMP_LU + #[inline] + pub fn get_perspective_transform_slice_def(src: &[core::Point2f; 4], dst: &[core::Point2f; 4]) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getPerspectiveTransform_const_Point2fXX_const_Point2fXX(src, dst, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Calculates a perspective transform from four pairs of the corresponding points. + /// + /// The function calculates the ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%203) matrix of a perspective transform so that: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20t%5Fi%20x%27%5Fi%20%5C%5C%20t%5Fi%20y%27%5Fi%20%5C%5C%20t%5Fi%20%5Cend%7Bbmatrix%7D%20%3D%20%5Ctexttt%7Bmap%5Fmatrix%7D%20%5Ccdot%20%5Cbegin%7Bbmatrix%7D%20x%5Fi%20%5C%5C%20y%5Fi%20%5C%5C%201%20%5Cend%7Bbmatrix%7D) + /// + /// where + /// + /// ![block formula](https://latex.codecogs.com/png.latex?dst%28i%29%3D%28x%27%5Fi%2Cy%27%5Fi%29%2C%20src%28i%29%3D%28x%5Fi%2C%20y%5Fi%29%2C%20i%3D0%2C1%2C2%2C3) + /// + /// ## Parameters + /// * src: Coordinates of quadrangle vertices in the source image. + /// * dst: Coordinates of the corresponding quadrangle vertices in the destination image. + /// * solveMethod: method passed to cv::solve (#DecompTypes) + /// ## See also + /// findHomography, warpPerspective, perspectiveTransform + /// + /// ## Overloaded parameters + /// + /// ## C++ default parameters + /// * solve_method: DECOMP_LU + #[inline] + pub fn get_perspective_transform_slice(src: &[core::Point2f; 4], dst: &[core::Point2f; 4], solve_method: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getPerspectiveTransform_const_Point2fXX_const_Point2fXX_int(src, dst, solve_method, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Calculates a perspective transform from four pairs of the corresponding points. + /// + /// The function calculates the ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%203) matrix of a perspective transform so that: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20t%5Fi%20x%27%5Fi%20%5C%5C%20t%5Fi%20y%27%5Fi%20%5C%5C%20t%5Fi%20%5Cend%7Bbmatrix%7D%20%3D%20%5Ctexttt%7Bmap%5Fmatrix%7D%20%5Ccdot%20%5Cbegin%7Bbmatrix%7D%20x%5Fi%20%5C%5C%20y%5Fi%20%5C%5C%201%20%5Cend%7Bbmatrix%7D) + /// + /// where + /// + /// ![block formula](https://latex.codecogs.com/png.latex?dst%28i%29%3D%28x%27%5Fi%2Cy%27%5Fi%29%2C%20src%28i%29%3D%28x%5Fi%2C%20y%5Fi%29%2C%20i%3D0%2C1%2C2%2C3) + /// + /// ## Parameters + /// * src: Coordinates of quadrangle vertices in the source image. + /// * dst: Coordinates of the corresponding quadrangle vertices in the destination image. + /// * solveMethod: method passed to cv::solve (#DecompTypes) + /// ## See also + /// findHomography, warpPerspective, perspectiveTransform + /// + /// ## Note + /// This alternative version of [get_perspective_transform] function uses the following default values for its arguments: + /// * solve_method: DECOMP_LU + #[inline] + pub fn get_perspective_transform_def(src: &impl ToInputArray, dst: &impl ToInputArray) -> Result { + input_array_arg!(src); + input_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_getPerspectiveTransform_const__InputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Calculates a perspective transform from four pairs of the corresponding points. + /// + /// The function calculates the ![inline formula](https://latex.codecogs.com/png.latex?3%20%5Ctimes%203) matrix of a perspective transform so that: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20t%5Fi%20x%27%5Fi%20%5C%5C%20t%5Fi%20y%27%5Fi%20%5C%5C%20t%5Fi%20%5Cend%7Bbmatrix%7D%20%3D%20%5Ctexttt%7Bmap%5Fmatrix%7D%20%5Ccdot%20%5Cbegin%7Bbmatrix%7D%20x%5Fi%20%5C%5C%20y%5Fi%20%5C%5C%201%20%5Cend%7Bbmatrix%7D) + /// + /// where + /// + /// ![block formula](https://latex.codecogs.com/png.latex?dst%28i%29%3D%28x%27%5Fi%2Cy%27%5Fi%29%2C%20src%28i%29%3D%28x%5Fi%2C%20y%5Fi%29%2C%20i%3D0%2C1%2C2%2C3) + /// + /// ## Parameters + /// * src: Coordinates of quadrangle vertices in the source image. + /// * dst: Coordinates of the corresponding quadrangle vertices in the destination image. + /// * solveMethod: method passed to cv::solve (#DecompTypes) + /// ## See also + /// findHomography, warpPerspective, perspectiveTransform + /// + /// ## C++ default parameters + /// * solve_method: DECOMP_LU + #[inline] + pub fn get_perspective_transform(src: &impl ToInputArray, dst: &impl ToInputArray, solve_method: i32) -> Result { + input_array_arg!(src); + input_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_getPerspectiveTransform_const__InputArrayR_const__InputArrayR_int(src.as_raw__InputArray(), dst.as_raw__InputArray(), solve_method, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Retrieves a pixel rectangle from an image with sub-pixel accuracy. + /// + /// The function getRectSubPix extracts pixels from src: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?patch%28x%2C%20y%29%20%3D%20src%28x%20%2B%20%20%5Ctexttt%7Bcenter%2Ex%7D%20%2D%20%28%20%5Ctexttt%7Bdst%2Ecols%7D%20%2D1%29%2A0%2E5%2C%20y%20%2B%20%20%5Ctexttt%7Bcenter%2Ey%7D%20%2D%20%28%20%5Ctexttt%7Bdst%2Erows%7D%20%2D1%29%2A0%2E5%29) + /// + /// where the values of the pixels at non-integer coordinates are retrieved using bilinear + /// interpolation. Every channel of multi-channel images is processed independently. Also + /// the image should be a single channel or three channel image. While the center of the + /// rectangle must be inside the image, parts of the rectangle may be outside. + /// + /// ## Parameters + /// * image: Source image. + /// * patchSize: Size of the extracted patch. + /// * center: Floating point coordinates of the center of the extracted rectangle within the + /// source image. The center must be inside the image. + /// * patch: Extracted patch that has the size patchSize and the same number of channels as src . + /// * patchType: Depth of the extracted pixels. By default, they have the same depth as src . + /// ## See also + /// warpAffine, warpPerspective + /// + /// ## Note + /// This alternative version of [get_rect_sub_pix] function uses the following default values for its arguments: + /// * patch_type: -1 + #[inline] + pub fn get_rect_sub_pix_def(image: &impl ToInputArray, patch_size: core::Size, center: core::Point2f, patch: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(image); + output_array_arg!(patch); + return_send!(via ocvrs_return); + unsafe { sys::cv_getRectSubPix_const__InputArrayR_Size_Point2f_const__OutputArrayR(image.as_raw__InputArray(), &patch_size, ¢er, patch.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Retrieves a pixel rectangle from an image with sub-pixel accuracy. + /// + /// The function getRectSubPix extracts pixels from src: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?patch%28x%2C%20y%29%20%3D%20src%28x%20%2B%20%20%5Ctexttt%7Bcenter%2Ex%7D%20%2D%20%28%20%5Ctexttt%7Bdst%2Ecols%7D%20%2D1%29%2A0%2E5%2C%20y%20%2B%20%20%5Ctexttt%7Bcenter%2Ey%7D%20%2D%20%28%20%5Ctexttt%7Bdst%2Erows%7D%20%2D1%29%2A0%2E5%29) + /// + /// where the values of the pixels at non-integer coordinates are retrieved using bilinear + /// interpolation. Every channel of multi-channel images is processed independently. Also + /// the image should be a single channel or three channel image. While the center of the + /// rectangle must be inside the image, parts of the rectangle may be outside. + /// + /// ## Parameters + /// * image: Source image. + /// * patchSize: Size of the extracted patch. + /// * center: Floating point coordinates of the center of the extracted rectangle within the + /// source image. The center must be inside the image. + /// * patch: Extracted patch that has the size patchSize and the same number of channels as src . + /// * patchType: Depth of the extracted pixels. By default, they have the same depth as src . + /// ## See also + /// warpAffine, warpPerspective + /// + /// ## C++ default parameters + /// * patch_type: -1 + #[inline] + pub fn get_rect_sub_pix(image: &impl ToInputArray, patch_size: core::Size, center: core::Point2f, patch: &mut impl ToOutputArray, patch_type: i32) -> Result<()> { + input_array_arg!(image); + output_array_arg!(patch); + return_send!(via ocvrs_return); + unsafe { sys::cv_getRectSubPix_const__InputArrayR_Size_Point2f_const__OutputArrayR_int(image.as_raw__InputArray(), &patch_size, ¢er, patch.as_raw__OutputArray(), patch_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates an affine matrix of 2D rotation. + /// + /// The function calculates the following matrix: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20%5Calpha%20%26%20%20%5Cbeta%20%26%20%281%2D%20%5Calpha%20%29%20%20%5Ccdot%20%5Ctexttt%7Bcenter%2Ex%7D%20%2D%20%20%5Cbeta%20%5Ccdot%20%5Ctexttt%7Bcenter%2Ey%7D%20%5C%5C%20%2D%20%5Cbeta%20%26%20%20%5Calpha%20%26%20%20%5Cbeta%20%5Ccdot%20%5Ctexttt%7Bcenter%2Ex%7D%20%2B%20%281%2D%20%5Calpha%20%29%20%20%5Ccdot%20%5Ctexttt%7Bcenter%2Ey%7D%20%5Cend%7Bbmatrix%7D) + /// + /// where + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20%5Calpha%20%3D%20%20%5Ctexttt%7Bscale%7D%20%5Ccdot%20%5Ccos%20%5Ctexttt%7Bangle%7D%20%2C%20%5C%5C%20%5Cbeta%20%3D%20%20%5Ctexttt%7Bscale%7D%20%5Ccdot%20%5Csin%20%5Ctexttt%7Bangle%7D%20%5Cend%7Barray%7D) + /// + /// The transformation maps the rotation center to itself. If this is not the target, adjust the shift. + /// + /// ## Parameters + /// * center: Center of the rotation in the source image. + /// * angle: Rotation angle in degrees. Positive values mean counter-clockwise rotation (the + /// coordinate origin is assumed to be the top-left corner). + /// * scale: Isotropic scale factor. + /// ## See also + /// getAffineTransform, warpAffine, transform + #[inline] + pub fn get_rotation_matrix_2d(center: core::Point2f, angle: f64, scale: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getRotationMatrix2D_Point2f_double_double(¢er, angle, scale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## See also + /// getRotationMatrix2D + #[inline] + #[cfg(not(target_os = "windows"))] + pub fn get_rotation_matrix_2d_matx(center: core::Point2f, angle: f64, scale: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getRotationMatrix2D__Point2f_double_double(¢er, angle, scale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a structuring element of the specified size and shape for morphological operations. + /// + /// The function constructs and returns the structuring element that can be further passed to #erode, + /// [dilate] or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as + /// the structuring element. + /// + /// ## Parameters + /// * shape: Element shape that could be one of [morph_shapes] + /// * ksize: Size of the structuring element. + /// * anchor: Anchor position within the element. The default value ![inline formula](https://latex.codecogs.com/png.latex?%28%2D1%2C%20%2D1%29) means that the + /// anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor + /// position. In other cases the anchor just regulates how much the result of the morphological + /// operation is shifted. + /// + /// ## Note + /// This alternative version of [get_structuring_element] function uses the following default values for its arguments: + /// * anchor: Point(-1,-1) + #[inline] + pub fn get_structuring_element_def(shape: i32, ksize: core::Size) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getStructuringElement_int_Size(shape, &ksize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns a structuring element of the specified size and shape for morphological operations. + /// + /// The function constructs and returns the structuring element that can be further passed to #erode, + /// [dilate] or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as + /// the structuring element. + /// + /// ## Parameters + /// * shape: Element shape that could be one of [morph_shapes] + /// * ksize: Size of the structuring element. + /// * anchor: Anchor position within the element. The default value ![inline formula](https://latex.codecogs.com/png.latex?%28%2D1%2C%20%2D1%29) means that the + /// anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor + /// position. In other cases the anchor just regulates how much the result of the morphological + /// operation is shifted. + /// + /// ## C++ default parameters + /// * anchor: Point(-1,-1) + #[inline] + pub fn get_structuring_element(shape: i32, ksize: core::Size, anchor: core::Point) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_getStructuringElement_int_Size_Point(shape, &ksize, &anchor, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Mat::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Calculates the width and height of a text string. + /// + /// The function cv::getTextSize calculates and returns the size of a box that contains the specified text. + /// That is, the following code renders some text, the tight box surrounding it, and the baseline: : + /// ```C++ + /// String text = "Funny text inside the box"; + /// int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; + /// double fontScale = 2; + /// int thickness = 3; + /// + /// Mat img(600, 800, CV_8UC3, Scalar::all(0)); + /// + /// int baseline=0; + /// Size textSize = getTextSize(text, fontFace, + /// fontScale, thickness, &baseline); + /// baseline += thickness; + /// + /// // center the text + /// Point textOrg((img.cols - textSize.width)/2, + /// (img.rows + textSize.height)/2); + /// + /// // draw the box + /// rectangle(img, textOrg + Point(0, baseline), + /// textOrg + Point(textSize.width, -textSize.height), + /// Scalar(0,0,255)); + /// // ... and the baseline first + /// line(img, textOrg + Point(0, thickness), + /// textOrg + Point(textSize.width, thickness), + /// Scalar(0, 0, 255)); + /// + /// // then put the text itself + /// putText(img, text, textOrg, fontFace, fontScale, + /// Scalar::all(255), thickness, 8); + /// ``` + /// + /// + /// ## Parameters + /// * text: Input text string. + /// * fontFace: Font to use, see #HersheyFonts. + /// * fontScale: Font scale factor that is multiplied by the font-specific base size. + /// * thickness: Thickness of lines used to render the text. See [put_text] for details. + /// * baseLine:[out] y-coordinate of the baseline relative to the bottom-most text + /// point. + /// ## Returns + /// The size of a box that contains the specified text. + /// ## See also + /// putText + #[inline] + pub fn get_text_size(text: &str, font_face: i32, font_scale: f64, thickness: i32, base_line: &mut i32) -> Result { + extern_container_arg!(text); + return_send!(via ocvrs_return); + unsafe { sys::cv_getTextSize_const_StringR_int_double_int_intX(text.opencv_as_extern(), font_face, font_scale, thickness, base_line, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Determines strong corners on an image. + /// + /// The function finds the most prominent corners in the image or in the specified image region, as + /// described in [Shi94](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Shi94) + /// + /// * Function calculates the corner quality measure at every source image pixel using the + /// [corner_min_eigen_val] or [corner_harris] . + /// * Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are + /// retained). + /// * The corners with the minimal eigenvalue less than + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BqualityLevel%7D%20%5Ccdot%20%5Cmax%5F%7Bx%2Cy%7D%20qualityMeasureMap%28x%2Cy%29) are rejected. + /// * The remaining corners are sorted by the quality measure in the descending order. + /// * Function throws away each corner for which there is a stronger corner at a distance less than + /// maxDistance. + /// + /// The function can be used to initialize a point-based tracker of an object. + /// + /// + /// Note: If the function is called with different values A and B of the parameter qualityLevel , and + /// A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector + /// with qualityLevel=B . + /// + /// ## Parameters + /// * image: Input 8-bit or floating-point 32-bit, single-channel image. + /// * corners: Output vector of detected corners. + /// * maxCorners: Maximum number of corners to return. If there are more corners than are found, + /// the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set + /// and all detected corners are returned. + /// * qualityLevel: Parameter characterizing the minimal accepted quality of image corners. The + /// parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue + /// (see [corner_min_eigen_val] ) or the Harris function response (see [corner_harris] ). The corners with the + /// quality measure less than the product are rejected. For example, if the best corner has the + /// quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure + /// less than 15 are rejected. + /// * minDistance: Minimum possible Euclidean distance between the returned corners. + /// * mask: Optional region of interest. If the image is not empty (it needs to have the type + /// CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. + /// * blockSize: Size of an average block for computing a derivative covariation matrix over each + /// pixel neighborhood. See cornerEigenValsAndVecs . + /// * useHarrisDetector: Parameter indicating whether to use a Harris detector (see #cornerHarris) + /// or #cornerMinEigenVal. + /// * k: Free parameter of the Harris detector. + /// ## See also + /// cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform, + /// + /// ## Note + /// This alternative version of [good_features_to_track] function uses the following default values for its arguments: + /// * mask: noArray() + /// * block_size: 3 + /// * use_harris_detector: false + /// * k: 0.04 + #[inline] + pub fn good_features_to_track_def(image: &impl ToInputArray, corners: &mut impl ToOutputArray, max_corners: i32, quality_level: f64, min_distance: f64) -> Result<()> { + input_array_arg!(image); + output_array_arg!(corners); + return_send!(via ocvrs_return); + unsafe { sys::cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double(image.as_raw__InputArray(), corners.as_raw__OutputArray(), max_corners, quality_level, min_distance, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Same as above, but returns also quality measure of the detected corners. + /// + /// ## Parameters + /// * image: Input 8-bit or floating-point 32-bit, single-channel image. + /// * corners: Output vector of detected corners. + /// * maxCorners: Maximum number of corners to return. If there are more corners than are found, + /// the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set + /// and all detected corners are returned. + /// * qualityLevel: Parameter characterizing the minimal accepted quality of image corners. The + /// parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue + /// (see [corner_min_eigen_val] ) or the Harris function response (see [corner_harris] ). The corners with the + /// quality measure less than the product are rejected. For example, if the best corner has the + /// quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure + /// less than 15 are rejected. + /// * minDistance: Minimum possible Euclidean distance between the returned corners. + /// * mask: Region of interest. If the image is not empty (it needs to have the type + /// CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. + /// * cornersQuality: Output vector of quality measure of the detected corners. + /// * blockSize: Size of an average block for computing a derivative covariation matrix over each + /// pixel neighborhood. See cornerEigenValsAndVecs . + /// * gradientSize: Aperture parameter for the Sobel operator used for derivatives computation. + /// See cornerEigenValsAndVecs . + /// * useHarrisDetector: Parameter indicating whether to use a Harris detector (see #cornerHarris) + /// or #cornerMinEigenVal. + /// * k: Free parameter of the Harris detector. + /// + /// ## Note + /// This alternative version of [good_features_to_track_with_quality] function uses the following default values for its arguments: + /// * block_size: 3 + /// * gradient_size: 3 + /// * use_harris_detector: false + /// * k: 0.04 + #[inline] + pub fn good_features_to_track_with_quality_def(image: &impl ToInputArray, corners: &mut impl ToOutputArray, max_corners: i32, quality_level: f64, min_distance: f64, mask: &impl ToInputArray, corners_quality: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(image); + output_array_arg!(corners); + input_array_arg!(mask); + output_array_arg!(corners_quality); + return_send!(via ocvrs_return); + unsafe { sys::cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_const__OutputArrayR(image.as_raw__InputArray(), corners.as_raw__OutputArray(), max_corners, quality_level, min_distance, mask.as_raw__InputArray(), corners_quality.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Same as above, but returns also quality measure of the detected corners. + /// + /// ## Parameters + /// * image: Input 8-bit or floating-point 32-bit, single-channel image. + /// * corners: Output vector of detected corners. + /// * maxCorners: Maximum number of corners to return. If there are more corners than are found, + /// the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set + /// and all detected corners are returned. + /// * qualityLevel: Parameter characterizing the minimal accepted quality of image corners. The + /// parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue + /// (see [corner_min_eigen_val] ) or the Harris function response (see [corner_harris] ). The corners with the + /// quality measure less than the product are rejected. For example, if the best corner has the + /// quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure + /// less than 15 are rejected. + /// * minDistance: Minimum possible Euclidean distance between the returned corners. + /// * mask: Region of interest. If the image is not empty (it needs to have the type + /// CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. + /// * cornersQuality: Output vector of quality measure of the detected corners. + /// * blockSize: Size of an average block for computing a derivative covariation matrix over each + /// pixel neighborhood. See cornerEigenValsAndVecs . + /// * gradientSize: Aperture parameter for the Sobel operator used for derivatives computation. + /// See cornerEigenValsAndVecs . + /// * useHarrisDetector: Parameter indicating whether to use a Harris detector (see #cornerHarris) + /// or #cornerMinEigenVal. + /// * k: Free parameter of the Harris detector. + /// + /// ## C++ default parameters + /// * block_size: 3 + /// * gradient_size: 3 + /// * use_harris_detector: false + /// * k: 0.04 + #[inline] + pub fn good_features_to_track_with_quality(image: &impl ToInputArray, corners: &mut impl ToOutputArray, max_corners: i32, quality_level: f64, min_distance: f64, mask: &impl ToInputArray, corners_quality: &mut impl ToOutputArray, block_size: i32, gradient_size: i32, use_harris_detector: bool, k: f64) -> Result<()> { + input_array_arg!(image); + output_array_arg!(corners); + input_array_arg!(mask); + output_array_arg!(corners_quality); + return_send!(via ocvrs_return); + unsafe { sys::cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_const__OutputArrayR_int_int_bool_double(image.as_raw__InputArray(), corners.as_raw__OutputArray(), max_corners, quality_level, min_distance, mask.as_raw__InputArray(), corners_quality.as_raw__OutputArray(), block_size, gradient_size, use_harris_detector, k, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Determines strong corners on an image. + /// + /// The function finds the most prominent corners in the image or in the specified image region, as + /// described in [Shi94](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Shi94) + /// + /// * Function calculates the corner quality measure at every source image pixel using the + /// [corner_min_eigen_val] or [corner_harris] . + /// * Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are + /// retained). + /// * The corners with the minimal eigenvalue less than + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7BqualityLevel%7D%20%5Ccdot%20%5Cmax%5F%7Bx%2Cy%7D%20qualityMeasureMap%28x%2Cy%29) are rejected. + /// * The remaining corners are sorted by the quality measure in the descending order. + /// * Function throws away each corner for which there is a stronger corner at a distance less than + /// maxDistance. + /// + /// The function can be used to initialize a point-based tracker of an object. + /// + /// + /// Note: If the function is called with different values A and B of the parameter qualityLevel , and + /// A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector + /// with qualityLevel=B . + /// + /// ## Parameters + /// * image: Input 8-bit or floating-point 32-bit, single-channel image. + /// * corners: Output vector of detected corners. + /// * maxCorners: Maximum number of corners to return. If there are more corners than are found, + /// the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set + /// and all detected corners are returned. + /// * qualityLevel: Parameter characterizing the minimal accepted quality of image corners. The + /// parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue + /// (see [corner_min_eigen_val] ) or the Harris function response (see [corner_harris] ). The corners with the + /// quality measure less than the product are rejected. For example, if the best corner has the + /// quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure + /// less than 15 are rejected. + /// * minDistance: Minimum possible Euclidean distance between the returned corners. + /// * mask: Optional region of interest. If the image is not empty (it needs to have the type + /// CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. + /// * blockSize: Size of an average block for computing a derivative covariation matrix over each + /// pixel neighborhood. See cornerEigenValsAndVecs . + /// * useHarrisDetector: Parameter indicating whether to use a Harris detector (see #cornerHarris) + /// or #cornerMinEigenVal. + /// * k: Free parameter of the Harris detector. + /// ## See also + /// cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform, + /// + /// ## C++ default parameters + /// * mask: noArray() + /// * block_size: 3 + /// * use_harris_detector: false + /// * k: 0.04 + #[inline] + pub fn good_features_to_track(image: &impl ToInputArray, corners: &mut impl ToOutputArray, max_corners: i32, quality_level: f64, min_distance: f64, mask: &impl ToInputArray, block_size: i32, use_harris_detector: bool, k: f64) -> Result<()> { + input_array_arg!(image); + output_array_arg!(corners); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_int_bool_double(image.as_raw__InputArray(), corners.as_raw__OutputArray(), max_corners, quality_level, min_distance, mask.as_raw__InputArray(), block_size, use_harris_detector, k, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [good_features_to_track_with_gradient] function uses the following default values for its arguments: + /// * use_harris_detector: false + /// * k: 0.04 + #[inline] + pub fn good_features_to_track_with_gradient_def(image: &impl ToInputArray, corners: &mut impl ToOutputArray, max_corners: i32, quality_level: f64, min_distance: f64, mask: &impl ToInputArray, block_size: i32, gradient_size: i32) -> Result<()> { + input_array_arg!(image); + output_array_arg!(corners); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_int_int(image.as_raw__InputArray(), corners.as_raw__OutputArray(), max_corners, quality_level, min_distance, mask.as_raw__InputArray(), block_size, gradient_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * use_harris_detector: false + /// * k: 0.04 + #[inline] + pub fn good_features_to_track_with_gradient(image: &impl ToInputArray, corners: &mut impl ToOutputArray, max_corners: i32, quality_level: f64, min_distance: f64, mask: &impl ToInputArray, block_size: i32, gradient_size: i32, use_harris_detector: bool, k: f64) -> Result<()> { + input_array_arg!(image); + output_array_arg!(corners); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_int_int_bool_double(image.as_raw__InputArray(), corners.as_raw__OutputArray(), max_corners, quality_level, min_distance, mask.as_raw__InputArray(), block_size, gradient_size, use_harris_detector, k, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Runs the GrabCut algorithm. + /// + /// The function implements the [GrabCut image segmentation algorithm](https://en.wikipedia.org/wiki/GrabCut). + /// + /// ## Parameters + /// * img: Input 8-bit 3-channel image. + /// * mask: Input/output 8-bit single-channel mask. The mask is initialized by the function when + /// mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses. + /// * rect: ROI containing a segmented object. The pixels outside of the ROI are marked as + /// "obvious background". The parameter is only used when mode==[GC_INIT_WITH_RECT] . + /// * bgdModel: Temporary array for the background model. Do not modify it while you are + /// processing the same image. + /// * fgdModel: Temporary arrays for the foreground model. Do not modify it while you are + /// processing the same image. + /// * iterCount: Number of iterations the algorithm should make before returning the result. Note + /// that the result can be refined with further calls with mode==[GC_INIT_WITH_MASK] or + /// mode==GC_EVAL . + /// * mode: Operation mode that could be one of the [grab_cut_modes] + /// + /// ## Note + /// This alternative version of [grab_cut] function uses the following default values for its arguments: + /// * mode: GC_EVAL + #[inline] + pub fn grab_cut_def(img: &impl ToInputArray, mask: &mut impl ToInputOutputArray, rect: core::Rect, bgd_model: &mut impl ToInputOutputArray, fgd_model: &mut impl ToInputOutputArray, iter_count: i32) -> Result<()> { + input_array_arg!(img); + input_output_array_arg!(mask); + input_output_array_arg!(bgd_model); + input_output_array_arg!(fgd_model); + return_send!(via ocvrs_return); + unsafe { sys::cv_grabCut_const__InputArrayR_const__InputOutputArrayR_Rect_const__InputOutputArrayR_const__InputOutputArrayR_int(img.as_raw__InputArray(), mask.as_raw__InputOutputArray(), &rect, bgd_model.as_raw__InputOutputArray(), fgd_model.as_raw__InputOutputArray(), iter_count, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Runs the GrabCut algorithm. + /// + /// The function implements the [GrabCut image segmentation algorithm](https://en.wikipedia.org/wiki/GrabCut). + /// + /// ## Parameters + /// * img: Input 8-bit 3-channel image. + /// * mask: Input/output 8-bit single-channel mask. The mask is initialized by the function when + /// mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses. + /// * rect: ROI containing a segmented object. The pixels outside of the ROI are marked as + /// "obvious background". The parameter is only used when mode==[GC_INIT_WITH_RECT] . + /// * bgdModel: Temporary array for the background model. Do not modify it while you are + /// processing the same image. + /// * fgdModel: Temporary arrays for the foreground model. Do not modify it while you are + /// processing the same image. + /// * iterCount: Number of iterations the algorithm should make before returning the result. Note + /// that the result can be refined with further calls with mode==[GC_INIT_WITH_MASK] or + /// mode==GC_EVAL . + /// * mode: Operation mode that could be one of the #GrabCutModes + /// + /// ## C++ default parameters + /// * mode: GC_EVAL + #[inline] + pub fn grab_cut(img: &impl ToInputArray, mask: &mut impl ToInputOutputArray, rect: core::Rect, bgd_model: &mut impl ToInputOutputArray, fgd_model: &mut impl ToInputOutputArray, iter_count: i32, mode: i32) -> Result<()> { + input_array_arg!(img); + input_output_array_arg!(mask); + input_output_array_arg!(bgd_model); + input_output_array_arg!(fgd_model); + return_send!(via ocvrs_return); + unsafe { sys::cv_grabCut_const__InputArrayR_const__InputOutputArrayR_Rect_const__InputOutputArrayR_const__InputOutputArrayR_int_int(img.as_raw__InputArray(), mask.as_raw__InputOutputArray(), &rect, bgd_model.as_raw__InputOutputArray(), fgd_model.as_raw__InputOutputArray(), iter_count, mode, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the integral of an image. + /// + /// The function calculates one or more integral images for the source image as follows: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsum%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7Bx%3CX%2Cy%3CY%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29) + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsqsum%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7Bx%3CX%2Cy%3CY%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29%5E2) + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Btilted%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7By%3CY%2Cabs%28x%2DX%2B1%29%20%5Cleq%20Y%2Dy%2D1%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29) + /// + /// Using these integral images, you can calculate sum, mean, and standard deviation over a specific + /// up-right or rotated rectangular region of the image in a constant time, for example: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5F%7Bx%5F1%20%5Cleq%20x%20%3C%20x%5F2%2C%20%20%5C%2C%20y%5F1%20%20%5Cleq%20y%20%3C%20y%5F2%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsum%7D%20%28x%5F2%2Cy%5F2%29%2D%20%5Ctexttt%7Bsum%7D%20%28x%5F1%2Cy%5F2%29%2D%20%5Ctexttt%7Bsum%7D%20%28x%5F2%2Cy%5F1%29%2B%20%5Ctexttt%7Bsum%7D%20%28x%5F1%2Cy%5F1%29) + /// + /// It makes possible to do a fast blurring or fast block correlation with a variable window size, for + /// example. In case of multi-channel images, sums for each channel are accumulated independently. + /// + /// As a practical example, the next figure shows the calculation of the integral of a straight + /// rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the + /// original image are shown, as well as the relative pixels in the integral images sum and tilted . + /// + /// ![integral calculation example](https://docs.opencv.org/4.13.0/integral.png) + /// + /// ## Parameters + /// * src: input image as ![inline formula](https://latex.codecogs.com/png.latex?W%20%5Ctimes%20H), 8-bit or floating-point (32f or 64f). + /// * sum: integral image as ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29) , 32-bit integer or floating-point (32f or 64f). + /// * sqsum: integral image for squared pixel values; it is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29), double-precision + /// floating-point (64f) array. + /// * tilted: integral for the image rotated by 45 degrees; it is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29) array with + /// the same data type as sum. + /// * sdepth: desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or + /// CV_64F. + /// * sqdepth: desired depth of the integral image of squared pixel values, CV_32F or CV_64F. + /// + /// ## Overloaded parameters + /// + /// + /// ## Note + /// This alternative version of [integral] function uses the following default values for its arguments: + /// * sdepth: -1 + #[inline] + pub fn integral_def(src: &impl ToInputArray, sum: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(sum); + return_send!(via ocvrs_return); + unsafe { sys::cv_integral_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), sum.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the integral of an image. + /// + /// The function calculates one or more integral images for the source image as follows: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsum%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7Bx%3CX%2Cy%3CY%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29) + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsqsum%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7Bx%3CX%2Cy%3CY%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29%5E2) + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Btilted%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7By%3CY%2Cabs%28x%2DX%2B1%29%20%5Cleq%20Y%2Dy%2D1%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29) + /// + /// Using these integral images, you can calculate sum, mean, and standard deviation over a specific + /// up-right or rotated rectangular region of the image in a constant time, for example: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5F%7Bx%5F1%20%5Cleq%20x%20%3C%20x%5F2%2C%20%20%5C%2C%20y%5F1%20%20%5Cleq%20y%20%3C%20y%5F2%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsum%7D%20%28x%5F2%2Cy%5F2%29%2D%20%5Ctexttt%7Bsum%7D%20%28x%5F1%2Cy%5F2%29%2D%20%5Ctexttt%7Bsum%7D%20%28x%5F2%2Cy%5F1%29%2B%20%5Ctexttt%7Bsum%7D%20%28x%5F1%2Cy%5F1%29) + /// + /// It makes possible to do a fast blurring or fast block correlation with a variable window size, for + /// example. In case of multi-channel images, sums for each channel are accumulated independently. + /// + /// As a practical example, the next figure shows the calculation of the integral of a straight + /// rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the + /// original image are shown, as well as the relative pixels in the integral images sum and tilted . + /// + /// ![integral calculation example](https://docs.opencv.org/4.13.0/integral.png) + /// + /// ## Parameters + /// * src: input image as ![inline formula](https://latex.codecogs.com/png.latex?W%20%5Ctimes%20H), 8-bit or floating-point (32f or 64f). + /// * sum: integral image as ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29) , 32-bit integer or floating-point (32f or 64f). + /// * sqsum: integral image for squared pixel values; it is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29), double-precision + /// floating-point (64f) array. + /// * tilted: integral for the image rotated by 45 degrees; it is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29) array with + /// the same data type as sum. + /// * sdepth: desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or + /// CV_64F. + /// * sqdepth: desired depth of the integral image of squared pixel values, CV_32F or CV_64F. + /// + /// ## Overloaded parameters + /// + /// + /// ## Note + /// This alternative version of [integral2] function uses the following default values for its arguments: + /// * sdepth: -1 + /// * sqdepth: -1 + #[inline] + pub fn integral2_def(src: &impl ToInputArray, sum: &mut impl ToOutputArray, sqsum: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(sum); + output_array_arg!(sqsum); + return_send!(via ocvrs_return); + unsafe { sys::cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src.as_raw__InputArray(), sum.as_raw__OutputArray(), sqsum.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the integral of an image. + /// + /// The function calculates one or more integral images for the source image as follows: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsum%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7Bx%3CX%2Cy%3CY%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29) + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsqsum%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7Bx%3CX%2Cy%3CY%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29%5E2) + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Btilted%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7By%3CY%2Cabs%28x%2DX%2B1%29%20%5Cleq%20Y%2Dy%2D1%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29) + /// + /// Using these integral images, you can calculate sum, mean, and standard deviation over a specific + /// up-right or rotated rectangular region of the image in a constant time, for example: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5F%7Bx%5F1%20%5Cleq%20x%20%3C%20x%5F2%2C%20%20%5C%2C%20y%5F1%20%20%5Cleq%20y%20%3C%20y%5F2%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsum%7D%20%28x%5F2%2Cy%5F2%29%2D%20%5Ctexttt%7Bsum%7D%20%28x%5F1%2Cy%5F2%29%2D%20%5Ctexttt%7Bsum%7D%20%28x%5F2%2Cy%5F1%29%2B%20%5Ctexttt%7Bsum%7D%20%28x%5F1%2Cy%5F1%29) + /// + /// It makes possible to do a fast blurring or fast block correlation with a variable window size, for + /// example. In case of multi-channel images, sums for each channel are accumulated independently. + /// + /// As a practical example, the next figure shows the calculation of the integral of a straight + /// rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the + /// original image are shown, as well as the relative pixels in the integral images sum and tilted . + /// + /// ![integral calculation example](https://docs.opencv.org/4.13.0/integral.png) + /// + /// ## Parameters + /// * src: input image as ![inline formula](https://latex.codecogs.com/png.latex?W%20%5Ctimes%20H), 8-bit or floating-point (32f or 64f). + /// * sum: integral image as ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29) , 32-bit integer or floating-point (32f or 64f). + /// * sqsum: integral image for squared pixel values; it is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29), double-precision + /// floating-point (64f) array. + /// * tilted: integral for the image rotated by 45 degrees; it is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29) array with + /// the same data type as sum. + /// * sdepth: desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or + /// CV_64F. + /// * sqdepth: desired depth of the integral image of squared pixel values, CV_32F or CV_64F. + /// + /// ## Note + /// This alternative version of [integral3] function uses the following default values for its arguments: + /// * sdepth: -1 + /// * sqdepth: -1 + #[inline] + pub fn integral3_def(src: &impl ToInputArray, sum: &mut impl ToOutputArray, sqsum: &mut impl ToOutputArray, tilted: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(sum); + output_array_arg!(sqsum); + output_array_arg!(tilted); + return_send!(via ocvrs_return); + unsafe { sys::cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(src.as_raw__InputArray(), sum.as_raw__OutputArray(), sqsum.as_raw__OutputArray(), tilted.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the integral of an image. + /// + /// The function calculates one or more integral images for the source image as follows: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsum%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7Bx%3CX%2Cy%3CY%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29) + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsqsum%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7Bx%3CX%2Cy%3CY%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29%5E2) + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Btilted%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7By%3CY%2Cabs%28x%2DX%2B1%29%20%5Cleq%20Y%2Dy%2D1%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29) + /// + /// Using these integral images, you can calculate sum, mean, and standard deviation over a specific + /// up-right or rotated rectangular region of the image in a constant time, for example: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5F%7Bx%5F1%20%5Cleq%20x%20%3C%20x%5F2%2C%20%20%5C%2C%20y%5F1%20%20%5Cleq%20y%20%3C%20y%5F2%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsum%7D%20%28x%5F2%2Cy%5F2%29%2D%20%5Ctexttt%7Bsum%7D%20%28x%5F1%2Cy%5F2%29%2D%20%5Ctexttt%7Bsum%7D%20%28x%5F2%2Cy%5F1%29%2B%20%5Ctexttt%7Bsum%7D%20%28x%5F1%2Cy%5F1%29) + /// + /// It makes possible to do a fast blurring or fast block correlation with a variable window size, for + /// example. In case of multi-channel images, sums for each channel are accumulated independently. + /// + /// As a practical example, the next figure shows the calculation of the integral of a straight + /// rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the + /// original image are shown, as well as the relative pixels in the integral images sum and tilted . + /// + /// ![integral calculation example](https://docs.opencv.org/4.13.0/integral.png) + /// + /// ## Parameters + /// * src: input image as ![inline formula](https://latex.codecogs.com/png.latex?W%20%5Ctimes%20H), 8-bit or floating-point (32f or 64f). + /// * sum: integral image as ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29) , 32-bit integer or floating-point (32f or 64f). + /// * sqsum: integral image for squared pixel values; it is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29), double-precision + /// floating-point (64f) array. + /// * tilted: integral for the image rotated by 45 degrees; it is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29) array with + /// the same data type as sum. + /// * sdepth: desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or + /// CV_64F. + /// * sqdepth: desired depth of the integral image of squared pixel values, CV_32F or CV_64F. + /// + /// ## C++ default parameters + /// * sdepth: -1 + /// * sqdepth: -1 + #[inline] + pub fn integral3(src: &impl ToInputArray, sum: &mut impl ToOutputArray, sqsum: &mut impl ToOutputArray, tilted: &mut impl ToOutputArray, sdepth: i32, sqdepth: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(sum); + output_array_arg!(sqsum); + output_array_arg!(tilted); + return_send!(via ocvrs_return); + unsafe { sys::cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), sum.as_raw__OutputArray(), sqsum.as_raw__OutputArray(), tilted.as_raw__OutputArray(), sdepth, sqdepth, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the integral of an image. + /// + /// The function calculates one or more integral images for the source image as follows: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsum%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7Bx%3CX%2Cy%3CY%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29) + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsqsum%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7Bx%3CX%2Cy%3CY%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29%5E2) + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Btilted%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7By%3CY%2Cabs%28x%2DX%2B1%29%20%5Cleq%20Y%2Dy%2D1%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29) + /// + /// Using these integral images, you can calculate sum, mean, and standard deviation over a specific + /// up-right or rotated rectangular region of the image in a constant time, for example: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5F%7Bx%5F1%20%5Cleq%20x%20%3C%20x%5F2%2C%20%20%5C%2C%20y%5F1%20%20%5Cleq%20y%20%3C%20y%5F2%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsum%7D%20%28x%5F2%2Cy%5F2%29%2D%20%5Ctexttt%7Bsum%7D%20%28x%5F1%2Cy%5F2%29%2D%20%5Ctexttt%7Bsum%7D%20%28x%5F2%2Cy%5F1%29%2B%20%5Ctexttt%7Bsum%7D%20%28x%5F1%2Cy%5F1%29) + /// + /// It makes possible to do a fast blurring or fast block correlation with a variable window size, for + /// example. In case of multi-channel images, sums for each channel are accumulated independently. + /// + /// As a practical example, the next figure shows the calculation of the integral of a straight + /// rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the + /// original image are shown, as well as the relative pixels in the integral images sum and tilted . + /// + /// ![integral calculation example](https://docs.opencv.org/4.13.0/integral.png) + /// + /// ## Parameters + /// * src: input image as ![inline formula](https://latex.codecogs.com/png.latex?W%20%5Ctimes%20H), 8-bit or floating-point (32f or 64f). + /// * sum: integral image as ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29) , 32-bit integer or floating-point (32f or 64f). + /// * sqsum: integral image for squared pixel values; it is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29), double-precision + /// floating-point (64f) array. + /// * tilted: integral for the image rotated by 45 degrees; it is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29) array with + /// the same data type as sum. + /// * sdepth: desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or + /// CV_64F. + /// * sqdepth: desired depth of the integral image of squared pixel values, CV_32F or CV_64F. + /// + /// ## Overloaded parameters + /// + /// ## C++ default parameters + /// * sdepth: -1 + /// * sqdepth: -1 + #[inline] + pub fn integral2(src: &impl ToInputArray, sum: &mut impl ToOutputArray, sqsum: &mut impl ToOutputArray, sdepth: i32, sqdepth: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(sum); + output_array_arg!(sqsum); + return_send!(via ocvrs_return); + unsafe { sys::cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), sum.as_raw__OutputArray(), sqsum.as_raw__OutputArray(), sdepth, sqdepth, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the integral of an image. + /// + /// The function calculates one or more integral images for the source image as follows: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsum%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7Bx%3CX%2Cy%3CY%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29) + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bsqsum%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7Bx%3CX%2Cy%3CY%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29%5E2) + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Btilted%7D%20%28X%2CY%29%20%3D%20%20%5Csum%20%5F%7By%3CY%2Cabs%28x%2DX%2B1%29%20%5Cleq%20Y%2Dy%2D1%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29) + /// + /// Using these integral images, you can calculate sum, mean, and standard deviation over a specific + /// up-right or rotated rectangular region of the image in a constant time, for example: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Csum%20%5F%7Bx%5F1%20%5Cleq%20x%20%3C%20x%5F2%2C%20%20%5C%2C%20y%5F1%20%20%5Cleq%20y%20%3C%20y%5F2%7D%20%20%5Ctexttt%7Bimage%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsum%7D%20%28x%5F2%2Cy%5F2%29%2D%20%5Ctexttt%7Bsum%7D%20%28x%5F1%2Cy%5F2%29%2D%20%5Ctexttt%7Bsum%7D%20%28x%5F2%2Cy%5F1%29%2B%20%5Ctexttt%7Bsum%7D%20%28x%5F1%2Cy%5F1%29) + /// + /// It makes possible to do a fast blurring or fast block correlation with a variable window size, for + /// example. In case of multi-channel images, sums for each channel are accumulated independently. + /// + /// As a practical example, the next figure shows the calculation of the integral of a straight + /// rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the + /// original image are shown, as well as the relative pixels in the integral images sum and tilted . + /// + /// ![integral calculation example](https://docs.opencv.org/4.13.0/integral.png) + /// + /// ## Parameters + /// * src: input image as ![inline formula](https://latex.codecogs.com/png.latex?W%20%5Ctimes%20H), 8-bit or floating-point (32f or 64f). + /// * sum: integral image as ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29) , 32-bit integer or floating-point (32f or 64f). + /// * sqsum: integral image for squared pixel values; it is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29), double-precision + /// floating-point (64f) array. + /// * tilted: integral for the image rotated by 45 degrees; it is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2B1%29%5Ctimes%20%28H%2B1%29) array with + /// the same data type as sum. + /// * sdepth: desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or + /// CV_64F. + /// * sqdepth: desired depth of the integral image of squared pixel values, CV_32F or CV_64F. + /// + /// ## Overloaded parameters + /// + /// ## C++ default parameters + /// * sdepth: -1 + #[inline] + pub fn integral(src: &impl ToInputArray, sum: &mut impl ToOutputArray, sdepth: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(sum); + return_send!(via ocvrs_return); + unsafe { sys::cv_integral_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), sum.as_raw__OutputArray(), sdepth, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds intersection of two convex polygons + /// + /// ## Parameters + /// * p1: First polygon + /// * p2: Second polygon + /// * p12: Output polygon describing the intersecting area + /// * handleNested: When true, an intersection is found if one of the polygons is fully enclosed in the other. + /// When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge + /// of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested. + /// + /// ## Returns + /// Area of intersecting polygon. May be negative, if algorithm has not converged, e.g. non-convex input. + /// + /// + /// Note: intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't. + /// + /// ## Note + /// This alternative version of [intersect_convex_convex] function uses the following default values for its arguments: + /// * handle_nested: true + #[inline] + pub fn intersect_convex_convex_def(p1: &impl ToInputArray, p2: &impl ToInputArray, p12: &mut impl ToOutputArray) -> Result { + input_array_arg!(p1); + input_array_arg!(p2); + output_array_arg!(p12); + return_send!(via ocvrs_return); + unsafe { sys::cv_intersectConvexConvex_const__InputArrayR_const__InputArrayR_const__OutputArrayR(p1.as_raw__InputArray(), p2.as_raw__InputArray(), p12.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds intersection of two convex polygons + /// + /// ## Parameters + /// * p1: First polygon + /// * p2: Second polygon + /// * p12: Output polygon describing the intersecting area + /// * handleNested: When true, an intersection is found if one of the polygons is fully enclosed in the other. + /// When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge + /// of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested. + /// + /// ## Returns + /// Area of intersecting polygon. May be negative, if algorithm has not converged, e.g. non-convex input. + /// + /// + /// Note: intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't. + /// + /// ## C++ default parameters + /// * handle_nested: true + #[inline] + pub fn intersect_convex_convex(p1: &impl ToInputArray, p2: &impl ToInputArray, p12: &mut impl ToOutputArray, handle_nested: bool) -> Result { + input_array_arg!(p1); + input_array_arg!(p2); + output_array_arg!(p12); + return_send!(via ocvrs_return); + unsafe { sys::cv_intersectConvexConvex_const__InputArrayR_const__InputArrayR_const__OutputArrayR_bool(p1.as_raw__InputArray(), p2.as_raw__InputArray(), p12.as_raw__OutputArray(), handle_nested, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Inverts an affine transformation. + /// + /// The function computes an inverse affine transformation represented by ![inline formula](https://latex.codecogs.com/png.latex?2%20%5Ctimes%203) matrix M: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Bbmatrix%7D%20a%5F%7B11%7D%20%26%20a%5F%7B12%7D%20%26%20b%5F1%20%20%5C%5C%20a%5F%7B21%7D%20%26%20a%5F%7B22%7D%20%26%20b%5F2%20%5Cend%7Bbmatrix%7D) + /// + /// The result is also a ![inline formula](https://latex.codecogs.com/png.latex?2%20%5Ctimes%203) matrix of the same type as M. + /// + /// ## Parameters + /// * M: Original affine transformation. + /// * iM: Output reverse affine transformation. + #[inline] + pub fn invert_affine_transform(m: &impl ToInputArray, i_m: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(m); + output_array_arg!(i_m); + return_send!(via ocvrs_return); + unsafe { sys::cv_invertAffineTransform_const__InputArrayR_const__OutputArrayR(m.as_raw__InputArray(), i_m.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Tests a contour convexity. + /// + /// The function tests whether the input contour is convex or not. The contour must be simple, that is, + /// without self-intersections. Otherwise, the function output is undefined. + /// + /// ## Parameters + /// * contour: Input vector of 2D points, stored in std::vector\<\> or Mat + #[inline] + pub fn is_contour_convex(contour: &impl ToInputArray) -> Result { + input_array_arg!(contour); + return_send!(via ocvrs_return); + unsafe { sys::cv_isContourConvex_const__InputArrayR(contour.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a line segment connecting two points. + /// + /// The function line draws the line segment between pt1 and pt2 points in the image. The line is + /// clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected + /// or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased + /// lines are drawn using Gaussian filtering. + /// + /// ## Parameters + /// * img: Image. + /// * pt1: First point of the line segment. + /// * pt2: Second point of the line segment. + /// * color: Line color. + /// * thickness: Line thickness. + /// * lineType: Type of the line. See #LineTypes. + /// * shift: Number of fractional bits in the point coordinates. + /// + /// ## Note + /// This alternative version of [line] function uses the following default values for its arguments: + /// * thickness: 1 + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn line_def(img: &mut impl ToInputOutputArray, pt1: core::Point, pt2: core::Point, color: core::Scalar) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_line_const__InputOutputArrayR_Point_Point_const_ScalarR(img.as_raw__InputOutputArray(), &pt1, &pt2, &color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a line segment connecting two points. + /// + /// The function line draws the line segment between pt1 and pt2 points in the image. The line is + /// clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected + /// or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased + /// lines are drawn using Gaussian filtering. + /// + /// ## Parameters + /// * img: Image. + /// * pt1: First point of the line segment. + /// * pt2: Second point of the line segment. + /// * color: Line color. + /// * thickness: Line thickness. + /// * lineType: Type of the line. See #LineTypes. + /// * shift: Number of fractional bits in the point coordinates. + /// + /// ## C++ default parameters + /// * thickness: 1 + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn line(img: &mut impl ToInputOutputArray, pt1: core::Point, pt2: core::Point, color: core::Scalar, thickness: i32, line_type: i32, shift: i32) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_line_const__InputOutputArrayR_Point_Point_const_ScalarR_int_int_int(img.as_raw__InputOutputArray(), &pt1, &pt2, &color, thickness, line_type, shift, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Remaps an image to polar coordinates space. + /// + /// + /// **Deprecated**: This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags) + /// + /// @internal + /// Transform the source image using the following transformation (See [polar_remaps_reference_image] "Polar remaps reference image c)"): + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%0A%20%20dst%28%20%5Crho%20%2C%20%5Cphi%20%29%20%3D%20src%28x%2Cy%29%20%5C%5C%0A%20%20dst%2Esize%28%29%20%5Cleftarrow%20src%2Esize%28%29%0A%5Cend%7Barray%7D) + /// + /// where + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%0A%20%20I%20%3D%20%28dx%2Cdy%29%20%3D%20%28x%20%2D%20center%2Ex%2Cy%20%2D%20center%2Ey%29%20%5C%5C%0A%20%20%5Crho%20%3D%20Kmag%20%5Ccdot%20%5Ctexttt%7Bmagnitude%7D%20%28I%29%20%2C%5C%5C%0A%20%20%5Cphi%20%3D%20angle%20%5Ccdot%20%5Ctexttt%7Bangle%7D%20%28I%29%0A%5Cend%7Barray%7D) + /// + /// and + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%0A%20%20Kx%20%3D%20src%2Ecols%20%2F%20maxRadius%20%5C%5C%0A%20%20Ky%20%3D%20src%2Erows%20%2F%202%5CPi%0A%5Cend%7Barray%7D) + /// + /// + /// ## Parameters + /// * src: Source image + /// * dst: Destination image. It will have same size and type as src. + /// * center: The transformation center; + /// * maxRadius: The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. + /// * flags: A combination of interpolation methods, see [interpolation_flags] + /// + /// + /// Note: + /// * The function can not operate in-place. + /// * To calculate magnitude and angle in degrees [cart_to_polar] is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. + /// ## See also + /// cv::logPolar + /// @endinternal + #[deprecated = "This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags)"] + #[inline] + pub fn linear_polar(src: &impl ToInputArray, dst: &mut impl ToOutputArray, center: core::Point2f, max_radius: f64, flags: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_linearPolar_const__InputArrayR_const__OutputArrayR_Point2f_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ¢er, max_radius, flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Remaps an image to semilog-polar coordinates space. + /// + /// + /// **Deprecated**: This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); + /// + /// @internal + /// Transform the source image using the following transformation (See [polar_remaps_reference_image] "Polar remaps reference image d)"): + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%0A%20%20dst%28%20%5Crho%20%2C%20%5Cphi%20%29%20%3D%20src%28x%2Cy%29%20%5C%5C%0A%20%20dst%2Esize%28%29%20%5Cleftarrow%20src%2Esize%28%29%0A%5Cend%7Barray%7D) + /// + /// where + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%0A%20%20I%20%3D%20%28dx%2Cdy%29%20%3D%20%28x%20%2D%20center%2Ex%2Cy%20%2D%20center%2Ey%29%20%5C%5C%0A%20%20%5Crho%20%3D%20M%20%5Ccdot%20log%5Fe%28%5Ctexttt%7Bmagnitude%7D%20%28I%29%29%20%2C%5C%5C%0A%20%20%5Cphi%20%3D%20Kangle%20%5Ccdot%20%5Ctexttt%7Bangle%7D%20%28I%29%20%5C%5C%0A%5Cend%7Barray%7D) + /// + /// and + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%0A%20%20M%20%3D%20src%2Ecols%20%2F%20log%5Fe%28maxRadius%29%20%5C%5C%0A%20%20Kangle%20%3D%20src%2Erows%20%2F%202%5CPi%20%5C%5C%0A%5Cend%7Barray%7D) + /// + /// The function emulates the human "foveal" vision and can be used for fast scale and + /// rotation-invariant template matching, for object tracking and so forth. + /// ## Parameters + /// * src: Source image + /// * dst: Destination image. It will have same size and type as src. + /// * center: The transformation center; where the output precision is maximal + /// * M: Magnitude scale parameter. It determines the radius of the bounding circle to transform too. + /// * flags: A combination of interpolation methods, see [interpolation_flags] + /// + /// + /// Note: + /// * The function can not operate in-place. + /// * To calculate magnitude and angle in degrees [cart_to_polar] is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. + /// ## See also + /// cv::linearPolar + /// @endinternal + #[deprecated = "This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG);"] + #[inline] + pub fn log_polar(src: &impl ToInputArray, dst: &mut impl ToOutputArray, center: core::Point2f, m: f64, flags: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_logPolar_const__InputArrayR_const__OutputArrayR_Point2f_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ¢er, m, flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Compares two shapes. + /// + /// The function compares two shapes. All three implemented methods use the Hu invariants (see #HuMoments) + /// + /// ## Parameters + /// * contour1: First contour or grayscale image. + /// * contour2: Second contour or grayscale image. + /// * method: Comparison method, see [shape_match_modes] + /// * parameter: Method-specific parameter (not supported now). + #[inline] + pub fn match_shapes(contour1: &impl ToInputArray, contour2: &impl ToInputArray, method: i32, parameter: f64) -> Result { + input_array_arg!(contour1); + input_array_arg!(contour2); + return_send!(via ocvrs_return); + unsafe { sys::cv_matchShapes_const__InputArrayR_const__InputArrayR_int_double(contour1.as_raw__InputArray(), contour2.as_raw__InputArray(), method, parameter, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Compares a template against overlapped image regions. + /// + /// The function slides through image , compares the overlapped patches of size ![inline formula](https://latex.codecogs.com/png.latex?w%20%5Ctimes%20h) against + /// templ using the specified method and stores the comparison results in result . [template_match_modes] + /// describes the formulae for the available comparison methods ( ![inline formula](https://latex.codecogs.com/png.latex?I) denotes image, ![inline formula](https://latex.codecogs.com/png.latex?T) + /// template, ![inline formula](https://latex.codecogs.com/png.latex?R) result, ![inline formula](https://latex.codecogs.com/png.latex?M) the optional mask ). The summation is done over template and/or + /// the image patch: ![inline formula](https://latex.codecogs.com/png.latex?x%27%20%3D%200%2E%2E%2Ew%2D1%2C%20y%27%20%3D%200%2E%2E%2Eh%2D1) + /// + /// After the function finishes the comparison, the best matches can be found as global minimums (when + /// [TM_SQDIFF] was used) or maximums (when [TM_CCORR] or [TM_CCOEFF] was used) using the + /// [min_max_loc] function. In case of a color image, template summation in the numerator and each sum in + /// the denominator is done over all of the channels and separate mean values are used for each channel. + /// That is, the function can take a color template and a color image. The result will still be a + /// single-channel image, which is easier to analyze. + /// + /// ## Parameters + /// * image: Image where the search is running. It must be 8-bit or 32-bit floating-point. + /// * templ: Searched template. It must be not greater than the source image and have the same + /// data type. + /// * result: Map of comparison results. It must be single-channel 32-bit floating-point. If image + /// is ![inline formula](https://latex.codecogs.com/png.latex?W%20%5Ctimes%20H) and templ is ![inline formula](https://latex.codecogs.com/png.latex?w%20%5Ctimes%20h) , then result is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2Dw%2B1%29%20%5Ctimes%20%28H%2Dh%2B1%29) . + /// * method: Parameter specifying the comparison method, see [template_match_modes] + /// * mask: Optional mask. It must have the same size as templ. It must either have the same number + /// of channels as template or only one channel, which is then used for all template and + /// image channels. If the data type is #CV_8U, the mask is interpreted as a binary mask, + /// meaning only elements where mask is nonzero are used and are kept unchanged independent + /// of the actual mask value (weight equals 1). For data type #CV_32F, the mask values are + /// used as weights. The exact formulas are documented in #TemplateMatchModes. + /// + /// ## Note + /// This alternative version of [match_template] function uses the following default values for its arguments: + /// * mask: noArray() + #[inline] + pub fn match_template_def(image: &impl ToInputArray, templ: &impl ToInputArray, result: &mut impl ToOutputArray, method: i32) -> Result<()> { + input_array_arg!(image); + input_array_arg!(templ); + output_array_arg!(result); + return_send!(via ocvrs_return); + unsafe { sys::cv_matchTemplate_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(image.as_raw__InputArray(), templ.as_raw__InputArray(), result.as_raw__OutputArray(), method, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Compares a template against overlapped image regions. + /// + /// The function slides through image , compares the overlapped patches of size ![inline formula](https://latex.codecogs.com/png.latex?w%20%5Ctimes%20h) against + /// templ using the specified method and stores the comparison results in result . [template_match_modes] + /// describes the formulae for the available comparison methods ( ![inline formula](https://latex.codecogs.com/png.latex?I) denotes image, ![inline formula](https://latex.codecogs.com/png.latex?T) + /// template, ![inline formula](https://latex.codecogs.com/png.latex?R) result, ![inline formula](https://latex.codecogs.com/png.latex?M) the optional mask ). The summation is done over template and/or + /// the image patch: ![inline formula](https://latex.codecogs.com/png.latex?x%27%20%3D%200%2E%2E%2Ew%2D1%2C%20y%27%20%3D%200%2E%2E%2Eh%2D1) + /// + /// After the function finishes the comparison, the best matches can be found as global minimums (when + /// [TM_SQDIFF] was used) or maximums (when [TM_CCORR] or [TM_CCOEFF] was used) using the + /// [min_max_loc] function. In case of a color image, template summation in the numerator and each sum in + /// the denominator is done over all of the channels and separate mean values are used for each channel. + /// That is, the function can take a color template and a color image. The result will still be a + /// single-channel image, which is easier to analyze. + /// + /// ## Parameters + /// * image: Image where the search is running. It must be 8-bit or 32-bit floating-point. + /// * templ: Searched template. It must be not greater than the source image and have the same + /// data type. + /// * result: Map of comparison results. It must be single-channel 32-bit floating-point. If image + /// is ![inline formula](https://latex.codecogs.com/png.latex?W%20%5Ctimes%20H) and templ is ![inline formula](https://latex.codecogs.com/png.latex?w%20%5Ctimes%20h) , then result is ![inline formula](https://latex.codecogs.com/png.latex?%28W%2Dw%2B1%29%20%5Ctimes%20%28H%2Dh%2B1%29) . + /// * method: Parameter specifying the comparison method, see [template_match_modes] + /// * mask: Optional mask. It must have the same size as templ. It must either have the same number + /// of channels as template or only one channel, which is then used for all template and + /// image channels. If the data type is #CV_8U, the mask is interpreted as a binary mask, + /// meaning only elements where mask is nonzero are used and are kept unchanged independent + /// of the actual mask value (weight equals 1). For data type #CV_32F, the mask values are + /// used as weights. The exact formulas are documented in #TemplateMatchModes. + /// + /// ## C++ default parameters + /// * mask: noArray() + #[inline] + pub fn match_template(image: &impl ToInputArray, templ: &impl ToInputArray, result: &mut impl ToOutputArray, method: i32, mask: &impl ToInputArray) -> Result<()> { + input_array_arg!(image); + input_array_arg!(templ); + output_array_arg!(result); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_matchTemplate_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR(image.as_raw__InputArray(), templ.as_raw__InputArray(), result.as_raw__OutputArray(), method, mask.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Blurs an image using the median filter. + /// + /// The function smoothes an image using the median filter with the ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bksize%7D%20%5Ctimes%0A%5Ctexttt%7Bksize%7D) aperture. Each channel of a multi-channel image is processed independently. + /// In-place operation is supported. + /// + /// + /// Note: The median filter uses [BORDER_REPLICATE] internally to cope with border pixels, see [border_types] + /// + /// ## Parameters + /// * src: input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be + /// CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U. + /// * dst: destination array of the same size and type as src. + /// * ksize: aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ... + /// ## See also + /// bilateralFilter, blur, boxFilter, GaussianBlur + #[inline] + pub fn median_blur(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ksize: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_medianBlur_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ksize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds a rotated rectangle of the minimum area enclosing the input 2D point set. + /// + /// The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a + /// specified point set. The angle of rotation represents the angle between the line connecting the starting + /// and ending points (based on the clockwise order with greatest index for the corner with greatest ![inline formula](https://latex.codecogs.com/png.latex?y)) + /// and the horizontal axis. This angle always falls between ![inline formula](https://latex.codecogs.com/png.latex?%5B%2D90%2C%200%29) because, if the object + /// rotates more than a rect angle, the next edge is used to measure the angle. The starting and ending points change + /// as the object rotates.Developer should keep in mind that the returned RotatedRect can contain negative + /// indices when data is close to the containing Mat element boundary. + /// + /// ## Parameters + /// * points: Input vector of 2D points, stored in std::vector\<\> or Mat + #[inline] + pub fn min_area_rect(points: &impl ToInputArray) -> Result { + input_array_arg!(points); + return_send!(via ocvrs_return); + unsafe { sys::cv_minAreaRect_const__InputArrayR(points.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds a circle of the minimum area enclosing a 2D point set. + /// + /// The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm. + /// + /// ## Parameters + /// * points: Input vector of 2D points, stored in std::vector\<\> or Mat + /// * center: Output center of the circle. + /// * radius: Output radius of the circle. + #[inline] + pub fn min_enclosing_circle(points: &impl ToInputArray, center: &mut core::Point2f, radius: &mut f32) -> Result<()> { + input_array_arg!(points); + return_send!(via ocvrs_return); + unsafe { sys::cv_minEnclosingCircle_const__InputArrayR_Point2fR_floatR(points.as_raw__InputArray(), center, radius, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds a convex polygon of minimum area enclosing a 2D point set and returns its area. + /// + /// This function takes a given set of 2D points and finds the enclosing polygon with k vertices and minimal + /// area. It takes the set of points and the parameter k as input and returns the area of the minimal + /// enclosing polygon. + /// + /// The Implementation is based on a paper by Aggarwal, Chang and Yap [Aggarwal1985](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Aggarwal1985). They + /// provide a ![inline formula](https://latex.codecogs.com/png.latex?%5Ctheta%28n%C2%B2log%28n%29log%28k%29%29) algorithm for finding the minimal convex polygon with k + /// vertices enclosing a 2D convex polygon with n vertices (k < n). Since the [min_enclosing_convex_polygon] + /// function takes a 2D point set as input, an additional preprocessing step of computing the convex hull + /// of the 2D point set is required. The complexity of the [convex_hull] function is ![inline formula](https://latex.codecogs.com/png.latex?O%28n%20log%28n%29%29) which + /// is lower than ![inline formula](https://latex.codecogs.com/png.latex?%5Ctheta%28n%C2%B2log%28n%29log%28k%29%29). Thus the overall complexity of the function is + /// ![inline formula](https://latex.codecogs.com/png.latex?O%28n%C2%B2log%28n%29log%28k%29%29). + /// + /// ## Parameters + /// * points: Input vector of 2D points, stored in std::vector\<\> or Mat + /// * polygon: Output vector of 2D points defining the vertices of the enclosing polygon + /// * k: Number of vertices of the output polygon + #[inline] + pub fn min_enclosing_convex_polygon(points: &impl ToInputArray, polygon: &mut impl ToOutputArray, k: i32) -> Result { + input_array_arg!(points); + output_array_arg!(polygon); + return_send!(via ocvrs_return); + unsafe { sys::cv_minEnclosingConvexPolygon_const__InputArrayR_const__OutputArrayR_int(points.as_raw__InputArray(), polygon.as_raw__OutputArray(), k, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds a triangle of minimum area enclosing a 2D point set and returns its area. + /// + /// The function finds a triangle of minimum area enclosing the given set of 2D points and returns its + /// area. The output for a given 2D point set is shown in the image below. 2D points are depicted in + /// *red* and the enclosing triangle in *yellow*. + /// + /// ![Sample output of the minimum enclosing triangle function](https://docs.opencv.org/4.13.0/minenclosingtriangle.png) + /// + /// The implementation of the algorithm is based on O'Rourke's [ORourke86](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_ORourke86) and Klee and Laskowski's + /// [KleeLaskowski85](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_KleeLaskowski85) papers. O'Rourke provides a ![inline formula](https://latex.codecogs.com/png.latex?%5Ctheta%28n%29) algorithm for finding the minimal + /// enclosing triangle of a 2D convex polygon with n vertices. Since the [min_enclosing_triangle] function + /// takes a 2D point set as input an additional preprocessing step of computing the convex hull of the + /// 2D point set is required. The complexity of the [convex_hull] function is ![inline formula](https://latex.codecogs.com/png.latex?O%28n%20log%28n%29%29) which is higher + /// than ![inline formula](https://latex.codecogs.com/png.latex?%5Ctheta%28n%29). Thus the overall complexity of the function is ![inline formula](https://latex.codecogs.com/png.latex?O%28n%20log%28n%29%29). + /// + /// ## Parameters + /// * points: Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat + /// * triangle: Output vector of three 2D points defining the vertices of the triangle. The depth + /// of the OutputArray must be CV_32F. + #[inline] + pub fn min_enclosing_triangle(points: &impl ToInputArray, triangle: &mut impl ToOutputArray) -> Result { + input_array_arg!(points); + output_array_arg!(triangle); + return_send!(via ocvrs_return); + unsafe { sys::cv_minEnclosingTriangle_const__InputArrayR_const__OutputArrayR(points.as_raw__InputArray(), triangle.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates all of the moments up to the third order of a polygon or rasterized shape. + /// + /// The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The + /// results are returned in the structure cv::Moments. + /// + /// ## Parameters + /// * array: Single chanel raster image (CV_8U, CV_16U, CV_16S, CV_32F, CV_64F) or an array ( + /// ![inline formula](https://latex.codecogs.com/png.latex?1%20%5Ctimes%20N) or ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Ctimes%201) ) of 2D points (Point or Point2f). + /// * binaryImage: If it is true, all non-zero image pixels are treated as 1's. The parameter is + /// used for images only. + /// ## Returns + /// moments. + /// + /// + /// Note: Only applicable to contour moments calculations from Python bindings: Note that the numpy + /// type for the input array should be either np.int32 or np.float32. + /// + /// + /// Note: For contour-based moments, the zeroth-order moment \c m00 represents + /// the contour area. + /// + /// If the input contour is degenerate (for example, a single point or all points + /// are collinear), the area is zero and therefore \c m00 == 0. + /// + /// In this case, the centroid coordinates (\c m10/m00, \c m01/m00) are undefined + /// and must be handled explicitly by the caller. + /// + /// A common workaround is to compute the center using cv::boundingRect() or by + /// averaging the input points. + /// ## See also + /// contourArea, arcLength + /// + /// ## Note + /// This alternative version of [moments] function uses the following default values for its arguments: + /// * binary_image: false + #[inline] + pub fn moments_def(array: &impl ToInputArray) -> Result { + input_array_arg!(array); + return_send!(via ocvrs_return); + unsafe { sys::cv_moments_const__InputArrayR(array.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates all of the moments up to the third order of a polygon or rasterized shape. + /// + /// The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The + /// results are returned in the structure cv::Moments. + /// + /// ## Parameters + /// * array: Single chanel raster image (CV_8U, CV_16U, CV_16S, CV_32F, CV_64F) or an array ( + /// ![inline formula](https://latex.codecogs.com/png.latex?1%20%5Ctimes%20N) or ![inline formula](https://latex.codecogs.com/png.latex?N%20%5Ctimes%201) ) of 2D points (Point or Point2f). + /// * binaryImage: If it is true, all non-zero image pixels are treated as 1's. The parameter is + /// used for images only. + /// ## Returns + /// moments. + /// + /// + /// Note: Only applicable to contour moments calculations from Python bindings: Note that the numpy + /// type for the input array should be either np.int32 or np.float32. + /// + /// + /// Note: For contour-based moments, the zeroth-order moment \c m00 represents + /// the contour area. + /// + /// If the input contour is degenerate (for example, a single point or all points + /// are collinear), the area is zero and therefore \c m00 == 0. + /// + /// In this case, the centroid coordinates (\c m10/m00, \c m01/m00) are undefined + /// and must be handled explicitly by the caller. + /// + /// A common workaround is to compute the center using cv::boundingRect() or by + /// averaging the input points. + /// ## See also + /// contourArea, arcLength + /// + /// ## C++ default parameters + /// * binary_image: false + #[inline] + pub fn moments(array: &impl ToInputArray, binary_image: bool) -> Result { + input_array_arg!(array); + return_send!(via ocvrs_return); + unsafe { sys::cv_moments_const__InputArrayR_bool(array.as_raw__InputArray(), binary_image, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation. + #[inline] + pub fn morphology_default_border_value() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_morphologyDefaultBorderValue(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs advanced morphological transformations. + /// + /// The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as + /// basic operations. + /// + /// Any of the operations can be done in-place. In case of multi-channel images, each channel is + /// processed independently. + /// + /// ## Parameters + /// * src: Source image. The number of channels can be arbitrary. The depth should be one of + /// CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. + /// * dst: Destination image of the same size and type as source image. + /// * op: Type of a morphological operation, see [morph_types] + /// * kernel: Structuring element. It can be created using #getStructuringElement. + /// * anchor: Anchor position with the kernel. Negative values mean that the anchor is at the + /// kernel center. + /// * iterations: Number of times erosion and dilation are applied. + /// * borderType: Pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// * borderValue: Border value in case of a constant border. The default value has a special + /// meaning. + /// ## See also + /// dilate, erode, getStructuringElement + /// + /// Note: The number of iterations is the number of times erosion or dilatation operation will be applied. + /// For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply + /// successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate). + /// + /// ## Note + /// This alternative version of [morphology_ex] function uses the following default values for its arguments: + /// * anchor: Point(-1,-1) + /// * iterations: 1 + /// * border_type: BORDER_CONSTANT + /// * border_value: morphologyDefaultBorderValue() + #[inline] + pub fn morphology_ex_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, op: i32, kernel: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(kernel); + return_send!(via ocvrs_return); + unsafe { sys::cv_morphologyEx_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), op, kernel.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs advanced morphological transformations. + /// + /// The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as + /// basic operations. + /// + /// Any of the operations can be done in-place. In case of multi-channel images, each channel is + /// processed independently. + /// + /// ## Parameters + /// * src: Source image. The number of channels can be arbitrary. The depth should be one of + /// CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. + /// * dst: Destination image of the same size and type as source image. + /// * op: Type of a morphological operation, see [morph_types] + /// * kernel: Structuring element. It can be created using #getStructuringElement. + /// * anchor: Anchor position with the kernel. Negative values mean that the anchor is at the + /// kernel center. + /// * iterations: Number of times erosion and dilation are applied. + /// * borderType: Pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// * borderValue: Border value in case of a constant border. The default value has a special + /// meaning. + /// ## See also + /// dilate, erode, getStructuringElement + /// + /// Note: The number of iterations is the number of times erosion or dilatation operation will be applied. + /// For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply + /// successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate). + /// + /// ## C++ default parameters + /// * anchor: Point(-1,-1) + /// * iterations: 1 + /// * border_type: BORDER_CONSTANT + /// * border_value: morphologyDefaultBorderValue() + #[inline] + pub fn morphology_ex(src: &impl ToInputArray, dst: &mut impl ToOutputArray, op: i32, kernel: &impl ToInputArray, anchor: core::Point, iterations: i32, border_type: i32, border_value: core::Scalar) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(kernel); + return_send!(via ocvrs_return); + unsafe { sys::cv_morphologyEx_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_Point_int_int_const_ScalarR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), op, kernel.as_raw__InputArray(), &anchor, iterations, border_type, &border_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Detects translational shifts between two images. + /// + /// This function extends the standard [phaseCorrelate] method by improving sub-pixel accuracy + /// through iterative shift refinement in the phase-correlation space, as described in + /// [hrazdira2020iterative](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_hrazdira2020iterative). + /// + /// ## Parameters + /// * src1: Source floating point array (CV_32FC1 or CV_64FC1) + /// * src2: Source floating point array (CV_32FC1 or CV_64FC1) + /// * L2size: The size of the correlation neighborhood used by the iterative shift refinement algorithm. + /// * maxIters: The maximum number of iterations the iterative refinement algorithm will run. + /// ## Returns + /// detected sub-pixel shift between the two arrays. + /// ## See also + /// phaseCorrelate, dft, idft, createHanningWindow + /// + /// ## Note + /// This alternative version of [phase_correlate_iterative] function uses the following default values for its arguments: + /// * l2size: 7 + /// * max_iters: 10 + #[inline] + pub fn phase_correlate_iterative_def(src1: &impl ToInputArray, src2: &impl ToInputArray) -> Result { + input_array_arg!(src1); + input_array_arg!(src2); + return_send!(via ocvrs_return); + unsafe { sys::cv_phaseCorrelateIterative_const__InputArrayR_const__InputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Detects translational shifts between two images. + /// + /// This function extends the standard [phaseCorrelate] method by improving sub-pixel accuracy + /// through iterative shift refinement in the phase-correlation space, as described in + /// [hrazdira2020iterative](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_hrazdira2020iterative). + /// + /// ## Parameters + /// * src1: Source floating point array (CV_32FC1 or CV_64FC1) + /// * src2: Source floating point array (CV_32FC1 or CV_64FC1) + /// * L2size: The size of the correlation neighborhood used by the iterative shift refinement algorithm. + /// * maxIters: The maximum number of iterations the iterative refinement algorithm will run. + /// ## Returns + /// detected sub-pixel shift between the two arrays. + /// ## See also + /// phaseCorrelate, dft, idft, createHanningWindow + /// + /// ## C++ default parameters + /// * l2size: 7 + /// * max_iters: 10 + #[inline] + pub fn phase_correlate_iterative(src1: &impl ToInputArray, src2: &impl ToInputArray, l2size: i32, max_iters: i32) -> Result { + input_array_arg!(src1); + input_array_arg!(src2); + return_send!(via ocvrs_return); + unsafe { sys::cv_phaseCorrelateIterative_const__InputArrayR_const__InputArrayR_int_int(src1.as_raw__InputArray(), src2.as_raw__InputArray(), l2size, max_iters, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// The function is used to detect translational shifts that occur between two images. + /// + /// The operation takes advantage of the Fourier shift theorem for detecting the translational shift in + /// the frequency domain. It can be used for fast image registration as well as motion estimation. For + /// more information please see + /// + /// Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed + /// with getOptimalDFTSize. + /// + /// The function performs the following equations: + /// - First it applies a Hanning window to each image to remove possible edge effects, if it's provided + /// by user. See [createHanningWindow] and . This window may + /// be cached until the array size changes to speed up processing time. + /// - Next it computes the forward DFTs of each source array: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BG%7D%5Fa%20%3D%20%5Cmathcal%7BF%7D%5C%7Bsrc%5F1%5C%7D%2C%20%5C%3B%20%5Cmathbf%7BG%7D%5Fb%20%3D%20%5Cmathcal%7BF%7D%5C%7Bsrc%5F2%5C%7D) + /// where ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathcal%7BF%7D) is the forward DFT. + /// - It then computes the cross-power spectrum of each frequency domain array: + /// ![block formula](https://latex.codecogs.com/png.latex?R%20%3D%20%5Cfrac%7B%20%5Cmathbf%7BG%7D%5Fa%20%5Cmathbf%7BG%7D%5Fb%5E%2A%7D%7B%7C%5Cmathbf%7BG%7D%5Fa%20%5Cmathbf%7BG%7D%5Fb%5E%2A%7C%7D) + /// - Next the cross-correlation is converted back into the time domain via the inverse DFT: + /// ![block formula](https://latex.codecogs.com/png.latex?r%20%3D%20%5Cmathcal%7BF%7D%5E%7B%2D1%7D%5C%7BR%5C%7D) + /// - Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to + /// achieve sub-pixel accuracy. + /// ![block formula](https://latex.codecogs.com/png.latex?%28%5CDelta%20x%2C%20%5CDelta%20y%29%20%3D%20%5Ctexttt%7BweightedCentroid%7D%20%5C%7B%5Carg%20%5Cmax%5F%7B%28x%2C%20y%29%7D%5C%7Br%5C%7D%5C%7D) + /// - If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 + /// centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single + /// peak) and will be smaller when there are multiple peaks. + /// + /// ## Parameters + /// * src1: Source floating point array (CV_32FC1 or CV_64FC1) + /// * src2: Source floating point array (CV_32FC1 or CV_64FC1) + /// * window: Floating point array with windowing coefficients to reduce edge effects (optional). + /// * response: Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional). + /// ## Returns + /// detected phase shift (sub-pixel) between the two arrays. + /// ## See also + /// dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow + /// + /// ## Note + /// This alternative version of [phase_correlate] function uses the following default values for its arguments: + /// * window: noArray() + /// * response: 0 + #[inline] + pub fn phase_correlate_def(src1: &impl ToInputArray, src2: &impl ToInputArray) -> Result { + input_array_arg!(src1); + input_array_arg!(src2); + return_send!(via ocvrs_return); + unsafe { sys::cv_phaseCorrelate_const__InputArrayR_const__InputArrayR(src1.as_raw__InputArray(), src2.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// The function is used to detect translational shifts that occur between two images. + /// + /// The operation takes advantage of the Fourier shift theorem for detecting the translational shift in + /// the frequency domain. It can be used for fast image registration as well as motion estimation. For + /// more information please see + /// + /// Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed + /// with getOptimalDFTSize. + /// + /// The function performs the following equations: + /// - First it applies a Hanning window to each image to remove possible edge effects, if it's provided + /// by user. See [createHanningWindow] and . This window may + /// be cached until the array size changes to speed up processing time. + /// - Next it computes the forward DFTs of each source array: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cmathbf%7BG%7D%5Fa%20%3D%20%5Cmathcal%7BF%7D%5C%7Bsrc%5F1%5C%7D%2C%20%5C%3B%20%5Cmathbf%7BG%7D%5Fb%20%3D%20%5Cmathcal%7BF%7D%5C%7Bsrc%5F2%5C%7D) + /// where ![inline formula](https://latex.codecogs.com/png.latex?%5Cmathcal%7BF%7D) is the forward DFT. + /// - It then computes the cross-power spectrum of each frequency domain array: + /// ![block formula](https://latex.codecogs.com/png.latex?R%20%3D%20%5Cfrac%7B%20%5Cmathbf%7BG%7D%5Fa%20%5Cmathbf%7BG%7D%5Fb%5E%2A%7D%7B%7C%5Cmathbf%7BG%7D%5Fa%20%5Cmathbf%7BG%7D%5Fb%5E%2A%7C%7D) + /// - Next the cross-correlation is converted back into the time domain via the inverse DFT: + /// ![block formula](https://latex.codecogs.com/png.latex?r%20%3D%20%5Cmathcal%7BF%7D%5E%7B%2D1%7D%5C%7BR%5C%7D) + /// - Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to + /// achieve sub-pixel accuracy. + /// ![block formula](https://latex.codecogs.com/png.latex?%28%5CDelta%20x%2C%20%5CDelta%20y%29%20%3D%20%5Ctexttt%7BweightedCentroid%7D%20%5C%7B%5Carg%20%5Cmax%5F%7B%28x%2C%20y%29%7D%5C%7Br%5C%7D%5C%7D) + /// - If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 + /// centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single + /// peak) and will be smaller when there are multiple peaks. + /// + /// ## Parameters + /// * src1: Source floating point array (CV_32FC1 or CV_64FC1) + /// * src2: Source floating point array (CV_32FC1 or CV_64FC1) + /// * window: Floating point array with windowing coefficients to reduce edge effects (optional). + /// * response: Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional). + /// ## Returns + /// detected phase shift (sub-pixel) between the two arrays. + /// ## See also + /// dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow + /// + /// ## C++ default parameters + /// * window: noArray() + /// * response: 0 + #[inline] + pub fn phase_correlate(src1: &impl ToInputArray, src2: &impl ToInputArray, window: &impl ToInputArray, response: &mut f64) -> Result { + input_array_arg!(src1); + input_array_arg!(src2); + input_array_arg!(window); + return_send!(via ocvrs_return); + unsafe { sys::cv_phaseCorrelate_const__InputArrayR_const__InputArrayR_const__InputArrayR_doubleX(src1.as_raw__InputArray(), src2.as_raw__InputArray(), window.as_raw__InputArray(), response, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs a point-in-contour test. + /// + /// The function determines whether the point is inside a contour, outside, or lies on an edge (or + /// coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) + /// value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. + /// Otherwise, the return value is a signed distance between the point and the nearest contour edge. + /// + /// See below a sample output of the function where each image pixel is tested against the contour: + /// + /// ![sample output](https://docs.opencv.org/4.13.0/pointpolygon.png) + /// + /// ## Parameters + /// * contour: Input contour. + /// * pt: Point tested against the contour. + /// * measureDist: If true, the function estimates the signed distance from the point to the + /// nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not. + #[inline] + pub fn point_polygon_test(contour: &impl ToInputArray, pt: core::Point2f, measure_dist: bool) -> Result { + input_array_arg!(contour); + return_send!(via ocvrs_return); + unsafe { sys::cv_pointPolygonTest_const__InputArrayR_Point2f_bool(contour.as_raw__InputArray(), &pt, measure_dist, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws several polygonal curves. + /// + /// ## Parameters + /// * img: Image. + /// * pts: Array of polygonal curves. + /// * isClosed: Flag indicating whether the drawn polylines are closed or not. If they are closed, + /// the function draws a line from the last vertex of each curve to its first vertex. + /// * color: Polyline color. + /// * thickness: Thickness of the polyline edges. + /// * lineType: Type of the line segments. See [line_types] + /// * shift: Number of fractional bits in the vertex coordinates. + /// + /// The function cv::polylines draws one or more polygonal curves. + /// + /// ## Note + /// This alternative version of [polylines] function uses the following default values for its arguments: + /// * thickness: 1 + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn polylines_def(img: &mut impl ToInputOutputArray, pts: &impl ToInputArray, is_closed: bool, color: core::Scalar) -> Result<()> { + input_output_array_arg!(img); + input_array_arg!(pts); + return_send!(via ocvrs_return); + unsafe { sys::cv_polylines_const__InputOutputArrayR_const__InputArrayR_bool_const_ScalarR(img.as_raw__InputOutputArray(), pts.as_raw__InputArray(), is_closed, &color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws several polygonal curves. + /// + /// ## Parameters + /// * img: Image. + /// * pts: Array of polygonal curves. + /// * isClosed: Flag indicating whether the drawn polylines are closed or not. If they are closed, + /// the function draws a line from the last vertex of each curve to its first vertex. + /// * color: Polyline color. + /// * thickness: Thickness of the polyline edges. + /// * lineType: Type of the line segments. See [line_types] + /// * shift: Number of fractional bits in the vertex coordinates. + /// + /// The function cv::polylines draws one or more polygonal curves. + /// + /// ## C++ default parameters + /// * thickness: 1 + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn polylines(img: &mut impl ToInputOutputArray, pts: &impl ToInputArray, is_closed: bool, color: core::Scalar, thickness: i32, line_type: i32, shift: i32) -> Result<()> { + input_output_array_arg!(img); + input_array_arg!(pts); + return_send!(via ocvrs_return); + unsafe { sys::cv_polylines_const__InputOutputArrayR_const__InputArrayR_bool_const_ScalarR_int_int_int(img.as_raw__InputOutputArray(), pts.as_raw__InputArray(), is_closed, &color, thickness, line_type, shift, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates a feature map for corner detection. + /// + /// The function calculates the complex spatial derivative-based function of the source image + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%28D%5Fx%20%20%5Ctexttt%7Bsrc%7D%20%29%5E2%20%20%5Ccdot%20D%5F%7Byy%7D%20%20%5Ctexttt%7Bsrc%7D%20%2B%20%28D%5Fy%20%20%5Ctexttt%7Bsrc%7D%20%29%5E2%20%20%5Ccdot%20D%5F%7Bxx%7D%20%20%5Ctexttt%7Bsrc%7D%20%2D%202%20D%5Fx%20%20%5Ctexttt%7Bsrc%7D%20%5Ccdot%20D%5Fy%20%20%5Ctexttt%7Bsrc%7D%20%5Ccdot%20D%5F%7Bxy%7D%20%20%5Ctexttt%7Bsrc%7D) + /// + /// where ![inline formula](https://latex.codecogs.com/png.latex?D%5Fx),![inline formula](https://latex.codecogs.com/png.latex?D%5Fy) are the first image derivatives, ![inline formula](https://latex.codecogs.com/png.latex?D%5F%7Bxx%7D),![inline formula](https://latex.codecogs.com/png.latex?D%5F%7Byy%7D) are the second image + /// derivatives, and ![inline formula](https://latex.codecogs.com/png.latex?D%5F%7Bxy%7D) is the mixed derivative. + /// + /// The corners can be found as local maximums of the functions, as shown below: + /// ```C++ + /// Mat corners, dilated_corners; + /// preCornerDetect(image, corners, 3); + /// // dilation with 3x3 rectangular structuring element + /// dilate(corners, dilated_corners, Mat(), 1); + /// Mat corner_mask = corners == dilated_corners; + /// ``` + /// + /// + /// ## Parameters + /// * src: Source single-channel 8-bit of floating-point image. + /// * dst: Output image that has the type CV_32F and the same size as src . + /// * ksize: %Aperture size of the Sobel . + /// * borderType: Pixel extrapolation method. See #BorderTypes. [BORDER_WRAP] is not supported. + /// + /// ## Note + /// This alternative version of [pre_corner_detect] function uses the following default values for its arguments: + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn pre_corner_detect_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ksize: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_preCornerDetect_const__InputArrayR_const__OutputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ksize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates a feature map for corner detection. + /// + /// The function calculates the complex spatial derivative-based function of the source image + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%3D%20%28D%5Fx%20%20%5Ctexttt%7Bsrc%7D%20%29%5E2%20%20%5Ccdot%20D%5F%7Byy%7D%20%20%5Ctexttt%7Bsrc%7D%20%2B%20%28D%5Fy%20%20%5Ctexttt%7Bsrc%7D%20%29%5E2%20%20%5Ccdot%20D%5F%7Bxx%7D%20%20%5Ctexttt%7Bsrc%7D%20%2D%202%20D%5Fx%20%20%5Ctexttt%7Bsrc%7D%20%5Ccdot%20D%5Fy%20%20%5Ctexttt%7Bsrc%7D%20%5Ccdot%20D%5F%7Bxy%7D%20%20%5Ctexttt%7Bsrc%7D) + /// + /// where ![inline formula](https://latex.codecogs.com/png.latex?D%5Fx),![inline formula](https://latex.codecogs.com/png.latex?D%5Fy) are the first image derivatives, ![inline formula](https://latex.codecogs.com/png.latex?D%5F%7Bxx%7D),![inline formula](https://latex.codecogs.com/png.latex?D%5F%7Byy%7D) are the second image + /// derivatives, and ![inline formula](https://latex.codecogs.com/png.latex?D%5F%7Bxy%7D) is the mixed derivative. + /// + /// The corners can be found as local maximums of the functions, as shown below: + /// ```C++ + /// Mat corners, dilated_corners; + /// preCornerDetect(image, corners, 3); + /// // dilation with 3x3 rectangular structuring element + /// dilate(corners, dilated_corners, Mat(), 1); + /// Mat corner_mask = corners == dilated_corners; + /// ``` + /// + /// + /// ## Parameters + /// * src: Source single-channel 8-bit of floating-point image. + /// * dst: Output image that has the type CV_32F and the same size as src . + /// * ksize: %Aperture size of the Sobel . + /// * borderType: Pixel extrapolation method. See #BorderTypes. [BORDER_WRAP] is not supported. + /// + /// ## C++ default parameters + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn pre_corner_detect(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ksize: i32, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_preCornerDetect_const__InputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ksize, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a text string. + /// + /// The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered + /// using the specified font are replaced by question marks. See [get_text_size] for a text rendering code + /// example. + /// + /// The `fontScale` parameter is a scale factor that is multiplied by the base font size: + /// - When scale > 1, the text is magnified. + /// - When 0 < scale < 1, the text is minimized. + /// - When scale < 0, the text is mirrored or reversed. + /// + /// ## Parameters + /// * img: Image. + /// * text: Text string to be drawn. + /// * org: Bottom-left corner of the text string in the image. + /// * fontFace: Font type, see #HersheyFonts. + /// * fontScale: Font scale factor that is multiplied by the font-specific base size. + /// * color: Text color. + /// * thickness: Thickness of the lines used to draw a text. + /// * lineType: Line type. See [line_types] + /// * bottomLeftOrigin: When true, the image data origin is at the bottom-left corner. Otherwise, + /// it is at the top-left corner. + /// + /// ## Note + /// This alternative version of [put_text] function uses the following default values for its arguments: + /// * thickness: 1 + /// * line_type: LINE_8 + /// * bottom_left_origin: false + #[inline] + pub fn put_text_def(img: &mut impl ToInputOutputArray, text: &str, org: core::Point, font_face: i32, font_scale: f64, color: core::Scalar) -> Result<()> { + input_output_array_arg!(img); + extern_container_arg!(text); + return_send!(via ocvrs_return); + unsafe { sys::cv_putText_const__InputOutputArrayR_const_StringR_Point_int_double_Scalar(img.as_raw__InputOutputArray(), text.opencv_as_extern(), &org, font_face, font_scale, &color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a text string. + /// + /// The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered + /// using the specified font are replaced by question marks. See [get_text_size] for a text rendering code + /// example. + /// + /// The `fontScale` parameter is a scale factor that is multiplied by the base font size: + /// - When scale > 1, the text is magnified. + /// - When 0 < scale < 1, the text is minimized. + /// - When scale < 0, the text is mirrored or reversed. + /// + /// ## Parameters + /// * img: Image. + /// * text: Text string to be drawn. + /// * org: Bottom-left corner of the text string in the image. + /// * fontFace: Font type, see #HersheyFonts. + /// * fontScale: Font scale factor that is multiplied by the font-specific base size. + /// * color: Text color. + /// * thickness: Thickness of the lines used to draw a text. + /// * lineType: Line type. See [line_types] + /// * bottomLeftOrigin: When true, the image data origin is at the bottom-left corner. Otherwise, + /// it is at the top-left corner. + /// + /// ## C++ default parameters + /// * thickness: 1 + /// * line_type: LINE_8 + /// * bottom_left_origin: false + #[inline] + pub fn put_text(img: &mut impl ToInputOutputArray, text: &str, org: core::Point, font_face: i32, font_scale: f64, color: core::Scalar, thickness: i32, line_type: i32, bottom_left_origin: bool) -> Result<()> { + input_output_array_arg!(img); + extern_container_arg!(text); + return_send!(via ocvrs_return); + unsafe { sys::cv_putText_const__InputOutputArrayR_const_StringR_Point_int_double_Scalar_int_int_bool(img.as_raw__InputOutputArray(), text.opencv_as_extern(), &org, font_face, font_scale, &color, thickness, line_type, bottom_left_origin, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Blurs an image and downsamples it. + /// + /// By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in + /// any case, the following conditions should be satisfied: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20%7C%20%5Ctexttt%7Bdstsize%2Ewidth%7D%20%2A2%2Dsrc%2Ecols%7C%20%5Cleq%202%20%5C%5C%20%7C%20%5Ctexttt%7Bdstsize%2Eheight%7D%20%2A2%2Dsrc%2Erows%7C%20%5Cleq%202%20%5Cend%7Barray%7D) + /// + /// The function performs the downsampling step of the Gaussian pyramid construction. First, it + /// convolves the source image with the kernel: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cfrac%7B1%7D%7B256%7D%20%5Cbegin%7Bbmatrix%7D%201%20%26%204%20%26%206%20%26%204%20%26%201%20%20%5C%5C%204%20%26%2016%20%26%2024%20%26%2016%20%26%204%20%20%5C%5C%206%20%26%2024%20%26%2036%20%26%2024%20%26%206%20%20%5C%5C%204%20%26%2016%20%26%2024%20%26%2016%20%26%204%20%20%5C%5C%201%20%26%204%20%26%206%20%26%204%20%26%201%20%5Cend%7Bbmatrix%7D) + /// + /// Then, it downsamples the image by rejecting even rows and columns. + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image; it has the specified size and the same type as src. + /// * dstsize: size of the output image. + /// * borderType: Pixel extrapolation method, see [border_types] ([BORDER_CONSTANT] isn't supported) + /// + /// ## Note + /// This alternative version of [pyr_down] function uses the following default values for its arguments: + /// * dstsize: Size() + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn pyr_down_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_pyrDown_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Blurs an image and downsamples it. + /// + /// By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in + /// any case, the following conditions should be satisfied: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20%7C%20%5Ctexttt%7Bdstsize%2Ewidth%7D%20%2A2%2Dsrc%2Ecols%7C%20%5Cleq%202%20%5C%5C%20%7C%20%5Ctexttt%7Bdstsize%2Eheight%7D%20%2A2%2Dsrc%2Erows%7C%20%5Cleq%202%20%5Cend%7Barray%7D) + /// + /// The function performs the downsampling step of the Gaussian pyramid construction. First, it + /// convolves the source image with the kernel: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cfrac%7B1%7D%7B256%7D%20%5Cbegin%7Bbmatrix%7D%201%20%26%204%20%26%206%20%26%204%20%26%201%20%20%5C%5C%204%20%26%2016%20%26%2024%20%26%2016%20%26%204%20%20%5C%5C%206%20%26%2024%20%26%2036%20%26%2024%20%26%206%20%20%5C%5C%204%20%26%2016%20%26%2024%20%26%2016%20%26%204%20%20%5C%5C%201%20%26%204%20%26%206%20%26%204%20%26%201%20%5Cend%7Bbmatrix%7D) + /// + /// Then, it downsamples the image by rejecting even rows and columns. + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image; it has the specified size and the same type as src. + /// * dstsize: size of the output image. + /// * borderType: Pixel extrapolation method, see [border_types] ([BORDER_CONSTANT] isn't supported) + /// + /// ## C++ default parameters + /// * dstsize: Size() + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn pyr_down(src: &impl ToInputArray, dst: &mut impl ToOutputArray, dstsize: core::Size, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_pyrDown_const__InputArrayR_const__OutputArrayR_const_SizeR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), &dstsize, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs initial step of meanshift segmentation of an image. + /// + /// The function implements the filtering stage of meanshift segmentation, that is, the output of the + /// function is the filtered "posterized" image with color gradients and fine-grain texture flattened. + /// At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes + /// meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is + /// considered: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29%3A%20X%2D%20%5Ctexttt%7Bsp%7D%20%5Cle%20x%20%20%5Cle%20X%2B%20%5Ctexttt%7Bsp%7D%20%2C%20Y%2D%20%5Ctexttt%7Bsp%7D%20%5Cle%20y%20%20%5Cle%20Y%2B%20%5Ctexttt%7Bsp%7D%20%2C%20%7C%7C%28R%2CG%2CB%29%2D%28r%2Cg%2Cb%29%7C%7C%20%20%20%5Cle%20%5Ctexttt%7Bsr%7D) + /// + /// where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively + /// (though, the algorithm does not depend on the color space used, so any 3-component color space can + /// be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector + /// (R',G',B') are found and they act as the neighborhood center on the next iteration: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%28X%2CY%29%7E%28X%27%2CY%27%29%2C%20%28R%2CG%2CB%29%7E%28R%27%2CG%27%2CB%27%29%2E) + /// + /// After the iterations over, the color components of the initial pixel (that is, the pixel from where + /// the iterations started) are set to the final value (average color at the last iteration): + /// + /// ![block formula](https://latex.codecogs.com/png.latex?I%28X%2CY%29%20%3C%2D%20%28R%2A%2CG%2A%2CB%2A%29) + /// + /// When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is + /// run on the smallest layer first. After that, the results are propagated to the larger layer and the + /// iterations are run again only on those pixels where the layer colors differ by more than sr from the + /// lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the + /// results will be actually different from the ones obtained by running the meanshift procedure on the + /// whole original image (i.e. when maxLevel==0). + /// + /// ## Parameters + /// * src: The source 8-bit, 3-channel image. + /// * dst: The destination image of the same format and the same size as the source. + /// * sp: The spatial window radius. + /// * sr: The color window radius. + /// * maxLevel: Maximum level of the pyramid for the segmentation. + /// * termcrit: Termination criteria: when to stop meanshift iterations. + /// + /// ## Note + /// This alternative version of [pyr_mean_shift_filtering] function uses the following default values for its arguments: + /// * max_level: 1 + /// * termcrit: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) + #[inline] + pub fn pyr_mean_shift_filtering_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, sp: f64, sr: f64) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_pyrMeanShiftFiltering_const__InputArrayR_const__OutputArrayR_double_double(src.as_raw__InputArray(), dst.as_raw__OutputArray(), sp, sr, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs initial step of meanshift segmentation of an image. + /// + /// The function implements the filtering stage of meanshift segmentation, that is, the output of the + /// function is the filtered "posterized" image with color gradients and fine-grain texture flattened. + /// At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes + /// meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is + /// considered: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29%3A%20X%2D%20%5Ctexttt%7Bsp%7D%20%5Cle%20x%20%20%5Cle%20X%2B%20%5Ctexttt%7Bsp%7D%20%2C%20Y%2D%20%5Ctexttt%7Bsp%7D%20%5Cle%20y%20%20%5Cle%20Y%2B%20%5Ctexttt%7Bsp%7D%20%2C%20%7C%7C%28R%2CG%2CB%29%2D%28r%2Cg%2Cb%29%7C%7C%20%20%20%5Cle%20%5Ctexttt%7Bsr%7D) + /// + /// where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively + /// (though, the algorithm does not depend on the color space used, so any 3-component color space can + /// be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector + /// (R',G',B') are found and they act as the neighborhood center on the next iteration: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%28X%2CY%29%7E%28X%27%2CY%27%29%2C%20%28R%2CG%2CB%29%7E%28R%27%2CG%27%2CB%27%29%2E) + /// + /// After the iterations over, the color components of the initial pixel (that is, the pixel from where + /// the iterations started) are set to the final value (average color at the last iteration): + /// + /// ![block formula](https://latex.codecogs.com/png.latex?I%28X%2CY%29%20%3C%2D%20%28R%2A%2CG%2A%2CB%2A%29) + /// + /// When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is + /// run on the smallest layer first. After that, the results are propagated to the larger layer and the + /// iterations are run again only on those pixels where the layer colors differ by more than sr from the + /// lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the + /// results will be actually different from the ones obtained by running the meanshift procedure on the + /// whole original image (i.e. when maxLevel==0). + /// + /// ## Parameters + /// * src: The source 8-bit, 3-channel image. + /// * dst: The destination image of the same format and the same size as the source. + /// * sp: The spatial window radius. + /// * sr: The color window radius. + /// * maxLevel: Maximum level of the pyramid for the segmentation. + /// * termcrit: Termination criteria: when to stop meanshift iterations. + /// + /// ## C++ default parameters + /// * max_level: 1 + /// * termcrit: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) + #[inline] + pub fn pyr_mean_shift_filtering(src: &impl ToInputArray, dst: &mut impl ToOutputArray, sp: f64, sr: f64, max_level: i32, termcrit: core::TermCriteria) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_pyrMeanShiftFiltering_const__InputArrayR_const__OutputArrayR_double_double_int_TermCriteria(src.as_raw__InputArray(), dst.as_raw__OutputArray(), sp, sr, max_level, &termcrit, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Upsamples an image and then blurs it. + /// + /// By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any + /// case, the following conditions should be satisfied: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20%7C%20%5Ctexttt%7Bdstsize%2Ewidth%7D%20%2Dsrc%2Ecols%2A2%7C%20%5Cleq%20%20%28%20%5Ctexttt%7Bdstsize%2Ewidth%7D%20%20%20%5Cmod%20%202%29%20%20%5C%5C%20%7C%20%5Ctexttt%7Bdstsize%2Eheight%7D%20%2Dsrc%2Erows%2A2%7C%20%5Cleq%20%20%28%20%5Ctexttt%7Bdstsize%2Eheight%7D%20%20%20%5Cmod%20%202%29%20%5Cend%7Barray%7D) + /// + /// The function performs the upsampling step of the Gaussian pyramid construction, though it can + /// actually be used to construct the Laplacian pyramid. First, it upsamples the source image by + /// injecting even zero rows and columns and then convolves the result with the same kernel as in + /// pyrDown multiplied by 4. + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image. It has the specified size and the same type as src . + /// * dstsize: size of the output image. + /// * borderType: Pixel extrapolation method, see [border_types] (only [BORDER_DEFAULT] is supported) + /// + /// ## Note + /// This alternative version of [pyr_up] function uses the following default values for its arguments: + /// * dstsize: Size() + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn pyr_up_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_pyrUp_const__InputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Upsamples an image and then blurs it. + /// + /// By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any + /// case, the following conditions should be satisfied: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%20%7C%20%5Ctexttt%7Bdstsize%2Ewidth%7D%20%2Dsrc%2Ecols%2A2%7C%20%5Cleq%20%20%28%20%5Ctexttt%7Bdstsize%2Ewidth%7D%20%20%20%5Cmod%20%202%29%20%20%5C%5C%20%7C%20%5Ctexttt%7Bdstsize%2Eheight%7D%20%2Dsrc%2Erows%2A2%7C%20%5Cleq%20%20%28%20%5Ctexttt%7Bdstsize%2Eheight%7D%20%20%20%5Cmod%20%202%29%20%5Cend%7Barray%7D) + /// + /// The function performs the upsampling step of the Gaussian pyramid construction, though it can + /// actually be used to construct the Laplacian pyramid. First, it upsamples the source image by + /// injecting even zero rows and columns and then convolves the result with the same kernel as in + /// pyrDown multiplied by 4. + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image. It has the specified size and the same type as src . + /// * dstsize: size of the output image. + /// * borderType: Pixel extrapolation method, see [border_types] (only [BORDER_DEFAULT] is supported) + /// + /// ## C++ default parameters + /// * dstsize: Size() + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn pyr_up(src: &impl ToInputArray, dst: &mut impl ToOutputArray, dstsize: core::Size, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_pyrUp_const__InputArrayR_const__OutputArrayR_const_SizeR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), &dstsize, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a simple, thick, or filled up-right rectangle. + /// + /// The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners + /// are pt1 and pt2. + /// + /// ## Parameters + /// * img: Image. + /// * pt1: Vertex of the rectangle. + /// * pt2: Vertex of the rectangle opposite to pt1 . + /// * color: Rectangle color or brightness (grayscale image). + /// * thickness: Thickness of lines that make up the rectangle. Negative values, like #FILLED, + /// mean that the function has to draw a filled rectangle. + /// * lineType: Type of the line. See [line_types] + /// * shift: Number of fractional bits in the point coordinates. + /// + /// ## Note + /// This alternative version of [rectangle_points] function uses the following default values for its arguments: + /// * thickness: 1 + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn rectangle_points_def(img: &mut impl ToInputOutputArray, pt1: core::Point, pt2: core::Point, color: core::Scalar) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_rectangle_const__InputOutputArrayR_Point_Point_const_ScalarR(img.as_raw__InputOutputArray(), &pt1, &pt2, &color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a simple, thick, or filled up-right rectangle. + /// + /// The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners + /// are pt1 and pt2. + /// + /// ## Parameters + /// * img: Image. + /// * pt1: Vertex of the rectangle. + /// * pt2: Vertex of the rectangle opposite to pt1 . + /// * color: Rectangle color or brightness (grayscale image). + /// * thickness: Thickness of lines that make up the rectangle. Negative values, like #FILLED, + /// mean that the function has to draw a filled rectangle. + /// * lineType: Type of the line. See [line_types] + /// * shift: Number of fractional bits in the point coordinates. + /// + /// ## C++ default parameters + /// * thickness: 1 + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn rectangle_points(img: &mut impl ToInputOutputArray, pt1: core::Point, pt2: core::Point, color: core::Scalar, thickness: i32, line_type: i32, shift: i32) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_rectangle_const__InputOutputArrayR_Point_Point_const_ScalarR_int_int_int(img.as_raw__InputOutputArray(), &pt1, &pt2, &color, thickness, line_type, shift, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a simple, thick, or filled up-right rectangle. + /// + /// The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners + /// are pt1 and pt2. + /// + /// ## Parameters + /// * img: Image. + /// * pt1: Vertex of the rectangle. + /// * pt2: Vertex of the rectangle opposite to pt1 . + /// * color: Rectangle color or brightness (grayscale image). + /// * thickness: Thickness of lines that make up the rectangle. Negative values, like #FILLED, + /// mean that the function has to draw a filled rectangle. + /// * lineType: Type of the line. See [line_types] + /// * shift: Number of fractional bits in the point coordinates. + /// + /// ## Overloaded parameters + /// + /// + /// use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and + /// r.br()-Point(1,1)` are opposite corners + /// + /// ## Note + /// This alternative version of [rectangle] function uses the following default values for its arguments: + /// * thickness: 1 + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn rectangle_def(img: &mut impl ToInputOutputArray, rec: core::Rect, color: core::Scalar) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_rectangle_const__InputOutputArrayR_Rect_const_ScalarR(img.as_raw__InputOutputArray(), &rec, &color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws a simple, thick, or filled up-right rectangle. + /// + /// The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners + /// are pt1 and pt2. + /// + /// ## Parameters + /// * img: Image. + /// * pt1: Vertex of the rectangle. + /// * pt2: Vertex of the rectangle opposite to pt1 . + /// * color: Rectangle color or brightness (grayscale image). + /// * thickness: Thickness of lines that make up the rectangle. Negative values, like #FILLED, + /// mean that the function has to draw a filled rectangle. + /// * lineType: Type of the line. See [line_types] + /// * shift: Number of fractional bits in the point coordinates. + /// + /// ## Overloaded parameters + /// + /// + /// use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and + /// r.br()-Point(1,1)` are opposite corners + /// + /// ## C++ default parameters + /// * thickness: 1 + /// * line_type: LINE_8 + /// * shift: 0 + #[inline] + pub fn rectangle(img: &mut impl ToInputOutputArray, rec: core::Rect, color: core::Scalar, thickness: i32, line_type: i32, shift: i32) -> Result<()> { + input_output_array_arg!(img); + return_send!(via ocvrs_return); + unsafe { sys::cv_rectangle_const__InputOutputArrayR_Rect_const_ScalarR_int_int_int(img.as_raw__InputOutputArray(), &rec, &color, thickness, line_type, shift, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies a generic geometrical transformation to an image. + /// + /// The function remap transforms the source image using the specified map: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsrc%7D%20%28map%5Fx%28x%2Cy%29%2Cmap%5Fy%28x%2Cy%29%29) + /// + /// with the WARP_RELATIVE_MAP flag : + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsrc%7D%20%28x%2Bmap%5Fx%28x%2Cy%29%2Cy%2Bmap%5Fy%28x%2Cy%29%29) + /// + /// where values of pixels with non-integer coordinates are computed using one of available + /// interpolation methods. ![inline formula](https://latex.codecogs.com/png.latex?map%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?map%5Fy) can be encoded as separate floating-point maps + /// in ![inline formula](https://latex.codecogs.com/png.latex?map%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?map%5F2) respectively, or interleaved floating-point maps of ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29) in + /// ![inline formula](https://latex.codecogs.com/png.latex?map%5F1), or fixed-point maps created by using #convertMaps. The reason you might want to + /// convert from floating to fixed-point representations of a map is that they can yield much faster + /// (\~2x) remapping operations. In the converted case, ![inline formula](https://latex.codecogs.com/png.latex?map%5F1) contains pairs (cvFloor(x), + /// cvFloor(y)) and ![inline formula](https://latex.codecogs.com/png.latex?map%5F2) contains indices in a table of interpolation coefficients. + /// + /// This function cannot operate in-place. + /// + /// ## Parameters + /// * src: Source image. + /// * dst: Destination image. It has the same size as map1 and the same type as src . + /// * map1: The first map of either (x,y) points or just x values having the type CV_16SC2 , + /// CV_32FC1, or CV_32FC2. See [convert_maps] for details on converting a floating point + /// representation to fixed-point for speed. + /// * map2: The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map + /// if map1 is (x,y) points), respectively. + /// * interpolation: Interpolation method (see #InterpolationFlags). The methods [INTER_AREA] + /// [INTER_LINEAR_EXACT] and [INTER_NEAREST_EXACT] are not supported by this function. + /// The extra flag WARP_RELATIVE_MAP can be ORed to the interpolation method + /// (e.g. INTER_LINEAR | WARP_RELATIVE_MAP) + /// * borderMode: Pixel extrapolation method (see #BorderTypes). When + /// borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that + /// corresponds to the "outliers" in the source image are not modified by the function. + /// * borderValue: Value used in case of a constant border. By default, it is 0. + /// + /// Note: + /// Due to current implementation limitations the size of an input and output images should be less than 32767x32767. + /// + /// ## Note + /// This alternative version of [remap] function uses the following default values for its arguments: + /// * border_mode: BORDER_CONSTANT + /// * border_value: Scalar() + #[inline] + pub fn remap_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, map1: &impl ToInputArray, map2: &impl ToInputArray, interpolation: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(map1); + input_array_arg!(map2); + return_send!(via ocvrs_return); + unsafe { sys::cv_remap_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), map1.as_raw__InputArray(), map2.as_raw__InputArray(), interpolation, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies a generic geometrical transformation to an image. + /// + /// The function remap transforms the source image using the specified map: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsrc%7D%20%28map%5Fx%28x%2Cy%29%2Cmap%5Fy%28x%2Cy%29%29) + /// + /// with the WARP_RELATIVE_MAP flag : + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsrc%7D%20%28x%2Bmap%5Fx%28x%2Cy%29%2Cy%2Bmap%5Fy%28x%2Cy%29%29) + /// + /// where values of pixels with non-integer coordinates are computed using one of available + /// interpolation methods. ![inline formula](https://latex.codecogs.com/png.latex?map%5Fx) and ![inline formula](https://latex.codecogs.com/png.latex?map%5Fy) can be encoded as separate floating-point maps + /// in ![inline formula](https://latex.codecogs.com/png.latex?map%5F1) and ![inline formula](https://latex.codecogs.com/png.latex?map%5F2) respectively, or interleaved floating-point maps of ![inline formula](https://latex.codecogs.com/png.latex?%28x%2Cy%29) in + /// ![inline formula](https://latex.codecogs.com/png.latex?map%5F1), or fixed-point maps created by using #convertMaps. The reason you might want to + /// convert from floating to fixed-point representations of a map is that they can yield much faster + /// (\~2x) remapping operations. In the converted case, ![inline formula](https://latex.codecogs.com/png.latex?map%5F1) contains pairs (cvFloor(x), + /// cvFloor(y)) and ![inline formula](https://latex.codecogs.com/png.latex?map%5F2) contains indices in a table of interpolation coefficients. + /// + /// This function cannot operate in-place. + /// + /// ## Parameters + /// * src: Source image. + /// * dst: Destination image. It has the same size as map1 and the same type as src . + /// * map1: The first map of either (x,y) points or just x values having the type CV_16SC2 , + /// CV_32FC1, or CV_32FC2. See [convert_maps] for details on converting a floating point + /// representation to fixed-point for speed. + /// * map2: The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map + /// if map1 is (x,y) points), respectively. + /// * interpolation: Interpolation method (see #InterpolationFlags). The methods [INTER_AREA] + /// [INTER_LINEAR_EXACT] and [INTER_NEAREST_EXACT] are not supported by this function. + /// The extra flag WARP_RELATIVE_MAP can be ORed to the interpolation method + /// (e.g. INTER_LINEAR | WARP_RELATIVE_MAP) + /// * borderMode: Pixel extrapolation method (see #BorderTypes). When + /// borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that + /// corresponds to the "outliers" in the source image are not modified by the function. + /// * borderValue: Value used in case of a constant border. By default, it is 0. + /// + /// Note: + /// Due to current implementation limitations the size of an input and output images should be less than 32767x32767. + /// + /// ## C++ default parameters + /// * border_mode: BORDER_CONSTANT + /// * border_value: Scalar() + #[inline] + pub fn remap(src: &impl ToInputArray, dst: &mut impl ToOutputArray, map1: &impl ToInputArray, map2: &impl ToInputArray, interpolation: i32, border_mode: i32, border_value: core::Scalar) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(map1); + input_array_arg!(map2); + return_send!(via ocvrs_return); + unsafe { sys::cv_remap_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_int_int_const_ScalarR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), map1.as_raw__InputArray(), map2.as_raw__InputArray(), interpolation, border_mode, &border_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Resizes an image. + /// + /// The function resize resizes the image src down to or up to the specified size. Note that the + /// initial dst type or size are not taken into account. Instead, the size and type are derived from + /// the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst, + /// you may call the function as follows: + /// ```C++ + /// // explicitly specify dsize=dst.size(); fx and fy will be computed from that. + /// resize(src, dst, dst.size(), 0, 0, interpolation); + /// ``` + /// + /// If you want to decimate the image by factor of 2 in each direction, you can call the function this + /// way: + /// ```C++ + /// // specify fx and fy and let the function compute the destination image size. + /// resize(src, dst, Size(), 0.5, 0.5, interpolation); + /// ``` + /// + /// To shrink an image, it will generally look best with [INTER_AREA] interpolation, whereas to + /// enlarge an image, it will generally look best with [INTER_CUBIC] (slow) or [INTER_LINEAR] + /// (faster but still looks OK). + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image; it has the size dsize (when it is non-zero) or the size computed from + /// src.size(), fx, and fy; the type of dst is the same as of src. + /// * dsize: output image size; if it equals zero (`None` in Python), it is computed as: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdsize%20%3D%20Size%28round%28fx%2Asrc%2Ecols%29%2C%20round%28fy%2Asrc%2Erows%29%29%7D) + /// Either dsize or both fx and fy must be non-zero. + /// * fx: scale factor along the horizontal axis; when it equals 0, it is computed as + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7B%28double%29dsize%2Ewidth%2Fsrc%2Ecols%7D) + /// * fy: scale factor along the vertical axis; when it equals 0, it is computed as + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7B%28double%29dsize%2Eheight%2Fsrc%2Erows%7D) + /// * interpolation: interpolation method, see [interpolation_flags] + /// ## See also + /// warpAffine, warpPerspective, remap + /// + /// ## Note + /// This alternative version of [resize] function uses the following default values for its arguments: + /// * fx: 0 + /// * fy: 0 + /// * interpolation: INTER_LINEAR + #[inline] + pub fn resize_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, dsize: core::Size) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_resize_const__InputArrayR_const__OutputArrayR_Size(src.as_raw__InputArray(), dst.as_raw__OutputArray(), &dsize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Resizes an image. + /// + /// The function resize resizes the image src down to or up to the specified size. Note that the + /// initial dst type or size are not taken into account. Instead, the size and type are derived from + /// the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst, + /// you may call the function as follows: + /// ```C++ + /// // explicitly specify dsize=dst.size(); fx and fy will be computed from that. + /// resize(src, dst, dst.size(), 0, 0, interpolation); + /// ``` + /// + /// If you want to decimate the image by factor of 2 in each direction, you can call the function this + /// way: + /// ```C++ + /// // specify fx and fy and let the function compute the destination image size. + /// resize(src, dst, Size(), 0.5, 0.5, interpolation); + /// ``` + /// + /// To shrink an image, it will generally look best with [INTER_AREA] interpolation, whereas to + /// enlarge an image, it will generally look best with [INTER_CUBIC] (slow) or [INTER_LINEAR] + /// (faster but still looks OK). + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image; it has the size dsize (when it is non-zero) or the size computed from + /// src.size(), fx, and fy; the type of dst is the same as of src. + /// * dsize: output image size; if it equals zero (`None` in Python), it is computed as: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdsize%20%3D%20Size%28round%28fx%2Asrc%2Ecols%29%2C%20round%28fy%2Asrc%2Erows%29%29%7D) + /// Either dsize or both fx and fy must be non-zero. + /// * fx: scale factor along the horizontal axis; when it equals 0, it is computed as + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7B%28double%29dsize%2Ewidth%2Fsrc%2Ecols%7D) + /// * fy: scale factor along the vertical axis; when it equals 0, it is computed as + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7B%28double%29dsize%2Eheight%2Fsrc%2Erows%7D) + /// * interpolation: interpolation method, see [interpolation_flags] + /// ## See also + /// warpAffine, warpPerspective, remap + /// + /// ## C++ default parameters + /// * fx: 0 + /// * fy: 0 + /// * interpolation: INTER_LINEAR + #[inline] + pub fn resize(src: &impl ToInputArray, dst: &mut impl ToOutputArray, dsize: core::Size, fx: f64, fy: f64, interpolation: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_resize_const__InputArrayR_const__OutputArrayR_Size_double_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), &dsize, fx, fy, interpolation, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds out if there is any intersection between two rotated rectangles. + /// + /// If there is then the vertices of the intersecting region are returned as well. + /// + /// Below are some examples of intersection configurations. The hatched pattern indicates the + /// intersecting region and the red vertices are returned by the function. + /// + /// ![intersection examples](https://docs.opencv.org/4.13.0/intersection.png) + /// + /// ## Parameters + /// * rect1: First rectangle + /// * rect2: Second rectangle + /// * intersectingRegion: The output array of the vertices of the intersecting region. It returns + /// at most 8 vertices. Stored as std::vector\ or cv::Mat as Mx1 of type CV_32FC2. + /// ## Returns + /// One of #RectanglesIntersectTypes + #[inline] + pub fn rotated_rectangle_intersection(rect1: core::RotatedRect, rect2: core::RotatedRect, intersecting_region: &mut impl ToOutputArray) -> Result { + output_array_arg!(intersecting_region); + return_send!(via ocvrs_return); + unsafe { sys::cv_rotatedRectangleIntersection_const_RotatedRectR_const_RotatedRectR_const__OutputArrayR(&rect1, &rect2, intersecting_region.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies a separable linear filter to an image. + /// + /// The function applies a separable linear filter to the image. That is, first, every row of src is + /// filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D + /// kernel kernelY. The final result shifted by delta is stored in dst . + /// + /// ## Parameters + /// * src: Source image. + /// * dst: Destination image of the same size and the same number of channels as src . + /// * ddepth: Destination image depth, see [filter_depths] "combinations" + /// * kernelX: Coefficients for filtering each row. + /// * kernelY: Coefficients for filtering each column. + /// * anchor: Anchor position within the kernel. The default value ![inline formula](https://latex.codecogs.com/png.latex?%28%2D1%2C%2D1%29) means that the anchor + /// is at the kernel center. + /// * delta: Value added to the filtered results before storing them. + /// * borderType: Pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// filter2D, Sobel, GaussianBlur, boxFilter, blur + /// + /// ## Note + /// This alternative version of [sep_filter_2d] function uses the following default values for its arguments: + /// * anchor: Point(-1,-1) + /// * delta: 0 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn sep_filter_2d_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32, kernel_x: &impl ToInputArray, kernel_y: &impl ToInputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(kernel_x); + input_array_arg!(kernel_y); + return_send!(via ocvrs_return); + unsafe { sys::cv_sepFilter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_const__InputArrayR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, kernel_x.as_raw__InputArray(), kernel_y.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies a separable linear filter to an image. + /// + /// The function applies a separable linear filter to the image. That is, first, every row of src is + /// filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D + /// kernel kernelY. The final result shifted by delta is stored in dst . + /// + /// ## Parameters + /// * src: Source image. + /// * dst: Destination image of the same size and the same number of channels as src . + /// * ddepth: Destination image depth, see [filter_depths] "combinations" + /// * kernelX: Coefficients for filtering each row. + /// * kernelY: Coefficients for filtering each column. + /// * anchor: Anchor position within the kernel. The default value ![inline formula](https://latex.codecogs.com/png.latex?%28%2D1%2C%2D1%29) means that the anchor + /// is at the kernel center. + /// * delta: Value added to the filtered results before storing them. + /// * borderType: Pixel extrapolation method, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// filter2D, Sobel, GaussianBlur, boxFilter, blur + /// + /// ## C++ default parameters + /// * anchor: Point(-1,-1) + /// * delta: 0 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn sep_filter_2d(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32, kernel_x: &impl ToInputArray, kernel_y: &impl ToInputArray, anchor: core::Point, delta: f64, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(kernel_x); + input_array_arg!(kernel_y); + return_send!(via ocvrs_return); + unsafe { sys::cv_sepFilter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_const__InputArrayR_Point_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, kernel_x.as_raw__InputArray(), kernel_y.as_raw__InputArray(), &anchor, delta, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the first order image derivative in both x and y using a Sobel operator + /// + /// Equivalent to calling: + /// + /// ```C++ + /// Sobel( src, dx, CV_16SC1, 1, 0, 3 ); + /// Sobel( src, dy, CV_16SC1, 0, 1, 3 ); + /// ``` + /// + /// + /// ## Parameters + /// * src: input image. + /// * dx: output image with first-order derivative in x. + /// * dy: output image with first-order derivative in y. + /// * ksize: size of Sobel kernel. It must be 3. + /// * borderType: pixel extrapolation method, see #BorderTypes. + /// Only #BORDER_DEFAULT=[BORDER_REFLECT_101] and [BORDER_REPLICATE] are supported. + /// ## See also + /// Sobel + /// + /// ## Note + /// This alternative version of [spatial_gradient] function uses the following default values for its arguments: + /// * ksize: 3 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn spatial_gradient_def(src: &impl ToInputArray, dx: &mut impl ToOutputArray, dy: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dx); + output_array_arg!(dy); + return_send!(via ocvrs_return); + unsafe { sys::cv_spatialGradient_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src.as_raw__InputArray(), dx.as_raw__OutputArray(), dy.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the first order image derivative in both x and y using a Sobel operator + /// + /// Equivalent to calling: + /// + /// ```C++ + /// Sobel( src, dx, CV_16SC1, 1, 0, 3 ); + /// Sobel( src, dy, CV_16SC1, 0, 1, 3 ); + /// ``` + /// + /// + /// ## Parameters + /// * src: input image. + /// * dx: output image with first-order derivative in x. + /// * dy: output image with first-order derivative in y. + /// * ksize: size of Sobel kernel. It must be 3. + /// * borderType: pixel extrapolation method, see #BorderTypes. + /// Only #BORDER_DEFAULT=[BORDER_REFLECT_101] and [BORDER_REPLICATE] are supported. + /// ## See also + /// Sobel + /// + /// ## C++ default parameters + /// * ksize: 3 + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn spatial_gradient(src: &impl ToInputArray, dx: &mut impl ToOutputArray, dy: &mut impl ToOutputArray, ksize: i32, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dx); + output_array_arg!(dy); + return_send!(via ocvrs_return); + unsafe { sys::cv_spatialGradient_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(src.as_raw__InputArray(), dx.as_raw__OutputArray(), dy.as_raw__OutputArray(), ksize, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the normalized sum of squares of the pixel values overlapping the filter. + /// + /// For every pixel ![inline formula](https://latex.codecogs.com/png.latex?%20%28x%2C%20y%29%20) in the source image, the function calculates the sum of squares of those neighboring + /// pixel values which overlap the filter placed over the pixel ![inline formula](https://latex.codecogs.com/png.latex?%20%28x%2C%20y%29%20). + /// + /// The unnormalized square box filter can be useful in computing local image statistics such as the local + /// variance and standard deviation around the neighborhood of a pixel. + /// + /// ## Parameters + /// * src: input image + /// * dst: output image of the same size and type as src + /// * ddepth: the output image depth (-1 to use src.depth()) + /// * ksize: kernel size + /// * anchor: kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel + /// center. + /// * normalize: flag, specifying whether the kernel is to be normalized by it's area or not. + /// * borderType: border mode used to extrapolate pixels outside of the image, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// boxFilter + /// + /// ## Note + /// This alternative version of [sqr_box_filter] function uses the following default values for its arguments: + /// * anchor: Point(-1,-1) + /// * normalize: true + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn sqr_box_filter_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32, ksize: core::Size) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_sqrBoxFilter_const__InputArrayR_const__OutputArrayR_int_Size(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, &ksize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Calculates the normalized sum of squares of the pixel values overlapping the filter. + /// + /// For every pixel ![inline formula](https://latex.codecogs.com/png.latex?%20%28x%2C%20y%29%20) in the source image, the function calculates the sum of squares of those neighboring + /// pixel values which overlap the filter placed over the pixel ![inline formula](https://latex.codecogs.com/png.latex?%20%28x%2C%20y%29%20). + /// + /// The unnormalized square box filter can be useful in computing local image statistics such as the local + /// variance and standard deviation around the neighborhood of a pixel. + /// + /// ## Parameters + /// * src: input image + /// * dst: output image of the same size and type as src + /// * ddepth: the output image depth (-1 to use src.depth()) + /// * ksize: kernel size + /// * anchor: kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel + /// center. + /// * normalize: flag, specifying whether the kernel is to be normalized by it's area or not. + /// * borderType: border mode used to extrapolate pixels outside of the image, see #BorderTypes. [BORDER_WRAP] is not supported. + /// ## See also + /// boxFilter + /// + /// ## C++ default parameters + /// * anchor: Point(-1,-1) + /// * normalize: true + /// * border_type: BORDER_DEFAULT + #[inline] + pub fn sqr_box_filter(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ddepth: i32, ksize: core::Size, anchor: core::Point, normalize: bool, border_type: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_sqrBoxFilter_const__InputArrayR_const__OutputArrayR_int_Size_Point_bool_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), ddepth, &ksize, &anchor, normalize, border_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Blurs an image using the stackBlur. + /// + /// The function applies and stackBlur to an image. + /// stackBlur can generate similar results as Gaussian blur, and the time consumption does not increase with the increase of kernel size. + /// It creates a kind of moving stack of colors whilst scanning through the image. Thereby it just has to add one new block of color to the right side + /// of the stack and remove the leftmost color. The remaining colors on the topmost layer of the stack are either added on or reduced by one, + /// depending on if they are on the right or on the left side of the stack. The only supported borderType is BORDER_REPLICATE. + /// Original paper was proposed by Mario Klingemann, which can be found . + /// + /// ## Parameters + /// * src: input image. The number of channels can be arbitrary, but the depth should be one of + /// CV_8U, CV_16U, CV_16S or CV_32F. + /// * dst: output image of the same size and type as src. + /// * ksize: stack-blurring kernel size. The ksize.width and ksize.height can differ but they both must be + /// positive and odd. + #[inline] + pub fn stack_blur(src: &impl ToInputArray, dst: &mut impl ToOutputArray, ksize: core::Size) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_stackBlur_const__InputArrayR_const__OutputArrayR_Size(src.as_raw__InputArray(), dst.as_raw__OutputArray(), &ksize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Same as #threshold, but with an optional mask + /// + /// + /// Note: If the mask is empty, [threshold_with_mask] is equivalent to #threshold. + /// If the mask is not empty, dst *must* be of the same size and type as src, so that + /// outliers pixels are left as-is + /// + /// ## Parameters + /// * src: input array (multiple-channel, 8-bit or 32-bit floating point). + /// * dst: output array of the same size and type and the same number of channels as src. + /// * mask: optional mask (same size as src, 8-bit). + /// * thresh: threshold value. + /// * maxval: maximum value to use with the [THRESH_BINARY] and [THRESH_BINARY_INV] thresholding + /// types. + /// * type: thresholding type (see #ThresholdTypes). + /// ## Returns + /// the computed threshold value if Otsu's or Triangle methods used. + /// ## See also + /// threshold, adaptiveThreshold, findContours, compare, min, max + #[inline] + pub fn threshold_with_mask(src: &impl ToInputArray, dst: &mut impl ToInputOutputArray, mask: &impl ToInputArray, thresh: f64, maxval: f64, typ: i32) -> Result { + input_array_arg!(src); + input_output_array_arg!(dst); + input_array_arg!(mask); + return_send!(via ocvrs_return); + unsafe { sys::cv_thresholdWithMask_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR_double_double_int(src.as_raw__InputArray(), dst.as_raw__InputOutputArray(), mask.as_raw__InputArray(), thresh, maxval, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies a fixed-level threshold to each array element. + /// + /// The function applies fixed-level thresholding to a multiple-channel array. The function is typically + /// used to get a bi-level (binary) image out of a grayscale image ( [compare] could be also used for + /// this purpose) or for removing a noise, that is, filtering out pixels with too small or too large + /// values. There are several types of thresholding supported by the function. They are determined by + /// type parameter. + /// + /// Also, the special values [THRESH_OTSU] or [THRESH_TRIANGLE] may be combined with one of the + /// above values. In these cases, the function determines the optimal threshold value using the Otsu's + /// or Triangle algorithm and uses it instead of the specified thresh. + /// + /// + /// Note: Currently, the Otsu's method is implemented only for CV_8UC1 and CV_16UC1 images, + /// and the Triangle's method is implemented only for CV_8UC1 images. + /// + /// ## Parameters + /// * src: input array (multiple-channel, CV_8U, CV_16S, CV_16U, CV_32F or CV_64F). + /// * dst: output array of the same size and type and the same number of channels as src. + /// * thresh: threshold value. + /// * maxval: maximum value to use with the [THRESH_BINARY] and [THRESH_BINARY_INV] thresholding + /// types. + /// * type: thresholding type (see #ThresholdTypes). + /// ## Returns + /// the computed threshold value if Otsu's or Triangle methods used. + /// ## See also + /// thresholdWithMask, adaptiveThreshold, findContours, compare, min, max + #[inline] + pub fn threshold(src: &impl ToInputArray, dst: &mut impl ToOutputArray, thresh: f64, maxval: f64, typ: i32) -> Result { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_threshold_const__InputArrayR_const__OutputArrayR_double_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), thresh, maxval, typ, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies an affine transformation to an image. + /// + /// The function warpAffine transforms the source image using the specified matrix: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BM%7D%20%5F%7B11%7D%20x%20%2B%20%20%5Ctexttt%7BM%7D%20%5F%7B12%7D%20y%20%2B%20%20%5Ctexttt%7BM%7D%20%5F%7B13%7D%2C%20%5Ctexttt%7BM%7D%20%5F%7B21%7D%20x%20%2B%20%20%5Ctexttt%7BM%7D%20%5F%7B22%7D%20y%20%2B%20%20%5Ctexttt%7BM%7D%20%5F%7B23%7D%29) + /// + /// when the flag [WARP_INVERSE_MAP] is set. Otherwise, the transformation is first inverted + /// with [invert_affine_transform] and then put in the formula above instead of M. The function cannot + /// operate in-place. + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image that has the size dsize and the same type as src . + /// * M: ![inline formula](https://latex.codecogs.com/png.latex?2%5Ctimes%203) transformation matrix. + /// * dsize: size of the output image. + /// * flags: combination of interpolation methods (see #InterpolationFlags) and the optional + /// flag [WARP_INVERSE_MAP] that means that M is the inverse transformation ( + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%5Crightarrow%5Ctexttt%7Bsrc%7D) ). + /// * borderMode: pixel extrapolation method (see #BorderTypes); when + /// borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to + /// the "outliers" in the source image are not modified by the function. + /// * borderValue: value used in case of a constant border; by default, it is 0. + /// ## See also + /// warpPerspective, resize, remap, getRectSubPix, transform + /// + /// ## Note + /// This alternative version of [warp_affine] function uses the following default values for its arguments: + /// * flags: INTER_LINEAR + /// * border_mode: BORDER_CONSTANT + /// * border_value: Scalar() + #[inline] + pub fn warp_affine_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, m: &impl ToInputArray, dsize: core::Size) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_warpAffine_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size(src.as_raw__InputArray(), dst.as_raw__OutputArray(), m.as_raw__InputArray(), &dsize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies an affine transformation to an image. + /// + /// The function warpAffine transforms the source image using the specified matrix: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsrc%7D%20%28%20%5Ctexttt%7BM%7D%20%5F%7B11%7D%20x%20%2B%20%20%5Ctexttt%7BM%7D%20%5F%7B12%7D%20y%20%2B%20%20%5Ctexttt%7BM%7D%20%5F%7B13%7D%2C%20%5Ctexttt%7BM%7D%20%5F%7B21%7D%20x%20%2B%20%20%5Ctexttt%7BM%7D%20%5F%7B22%7D%20y%20%2B%20%20%5Ctexttt%7BM%7D%20%5F%7B23%7D%29) + /// + /// when the flag [WARP_INVERSE_MAP] is set. Otherwise, the transformation is first inverted + /// with [invert_affine_transform] and then put in the formula above instead of M. The function cannot + /// operate in-place. + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image that has the size dsize and the same type as src . + /// * M: ![inline formula](https://latex.codecogs.com/png.latex?2%5Ctimes%203) transformation matrix. + /// * dsize: size of the output image. + /// * flags: combination of interpolation methods (see #InterpolationFlags) and the optional + /// flag [WARP_INVERSE_MAP] that means that M is the inverse transformation ( + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%5Crightarrow%5Ctexttt%7Bsrc%7D) ). + /// * borderMode: pixel extrapolation method (see #BorderTypes); when + /// borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to + /// the "outliers" in the source image are not modified by the function. + /// * borderValue: value used in case of a constant border; by default, it is 0. + /// ## See also + /// warpPerspective, resize, remap, getRectSubPix, transform + /// + /// ## C++ default parameters + /// * flags: INTER_LINEAR + /// * border_mode: BORDER_CONSTANT + /// * border_value: Scalar() + #[inline] + pub fn warp_affine(src: &impl ToInputArray, dst: &mut impl ToOutputArray, m: &impl ToInputArray, dsize: core::Size, flags: i32, border_mode: i32, border_value: core::Scalar) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_warpAffine_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size_int_int_const_ScalarR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), m.as_raw__InputArray(), &dsize, flags, border_mode, &border_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies a perspective transformation to an image. + /// + /// The function warpPerspective transforms the source image using the specified matrix: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsrc%7D%20%5Cleft%20%28%20%5Cfrac%7BM%5F%7B11%7D%20x%20%2B%20M%5F%7B12%7D%20y%20%2B%20M%5F%7B13%7D%7D%7BM%5F%7B31%7D%20x%20%2B%20M%5F%7B32%7D%20y%20%2B%20M%5F%7B33%7D%7D%20%2C%0A%20%20%20%20%20%5Cfrac%7BM%5F%7B21%7D%20x%20%2B%20M%5F%7B22%7D%20y%20%2B%20M%5F%7B23%7D%7D%7BM%5F%7B31%7D%20x%20%2B%20M%5F%7B32%7D%20y%20%2B%20M%5F%7B33%7D%7D%20%5Cright%20%29) + /// + /// when the flag [WARP_INVERSE_MAP] is set. Otherwise, the transformation is first inverted with invert + /// and then put in the formula above instead of M. The function cannot operate in-place. + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image that has the size dsize and the same type as src . + /// * M: ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes%203) transformation matrix. + /// * dsize: size of the output image. + /// * flags: combination of interpolation methods ([INTER_LINEAR] or #INTER_NEAREST) and the + /// optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%5Crightarrow%5Ctexttt%7Bsrc%7D) ). + /// * borderMode: pixel extrapolation method ([BORDER_CONSTANT] or #BORDER_REPLICATE). + /// * borderValue: value used in case of a constant border; by default, it equals 0. + /// ## See also + /// warpAffine, resize, remap, getRectSubPix, perspectiveTransform + /// + /// ## Note + /// This alternative version of [warp_perspective] function uses the following default values for its arguments: + /// * flags: INTER_LINEAR + /// * border_mode: BORDER_CONSTANT + /// * border_value: Scalar() + #[inline] + pub fn warp_perspective_def(src: &impl ToInputArray, dst: &mut impl ToOutputArray, m: &impl ToInputArray, dsize: core::Size) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_warpPerspective_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size(src.as_raw__InputArray(), dst.as_raw__OutputArray(), m.as_raw__InputArray(), &dsize, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Applies a perspective transformation to an image. + /// + /// The function warpPerspective transforms the source image using the specified matrix: + /// + /// ![block formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%20%28x%2Cy%29%20%3D%20%20%5Ctexttt%7Bsrc%7D%20%5Cleft%20%28%20%5Cfrac%7BM%5F%7B11%7D%20x%20%2B%20M%5F%7B12%7D%20y%20%2B%20M%5F%7B13%7D%7D%7BM%5F%7B31%7D%20x%20%2B%20M%5F%7B32%7D%20y%20%2B%20M%5F%7B33%7D%7D%20%2C%0A%20%20%20%20%20%5Cfrac%7BM%5F%7B21%7D%20x%20%2B%20M%5F%7B22%7D%20y%20%2B%20M%5F%7B23%7D%7D%7BM%5F%7B31%7D%20x%20%2B%20M%5F%7B32%7D%20y%20%2B%20M%5F%7B33%7D%7D%20%5Cright%20%29) + /// + /// when the flag [WARP_INVERSE_MAP] is set. Otherwise, the transformation is first inverted with invert + /// and then put in the formula above instead of M. The function cannot operate in-place. + /// + /// ## Parameters + /// * src: input image. + /// * dst: output image that has the size dsize and the same type as src . + /// * M: ![inline formula](https://latex.codecogs.com/png.latex?3%5Ctimes%203) transformation matrix. + /// * dsize: size of the output image. + /// * flags: combination of interpolation methods ([INTER_LINEAR] or #INTER_NEAREST) and the + /// optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( + /// ![inline formula](https://latex.codecogs.com/png.latex?%5Ctexttt%7Bdst%7D%5Crightarrow%5Ctexttt%7Bsrc%7D) ). + /// * borderMode: pixel extrapolation method ([BORDER_CONSTANT] or #BORDER_REPLICATE). + /// * borderValue: value used in case of a constant border; by default, it equals 0. + /// ## See also + /// warpAffine, resize, remap, getRectSubPix, perspectiveTransform + /// + /// ## C++ default parameters + /// * flags: INTER_LINEAR + /// * border_mode: BORDER_CONSTANT + /// * border_value: Scalar() + #[inline] + pub fn warp_perspective(src: &impl ToInputArray, dst: &mut impl ToOutputArray, m: &impl ToInputArray, dsize: core::Size, flags: i32, border_mode: i32, border_value: core::Scalar) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + input_array_arg!(m); + return_send!(via ocvrs_return); + unsafe { sys::cv_warpPerspective_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size_int_int_const_ScalarR(src.as_raw__InputArray(), dst.as_raw__OutputArray(), m.as_raw__InputArray(), &dsize, flags, border_mode, &border_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// \brief Remaps an image to polar or semilog-polar coordinates space + /// + /// @anchor polar_remaps_reference_image + /// ![Polar remaps reference](https://docs.opencv.org/4.13.0/polar_remap_doc.png) + /// + /// Transform the source image using the following transformation: + /// ![block formula](https://latex.codecogs.com/png.latex?%0Adst%28%5Crho%20%2C%20%5Cphi%20%29%20%3D%20src%28x%2Cy%29%0A) + /// + /// where + /// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Barray%7D%7Bl%7D%0A%5Cvec%7BI%7D%20%3D%20%28x%20%2D%20center%2Ex%2C%20%5C%3By%20%2D%20center%2Ey%29%20%5C%5C%0A%5Cphi%20%3D%20Kangle%20%5Ccdot%20%5Ctexttt%7Bangle%7D%20%28%5Cvec%7BI%7D%29%20%5C%5C%0A%5Crho%20%3D%20%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D%0AKlin%20%5Ccdot%20%5Ctexttt%7Bmagnitude%7D%20%28%5Cvec%7BI%7D%29%20%26%20default%20%5C%5C%0AKlog%20%5Ccdot%20log%5Fe%28%5Ctexttt%7Bmagnitude%7D%20%28%5Cvec%7BI%7D%29%29%20%26%20if%20%5C%3B%20semilog%20%5C%5C%0A%5Cend%7Bmatrix%7D%5Cright%2E%0A%5Cend%7Barray%7D%0A) + /// + /// and + /// ![block formula](https://latex.codecogs.com/png.latex?%0A%5Cbegin%7Barray%7D%7Bl%7D%0AKangle%20%3D%20dsize%2Eheight%20%2F%202%5CPi%20%5C%5C%0AKlin%20%3D%20dsize%2Ewidth%20%2F%20maxRadius%20%5C%5C%0AKlog%20%3D%20dsize%2Ewidth%20%2F%20log%5Fe%28maxRadius%29%20%5C%5C%0A%5Cend%7Barray%7D%0A) + /// + /// + /// \par Linear vs semilog mapping + /// + /// Polar mapping can be linear or semi-log. Add one of [warp_polar_mode] to `flags` to specify the polar mapping mode. + /// + /// Linear is the default mode. + /// + /// The semilog mapping emulates the human "foveal" vision that permit very high acuity on the line of sight (central vision) + /// in contrast to peripheral vision where acuity is minor. + /// + /// \par Option on `dsize`: + /// + /// - if both values in `dsize <=0 ` (default), + /// the destination image will have (almost) same area of source bounding circle: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%0Adsize%2Earea%20%20%5Cleftarrow%20%28maxRadius%5E2%20%5Ccdot%20%5CPi%29%20%5C%5C%0Adsize%2Ewidth%20%3D%20%5Ctexttt%7BcvRound%7D%28maxRadius%29%20%5C%5C%0Adsize%2Eheight%20%3D%20%5Ctexttt%7BcvRound%7D%28maxRadius%20%5Ccdot%20%5CPi%29%20%5C%5C%0A%5Cend%7Barray%7D) + /// + /// + /// - if only `dsize.height <= 0`, + /// the destination image area will be proportional to the bounding circle area but scaled by `Kx * Kx`: + /// ![block formula](https://latex.codecogs.com/png.latex?%5Cbegin%7Barray%7D%7Bl%7D%0Adsize%2Eheight%20%3D%20%5Ctexttt%7BcvRound%7D%28dsize%2Ewidth%20%5Ccdot%20%5CPi%29%20%5C%5C%0A%5Cend%7Barray%7D%0A) + /// + /// - if both values in `dsize > 0 `, + /// the destination image will have the given size therefore the area of the bounding circle will be scaled to `dsize`. + /// + /// + /// \par Reverse mapping + /// + /// You can get reverse mapping adding [WARP_INVERSE_MAP] to `flags` + /// \snippet polar_transforms.cpp InverseMap + /// + /// In addiction, to calculate the original coordinate from a polar mapped coordinate ![inline formula](https://latex.codecogs.com/png.latex?%28rho%2C%20phi%29%2D%3E%28x%2C%20y%29): + /// \snippet polar_transforms.cpp InverseCoordinate + /// + /// ## Parameters + /// * src: Source image. + /// * dst: Destination image. It will have same type as src. + /// * dsize: The destination image size (see description for valid options). + /// * center: The transformation center. + /// * maxRadius: The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. + /// * flags: A combination of interpolation methods, [interpolation_flags] + #WarpPolarMode. + /// - Add [WARP_POLAR_LINEAR] to select linear polar mapping (default) + /// - Add [WARP_POLAR_LOG] to select semilog polar mapping + /// - Add [WARP_INVERSE_MAP] for reverse mapping. + /// + /// Note: + /// * The function can not operate in-place. + /// * To calculate magnitude and angle in degrees [cart_to_polar] is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. + /// * This function uses #remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767. + /// ## See also + /// cv::remap + #[inline] + pub fn warp_polar(src: &impl ToInputArray, dst: &mut impl ToOutputArray, dsize: core::Size, center: core::Point2f, max_radius: f64, flags: i32) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_warpPolar_const__InputArrayR_const__OutputArrayR_Size_Point2f_double_int(src.as_raw__InputArray(), dst.as_raw__OutputArray(), &dsize, ¢er, max_radius, flags, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Performs a marker-based image segmentation using the watershed algorithm. + /// + /// The function implements one of the variants of watershed, non-parametric marker-based segmentation + /// algorithm, described in [Meyer92](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Meyer92) . + /// + /// Before passing the image to the function, you have to roughly outline the desired regions in the + /// image markers with positive (\>0) indices. So, every region is represented as one or more connected + /// components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary + /// mask using [find_contours] and [draw_contours] (see the watershed.cpp demo). The markers are "seeds" of + /// the future image regions. All the other pixels in markers , whose relation to the outlined regions + /// is not known and should be defined by the algorithm, should be set to 0's. In the function output, + /// each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the + /// regions. + /// + /// + /// Note: Any two neighbor connected components are not necessarily separated by a watershed boundary + /// (-1's pixels); for example, they can touch each other in the initial marker image passed to the + /// function. + /// + /// ## Parameters + /// * image: Input 8-bit 3-channel image. + /// * markers: Input/output 32-bit single-channel image (map) of markers. It should have the same + /// size as image . + /// ## See also + /// findContours + #[inline] + pub fn watershed(image: &impl ToInputArray, markers: &mut impl ToInputOutputArray) -> Result<()> { + input_array_arg!(image); + input_output_array_arg!(markers); + return_send!(via ocvrs_return); + unsafe { sys::cv_watershed_const__InputArrayR_const__InputOutputArrayR(image.as_raw__InputArray(), markers.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [emd_1] function uses the following default values for its arguments: + /// * cost: noArray() + /// * lower_bound: Ptr() + /// * flow: noArray() + #[inline] + pub fn emd_1_def(signature1: &impl ToInputArray, signature2: &impl ToInputArray, dist_type: i32) -> Result { + input_array_arg!(signature1); + input_array_arg!(signature2); + return_send!(via ocvrs_return); + unsafe { sys::cv_wrapperEMD_const__InputArrayR_const__InputArrayR_int(signature1.as_raw__InputArray(), signature2.as_raw__InputArray(), dist_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * cost: noArray() + /// * lower_bound: Ptr() + /// * flow: noArray() + #[inline] + pub fn emd_1(signature1: &impl ToInputArray, signature2: &impl ToInputArray, dist_type: i32, cost: &impl ToInputArray, mut lower_bound: core::Ptr, flow: &mut impl ToOutputArray) -> Result { + input_array_arg!(signature1); + input_array_arg!(signature2); + input_array_arg!(cost); + output_array_arg!(flow); + return_send!(via ocvrs_return); + unsafe { sys::cv_wrapperEMD_const__InputArrayR_const__InputArrayR_int_const__InputArrayR_PtrLfloatG_const__OutputArrayR(signature1.as_raw__InputArray(), signature2.as_raw__InputArray(), dist_type, cost.as_raw__InputArray(), lower_bound.as_raw_mut_PtrOff32(), flow.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Base class for Contrast Limited Adaptive Histogram Equalization. + pub struct CLAHE { + ptr: *mut c_void, + } + + opencv_type_boxed! { CLAHE } + + impl Drop for CLAHE { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_CLAHE_delete(self.as_raw_mut_CLAHE()) }; + } + } + + unsafe impl Send for CLAHE {} + + /// Constant methods for [crate::imgproc::CLAHE] + pub trait CLAHETraitConst: core::AlgorithmTraitConst { + fn as_raw_CLAHE(&self) -> *const c_void; + + /// Returns threshold value for contrast limiting. + #[inline] + fn get_clip_limit(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CLAHE_getClipLimit_const(self.as_raw_CLAHE(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns Size defines the number of tiles in row and column. + #[inline] + fn get_tiles_grid_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CLAHE_getTilesGridSize_const(self.as_raw_CLAHE(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the bit shift parameter for histogram bins. + /// + /// ## Returns + /// current bit shift value. + #[inline] + fn get_bit_shift(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_CLAHE_getBitShift_const(self.as_raw_CLAHE(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::imgproc::CLAHE] + pub trait CLAHETrait: core::AlgorithmTrait + crate::imgproc::CLAHETraitConst { + fn as_raw_mut_CLAHE(&mut self) -> *mut c_void; + + /// Equalizes the histogram of a grayscale image using Contrast Limited Adaptive Histogram Equalization. + /// + /// ## Parameters + /// * src: Source image of type CV_8UC1 or CV_16UC1. + /// * dst: Destination image. + #[inline] + fn apply(&mut self, src: &impl ToInputArray, dst: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(src); + output_array_arg!(dst); + return_send!(via ocvrs_return); + unsafe { sys::cv_CLAHE_apply_const__InputArrayR_const__OutputArrayR(self.as_raw_mut_CLAHE(), src.as_raw__InputArray(), dst.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets threshold for contrast limiting. + /// + /// ## Parameters + /// * clipLimit: threshold value. + #[inline] + fn set_clip_limit(&mut self, clip_limit: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_CLAHE_setClipLimit_double(self.as_raw_mut_CLAHE(), clip_limit, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets size of grid for histogram equalization. Input image will be divided into + /// equally sized rectangular tiles. + /// + /// ## Parameters + /// * tileGridSize: defines the number of tiles in row and column. + #[inline] + fn set_tiles_grid_size(&mut self, tile_grid_size: core::Size) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_CLAHE_setTilesGridSize_Size(self.as_raw_mut_CLAHE(), &tile_grid_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets bit shift parameter for histogram bins. + /// + /// ## Parameters + /// * bitShift: bit shift value (default is 0). + #[inline] + fn set_bit_shift(&mut self, bit_shift: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_CLAHE_setBitShift_int(self.as_raw_mut_CLAHE(), bit_shift, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn collect_garbage(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_CLAHE_collectGarbage(self.as_raw_mut_CLAHE(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for CLAHE { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("CLAHE") + .finish() + } + } + + boxed_cast_base! { CLAHE, core::Algorithm, cv_CLAHE_to_Algorithm } + + impl core::AlgorithmTraitConst for CLAHE { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for CLAHE { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CLAHE, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::imgproc::CLAHETraitConst for CLAHE { + #[inline] fn as_raw_CLAHE(&self) -> *const c_void { self.as_raw() } + } + + impl crate::imgproc::CLAHETrait for CLAHE { + #[inline] fn as_raw_mut_CLAHE(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { CLAHE, crate::imgproc::CLAHETraitConst, as_raw_CLAHE, crate::imgproc::CLAHETrait, as_raw_mut_CLAHE } + + /// finds arbitrary template in the grayscale image using Generalized Hough Transform + pub struct GeneralizedHough { + ptr: *mut c_void, + } + + opencv_type_boxed! { GeneralizedHough } + + impl Drop for GeneralizedHough { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_GeneralizedHough_delete(self.as_raw_mut_GeneralizedHough()) }; + } + } + + unsafe impl Send for GeneralizedHough {} + + /// Constant methods for [crate::imgproc::GeneralizedHough] + pub trait GeneralizedHoughTraitConst: core::AlgorithmTraitConst { + fn as_raw_GeneralizedHough(&self) -> *const c_void; + + #[inline] + fn get_canny_low_thresh(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_getCannyLowThresh_const(self.as_raw_GeneralizedHough(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_canny_high_thresh(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_getCannyHighThresh_const(self.as_raw_GeneralizedHough(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_min_dist(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_getMinDist_const(self.as_raw_GeneralizedHough(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_dp(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_getDp_const(self.as_raw_GeneralizedHough(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_max_buffer_size(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_getMaxBufferSize_const(self.as_raw_GeneralizedHough(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::imgproc::GeneralizedHough] + pub trait GeneralizedHoughTrait: core::AlgorithmTrait + crate::imgproc::GeneralizedHoughTraitConst { + fn as_raw_mut_GeneralizedHough(&mut self) -> *mut c_void; + + /// set template to search + /// + /// ## C++ default parameters + /// * templ_center: Point(-1,-1) + #[inline] + fn set_template(&mut self, templ: &impl ToInputArray, templ_center: core::Point) -> Result<()> { + input_array_arg!(templ); + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_setTemplate_const__InputArrayR_Point(self.as_raw_mut_GeneralizedHough(), templ.as_raw__InputArray(), &templ_center, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// set template to search + /// + /// ## Note + /// This alternative version of [GeneralizedHoughTrait::set_template] function uses the following default values for its arguments: + /// * templ_center: Point(-1,-1) + #[inline] + fn set_template_def(&mut self, templ: &impl ToInputArray) -> Result<()> { + input_array_arg!(templ); + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_setTemplate_const__InputArrayR(self.as_raw_mut_GeneralizedHough(), templ.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * templ_center: Point(-1,-1) + #[inline] + fn set_template_1(&mut self, edges: &impl ToInputArray, dx: &impl ToInputArray, dy: &impl ToInputArray, templ_center: core::Point) -> Result<()> { + input_array_arg!(edges); + input_array_arg!(dx); + input_array_arg!(dy); + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_setTemplate_const__InputArrayR_const__InputArrayR_const__InputArrayR_Point(self.as_raw_mut_GeneralizedHough(), edges.as_raw__InputArray(), dx.as_raw__InputArray(), dy.as_raw__InputArray(), &templ_center, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [GeneralizedHoughTrait::set_template] function uses the following default values for its arguments: + /// * templ_center: Point(-1,-1) + #[inline] + fn set_template_def_1(&mut self, edges: &impl ToInputArray, dx: &impl ToInputArray, dy: &impl ToInputArray) -> Result<()> { + input_array_arg!(edges); + input_array_arg!(dx); + input_array_arg!(dy); + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_setTemplate_const__InputArrayR_const__InputArrayR_const__InputArrayR(self.as_raw_mut_GeneralizedHough(), edges.as_raw__InputArray(), dx.as_raw__InputArray(), dy.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// find template on image + /// + /// ## C++ default parameters + /// * votes: noArray() + #[inline] + fn detect(&mut self, image: &impl ToInputArray, positions: &mut impl ToOutputArray, votes: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(image); + output_array_arg!(positions); + output_array_arg!(votes); + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_detect_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(self.as_raw_mut_GeneralizedHough(), image.as_raw__InputArray(), positions.as_raw__OutputArray(), votes.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// find template on image + /// + /// ## Note + /// This alternative version of [GeneralizedHoughTrait::detect] function uses the following default values for its arguments: + /// * votes: noArray() + #[inline] + fn detect_def(&mut self, image: &impl ToInputArray, positions: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(image); + output_array_arg!(positions); + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_detect_const__InputArrayR_const__OutputArrayR(self.as_raw_mut_GeneralizedHough(), image.as_raw__InputArray(), positions.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## C++ default parameters + /// * votes: noArray() + #[inline] + fn detect_with_edges(&mut self, edges: &impl ToInputArray, dx: &impl ToInputArray, dy: &impl ToInputArray, positions: &mut impl ToOutputArray, votes: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(edges); + input_array_arg!(dx); + input_array_arg!(dy); + output_array_arg!(positions); + output_array_arg!(votes); + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_detect_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(self.as_raw_mut_GeneralizedHough(), edges.as_raw__InputArray(), dx.as_raw__InputArray(), dy.as_raw__InputArray(), positions.as_raw__OutputArray(), votes.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// ## Note + /// This alternative version of [GeneralizedHoughTrait::detect_with_edges] function uses the following default values for its arguments: + /// * votes: noArray() + #[inline] + fn detect_with_edges_def(&mut self, edges: &impl ToInputArray, dx: &impl ToInputArray, dy: &impl ToInputArray, positions: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(edges); + input_array_arg!(dx); + input_array_arg!(dy); + output_array_arg!(positions); + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_detect_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(self.as_raw_mut_GeneralizedHough(), edges.as_raw__InputArray(), dx.as_raw__InputArray(), dy.as_raw__InputArray(), positions.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Canny low threshold. + #[inline] + fn set_canny_low_thresh(&mut self, canny_low_thresh: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_setCannyLowThresh_int(self.as_raw_mut_GeneralizedHough(), canny_low_thresh, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Canny high threshold. + #[inline] + fn set_canny_high_thresh(&mut self, canny_high_thresh: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_setCannyHighThresh_int(self.as_raw_mut_GeneralizedHough(), canny_high_thresh, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Minimum distance between the centers of the detected objects. + #[inline] + fn set_min_dist(&mut self, min_dist: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_setMinDist_double(self.as_raw_mut_GeneralizedHough(), min_dist, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Inverse ratio of the accumulator resolution to the image resolution. + #[inline] + fn set_dp(&mut self, dp: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_setDp_double(self.as_raw_mut_GeneralizedHough(), dp, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Maximal size of inner buffers. + #[inline] + fn set_max_buffer_size(&mut self, max_buffer_size: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHough_setMaxBufferSize_int(self.as_raw_mut_GeneralizedHough(), max_buffer_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for GeneralizedHough { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("GeneralizedHough") + .finish() + } + } + + boxed_cast_base! { GeneralizedHough, core::Algorithm, cv_GeneralizedHough_to_Algorithm } + + boxed_cast_descendant! { GeneralizedHough, crate::imgproc::GeneralizedHoughBallard, cv_GeneralizedHough_to_GeneralizedHoughBallard } + + boxed_cast_descendant! { GeneralizedHough, crate::imgproc::GeneralizedHoughGuil, cv_GeneralizedHough_to_GeneralizedHoughGuil } + + impl core::AlgorithmTraitConst for GeneralizedHough { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for GeneralizedHough { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeneralizedHough, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::imgproc::GeneralizedHoughTraitConst for GeneralizedHough { + #[inline] fn as_raw_GeneralizedHough(&self) -> *const c_void { self.as_raw() } + } + + impl crate::imgproc::GeneralizedHoughTrait for GeneralizedHough { + #[inline] fn as_raw_mut_GeneralizedHough(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeneralizedHough, crate::imgproc::GeneralizedHoughTraitConst, as_raw_GeneralizedHough, crate::imgproc::GeneralizedHoughTrait, as_raw_mut_GeneralizedHough } + + /// finds arbitrary template in the grayscale image using Generalized Hough Transform + /// + /// Detects position only without translation and rotation [Ballard1981](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Ballard1981) . + pub struct GeneralizedHoughBallard { + ptr: *mut c_void, + } + + opencv_type_boxed! { GeneralizedHoughBallard } + + impl Drop for GeneralizedHoughBallard { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_GeneralizedHoughBallard_delete(self.as_raw_mut_GeneralizedHoughBallard()) }; + } + } + + unsafe impl Send for GeneralizedHoughBallard {} + + /// Constant methods for [crate::imgproc::GeneralizedHoughBallard] + pub trait GeneralizedHoughBallardTraitConst: crate::imgproc::GeneralizedHoughTraitConst { + fn as_raw_GeneralizedHoughBallard(&self) -> *const c_void; + + #[inline] + fn get_levels(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughBallard_getLevels_const(self.as_raw_GeneralizedHoughBallard(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_votes_threshold(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughBallard_getVotesThreshold_const(self.as_raw_GeneralizedHoughBallard(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::imgproc::GeneralizedHoughBallard] + pub trait GeneralizedHoughBallardTrait: crate::imgproc::GeneralizedHoughBallardTraitConst + crate::imgproc::GeneralizedHoughTrait { + fn as_raw_mut_GeneralizedHoughBallard(&mut self) -> *mut c_void; + + /// R-Table levels. + #[inline] + fn set_levels(&mut self, levels: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughBallard_setLevels_int(self.as_raw_mut_GeneralizedHoughBallard(), levels, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected. + #[inline] + fn set_votes_threshold(&mut self, votes_threshold: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughBallard_setVotesThreshold_int(self.as_raw_mut_GeneralizedHoughBallard(), votes_threshold, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for GeneralizedHoughBallard { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("GeneralizedHoughBallard") + .finish() + } + } + + boxed_cast_base! { GeneralizedHoughBallard, core::Algorithm, cv_GeneralizedHoughBallard_to_Algorithm } + + boxed_cast_base! { GeneralizedHoughBallard, crate::imgproc::GeneralizedHough, cv_GeneralizedHoughBallard_to_GeneralizedHough } + + impl core::AlgorithmTraitConst for GeneralizedHoughBallard { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for GeneralizedHoughBallard { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeneralizedHoughBallard, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::imgproc::GeneralizedHoughTraitConst for GeneralizedHoughBallard { + #[inline] fn as_raw_GeneralizedHough(&self) -> *const c_void { self.as_raw() } + } + + impl crate::imgproc::GeneralizedHoughTrait for GeneralizedHoughBallard { + #[inline] fn as_raw_mut_GeneralizedHough(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeneralizedHoughBallard, crate::imgproc::GeneralizedHoughTraitConst, as_raw_GeneralizedHough, crate::imgproc::GeneralizedHoughTrait, as_raw_mut_GeneralizedHough } + + impl crate::imgproc::GeneralizedHoughBallardTraitConst for GeneralizedHoughBallard { + #[inline] fn as_raw_GeneralizedHoughBallard(&self) -> *const c_void { self.as_raw() } + } + + impl crate::imgproc::GeneralizedHoughBallardTrait for GeneralizedHoughBallard { + #[inline] fn as_raw_mut_GeneralizedHoughBallard(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeneralizedHoughBallard, crate::imgproc::GeneralizedHoughBallardTraitConst, as_raw_GeneralizedHoughBallard, crate::imgproc::GeneralizedHoughBallardTrait, as_raw_mut_GeneralizedHoughBallard } + + /// finds arbitrary template in the grayscale image using Generalized Hough Transform + /// + /// Detects position, translation and rotation [Guil1999](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Guil1999) . + pub struct GeneralizedHoughGuil { + ptr: *mut c_void, + } + + opencv_type_boxed! { GeneralizedHoughGuil } + + impl Drop for GeneralizedHoughGuil { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_GeneralizedHoughGuil_delete(self.as_raw_mut_GeneralizedHoughGuil()) }; + } + } + + unsafe impl Send for GeneralizedHoughGuil {} + + /// Constant methods for [crate::imgproc::GeneralizedHoughGuil] + pub trait GeneralizedHoughGuilTraitConst: crate::imgproc::GeneralizedHoughTraitConst { + fn as_raw_GeneralizedHoughGuil(&self) -> *const c_void; + + #[inline] + fn get_xi(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_getXi_const(self.as_raw_GeneralizedHoughGuil(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_levels(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_getLevels_const(self.as_raw_GeneralizedHoughGuil(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_angle_epsilon(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_getAngleEpsilon_const(self.as_raw_GeneralizedHoughGuil(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_min_angle(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_getMinAngle_const(self.as_raw_GeneralizedHoughGuil(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_max_angle(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_getMaxAngle_const(self.as_raw_GeneralizedHoughGuil(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_angle_step(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_getAngleStep_const(self.as_raw_GeneralizedHoughGuil(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_angle_thresh(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_getAngleThresh_const(self.as_raw_GeneralizedHoughGuil(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_min_scale(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_getMinScale_const(self.as_raw_GeneralizedHoughGuil(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_max_scale(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_getMaxScale_const(self.as_raw_GeneralizedHoughGuil(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_scale_step(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_getScaleStep_const(self.as_raw_GeneralizedHoughGuil(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_scale_thresh(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_getScaleThresh_const(self.as_raw_GeneralizedHoughGuil(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn get_pos_thresh(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_getPosThresh_const(self.as_raw_GeneralizedHoughGuil(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::imgproc::GeneralizedHoughGuil] + pub trait GeneralizedHoughGuilTrait: crate::imgproc::GeneralizedHoughGuilTraitConst + crate::imgproc::GeneralizedHoughTrait { + fn as_raw_mut_GeneralizedHoughGuil(&mut self) -> *mut c_void; + + /// Angle difference in degrees between two points in feature. + #[inline] + fn set_xi(&mut self, xi: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_setXi_double(self.as_raw_mut_GeneralizedHoughGuil(), xi, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Feature table levels. + #[inline] + fn set_levels(&mut self, levels: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_setLevels_int(self.as_raw_mut_GeneralizedHoughGuil(), levels, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Maximal difference between angles that treated as equal. + #[inline] + fn set_angle_epsilon(&mut self, angle_epsilon: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_setAngleEpsilon_double(self.as_raw_mut_GeneralizedHoughGuil(), angle_epsilon, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Minimal rotation angle to detect in degrees. + #[inline] + fn set_min_angle(&mut self, min_angle: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_setMinAngle_double(self.as_raw_mut_GeneralizedHoughGuil(), min_angle, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Maximal rotation angle to detect in degrees. + #[inline] + fn set_max_angle(&mut self, max_angle: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_setMaxAngle_double(self.as_raw_mut_GeneralizedHoughGuil(), max_angle, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Angle step in degrees. + #[inline] + fn set_angle_step(&mut self, angle_step: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_setAngleStep_double(self.as_raw_mut_GeneralizedHoughGuil(), angle_step, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Angle votes threshold. + #[inline] + fn set_angle_thresh(&mut self, angle_thresh: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_setAngleThresh_int(self.as_raw_mut_GeneralizedHoughGuil(), angle_thresh, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Minimal scale to detect. + #[inline] + fn set_min_scale(&mut self, min_scale: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_setMinScale_double(self.as_raw_mut_GeneralizedHoughGuil(), min_scale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Maximal scale to detect. + #[inline] + fn set_max_scale(&mut self, max_scale: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_setMaxScale_double(self.as_raw_mut_GeneralizedHoughGuil(), max_scale, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Scale step. + #[inline] + fn set_scale_step(&mut self, scale_step: f64) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_setScaleStep_double(self.as_raw_mut_GeneralizedHoughGuil(), scale_step, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Scale votes threshold. + #[inline] + fn set_scale_thresh(&mut self, scale_thresh: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_setScaleThresh_int(self.as_raw_mut_GeneralizedHoughGuil(), scale_thresh, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Position votes threshold. + #[inline] + fn set_pos_thresh(&mut self, pos_thresh: i32) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_GeneralizedHoughGuil_setPosThresh_int(self.as_raw_mut_GeneralizedHoughGuil(), pos_thresh, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for GeneralizedHoughGuil { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("GeneralizedHoughGuil") + .finish() + } + } + + boxed_cast_base! { GeneralizedHoughGuil, core::Algorithm, cv_GeneralizedHoughGuil_to_Algorithm } + + boxed_cast_base! { GeneralizedHoughGuil, crate::imgproc::GeneralizedHough, cv_GeneralizedHoughGuil_to_GeneralizedHough } + + impl core::AlgorithmTraitConst for GeneralizedHoughGuil { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for GeneralizedHoughGuil { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeneralizedHoughGuil, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::imgproc::GeneralizedHoughTraitConst for GeneralizedHoughGuil { + #[inline] fn as_raw_GeneralizedHough(&self) -> *const c_void { self.as_raw() } + } + + impl crate::imgproc::GeneralizedHoughTrait for GeneralizedHoughGuil { + #[inline] fn as_raw_mut_GeneralizedHough(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeneralizedHoughGuil, crate::imgproc::GeneralizedHoughTraitConst, as_raw_GeneralizedHough, crate::imgproc::GeneralizedHoughTrait, as_raw_mut_GeneralizedHough } + + impl crate::imgproc::GeneralizedHoughGuilTraitConst for GeneralizedHoughGuil { + #[inline] fn as_raw_GeneralizedHoughGuil(&self) -> *const c_void { self.as_raw() } + } + + impl crate::imgproc::GeneralizedHoughGuilTrait for GeneralizedHoughGuil { + #[inline] fn as_raw_mut_GeneralizedHoughGuil(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { GeneralizedHoughGuil, crate::imgproc::GeneralizedHoughGuilTraitConst, as_raw_GeneralizedHoughGuil, crate::imgproc::GeneralizedHoughGuilTrait, as_raw_mut_GeneralizedHoughGuil } + + /// Class for iterating over all pixels on a raster line segment. + /// + /// The class LineIterator is used to get each pixel of a raster line connecting + /// two specified points. + /// It can be treated as a versatile implementation of the Bresenham algorithm + /// where you can stop at each pixel and do some extra processing, for + /// example, grab pixel values along the line or draw a line with an effect + /// (for example, with XOR operation). + /// + /// The number of pixels along the line is stored in LineIterator::count. + /// The method LineIterator::pos returns the current position in the image: + /// + /// ```C++ + /// // grabs pixels along the line (pt1, pt2) + /// // from 8-bit 3-channel image to the buffer + /// LineIterator it(img, pt1, pt2, 8); + /// LineIterator it2 = it; + /// vector buf(it.count); + /// + /// for(int i = 0; i < it.count; i++, ++it) + /// buf[i] = *(const Vec3b*)*it; + /// + /// // alternative way of iterating through the line + /// for(int i = 0; i < it2.count; i++, ++it2) + /// { + /// Vec3b val = img.at(it2.pos()); + /// CV_Assert(buf[i] == val); + /// } + /// ``` + /// + pub struct LineIterator { + ptr: *mut c_void, + } + + opencv_type_boxed! { LineIterator } + + impl Drop for LineIterator { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_LineIterator_delete(self.as_raw_mut_LineIterator()) }; + } + } + + unsafe impl Send for LineIterator {} + + impl LineIterator { + /// Initializes iterator object for the given line and image. + /// + /// The returned iterator can be used to traverse all pixels on a line that + /// connects the given two points. + /// The line will be clipped on the image boundaries. + /// + /// ## Parameters + /// * img: Underlying image. + /// * pt1: First endpoint of the line. + /// * pt2: The other endpoint of the line. + /// * connectivity: Pixel connectivity of the iterator. Valid values are 4 (iterator can move + /// up, down, left and right) and 8 (iterator can also move diagonally). + /// * leftToRight: If true, the line is traversed from the leftmost endpoint to the rightmost + /// endpoint. Otherwise, the line is traversed from \p pt1 to \p pt2. + /// + /// ## C++ default parameters + /// * connectivity: 8 + /// * left_to_right: false + #[inline] + pub fn new(img: &impl core::MatTraitConst, pt1: core::Point, pt2: core::Point, connectivity: i32, left_to_right: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LineIterator_LineIterator_const_MatR_Point_Point_int_bool(img.as_raw_Mat(), &pt1, &pt2, connectivity, left_to_right, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::LineIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Initializes iterator object for the given line and image. + /// + /// The returned iterator can be used to traverse all pixels on a line that + /// connects the given two points. + /// The line will be clipped on the image boundaries. + /// + /// ## Parameters + /// * img: Underlying image. + /// * pt1: First endpoint of the line. + /// * pt2: The other endpoint of the line. + /// * connectivity: Pixel connectivity of the iterator. Valid values are 4 (iterator can move + /// up, down, left and right) and 8 (iterator can also move diagonally). + /// * leftToRight: If true, the line is traversed from the leftmost endpoint to the rightmost + /// endpoint. Otherwise, the line is traversed from \p pt1 to \p pt2. + /// + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * connectivity: 8 + /// * left_to_right: false + #[inline] + pub fn new_def(img: &impl core::MatTraitConst, pt1: core::Point, pt2: core::Point) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LineIterator_LineIterator_const_MatR_Point_Point(img.as_raw_Mat(), &pt1, &pt2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::LineIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * connectivity: 8 + /// * left_to_right: false + #[inline] + pub fn new_1(pt1: core::Point, pt2: core::Point, connectivity: i32, left_to_right: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LineIterator_LineIterator_Point_Point_int_bool(&pt1, &pt2, connectivity, left_to_right, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::LineIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * connectivity: 8 + /// * left_to_right: false + #[inline] + pub fn new_def_1(pt1: core::Point, pt2: core::Point) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LineIterator_LineIterator_Point_Point(&pt1, &pt2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::LineIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * connectivity: 8 + /// * left_to_right: false + #[inline] + pub fn new_2(bounding_area_size: core::Size, pt1: core::Point, pt2: core::Point, connectivity: i32, left_to_right: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LineIterator_LineIterator_Size_Point_Point_int_bool(&bounding_area_size, &pt1, &pt2, connectivity, left_to_right, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::LineIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * connectivity: 8 + /// * left_to_right: false + #[inline] + pub fn new_def_2(bounding_area_size: core::Size, pt1: core::Point, pt2: core::Point) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LineIterator_LineIterator_Size_Point_Point(&bounding_area_size, &pt1, &pt2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::LineIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## C++ default parameters + /// * connectivity: 8 + /// * left_to_right: false + #[inline] + pub fn new_3(bounding_area_rect: core::Rect, pt1: core::Point, pt2: core::Point, connectivity: i32, left_to_right: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LineIterator_LineIterator_Rect_Point_Point_int_bool(&bounding_area_rect, &pt1, &pt2, connectivity, left_to_right, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::LineIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * connectivity: 8 + /// * left_to_right: false + #[inline] + pub fn new_def_3(bounding_area_rect: core::Rect, pt1: core::Point, pt2: core::Point) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LineIterator_LineIterator_Rect_Point_Point(&bounding_area_rect, &pt1, &pt2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::LineIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::imgproc::LineIterator] + pub trait LineIteratorTraitConst { + fn as_raw_LineIterator(&self) -> *const c_void; + + #[inline] + fn ptr(&self) -> *const u8 { + let ret = unsafe { sys::cv_LineIterator_propPtr_const(self.as_raw_LineIterator()) }; + ret + } + + #[inline] + fn ptr0(&self) -> *const u8 { + let ret = unsafe { sys::cv_LineIterator_propPtr0_const(self.as_raw_LineIterator()) }; + ret + } + + #[inline] + fn step(&self) -> i32 { + let ret = unsafe { sys::cv_LineIterator_propStep_const(self.as_raw_LineIterator()) }; + ret + } + + #[inline] + fn elem_size(&self) -> i32 { + let ret = unsafe { sys::cv_LineIterator_propElemSize_const(self.as_raw_LineIterator()) }; + ret + } + + #[inline] + fn err(&self) -> i32 { + let ret = unsafe { sys::cv_LineIterator_propErr_const(self.as_raw_LineIterator()) }; + ret + } + + #[inline] + fn count(&self) -> i32 { + let ret = unsafe { sys::cv_LineIterator_propCount_const(self.as_raw_LineIterator()) }; + ret + } + + #[inline] + fn minus_delta(&self) -> i32 { + let ret = unsafe { sys::cv_LineIterator_propMinusDelta_const(self.as_raw_LineIterator()) }; + ret + } + + #[inline] + fn plus_delta(&self) -> i32 { + let ret = unsafe { sys::cv_LineIterator_propPlusDelta_const(self.as_raw_LineIterator()) }; + ret + } + + #[inline] + fn minus_step(&self) -> i32 { + let ret = unsafe { sys::cv_LineIterator_propMinusStep_const(self.as_raw_LineIterator()) }; + ret + } + + #[inline] + fn plus_step(&self) -> i32 { + let ret = unsafe { sys::cv_LineIterator_propPlusStep_const(self.as_raw_LineIterator()) }; + ret + } + + #[inline] + fn minus_shift(&self) -> i32 { + let ret = unsafe { sys::cv_LineIterator_propMinusShift_const(self.as_raw_LineIterator()) }; + ret + } + + #[inline] + fn plus_shift(&self) -> i32 { + let ret = unsafe { sys::cv_LineIterator_propPlusShift_const(self.as_raw_LineIterator()) }; + ret + } + + #[inline] + fn p(&self) -> core::Point { + return_send!(via ocvrs_return); + unsafe { sys::cv_LineIterator_propP_const(self.as_raw_LineIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + ret + } + + #[inline] + fn ptmode(&self) -> bool { + let ret = unsafe { sys::cv_LineIterator_propPtmode_const(self.as_raw_LineIterator()) }; + ret + } + + /// Returns coordinates of the current pixel. + #[inline] + fn pos(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LineIterator_pos_const(self.as_raw_LineIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::imgproc::LineIterator] + pub trait LineIteratorTrait: crate::imgproc::LineIteratorTraitConst { + fn as_raw_mut_LineIterator(&mut self) -> *mut c_void; + + #[inline] + fn ptr_mut(&mut self) -> *mut u8 { + let ret = unsafe { sys::cv_LineIterator_propPtr(self.as_raw_mut_LineIterator()) }; + ret + } + + #[inline] + unsafe fn set_ptr(&mut self, val: *const u8) { + let ret = unsafe { sys::cv_LineIterator_propPtr_unsigned_charX(self.as_raw_mut_LineIterator(), val) }; + ret + } + + #[inline] + fn set_step(&mut self, val: i32) { + let ret = unsafe { sys::cv_LineIterator_propStep_const_int(self.as_raw_mut_LineIterator(), val) }; + ret + } + + #[inline] + fn set_elem_size(&mut self, val: i32) { + let ret = unsafe { sys::cv_LineIterator_propElemSize_const_int(self.as_raw_mut_LineIterator(), val) }; + ret + } + + #[inline] + fn set_err(&mut self, val: i32) { + let ret = unsafe { sys::cv_LineIterator_propErr_const_int(self.as_raw_mut_LineIterator(), val) }; + ret + } + + #[inline] + fn set_count(&mut self, val: i32) { + let ret = unsafe { sys::cv_LineIterator_propCount_const_int(self.as_raw_mut_LineIterator(), val) }; + ret + } + + #[inline] + fn set_minus_delta(&mut self, val: i32) { + let ret = unsafe { sys::cv_LineIterator_propMinusDelta_const_int(self.as_raw_mut_LineIterator(), val) }; + ret + } + + #[inline] + fn set_plus_delta(&mut self, val: i32) { + let ret = unsafe { sys::cv_LineIterator_propPlusDelta_const_int(self.as_raw_mut_LineIterator(), val) }; + ret + } + + #[inline] + fn set_minus_step(&mut self, val: i32) { + let ret = unsafe { sys::cv_LineIterator_propMinusStep_const_int(self.as_raw_mut_LineIterator(), val) }; + ret + } + + #[inline] + fn set_plus_step(&mut self, val: i32) { + let ret = unsafe { sys::cv_LineIterator_propPlusStep_const_int(self.as_raw_mut_LineIterator(), val) }; + ret + } + + #[inline] + fn set_minus_shift(&mut self, val: i32) { + let ret = unsafe { sys::cv_LineIterator_propMinusShift_const_int(self.as_raw_mut_LineIterator(), val) }; + ret + } + + #[inline] + fn set_plus_shift(&mut self, val: i32) { + let ret = unsafe { sys::cv_LineIterator_propPlusShift_const_int(self.as_raw_mut_LineIterator(), val) }; + ret + } + + #[inline] + fn set_p(&mut self, val: core::Point) { + let ret = unsafe { sys::cv_LineIterator_propP_const_Point(self.as_raw_mut_LineIterator(), &val) }; + ret + } + + #[inline] + fn set_ptmode(&mut self, val: bool) { + let ret = unsafe { sys::cv_LineIterator_propPtmode_const_bool(self.as_raw_mut_LineIterator(), val) }; + ret + } + + #[inline] + fn init(&mut self, img: &impl core::MatTraitConst, bounding_area_rect: core::Rect, pt1: core::Point, pt2: core::Point, connectivity: i32, left_to_right: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_LineIterator_init_const_MatX_Rect_Point_Point_int_bool(self.as_raw_mut_LineIterator(), img.as_raw_Mat(), &bounding_area_rect, &pt1, &pt2, connectivity, left_to_right, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns pointer to the current pixel. + #[inline] + fn try_deref_mut(&mut self) -> Result<*mut u8> { + return_send!(via ocvrs_return); + unsafe { sys::cv_LineIterator_operatorX(self.as_raw_mut_LineIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Moves iterator to the next pixel on the line. + /// + /// This is the prefix version (++it). + #[inline] + fn incr(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_LineIterator_operatorAA(self.as_raw_mut_LineIterator(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::LineIterator::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl std::fmt::Debug for LineIterator { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("LineIterator") + .field("ptr", &crate::imgproc::LineIteratorTraitConst::ptr(self)) + .field("ptr0", &crate::imgproc::LineIteratorTraitConst::ptr0(self)) + .field("step", &crate::imgproc::LineIteratorTraitConst::step(self)) + .field("elem_size", &crate::imgproc::LineIteratorTraitConst::elem_size(self)) + .field("err", &crate::imgproc::LineIteratorTraitConst::err(self)) + .field("count", &crate::imgproc::LineIteratorTraitConst::count(self)) + .field("minus_delta", &crate::imgproc::LineIteratorTraitConst::minus_delta(self)) + .field("plus_delta", &crate::imgproc::LineIteratorTraitConst::plus_delta(self)) + .field("minus_step", &crate::imgproc::LineIteratorTraitConst::minus_step(self)) + .field("plus_step", &crate::imgproc::LineIteratorTraitConst::plus_step(self)) + .field("minus_shift", &crate::imgproc::LineIteratorTraitConst::minus_shift(self)) + .field("plus_shift", &crate::imgproc::LineIteratorTraitConst::plus_shift(self)) + .field("p", &crate::imgproc::LineIteratorTraitConst::p(self)) + .field("ptmode", &crate::imgproc::LineIteratorTraitConst::ptmode(self)) + .finish() + } + } + + impl crate::imgproc::LineIteratorTraitConst for LineIterator { + #[inline] fn as_raw_LineIterator(&self) -> *const c_void { self.as_raw() } + } + + impl crate::imgproc::LineIteratorTrait for LineIterator { + #[inline] fn as_raw_mut_LineIterator(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LineIterator, crate::imgproc::LineIteratorTraitConst, as_raw_LineIterator, crate::imgproc::LineIteratorTrait, as_raw_mut_LineIterator } + + /// Line segment detector class + /// + /// following the algorithm described at [Rafael12](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Rafael12) . + /// + /// + /// Note: Implementation has been removed from OpenCV version 3.4.6 to 3.4.15 and version 4.1.0 to 4.5.3 due original code license conflict. + /// restored again after [Computation of a NFA](https://github.com/rafael-grompone-von-gioi/binomial_nfa) code published under the MIT license. + pub struct LineSegmentDetector { + ptr: *mut c_void, + } + + opencv_type_boxed! { LineSegmentDetector } + + impl Drop for LineSegmentDetector { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_LineSegmentDetector_delete(self.as_raw_mut_LineSegmentDetector()) }; + } + } + + unsafe impl Send for LineSegmentDetector {} + + /// Constant methods for [crate::imgproc::LineSegmentDetector] + pub trait LineSegmentDetectorTraitConst: core::AlgorithmTraitConst { + fn as_raw_LineSegmentDetector(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::imgproc::LineSegmentDetector] + pub trait LineSegmentDetectorTrait: core::AlgorithmTrait + crate::imgproc::LineSegmentDetectorTraitConst { + fn as_raw_mut_LineSegmentDetector(&mut self) -> *mut c_void; + + /// Finds lines in the input image. + /// + /// This is the output of the default parameters of the algorithm on the above shown image. + /// + /// ![image](https://docs.opencv.org/4.13.0/building_lsd.png) + /// + /// ## Parameters + /// * image: A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use: + /// `lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);` + /// * lines: A vector of Vec4f elements specifying the beginning and ending point of a line. Where + /// Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly + /// oriented depending on the gradient. + /// * width: Vector of widths of the regions, where the lines are found. E.g. Width of line. + /// * prec: Vector of precisions with which the lines are found. + /// * nfa: Vector containing number of false alarms in the line region, with precision of 10%. The + /// bigger the value, logarithmically better the detection. + /// - -1 corresponds to 10 mean false alarms + /// - 0 corresponds to 1 mean false alarm + /// - 1 corresponds to 0.1 mean false alarms + /// This vector will be calculated only when the objects type is #LSD_REFINE_ADV. + /// + /// ## C++ default parameters + /// * width: noArray() + /// * prec: noArray() + /// * nfa: noArray() + #[inline] + fn detect(&mut self, image: &impl ToInputArray, lines: &mut impl ToOutputArray, width: &mut impl ToOutputArray, prec: &mut impl ToOutputArray, nfa: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(image); + output_array_arg!(lines); + output_array_arg!(width); + output_array_arg!(prec); + output_array_arg!(nfa); + return_send!(via ocvrs_return); + unsafe { sys::cv_LineSegmentDetector_detect_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(self.as_raw_mut_LineSegmentDetector(), image.as_raw__InputArray(), lines.as_raw__OutputArray(), width.as_raw__OutputArray(), prec.as_raw__OutputArray(), nfa.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds lines in the input image. + /// + /// This is the output of the default parameters of the algorithm on the above shown image. + /// + /// ![image](https://docs.opencv.org/4.13.0/building_lsd.png) + /// + /// ## Parameters + /// * image: A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use: + /// `lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);` + /// * lines: A vector of Vec4f elements specifying the beginning and ending point of a line. Where + /// Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly + /// oriented depending on the gradient. + /// * width: Vector of widths of the regions, where the lines are found. E.g. Width of line. + /// * prec: Vector of precisions with which the lines are found. + /// * nfa: Vector containing number of false alarms in the line region, with precision of 10%. The + /// bigger the value, logarithmically better the detection. + /// - -1 corresponds to 10 mean false alarms + /// - 0 corresponds to 1 mean false alarm + /// - 1 corresponds to 0.1 mean false alarms + /// This vector will be calculated only when the objects type is #LSD_REFINE_ADV. + /// + /// ## Note + /// This alternative version of [LineSegmentDetectorTrait::detect] function uses the following default values for its arguments: + /// * width: noArray() + /// * prec: noArray() + /// * nfa: noArray() + #[inline] + fn detect_def(&mut self, image: &impl ToInputArray, lines: &mut impl ToOutputArray) -> Result<()> { + input_array_arg!(image); + output_array_arg!(lines); + return_send!(via ocvrs_return); + unsafe { sys::cv_LineSegmentDetector_detect_const__InputArrayR_const__OutputArrayR(self.as_raw_mut_LineSegmentDetector(), image.as_raw__InputArray(), lines.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws the line segments on a given image. + /// ## Parameters + /// * image: The image, where the lines will be drawn. Should be bigger or equal to the image, + /// where the lines were found. + /// * lines: A vector of the lines that needed to be drawn. + #[inline] + fn draw_segments(&mut self, image: &mut impl ToInputOutputArray, lines: &impl ToInputArray) -> Result<()> { + input_output_array_arg!(image); + input_array_arg!(lines); + return_send!(via ocvrs_return); + unsafe { sys::cv_LineSegmentDetector_drawSegments_const__InputOutputArrayR_const__InputArrayR(self.as_raw_mut_LineSegmentDetector(), image.as_raw__InputOutputArray(), lines.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels. + /// + /// ## Parameters + /// * size: The size of the image, where lines1 and lines2 were found. + /// * lines1: The first group of lines that needs to be drawn. It is visualized in blue color. + /// * lines2: The second group of lines. They visualized in red color. + /// * image: Optional image, where the lines will be drawn. The image should be color(3-channel) + /// in order for lines1 and lines2 to be drawn in the above mentioned colors. + /// + /// ## C++ default parameters + /// * image: noArray() + #[inline] + fn compare_segments(&mut self, size: core::Size, lines1: &impl ToInputArray, lines2: &impl ToInputArray, image: &mut impl ToInputOutputArray) -> Result { + input_array_arg!(lines1); + input_array_arg!(lines2); + input_output_array_arg!(image); + return_send!(via ocvrs_return); + unsafe { sys::cv_LineSegmentDetector_compareSegments_const_SizeR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR(self.as_raw_mut_LineSegmentDetector(), &size, lines1.as_raw__InputArray(), lines2.as_raw__InputArray(), image.as_raw__InputOutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels. + /// + /// ## Parameters + /// * size: The size of the image, where lines1 and lines2 were found. + /// * lines1: The first group of lines that needs to be drawn. It is visualized in blue color. + /// * lines2: The second group of lines. They visualized in red color. + /// * image: Optional image, where the lines will be drawn. The image should be color(3-channel) + /// in order for lines1 and lines2 to be drawn in the above mentioned colors. + /// + /// ## Note + /// This alternative version of [LineSegmentDetectorTrait::compare_segments] function uses the following default values for its arguments: + /// * image: noArray() + #[inline] + fn compare_segments_def(&mut self, size: core::Size, lines1: &impl ToInputArray, lines2: &impl ToInputArray) -> Result { + input_array_arg!(lines1); + input_array_arg!(lines2); + return_send!(via ocvrs_return); + unsafe { sys::cv_LineSegmentDetector_compareSegments_const_SizeR_const__InputArrayR_const__InputArrayR(self.as_raw_mut_LineSegmentDetector(), &size, lines1.as_raw__InputArray(), lines2.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for LineSegmentDetector { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("LineSegmentDetector") + .finish() + } + } + + boxed_cast_base! { LineSegmentDetector, core::Algorithm, cv_LineSegmentDetector_to_Algorithm } + + impl core::AlgorithmTraitConst for LineSegmentDetector { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } + } + + impl core::AlgorithmTrait for LineSegmentDetector { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LineSegmentDetector, core::AlgorithmTraitConst, as_raw_Algorithm, core::AlgorithmTrait, as_raw_mut_Algorithm } + + impl crate::imgproc::LineSegmentDetectorTraitConst for LineSegmentDetector { + #[inline] fn as_raw_LineSegmentDetector(&self) -> *const c_void { self.as_raw() } + } + + impl crate::imgproc::LineSegmentDetectorTrait for LineSegmentDetector { + #[inline] fn as_raw_mut_LineSegmentDetector(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { LineSegmentDetector, crate::imgproc::LineSegmentDetectorTraitConst, as_raw_LineSegmentDetector, crate::imgproc::LineSegmentDetectorTrait, as_raw_mut_LineSegmentDetector } + + pub struct Subdiv2D { + ptr: *mut c_void, + } + + opencv_type_boxed! { Subdiv2D } + + impl Drop for Subdiv2D { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_Subdiv2D_delete(self.as_raw_mut_Subdiv2D()) }; + } + } + + unsafe impl Send for Subdiv2D {} + + impl Subdiv2D { + /// creates an empty Subdiv2D object. + /// To create a new empty Delaunay subdivision you need to use the [init_delaunay] function. + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_Subdiv2D(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::Subdiv2D::opencv_from_extern(ret) }; + Ok(ret) + } + + /// creates an empty Subdiv2D object. + /// To create a new empty Delaunay subdivision you need to use the [init_delaunay] function. + /// + /// ## Overloaded parameters + /// + /// + /// ## Parameters + /// * rect: Rectangle that includes all of the 2D points that are to be added to the subdivision. + /// + /// The function creates an empty Delaunay subdivision where 2D points can be added using the function + /// insert() . All of the points to be added must be within the specified rectangle, otherwise a runtime + /// error is raised. + #[inline] + pub fn new(rect: core::Rect) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_Subdiv2D_Rect(&rect, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::Subdiv2D::opencv_from_extern(ret) }; + Ok(ret) + } + + /// creates an empty Subdiv2D object. + /// To create a new empty Delaunay subdivision you need to use the [init_delaunay] function. + /// + /// ## Overloaded parameters + #[inline] + pub fn new_1(rect2f: core::Rect2f) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_Subdiv2D_Rect2f(&rect2f, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::Subdiv2D::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::imgproc::Subdiv2D] + pub trait Subdiv2DTraitConst { + fn as_raw_Subdiv2D(&self) -> *const c_void; + + /// Returns a list of all edges. + /// + /// ## Parameters + /// * edgeList: Output vector. + /// + /// The function gives each edge as a 4 numbers vector, where each two are one of the edge + /// vertices. i.e. org_x = v[0], org_y = v[1], dst_x = v[2], dst_y = v[3]. + #[inline] + fn get_edge_list(&self, edge_list: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_getEdgeList_const_vectorLVec4fGR(self.as_raw_Subdiv2D(), edge_list.as_raw_mut_VectorOfVec4f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a list of the leading edge ID connected to each triangle. + /// + /// ## Parameters + /// * leadingEdgeList: Output vector. + /// + /// The function gives one edge ID for each triangle. + #[inline] + fn get_leading_edge_list(&self, leading_edge_list: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_getLeadingEdgeList_const_vectorLintGR(self.as_raw_Subdiv2D(), leading_edge_list.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a list of all triangles. + /// + /// ## Parameters + /// * triangleList: Output vector. + /// + /// The function gives each triangle as a 6 numbers vector, where each two are one of the triangle + /// vertices. i.e. p1_x = v[0], p1_y = v[1], p2_x = v[2], p2_y = v[3], p3_x = v[4], p3_y = v[5]. + #[inline] + fn get_triangle_list(&self, triangle_list: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_getTriangleList_const_vectorLVec6fGR(self.as_raw_Subdiv2D(), triangle_list.as_raw_mut_VectorOfVec6f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns vertex location from vertex ID. + /// + /// ## Parameters + /// * vertex: vertex ID. + /// * firstEdge: Optional. The first edge ID which is connected to the vertex. + /// ## Returns + /// vertex (x,y) + /// + /// ## C++ default parameters + /// * first_edge: 0 + #[inline] + fn get_vertex(&self, vertex: i32, first_edge: &mut i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_getVertex_const_int_intX(self.as_raw_Subdiv2D(), vertex, first_edge, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns vertex location from vertex ID. + /// + /// ## Parameters + /// * vertex: vertex ID. + /// * firstEdge: Optional. The first edge ID which is connected to the vertex. + /// ## Returns + /// vertex (x,y) + /// + /// ## Note + /// This alternative version of [Subdiv2DTraitConst::get_vertex] function uses the following default values for its arguments: + /// * first_edge: 0 + #[inline] + fn get_vertex_def(&self, vertex: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_getVertex_const_int(self.as_raw_Subdiv2D(), vertex, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns one of the edges related to the given edge. + /// + /// ## Parameters + /// * edge: Subdivision edge ID. + /// * nextEdgeType: Parameter specifying which of the related edges to return. + /// The following values are possible: + /// * NEXT_AROUND_ORG next around the edge origin ( eOnext on the picture below if e is the input edge) + /// * NEXT_AROUND_DST next around the edge vertex ( eDnext ) + /// * PREV_AROUND_ORG previous around the edge origin (reversed eRnext ) + /// * PREV_AROUND_DST previous around the edge destination (reversed eLnext ) + /// * NEXT_AROUND_LEFT next around the left facet ( eLnext ) + /// * NEXT_AROUND_RIGHT next around the right facet ( eRnext ) + /// * PREV_AROUND_LEFT previous around the left facet (reversed eOnext ) + /// * PREV_AROUND_RIGHT previous around the right facet (reversed eDnext ) + /// + /// ![sample output](https://docs.opencv.org/4.13.0/quadedge.png) + /// + /// ## Returns + /// edge ID related to the input edge. + #[inline] + fn get_edge(&self, edge: i32, next_edge_type: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_getEdge_const_int_int(self.as_raw_Subdiv2D(), edge, next_edge_type, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns next edge around the edge origin. + /// + /// ## Parameters + /// * edge: Subdivision edge ID. + /// + /// ## Returns + /// an integer which is next edge ID around the edge origin: eOnext on the + /// picture above if e is the input edge). + #[inline] + fn next_edge(&self, edge: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_nextEdge_const_int(self.as_raw_Subdiv2D(), edge, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns another edge of the same quad-edge. + /// + /// ## Parameters + /// * edge: Subdivision edge ID. + /// * rotate: Parameter specifying which of the edges of the same quad-edge as the input + /// one to return. The following values are possible: + /// * 0 - the input edge ( e on the picture below if e is the input edge) + /// * 1 - the rotated edge ( eRot ) + /// * 2 - the reversed edge (reversed e (in green)) + /// * 3 - the reversed rotated edge (reversed eRot (in green)) + /// + /// ## Returns + /// one of the edges ID of the same quad-edge as the input edge. + #[inline] + fn rotate_edge(&self, edge: i32, rotate: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_rotateEdge_const_int_int(self.as_raw_Subdiv2D(), edge, rotate, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + #[inline] + fn sym_edge(&self, edge: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_symEdge_const_int(self.as_raw_Subdiv2D(), edge, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the edge origin. + /// + /// ## Parameters + /// * edge: Subdivision edge ID. + /// * orgpt: Output vertex location. + /// + /// ## Returns + /// vertex ID. + /// + /// ## C++ default parameters + /// * orgpt: 0 + #[inline] + fn edge_org(&self, edge: i32, orgpt: &mut core::Point2f) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_edgeOrg_const_int_Point2fX(self.as_raw_Subdiv2D(), edge, orgpt, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the edge origin. + /// + /// ## Parameters + /// * edge: Subdivision edge ID. + /// * orgpt: Output vertex location. + /// + /// ## Returns + /// vertex ID. + /// + /// ## Note + /// This alternative version of [Subdiv2DTraitConst::edge_org] function uses the following default values for its arguments: + /// * orgpt: 0 + #[inline] + fn edge_org_def(&self, edge: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_edgeOrg_const_int(self.as_raw_Subdiv2D(), edge, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the edge destination. + /// + /// ## Parameters + /// * edge: Subdivision edge ID. + /// * dstpt: Output vertex location. + /// + /// ## Returns + /// vertex ID. + /// + /// ## C++ default parameters + /// * dstpt: 0 + #[inline] + fn edge_dst(&self, edge: i32, dstpt: &mut core::Point2f) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_edgeDst_const_int_Point2fX(self.as_raw_Subdiv2D(), edge, dstpt, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the edge destination. + /// + /// ## Parameters + /// * edge: Subdivision edge ID. + /// * dstpt: Output vertex location. + /// + /// ## Returns + /// vertex ID. + /// + /// ## Note + /// This alternative version of [Subdiv2DTraitConst::edge_dst] function uses the following default values for its arguments: + /// * dstpt: 0 + #[inline] + fn edge_dst_def(&self, edge: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_edgeDst_const_int(self.as_raw_Subdiv2D(), edge, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::imgproc::Subdiv2D] + pub trait Subdiv2DTrait: crate::imgproc::Subdiv2DTraitConst { + fn as_raw_mut_Subdiv2D(&mut self) -> *mut c_void; + + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + /// + /// Creates a new empty Delaunay subdivision + /// + /// ## Parameters + /// * rect: Rectangle that includes all of the 2D points that are to be added to the subdivision. + #[inline] + fn init_delaunay(&mut self, rect: core::Rect) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_initDelaunay_Rect(self.as_raw_mut_Subdiv2D(), &rect, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + /// + /// Creates a new empty Delaunay subdivision + /// + /// ## Parameters + /// * rect: Rectangle that includes all of the 2d points that are to be added to the subdivision. + #[inline] + fn init_delaunay2f(&mut self, rect: core::Rect2f) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_initDelaunay_Rect2f(self.as_raw_mut_Subdiv2D(), &rect, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Insert a single point into a Delaunay triangulation. + /// + /// ## Parameters + /// * pt: Point to insert. + /// + /// The function inserts a single point into a subdivision and modifies the subdivision topology + /// appropriately. If a point with the same coordinates exists already, no new point is added. + /// ## Returns + /// the ID of the point. + /// + /// + /// Note: If the point is outside of the triangulation specified rect a runtime error is raised. + #[inline] + fn insert(&mut self, pt: core::Point2f) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_insert_Point2f(self.as_raw_mut_Subdiv2D(), &pt, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Insert multiple points into a Delaunay triangulation. + /// + /// ## Parameters + /// * ptvec: Points to insert. + /// + /// The function inserts a vector of points into a subdivision and modifies the subdivision topology + /// appropriately. + #[inline] + fn insert_multiple(&mut self, ptvec: &core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_insert_const_vectorLPoint2fGR(self.as_raw_mut_Subdiv2D(), ptvec.as_raw_VectorOfPoint2f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the location of a point within a Delaunay triangulation. + /// + /// ## Parameters + /// * pt: Point to locate. + /// * edge: Output edge that the point belongs to or is located to the right of it. + /// * vertex: Optional output vertex the input point coincides with. + /// + /// The function locates the input point within the subdivision and gives one of the triangle edges + /// or vertices. + /// + /// ## Returns + /// an integer which specify one of the following five cases for point location: + /// * The point falls into some facet. The function returns [PTLOC_INSIDE] and edge will contain one of + /// edges of the facet. + /// * The point falls onto the edge. The function returns [PTLOC_ON_EDGE] and edge will contain this edge. + /// * The point coincides with one of the subdivision vertices. The function returns [PTLOC_VERTEX] and + /// vertex will contain a pointer to the vertex. + /// * The point is outside the subdivision reference rectangle. The function returns [PTLOC_OUTSIDE_RECT] + /// and no pointers are filled. + /// * One of input arguments is invalid. A runtime error is raised or, if silent or "parent" error + /// processing mode is selected, [PTLOC_ERROR] is returned. + #[inline] + fn locate(&mut self, pt: core::Point2f, edge: &mut i32, vertex: &mut i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_locate_Point2f_intR_intR(self.as_raw_mut_Subdiv2D(), &pt, edge, vertex, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the subdivision vertex closest to the given point. + /// + /// ## Parameters + /// * pt: Input point. + /// * nearestPt: Output subdivision vertex point. + /// + /// The function is another function that locates the input point within the subdivision. It finds the + /// subdivision vertex that is the closest to the input point. It is not necessarily one of vertices + /// of the facet containing the input point, though the facet (located using locate() ) is used as a + /// starting point. + /// + /// ## Returns + /// vertex ID. + /// + /// ## C++ default parameters + /// * nearest_pt: 0 + #[inline] + fn find_nearest(&mut self, pt: core::Point2f, nearest_pt: &mut core::Point2f) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_findNearest_Point2f_Point2fX(self.as_raw_mut_Subdiv2D(), &pt, nearest_pt, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Finds the subdivision vertex closest to the given point. + /// + /// ## Parameters + /// * pt: Input point. + /// * nearestPt: Output subdivision vertex point. + /// + /// The function is another function that locates the input point within the subdivision. It finds the + /// subdivision vertex that is the closest to the input point. It is not necessarily one of vertices + /// of the facet containing the input point, though the facet (located using locate() ) is used as a + /// starting point. + /// + /// ## Returns + /// vertex ID. + /// + /// ## Note + /// This alternative version of [Subdiv2DTrait::find_nearest] function uses the following default values for its arguments: + /// * nearest_pt: 0 + #[inline] + fn find_nearest_def(&mut self, pt: core::Point2f) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_findNearest_Point2f(self.as_raw_mut_Subdiv2D(), &pt, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns a list of all Voronoi facets. + /// + /// ## Parameters + /// * idx: Vector of vertices IDs to consider. For all vertices you can pass empty vector. + /// * facetList: Output vector of the Voronoi facets. + /// * facetCenters: Output vector of the Voronoi facets center points. + #[inline] + fn get_voronoi_facet_list(&mut self, idx: &core::Vector, facet_list: &mut core::Vector>, facet_centers: &mut core::Vector) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_Subdiv2D_getVoronoiFacetList_const_vectorLintGR_vectorLvectorLPoint2fGGR_vectorLPoint2fGR(self.as_raw_mut_Subdiv2D(), idx.as_raw_VectorOfi32(), facet_list.as_raw_mut_VectorOfVectorOfPoint2f(), facet_centers.as_raw_mut_VectorOfPoint2f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for Subdiv2D { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("Subdiv2D") + .finish() + } + } + + impl crate::imgproc::Subdiv2DTraitConst for Subdiv2D { + #[inline] fn as_raw_Subdiv2D(&self) -> *const c_void { self.as_raw() } + } + + impl crate::imgproc::Subdiv2DTrait for Subdiv2D { + #[inline] fn as_raw_mut_Subdiv2D(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { Subdiv2D, crate::imgproc::Subdiv2DTraitConst, as_raw_Subdiv2D, crate::imgproc::Subdiv2DTrait, as_raw_mut_Subdiv2D } + + /// Intelligent Scissors image segmentation + /// + /// This class is used to find the path (contour) between two points + /// which can be used for image segmentation. + /// + /// Usage example: + /// [usage_example_intelligent_scissors](https://github.com/opencv/opencv/blob/4.13.0/samples/cpp/tutorial_code/snippets/imgproc_segmentation.cpp#L1) + /// + /// Reference: "Intelligent Scissors for Image Composition" + /// algorithm designed by Eric N. Mortensen and William A. Barrett, Brigham Young University + /// [Mortensen95intelligentscissors](https://docs.opencv.org/4.13.0/d0/de3/citelist.html#CITEREF_Mortensen95intelligentscissors) + pub struct IntelligentScissorsMB { + ptr: *mut c_void, + } + + opencv_type_boxed! { IntelligentScissorsMB } + + impl Drop for IntelligentScissorsMB { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_segmentation_IntelligentScissorsMB_delete(self.as_raw_mut_IntelligentScissorsMB()) }; + } + } + + unsafe impl Send for IntelligentScissorsMB {} + + impl IntelligentScissorsMB { + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_IntelligentScissorsMB(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::IntelligentScissorsMB::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Constant methods for [crate::imgproc::IntelligentScissorsMB] + pub trait IntelligentScissorsMBTraitConst { + fn as_raw_IntelligentScissorsMB(&self) -> *const c_void; + + /// Extracts optimal contour for the given target point on the image + /// + /// + /// Note: buildMap() must be called before this call + /// + /// ## Parameters + /// * targetPt: The target point + /// * contour:[out] The list of pixels which contains optimal path between the source and the target points of the image. Type is CV_32SC2 (compatible with `std::vector`) + /// * backward: Flag to indicate reverse order of retrieved pixels (use "true" value to fetch points from the target to the source point) + /// + /// ## C++ default parameters + /// * backward: false + #[inline] + fn get_contour(&self, target_pt: core::Point, contour: &mut impl ToOutputArray, backward: bool) -> Result<()> { + output_array_arg!(contour); + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_getContour_const_const_PointR_const__OutputArrayR_bool(self.as_raw_IntelligentScissorsMB(), &target_pt, contour.as_raw__OutputArray(), backward, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Extracts optimal contour for the given target point on the image + /// + /// + /// Note: buildMap() must be called before this call + /// + /// ## Parameters + /// * targetPt: The target point + /// * contour:[out] The list of pixels which contains optimal path between the source and the target points of the image. Type is CV_32SC2 (compatible with `std::vector`) + /// * backward: Flag to indicate reverse order of retrieved pixels (use "true" value to fetch points from the target to the source point) + /// + /// ## Note + /// This alternative version of [IntelligentScissorsMBTraitConst::get_contour] function uses the following default values for its arguments: + /// * backward: false + #[inline] + fn get_contour_def(&self, target_pt: core::Point, contour: &mut impl ToOutputArray) -> Result<()> { + output_array_arg!(contour); + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_getContour_const_const_PointR_const__OutputArrayR(self.as_raw_IntelligentScissorsMB(), &target_pt, contour.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::imgproc::IntelligentScissorsMB] + pub trait IntelligentScissorsMBTrait: crate::imgproc::IntelligentScissorsMBTraitConst { + fn as_raw_mut_IntelligentScissorsMB(&mut self) -> *mut c_void; + + /// Specify weights of feature functions + /// + /// Consider keeping weights normalized (sum of weights equals to 1.0) + /// Discrete dynamic programming (DP) goal is minimization of costs between pixels. + /// + /// ## Parameters + /// * weight_non_edge: Specify cost of non-edge pixels (default: 0.43f) + /// * weight_gradient_direction: Specify cost of gradient direction function (default: 0.43f) + /// * weight_gradient_magnitude: Specify cost of gradient magnitude function (default: 0.14f) + #[inline] + fn set_weights(&mut self, weight_non_edge: f32, weight_gradient_direction: f32, weight_gradient_magnitude: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_setWeights_float_float_float(self.as_raw_mut_IntelligentScissorsMB(), weight_non_edge, weight_gradient_direction, weight_gradient_magnitude, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::IntelligentScissorsMB::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Specify gradient magnitude max value threshold + /// + /// Zero limit value is used to disable gradient magnitude thresholding (default behavior, as described in original article). + /// Otherwize pixels with `gradient magnitude >= threshold` have zero cost. + /// + /// + /// Note: Thresholding should be used for images with irregular regions (to avoid stuck on parameters from high-contract areas, like embedded logos). + /// + /// ## Parameters + /// * gradient_magnitude_threshold_max: Specify gradient magnitude max value threshold (default: 0, disabled) + /// + /// ## C++ default parameters + /// * gradient_magnitude_threshold_max: 0.0f + #[inline] + fn set_gradient_magnitude_max_limit(&mut self, gradient_magnitude_threshold_max: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_setGradientMagnitudeMaxLimit_float(self.as_raw_mut_IntelligentScissorsMB(), gradient_magnitude_threshold_max, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::IntelligentScissorsMB::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Specify gradient magnitude max value threshold + /// + /// Zero limit value is used to disable gradient magnitude thresholding (default behavior, as described in original article). + /// Otherwize pixels with `gradient magnitude >= threshold` have zero cost. + /// + /// + /// Note: Thresholding should be used for images with irregular regions (to avoid stuck on parameters from high-contract areas, like embedded logos). + /// + /// ## Parameters + /// * gradient_magnitude_threshold_max: Specify gradient magnitude max value threshold (default: 0, disabled) + /// + /// ## Note + /// This alternative version of [IntelligentScissorsMBTrait::set_gradient_magnitude_max_limit] function uses the following default values for its arguments: + /// * gradient_magnitude_threshold_max: 0.0f + #[inline] + fn set_gradient_magnitude_max_limit_def(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_setGradientMagnitudeMaxLimit(self.as_raw_mut_IntelligentScissorsMB(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::IntelligentScissorsMB::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Switch to "Laplacian Zero-Crossing" edge feature extractor and specify its parameters + /// + /// This feature extractor is used by default according to article. + /// + /// Implementation has additional filtering for regions with low-amplitude noise. + /// This filtering is enabled through parameter of minimal gradient amplitude (use some small value 4, 8, 16). + /// + /// + /// Note: Current implementation of this feature extractor is based on processing of grayscale images (color image is converted to grayscale image first). + /// + /// + /// Note: Canny edge detector is a bit slower, but provides better results (especially on color images): use setEdgeFeatureCannyParameters(). + /// + /// ## Parameters + /// * gradient_magnitude_min_value: Minimal gradient magnitude value for edge pixels (default: 0, check is disabled) + /// + /// ## C++ default parameters + /// * gradient_magnitude_min_value: 0.0f + #[inline] + fn set_edge_feature_zero_crossing_parameters(&mut self, gradient_magnitude_min_value: f32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_setEdgeFeatureZeroCrossingParameters_float(self.as_raw_mut_IntelligentScissorsMB(), gradient_magnitude_min_value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::IntelligentScissorsMB::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Switch to "Laplacian Zero-Crossing" edge feature extractor and specify its parameters + /// + /// This feature extractor is used by default according to article. + /// + /// Implementation has additional filtering for regions with low-amplitude noise. + /// This filtering is enabled through parameter of minimal gradient amplitude (use some small value 4, 8, 16). + /// + /// + /// Note: Current implementation of this feature extractor is based on processing of grayscale images (color image is converted to grayscale image first). + /// + /// + /// Note: Canny edge detector is a bit slower, but provides better results (especially on color images): use setEdgeFeatureCannyParameters(). + /// + /// ## Parameters + /// * gradient_magnitude_min_value: Minimal gradient magnitude value for edge pixels (default: 0, check is disabled) + /// + /// ## Note + /// This alternative version of [IntelligentScissorsMBTrait::set_edge_feature_zero_crossing_parameters] function uses the following default values for its arguments: + /// * gradient_magnitude_min_value: 0.0f + #[inline] + fn set_edge_feature_zero_crossing_parameters_def(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_setEdgeFeatureZeroCrossingParameters(self.as_raw_mut_IntelligentScissorsMB(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::IntelligentScissorsMB::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Switch edge feature extractor to use Canny edge detector + /// + /// + /// Note: "Laplacian Zero-Crossing" feature extractor is used by default (following to original article) + /// ## See also + /// Canny + /// + /// ## C++ default parameters + /// * aperture_size: 3 + /// * l2gradient: false + #[inline] + fn set_edge_feature_canny_parameters(&mut self, threshold1: f64, threshold2: f64, aperture_size: i32, l2gradient: bool) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_setEdgeFeatureCannyParameters_double_double_int_bool(self.as_raw_mut_IntelligentScissorsMB(), threshold1, threshold2, aperture_size, l2gradient, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::IntelligentScissorsMB::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Switch edge feature extractor to use Canny edge detector + /// + /// + /// Note: "Laplacian Zero-Crossing" feature extractor is used by default (following to original article) + /// ## See also + /// Canny + /// + /// ## Note + /// This alternative version of [IntelligentScissorsMBTrait::set_edge_feature_canny_parameters] function uses the following default values for its arguments: + /// * aperture_size: 3 + /// * l2gradient: false + #[inline] + fn set_edge_feature_canny_parameters_def(&mut self, threshold1: f64, threshold2: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_setEdgeFeatureCannyParameters_double_double(self.as_raw_mut_IntelligentScissorsMB(), threshold1, threshold2, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::IntelligentScissorsMB::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Specify input image and extract image features + /// + /// ## Parameters + /// * image: input image. Type is [CV_8UC1] / #CV_8UC3 + #[inline] + fn apply_image(&mut self, image: &impl ToInputArray) -> Result { + input_array_arg!(image); + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_applyImage_const__InputArrayR(self.as_raw_mut_IntelligentScissorsMB(), image.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::IntelligentScissorsMB::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Specify custom features of input image + /// + /// Customized advanced variant of applyImage() call. + /// + /// ## Parameters + /// * non_edge: Specify cost of non-edge pixels. Type is CV_8UC1. Expected values are `{0, 1}`. + /// * gradient_direction: Specify gradient direction feature. Type is CV_32FC2. Values are expected to be normalized: `x^2 + y^2 == 1` + /// * gradient_magnitude: Specify cost of gradient magnitude function: Type is CV_32FC1. Values should be in range `[0, 1]`. + /// * image: **Optional parameter**. Must be specified if subset of features is specified (non-specified features are calculated internally) + /// + /// ## C++ default parameters + /// * image: noArray() + #[inline] + fn apply_image_features(&mut self, non_edge: &impl ToInputArray, gradient_direction: &impl ToInputArray, gradient_magnitude: &impl ToInputArray, image: &impl ToInputArray) -> Result { + input_array_arg!(non_edge); + input_array_arg!(gradient_direction); + input_array_arg!(gradient_magnitude); + input_array_arg!(image); + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_applyImageFeatures_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR(self.as_raw_mut_IntelligentScissorsMB(), non_edge.as_raw__InputArray(), gradient_direction.as_raw__InputArray(), gradient_magnitude.as_raw__InputArray(), image.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::IntelligentScissorsMB::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Specify custom features of input image + /// + /// Customized advanced variant of applyImage() call. + /// + /// ## Parameters + /// * non_edge: Specify cost of non-edge pixels. Type is CV_8UC1. Expected values are `{0, 1}`. + /// * gradient_direction: Specify gradient direction feature. Type is CV_32FC2. Values are expected to be normalized: `x^2 + y^2 == 1` + /// * gradient_magnitude: Specify cost of gradient magnitude function: Type is CV_32FC1. Values should be in range `[0, 1]`. + /// * image: **Optional parameter**. Must be specified if subset of features is specified (non-specified features are calculated internally) + /// + /// ## Note + /// This alternative version of [IntelligentScissorsMBTrait::apply_image_features] function uses the following default values for its arguments: + /// * image: noArray() + #[inline] + fn apply_image_features_def(&mut self, non_edge: &impl ToInputArray, gradient_direction: &impl ToInputArray, gradient_magnitude: &impl ToInputArray) -> Result { + input_array_arg!(non_edge); + input_array_arg!(gradient_direction); + input_array_arg!(gradient_magnitude); + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_applyImageFeatures_const__InputArrayR_const__InputArrayR_const__InputArrayR(self.as_raw_mut_IntelligentScissorsMB(), non_edge.as_raw__InputArray(), gradient_direction.as_raw__InputArray(), gradient_magnitude.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::imgproc::IntelligentScissorsMB::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Prepares a map of optimal paths for the given source point on the image + /// + /// + /// Note: applyImage() / applyImageFeatures() must be called before this call + /// + /// ## Parameters + /// * sourcePt: The source point used to find the paths + #[inline] + fn build_map(&mut self, source_pt: core::Point) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_segmentation_IntelligentScissorsMB_buildMap_const_PointR(self.as_raw_mut_IntelligentScissorsMB(), &source_pt, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl Clone for IntelligentScissorsMB { + #[inline] + fn clone(&self) -> Self { + unsafe { Self::from_raw(sys::cv_segmentation_IntelligentScissorsMB_implicitClone_const(self.as_raw_IntelligentScissorsMB())) } + } + } + + impl std::fmt::Debug for IntelligentScissorsMB { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("IntelligentScissorsMB") + .finish() + } + } + + impl crate::imgproc::IntelligentScissorsMBTraitConst for IntelligentScissorsMB { + #[inline] fn as_raw_IntelligentScissorsMB(&self) -> *const c_void { self.as_raw() } + } + + impl crate::imgproc::IntelligentScissorsMBTrait for IntelligentScissorsMB { + #[inline] fn as_raw_mut_IntelligentScissorsMB(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { IntelligentScissorsMB, crate::imgproc::IntelligentScissorsMBTraitConst, as_raw_IntelligentScissorsMB, crate::imgproc::IntelligentScissorsMBTrait, as_raw_mut_IntelligentScissorsMB } + +} diff --git a/target/debug/build/opencv-4e105b5546afc119/out/opencv/sys.rs b/target/debug/build/opencv-4e105b5546afc119/out/opencv/sys.rs new file mode 100644 index 0000000..e9c2162 --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/opencv/sys.rs @@ -0,0 +1,5250 @@ +use crate::{mod_prelude_sys::*, core}; + +mod core_sys { + use super::*; + + unsafe extern "C" { + pub fn cv_Cholesky_doubleX_size_t_int_doubleX_size_t_int(a: *mut f64, astep: size_t, m: i32, b: *mut f64, bstep: size_t, n: i32, ocvrs_return: *mut Result); + pub fn cv_Cholesky_floatX_size_t_int_floatX_size_t_int(a: *mut f32, astep: size_t, m: i32, b: *mut f32, bstep: size_t, n: i32, ocvrs_return: *mut Result); + pub fn cv_LUT_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src: *const c_void, lut: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_LU_doubleX_size_t_int_doubleX_size_t_int(a: *mut f64, astep: size_t, m: i32, b: *mut f64, bstep: size_t, n: i32, ocvrs_return: *mut Result); + pub fn cv_LU_floatX_size_t_int_floatX_size_t_int(a: *mut f32, astep: size_t, m: i32, b: *mut f32, bstep: size_t, n: i32, ocvrs_return: *mut Result); + pub fn cv_Mahalanobis_const__InputArrayR_const__InputArrayR_const__InputArrayR(v1: *const c_void, v2: *const c_void, icovar: *const c_void, ocvrs_return: *mut Result); + pub fn cv_PCABackProject_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(data: *const c_void, mean: *const c_void, eigenvectors: *const c_void, result: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR(data: *const c_void, mean: *const c_void, eigenvectors: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR(data: *const c_void, mean: *const c_void, eigenvectors: *const c_void, eigenvalues: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_double(data: *const c_void, mean: *const c_void, eigenvectors: *const c_void, eigenvalues: *const c_void, retained_variance: f64, ocvrs_return: *mut Result<()>); + pub fn cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_int(data: *const c_void, mean: *const c_void, eigenvectors: *const c_void, eigenvalues: *const c_void, max_components: i32, ocvrs_return: *mut Result<()>); + pub fn cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_double(data: *const c_void, mean: *const c_void, eigenvectors: *const c_void, retained_variance: f64, ocvrs_return: *mut Result<()>); + pub fn cv_PCACompute_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_int(data: *const c_void, mean: *const c_void, eigenvectors: *const c_void, max_components: i32, ocvrs_return: *mut Result<()>); + pub fn cv_PCAProject_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(data: *const c_void, mean: *const c_void, eigenvectors: *const c_void, result: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_PSNR_const__InputArrayR_const__InputArrayR(src1: *const c_void, src2: *const c_void, ocvrs_return: *mut Result); + pub fn cv_PSNR_const__InputArrayR_const__InputArrayR_double(src1: *const c_void, src2: *const c_void, r: f64, ocvrs_return: *mut Result); + pub fn cv_SVBackSubst_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(w: *const c_void, u: *const c_void, vt: *const c_void, rhs: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SVDecomp_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(src: *const c_void, w: *const c_void, u: *const c_void, vt: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SVDecomp_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int(src: *const c_void, w: *const c_void, u: *const c_void, vt: *const c_void, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_abs_const_MatExprR(e: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_abs_const_MatR(m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_absdiff_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_addWeighted_const__InputArrayR_double_const__InputArrayR_double_double_const__OutputArrayR(src1: *const c_void, alpha: f64, src2: *const c_void, beta: f64, gamma: f64, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_addWeighted_const__InputArrayR_double_const__InputArrayR_double_double_const__OutputArrayR_int(src1: *const c_void, alpha: f64, src2: *const c_void, beta: f64, gamma: f64, dst: *const c_void, dtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_add_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_add_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR_int(src1: *const c_void, src2: *const c_void, dst: *const c_void, mask: *const c_void, dtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_batchDistance_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dist: *const c_void, dtype: i32, nidx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_batchDistance_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_const__OutputArrayR_int_int_const__InputArrayR_int_bool(src1: *const c_void, src2: *const c_void, dist: *const c_void, dtype: i32, nidx: *const c_void, norm_type: i32, k: i32, mask: *const c_void, update: i32, crosscheck: bool, ocvrs_return: *mut Result<()>); + pub fn cv_bitwise_and_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_bitwise_and_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_bitwise_not_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_bitwise_not_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src: *const c_void, dst: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_bitwise_or_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_bitwise_or_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_bitwise_xor_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_bitwise_xor_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_borderInterpolate_int_int_int(p: i32, len: i32, border_type: i32, ocvrs_return: *mut Result); + pub fn cv_broadcast_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src: *const c_void, shape: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_calcCovarMatrix_const__InputArrayR_const__OutputArrayR_const__InputOutputArrayR_int(samples: *const c_void, covar: *const c_void, mean: *const c_void, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_calcCovarMatrix_const__InputArrayR_const__OutputArrayR_const__InputOutputArrayR_int_int(samples: *const c_void, covar: *const c_void, mean: *const c_void, flags: i32, ctype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_cartToPolar_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(x: *const c_void, y: *const c_void, magnitude: *const c_void, angle: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_cartToPolar_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_bool(x: *const c_void, y: *const c_void, magnitude: *const c_void, angle: *const c_void, angle_in_degrees: bool, ocvrs_return: *mut Result<()>); + pub fn cv_checkHardwareSupport_int(feature: i32, ocvrs_return: *mut Result); + pub fn cv_checkRange_const__InputArrayR(a: *const c_void, ocvrs_return: *mut Result); + pub fn cv_checkRange_const__InputArrayR_bool_PointX_double_double(a: *const c_void, quiet: bool, pos: *mut core::Point, min_val: f64, max_val: f64, ocvrs_return: *mut Result); + pub fn cv_compare_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(src1: *const c_void, src2: *const c_void, dst: *const c_void, cmpop: i32, ocvrs_return: *mut Result<()>); + pub fn cv_completeSymm_const__InputOutputArrayR(m: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_completeSymm_const__InputOutputArrayR_bool(m: *const c_void, lower_to_upper: bool, ocvrs_return: *mut Result<()>); + pub fn cv_convertFp16_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_convertScaleAbs_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_convertScaleAbs_const__InputArrayR_const__OutputArrayR_double_double(src: *const c_void, dst: *const c_void, alpha: f64, beta: f64, ocvrs_return: *mut Result<()>); + pub fn cv_copyMakeBorder_const__InputArrayR_const__OutputArrayR_int_int_int_int_int(src: *const c_void, dst: *const c_void, top: i32, bottom: i32, left: i32, right: i32, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_copyMakeBorder_const__InputArrayR_const__OutputArrayR_int_int_int_int_int_const_ScalarR(src: *const c_void, dst: *const c_void, top: i32, bottom: i32, left: i32, right: i32, border_type: i32, value: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_copyTo_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src: *const c_void, dst: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_countNonZero_const__InputArrayR(src: *const c_void, ocvrs_return: *mut Result); + pub fn cv_cubeRoot_float(val: f32, ocvrs_return: *mut Result); + pub fn cv_dct_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dct_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_depthToString_int(depth: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_detail_check_failed_MatChannels_const_int_const_CheckContextR(v: i32, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_MatChannels_const_int_const_int_const_CheckContextR(v1: i32, v2: i32, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_MatDepth_const_int_const_CheckContextR(v: i32, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_MatDepth_const_int_const_int_const_CheckContextR(v1: i32, v2: i32, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_MatType_const_int_const_CheckContextR(v: i32, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_MatType_const_int_const_int_const_CheckContextR(v1: i32, v2: i32, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_auto_const_Size_LintG_const_CheckContextR(v: *const core::Size_, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_auto_const_Size_LintG_const_Size_LintG_const_CheckContextR(v1: *const core::Size_, v2: *const core::Size_, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_auto_const_bool_const_bool_const_CheckContextR(v1: bool, v2: bool, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_auto_const_double_const_CheckContextR(v: f64, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_auto_const_double_const_double_const_CheckContextR(v1: f64, v2: f64, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_auto_const_float_const_CheckContextR(v: f32, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_auto_const_float_const_float_const_CheckContextR(v1: f32, v2: f32, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_auto_const_int_const_CheckContextR(v: i32, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_auto_const_int_const_int_const_CheckContextR(v1: i32, v2: i32, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_auto_const_size_t_const_CheckContextR(v: size_t, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_auto_const_size_t_const_size_t_const_CheckContextR(v1: size_t, v2: size_t, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_auto_const_stringR_const_CheckContextR(v1: *const c_char, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_false_const_bool_const_CheckContextR(v: bool, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_detail_check_failed_true_const_bool_const_CheckContextR(v: bool, ctx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_determinant_const__InputArrayR(mtx: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dft_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dft_const__InputArrayR_const__OutputArrayR_int_int(src: *const c_void, dst: *const c_void, flags: i32, nonzero_rows: i32, ocvrs_return: *mut Result<()>); + pub fn cv_directx_convertFromD3D10Texture2D_ID3D10Texture2DX_const__OutputArrayR(p_d3d10_texture_2d: *mut c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_directx_convertFromD3D11Texture2D_ID3D11Texture2DX_const__OutputArrayR(p_d3d11_texture_2d: *mut c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_directx_convertFromDirect3DSurface9_IDirect3DSurface9X_const__OutputArrayR(p_direct_3d_surface9: *mut c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_directx_convertFromDirect3DSurface9_IDirect3DSurface9X_const__OutputArrayR_voidX(p_direct_3d_surface9: *mut c_void, dst: *const c_void, surface_shared_handle: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_directx_convertToD3D10Texture2D_const__InputArrayR_ID3D10Texture2DX(src: *const c_void, p_d3d10_texture_2d: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_directx_convertToD3D11Texture2D_const__InputArrayR_ID3D11Texture2DX(src: *const c_void, p_d3d11_texture_2d: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_directx_convertToDirect3DSurface9_const__InputArrayR_IDirect3DSurface9X(src: *const c_void, p_direct_3d_surface9: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_directx_convertToDirect3DSurface9_const__InputArrayR_IDirect3DSurface9X_voidX(src: *const c_void, p_direct_3d_surface9: *mut c_void, surface_shared_handle: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_directx_getTypeFromD3DFORMAT_const_int(id_3d_format: i32, ocvrs_return: *mut Result); + pub fn cv_directx_getTypeFromDXGI_FORMAT_const_int(i_dxgi_format: i32, ocvrs_return: *mut Result); + pub fn cv_directx_ocl_initializeContextFromD3D10Device_ID3D10DeviceX(p_d3d10_device: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_directx_ocl_initializeContextFromD3D11Device_ID3D11DeviceX(p_d3d11_device: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_directx_ocl_initializeContextFromDirect3DDevice9Ex_IDirect3DDevice9ExX(p_direct_3d_device9_ex: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_directx_ocl_initializeContextFromDirect3DDevice9_IDirect3DDevice9X(p_direct_3d_device9: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_divide_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_divide_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double_int(src1: *const c_void, src2: *const c_void, dst: *const c_void, scale: f64, dtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_divide_double_const__InputArrayR_const__OutputArrayR(scale: f64, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_divide_double_const__InputArrayR_const__OutputArrayR_int(scale: f64, src2: *const c_void, dst: *const c_void, dtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_eigenNonSymmetric_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src: *const c_void, eigenvalues: *const c_void, eigenvectors: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_eigen_const__InputArrayR_const__OutputArrayR(src: *const c_void, eigenvalues: *const c_void, ocvrs_return: *mut Result); + pub fn cv_eigen_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src: *const c_void, eigenvalues: *const c_void, eigenvectors: *const c_void, ocvrs_return: *mut Result); + pub fn cv_error_const_ExceptionR(exc: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_error_int_const_StringR_const_charX_const_charX_int(code: i32, err: *const c_char, func: *const c_char, file: *const c_char, line: i32, ocvrs_return: *mut Result<()>); + pub fn cv_exp_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_extractChannel_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, coi: i32, ocvrs_return: *mut Result<()>); + pub fn cv_fastAtan2_float_float(y: f32, x: f32, ocvrs_return: *mut Result); + pub fn cv_findNonZero_const__InputArrayR_const__OutputArrayR(src: *const c_void, idx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_flipND_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, axis: i32, ocvrs_return: *mut Result<()>); + pub fn cv_flip_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, flip_code: i32, ocvrs_return: *mut Result<()>); + pub fn cv_gemm_const__InputArrayR_const__InputArrayR_double_const__InputArrayR_double_const__OutputArrayR(src1: *const c_void, src2: *const c_void, alpha: f64, src3: *const c_void, beta: f64, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_gemm_const__InputArrayR_const__InputArrayR_double_const__InputArrayR_double_const__OutputArrayR_int(src1: *const c_void, src2: *const c_void, alpha: f64, src3: *const c_void, beta: f64, dst: *const c_void, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_getBuildInformation(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getCPUFeaturesLine(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getCPUTickCount(ocvrs_return: *mut Result); + pub fn cv_getDefaultAlgorithmHint(ocvrs_return: *mut Result); + pub fn cv_getElemSize_int(typ: i32, ocvrs_return: *mut Result); + pub fn cv_getHardwareFeatureName_int(feature: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getNumThreads(ocvrs_return: *mut Result); + pub fn cv_getNumberOfCPUs(ocvrs_return: *mut Result); + pub fn cv_getOptimalDFTSize_int(vecsize: i32, ocvrs_return: *mut Result); + pub fn cv_getThreadNum(ocvrs_return: *mut Result); + pub fn cv_getTickCount(ocvrs_return: *mut Result); + pub fn cv_getTickFrequency(ocvrs_return: *mut Result); + pub fn cv_getVersionMajor() -> i32; + pub fn cv_getVersionMinor() -> i32; + pub fn cv_getVersionRevision() -> i32; + pub fn cv_getVersionString(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_glob_String_vectorLStringGR(pattern: *const c_char, result: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_glob_String_vectorLStringGR_bool(pattern: *const c_char, result: *mut c_void, recursive: bool, ocvrs_return: *mut Result<()>); + pub fn cv_hasNonZero_const__InputArrayR(src: *const c_void, ocvrs_return: *mut Result); + pub fn cv_haveOpenVX(ocvrs_return: *mut Result); + pub fn cv_hconcat_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_hconcat_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_idct_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_idct_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_idft_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_idft_const__InputArrayR_const__OutputArrayR_int_int(src: *const c_void, dst: *const c_void, flags: i32, nonzero_rows: i32, ocvrs_return: *mut Result<()>); + pub fn cv_inRange_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src: *const c_void, lowerb: *const c_void, upperb: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_insertChannel_const__InputArrayR_const__InputOutputArrayR_int(src: *const c_void, dst: *const c_void, coi: i32, ocvrs_return: *mut Result<()>); + pub fn cv_instr_getFlags(ocvrs_return: *mut Result); + pub fn cv_instr_resetTrace(ocvrs_return: *mut Result<()>); + pub fn cv_instr_setFlags_FLAGS(mode_flags: core::FLAGS, ocvrs_return: *mut Result<()>); + pub fn cv_instr_setUseInstrumentation_bool(flag: bool, ocvrs_return: *mut Result<()>); + pub fn cv_instr_useInstrumentation(ocvrs_return: *mut Result); + pub fn cv_invert_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result); + pub fn cv_invert_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, flags: i32, ocvrs_return: *mut Result); + pub fn cv_ipp_getIppErrorLocation(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ipp_getIppFeatures(ocvrs_return: *mut Result); + pub fn cv_ipp_getIppStatus(ocvrs_return: *mut Result); + pub fn cv_ipp_getIppVersion(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ipp_setIppStatus_int(status: i32, ocvrs_return: *mut Result<()>); + pub fn cv_ipp_setIppStatus_int_const_charX_const_charX_int(status: i32, funcname: *const c_char, filename: *const c_char, line: i32, ocvrs_return: *mut Result<()>); + pub fn cv_ipp_setUseIPP_NotExact_bool(flag: bool, ocvrs_return: *mut Result<()>); + pub fn cv_ipp_setUseIPP_bool(flag: bool, ocvrs_return: *mut Result<()>); + pub fn cv_ipp_useIPP(ocvrs_return: *mut Result); + pub fn cv_ipp_useIPP_NotExact(ocvrs_return: *mut Result); + pub fn cv_kmeans_const__InputArrayR_int_const__InputOutputArrayR_TermCriteria_int_int(data: *const c_void, k: i32, best_labels: *const c_void, criteria: *const core::TermCriteria, attempts: i32, flags: i32, ocvrs_return: *mut Result); + pub fn cv_kmeans_const__InputArrayR_int_const__InputOutputArrayR_TermCriteria_int_int_const__OutputArrayR(data: *const c_void, k: i32, best_labels: *const c_void, criteria: *const core::TermCriteria, attempts: i32, flags: i32, centers: *const c_void, ocvrs_return: *mut Result); + pub fn cv_log_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_magnitude_const__InputArrayR_const__InputArrayR_const__OutputArrayR(x: *const c_void, y: *const c_void, magnitude: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_max_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_max_const_MatR_const_MatR_MatR(src1: *const c_void, src2: *const c_void, dst: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_max_const_MatR_double(a: *const c_void, s: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_max_const_UMatR_const_UMatR_UMatR(src1: *const c_void, src2: *const c_void, dst: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_max_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_max_double_const_MatR(s: f64, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_meanStdDev_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src: *const c_void, mean: *const c_void, stddev: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_meanStdDev_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__InputArrayR(src: *const c_void, mean: *const c_void, stddev: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_mean_const__InputArrayR(src: *const c_void, ocvrs_return: *mut Result); + pub fn cv_mean_const__InputArrayR_const__InputArrayR(src: *const c_void, mask: *const c_void, ocvrs_return: *mut Result); + pub fn cv_merge_const__InputArrayR_const__OutputArrayR(mv: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_minMaxIdx_const__InputArrayR_doubleX(src: *const c_void, min_val: *mut f64, ocvrs_return: *mut Result<()>); + pub fn cv_minMaxIdx_const__InputArrayR_doubleX_doubleX_intX_intX_const__InputArrayR(src: *const c_void, min_val: *mut f64, max_val: *mut f64, min_idx: *mut i32, max_idx: *mut i32, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_minMaxLoc_const_SparseMatR_doubleX_doubleX(a: *const c_void, min_val: *mut f64, max_val: *mut f64, ocvrs_return: *mut Result<()>); + pub fn cv_minMaxLoc_const_SparseMatR_doubleX_doubleX_intX_intX(a: *const c_void, min_val: *mut f64, max_val: *mut f64, min_idx: *mut i32, max_idx: *mut i32, ocvrs_return: *mut Result<()>); + pub fn cv_minMaxLoc_const__InputArrayR_doubleX(src: *const c_void, min_val: *mut f64, ocvrs_return: *mut Result<()>); + pub fn cv_minMaxLoc_const__InputArrayR_doubleX_doubleX_PointX_PointX_const__InputArrayR(src: *const c_void, min_val: *mut f64, max_val: *mut f64, min_loc: *mut core::Point, max_loc: *mut core::Point, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_min_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_min_const_MatR_const_MatR_MatR(src1: *const c_void, src2: *const c_void, dst: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_min_const_MatR_double(a: *const c_void, s: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_min_const_UMatR_const_UMatR_UMatR(src1: *const c_void, src2: *const c_void, dst: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_min_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_min_double_const_MatR(s: f64, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_mixChannels_const__InputArrayR_const__InputOutputArrayR_const_intX_size_t(src: *const c_void, dst: *const c_void, from_to: *const i32, npairs: size_t, ocvrs_return: *mut Result<()>); + pub fn cv_mixChannels_const__InputArrayR_const__InputOutputArrayR_const_vectorLintGR(src: *const c_void, dst: *const c_void, from_to: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_mulSpectrums_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(a: *const c_void, b: *const c_void, c: *const c_void, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_mulSpectrums_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_bool(a: *const c_void, b: *const c_void, c: *const c_void, flags: i32, conj_b: bool, ocvrs_return: *mut Result<()>); + pub fn cv_mulTransposed_const__InputArrayR_const__OutputArrayR_bool(src: *const c_void, dst: *const c_void, a_ta: bool, ocvrs_return: *mut Result<()>); + pub fn cv_mulTransposed_const__InputArrayR_const__OutputArrayR_bool_const__InputArrayR_double_int(src: *const c_void, dst: *const c_void, a_ta: bool, delta: *const c_void, scale: f64, dtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_multiply_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_multiply_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double_int(src1: *const c_void, src2: *const c_void, dst: *const c_void, scale: f64, dtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_noArray() -> *mut c_void; + pub fn cv_norm_const_SparseMatR_int(src: *const c_void, norm_type: i32, ocvrs_return: *mut Result); + pub fn cv_norm_const__InputArrayR(src1: *const c_void, ocvrs_return: *mut Result); + pub fn cv_norm_const__InputArrayR_const__InputArrayR(src1: *const c_void, src2: *const c_void, ocvrs_return: *mut Result); + pub fn cv_norm_const__InputArrayR_const__InputArrayR_int_const__InputArrayR(src1: *const c_void, src2: *const c_void, norm_type: i32, mask: *const c_void, ocvrs_return: *mut Result); + pub fn cv_norm_const__InputArrayR_int_const__InputArrayR(src1: *const c_void, norm_type: i32, mask: *const c_void, ocvrs_return: *mut Result); + pub fn cv_normalize_const_SparseMatR_SparseMatR_double_int(src: *const c_void, dst: *mut c_void, alpha: f64, norm_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_normalize_const__InputArrayR_const__InputOutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_normalize_const__InputArrayR_const__InputOutputArrayR_double_double_int_int_const__InputArrayR(src: *const c_void, dst: *const c_void, alpha: f64, beta: f64, norm_type: i32, dtype: i32, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_attachContext_const_StringR_voidX_voidX_voidX(platform_name: *const c_char, platform_id: *mut c_void, context: *mut c_void, device_id: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_buildOptionsAddMatrixDescription_StringR_const_StringR_const__InputArrayR(build_options: *mut *mut c_void, name: *const c_char, _m: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_checkOptimalVectorWidth_const_intX_const__InputArrayR(vector_widths: *const i32, src1: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_checkOptimalVectorWidth_const_intX_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_OclVectorStrategy(vector_widths: *const i32, src1: *const c_void, src2: *const c_void, src3: *const c_void, src4: *const c_void, src5: *const c_void, src6: *const c_void, src7: *const c_void, src8: *const c_void, src9: *const c_void, strat: core::OclVectorStrategy, ocvrs_return: *mut Result); + pub fn cv_ocl_convertFromBuffer_voidX_size_t_int_int_int_UMatR(cl_mem_buffer: *mut c_void, step: size_t, rows: i32, cols: i32, typ: i32, dst: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_convertFromImage_voidX_UMatR(cl_mem_image: *mut c_void, dst: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_convertTypeStr_int_int_int_charX(sdepth: i32, ddepth: i32, cn: i32, buf: *mut *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_convertTypeStr_int_int_int_charX_size_t(sdepth: i32, ddepth: i32, cn: i32, buf: *mut *mut c_void, buf_size: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_finish(ocvrs_return: *mut Result<()>); + pub fn cv_ocl_getOpenCLErrorString_int(error_code: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_getPlatfomsInfo_vectorLPlatformInfoGR(platform_info: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_haveAmdBlas(ocvrs_return: *mut Result); + pub fn cv_ocl_haveAmdFft(ocvrs_return: *mut Result); + pub fn cv_ocl_haveOpenCL(ocvrs_return: *mut Result); + pub fn cv_ocl_haveSVM(ocvrs_return: *mut Result); + pub fn cv_ocl_kernelToStr_const__InputArrayR(_kernel: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_kernelToStr_const__InputArrayR_int_const_charX(_kernel: *const c_void, ddepth: i32, name: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_memopTypeToStr_int(t: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_predictOptimalVectorWidthMax_const__InputArrayR(src1: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_predictOptimalVectorWidthMax_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR(src1: *const c_void, src2: *const c_void, src3: *const c_void, src4: *const c_void, src5: *const c_void, src6: *const c_void, src7: *const c_void, src8: *const c_void, src9: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_predictOptimalVectorWidth_const__InputArrayR(src1: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_predictOptimalVectorWidth_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_OclVectorStrategy(src1: *const c_void, src2: *const c_void, src3: *const c_void, src4: *const c_void, src5: *const c_void, src6: *const c_void, src7: *const c_void, src8: *const c_void, src9: *const c_void, strat: core::OclVectorStrategy, ocvrs_return: *mut Result); + pub fn cv_ocl_setUseOpenCL_bool(flag: bool, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_typeToStr_int(t: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_useOpenCL(ocvrs_return: *mut Result); + pub fn cv_ocl_vecopTypeToStr_int(t: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorA_const_MatExprR_const_MatExprR(e1: *const c_void, e2: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorA_const_MatExprR_const_MatR(e: *const c_void, m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorA_const_MatExprR_const_ScalarR(e: *const c_void, s: *const core::Scalar, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorA_const_MatR_const_MatExprR(m: *const c_void, e: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorA_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorA_const_MatR_const_ScalarR(a: *const c_void, s: *const core::Scalar, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorA_const_ScalarR_const_MatExprR(s: *const core::Scalar, e: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorA_const_ScalarR_const_MatR(s: *const core::Scalar, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorD_const_MatExprR_const_MatExprR(e1: *const c_void, e2: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorD_const_MatExprR_const_MatR(e: *const c_void, m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorD_const_MatExprR_double(e: *const c_void, s: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorD_const_MatR_const_MatExprR(m: *const c_void, e: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorD_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorD_const_MatR_double(a: *const c_void, s: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorD_double_const_MatExprR(s: f64, e: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorD_double_const_MatR(s: f64, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorEQ_const_FileNodeIteratorR_const_FileNodeIteratorR(it1: *const c_void, it2: *const c_void, ocvrs_return: *mut Result); + pub fn cv_operatorEQ_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorEQ_const_MatR_double(a: *const c_void, s: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorEQ_double_const_MatR(s: f64, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorGE_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorGE_const_MatR_double(a: *const c_void, s: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorGE_double_const_MatR(s: f64, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorG_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorG_const_MatR_double(a: *const c_void, s: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorG_double_const_MatR(s: f64, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorLE_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorLE_const_MatR_double(a: *const c_void, s: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorLE_double_const_MatR(s: f64, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorL_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorL_const_MatR_double(a: *const c_void, s: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorL_double_const_MatR(s: f64, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorNE_const_FileNodeIteratorR_const_FileNodeIteratorR(it1: *const c_void, it2: *const c_void, ocvrs_return: *mut Result); + pub fn cv_operatorNE_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorNE_const_MatR_double(a: *const c_void, s: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorNE_double_const_MatR(s: f64, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorNOTB_const_MatR(m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorOR_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorOR_const_MatR_const_ScalarR(a: *const c_void, s: *const core::Scalar, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorOR_const_ScalarR_const_MatR(s: *const core::Scalar, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorR_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorR_const_MatR_const_ScalarR(a: *const c_void, s: *const core::Scalar, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorR_const_ScalarR_const_MatR(s: *const core::Scalar, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorS_const_MatExprR(e: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorS_const_MatExprR_const_MatExprR(e1: *const c_void, e2: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorS_const_MatExprR_const_MatR(e: *const c_void, m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorS_const_MatExprR_const_ScalarR(e: *const c_void, s: *const core::Scalar, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorS_const_MatR(m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorS_const_MatR_const_MatExprR(m: *const c_void, e: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorS_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorS_const_MatR_const_ScalarR(a: *const c_void, s: *const core::Scalar, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorS_const_ScalarR_const_MatExprR(s: *const core::Scalar, e: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorS_const_ScalarR_const_MatR(s: *const core::Scalar, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorXOR_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorXOR_const_MatR_const_ScalarR(a: *const c_void, s: *const core::Scalar, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorXOR_const_ScalarR_const_MatR(s: *const core::Scalar, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorX_const_MatExprR_const_MatExprR(e1: *const c_void, e2: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorX_const_MatExprR_const_MatR(e: *const c_void, m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorX_const_MatExprR_double(e: *const c_void, s: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorX_const_MatR_const_MatExprR(m: *const c_void, e: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorX_const_MatR_const_MatR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorX_const_MatR_double(a: *const c_void, s: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorX_double_const_MatExprR(s: f64, e: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_operatorX_double_const_MatR(s: f64, a: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_parallel_for__const_RangeR_const_ParallelLoopBodyR(range: *const c_void, body: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_parallel_for__const_RangeR_const_ParallelLoopBodyR_double(range: *const c_void, body: *const c_void, nstripes: f64, ocvrs_return: *mut Result<()>); + pub fn cv_patchNaNs_const__InputOutputArrayR(a: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_patchNaNs_const__InputOutputArrayR_double(a: *const c_void, val: f64, ocvrs_return: *mut Result<()>); + pub fn cv_perspectiveTransform_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src: *const c_void, dst: *const c_void, m: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_phase_const__InputArrayR_const__InputArrayR_const__OutputArrayR(x: *const c_void, y: *const c_void, angle: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_phase_const__InputArrayR_const__InputArrayR_const__OutputArrayR_bool(x: *const c_void, y: *const c_void, angle: *const c_void, angle_in_degrees: bool, ocvrs_return: *mut Result<()>); + pub fn cv_polarToCart_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(magnitude: *const c_void, angle: *const c_void, x: *const c_void, y: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_polarToCart_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_bool(magnitude: *const c_void, angle: *const c_void, x: *const c_void, y: *const c_void, angle_in_degrees: bool, ocvrs_return: *mut Result<()>); + pub fn cv_pow_const__InputArrayR_double_const__OutputArrayR(src: *const c_void, power: f64, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_randn_const__InputOutputArrayR_const__InputArrayR_const__InputArrayR(dst: *const c_void, mean: *const c_void, stddev: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_randu_const__InputOutputArrayR_const__InputArrayR_const__InputArrayR(dst: *const c_void, low: *const c_void, high: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_read_const_FileNodeR_DMatchR_const_DMatchR(node: *const c_void, value: *mut core::DMatch, default_value: *const core::DMatch, ocvrs_return: *mut Result<()>); + pub fn cv_read_const_FileNodeR_KeyPointR_const_KeyPointR(node: *const c_void, value: *mut c_void, default_value: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_read_const_FileNodeR_MatR(node: *const c_void, mat: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_read_const_FileNodeR_MatR_const_MatR(node: *const c_void, mat: *mut c_void, default_mat: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_read_const_FileNodeR_SparseMatR(node: *const c_void, mat: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_read_const_FileNodeR_SparseMatR_const_SparseMatR(node: *const c_void, mat: *mut c_void, default_mat: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_read_const_FileNodeR_doubleR_double(node: *const c_void, value: *mut f64, default_value: f64, ocvrs_return: *mut Result<()>); + pub fn cv_read_const_FileNodeR_floatR_float(node: *const c_void, value: *mut f32, default_value: f32, ocvrs_return: *mut Result<()>); + pub fn cv_read_const_FileNodeR_int64_tR_int64_t(node: *const c_void, value: *mut i64, default_value: i64, ocvrs_return: *mut Result<()>); + pub fn cv_read_const_FileNodeR_intR_int(node: *const c_void, value: *mut i32, default_value: i32, ocvrs_return: *mut Result<()>); + pub fn cv_read_const_FileNodeR_stringR_const_stringR(node: *const c_void, value: *mut *mut c_void, default_value: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_read_const_FileNodeR_vectorLDMatchGR(node: *const c_void, matches: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_read_const_FileNodeR_vectorLKeyPointGR(node: *const c_void, keypoints: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_rectangleIntersectionArea_const_Rect2dR_const_Rect2dR(a: *const core::Rect2d, b: *const core::Rect2d, ocvrs_return: *mut Result); + pub fn cv_reduceArgMax_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, axis: i32, ocvrs_return: *mut Result<()>); + pub fn cv_reduceArgMax_const__InputArrayR_const__OutputArrayR_int_bool(src: *const c_void, dst: *const c_void, axis: i32, last_index: bool, ocvrs_return: *mut Result<()>); + pub fn cv_reduceArgMin_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, axis: i32, ocvrs_return: *mut Result<()>); + pub fn cv_reduceArgMin_const__InputArrayR_const__OutputArrayR_int_bool(src: *const c_void, dst: *const c_void, axis: i32, last_index: bool, ocvrs_return: *mut Result<()>); + pub fn cv_reduce_const__InputArrayR_const__OutputArrayR_int_int(src: *const c_void, dst: *const c_void, dim: i32, rtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_reduce_const__InputArrayR_const__OutputArrayR_int_int_int(src: *const c_void, dst: *const c_void, dim: i32, rtype: i32, dtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_repeat_const_MatR_int_int(src: *const c_void, ny: i32, nx: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_repeat_const__InputArrayR_int_int_const__OutputArrayR(src: *const c_void, ny: i32, nx: i32, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_rotate_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, rotate_code: i32, ocvrs_return: *mut Result<()>); + pub fn cv_samples_addSamplesDataSearchPath_const_StringR(path: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_samples_addSamplesDataSearchSubDirectory_const_StringR(subdir: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_samples_findFileOrKeep_const_StringR(relative_path: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_samples_findFileOrKeep_const_StringR_bool(relative_path: *const c_char, silent_mode: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_samples_findFile_const_StringR(relative_path: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_samples_findFile_const_StringR_bool_bool(relative_path: *const c_char, required: bool, silent_mode: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_scaleAdd_const__InputArrayR_double_const__InputArrayR_const__OutputArrayR(src1: *const c_void, alpha: f64, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_setBreakOnError_bool(flag: bool, ocvrs_return: *mut Result); + pub fn cv_setIdentity_const__InputOutputArrayR(mtx: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_setIdentity_const__InputOutputArrayR_const_ScalarR(mtx: *const c_void, s: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_setNumThreads_int(nthreads: i32, ocvrs_return: *mut Result<()>); + pub fn cv_setRNGSeed_int(seed: i32, ocvrs_return: *mut Result<()>); + pub fn cv_setUseOpenVX_bool(flag: bool, ocvrs_return: *mut Result<()>); + pub fn cv_setUseOptimized_bool(onoff: bool, ocvrs_return: *mut Result<()>); + pub fn cv_solveCubic_const__InputArrayR_const__OutputArrayR(coeffs: *const c_void, roots: *const c_void, ocvrs_return: *mut Result); + pub fn cv_solveLP_const__InputArrayR_const__InputArrayR_const__OutputArrayR(func: *const c_void, constr: *const c_void, z: *const c_void, ocvrs_return: *mut Result); + pub fn cv_solveLP_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double(func: *const c_void, constr: *const c_void, z: *const c_void, constr_eps: f64, ocvrs_return: *mut Result); + pub fn cv_solvePoly_const__InputArrayR_const__OutputArrayR(coeffs: *const c_void, roots: *const c_void, ocvrs_return: *mut Result); + pub fn cv_solvePoly_const__InputArrayR_const__OutputArrayR_int(coeffs: *const c_void, roots: *const c_void, max_iters: i32, ocvrs_return: *mut Result); + pub fn cv_solve_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result); + pub fn cv_solve_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(src1: *const c_void, src2: *const c_void, dst: *const c_void, flags: i32, ocvrs_return: *mut Result); + pub fn cv_sortIdx_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_sort_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_split_const_MatR_MatX(src: *const c_void, mvbegin: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_split_const__InputArrayR_const__OutputArrayR(m: *const c_void, mv: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_sqrt_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_subtract_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_subtract_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__InputArrayR_int(src1: *const c_void, src2: *const c_void, dst: *const c_void, mask: *const c_void, dtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_sum_const__InputArrayR(src: *const c_void, ocvrs_return: *mut Result); + pub fn cv_swap_MatR_MatR(a: *mut c_void, b: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_swap_UMatR_UMatR(a: *mut c_void, b: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_tempfile(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_tempfile_const_charX(suffix: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_terminate_int_const_StringR_const_charX_const_charX_int(code: i32, err: *const c_char, func: *const c_char, file: *const c_char, line: i32); + pub fn cv_trace_const__InputArrayR(mtx: *const c_void, ocvrs_return: *mut Result); + pub fn cv_transform_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src: *const c_void, dst: *const c_void, m: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_transposeND_const__InputArrayR_const_vectorLintGR_const__OutputArrayR(src: *const c_void, order: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_transpose_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_typeToString_int(typ: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_useOpenVX(ocvrs_return: *mut Result); + pub fn cv_useOptimized(ocvrs_return: *mut Result); + pub fn cv_utils_copyMatAndDumpNamedArguments_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_copyMatAndDumpNamedArguments_const__InputArrayR_const__OutputArrayR_const_FunctionParamsR(src: *const c_void, dst: *const c_void, params: *const core::FunctionParams, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpBool_bool(argument: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpCString_const_charX(argument: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpDouble_double(argument: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpFloat_float(argument: f32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpInputArrayOfArrays_const__InputArrayR(argument: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpInputArray_const__InputArrayR(argument: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpInputOutputArrayOfArrays_const__InputOutputArrayR(argument: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpInputOutputArray_const__InputOutputArrayR(argument: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpInt64_int64_t(argument: i64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpInt_int(argument: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpRange_const_RangeR(argument: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpRect_const_RectR(argument: *const core::Rect, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpRotatedRect_const_RotatedRectR(argument: *const core::RotatedRect, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpSizeT_size_t(argument: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpString_const_StringR(argument: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpTermCriteria_const_TermCriteriaR(argument: *const core::TermCriteria, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpVec2i(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpVec2i_const_Vec2i(value: *const core::Vec2i, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpVectorOfDouble_const_vectorLdoubleGR(vec: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpVectorOfInt_const_vectorLintGR(vec: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_dumpVectorOfRect_const_vectorLRectGR(vec: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_fs_getCacheDirectoryForDownloads(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_generateVectorOfInt_size_t_vectorLintGR(len: size_t, vec: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_utils_generateVectorOfMat_size_t_int_int_int_vectorLMatGR(len: size_t, rows: i32, cols: i32, dtype: i32, vec: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_utils_generateVectorOfRect_size_t_vectorLRectGR(len: size_t, vec: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_utils_getThreadID(ocvrs_return: *mut Result); + pub fn cv_utils_logging_getLogLevel(ocvrs_return: *mut Result); + pub fn cv_utils_logging_getLogTagLevel_const_charX(tag: *const c_char, ocvrs_return: *mut Result); + pub fn cv_utils_logging_internal_getGlobalLogTag(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_logging_internal_replaceWriteLogMessageEx_WriteLogMessageExFuncType(f: Option ()>, ocvrs_return: *mut Result<()>); + pub fn cv_utils_logging_internal_replaceWriteLogMessage_WriteLogMessageFuncType(f: Option ()>, ocvrs_return: *mut Result<()>); + pub fn cv_utils_logging_internal_writeLogMessageEx_LogLevel_const_charX_const_charX_int_const_charX_const_charX(log_level: core::LogLevel, tag: *const c_char, file: *const c_char, line: i32, func: *const c_char, message: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_utils_logging_internal_writeLogMessage_LogLevel_const_charX(log_level: core::LogLevel, message: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_utils_logging_registerLogTag_LogTagX(plogtag: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_utils_logging_setLogLevel_LogLevel(log_level: core::LogLevel, ocvrs_return: *mut Result); + pub fn cv_utils_logging_setLogTagLevel_const_charX_LogLevel(tag: *const c_char, level: core::LogLevel, ocvrs_return: *mut Result<()>); + pub fn cv_utils_nested_testEchoBooleanFunction_bool(flag: bool, ocvrs_return: *mut Result); + pub fn cv_utils_testAsyncArray_const__InputArrayR(argument: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_testAsyncException(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_testOverloadResolution_const_RectR(rect: *const core::Rect, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_testOverloadResolution_int(value: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_testOverloadResolution_int_const_PointR(value: i32, point: *const core::Point, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_testOverwriteNativeMethod_int(argument: i32, ocvrs_return: *mut Result); + pub fn cv_utils_testRaiseGeneralException(ocvrs_return: *mut Result<()>); + pub fn cv_utils_testReservedKeywordConversion_int(positional_argument: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_testReservedKeywordConversion_int_int_int(positional_argument: i32, lambda: i32, from: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_testRotatedRectVector_float_float_float_float_float(x: f32, y: f32, w: f32, h: f32, angle: f32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_testRotatedRect_float_float_float_float_float(x: f32, y: f32, w: f32, h: f32, angle: f32, ocvrs_return: *mut Result); + pub fn cv_va_intel_convertFromVASurface_VADisplay_VASurfaceID_Size_const__OutputArrayR(display: core::VADisplay, surface: core::VASurfaceID, size: *const core::Size, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_va_intel_convertToVASurface_VADisplay_const__InputArrayR_VASurfaceID_Size(display: core::VADisplay, src: *const c_void, surface: core::VASurfaceID, size: *const core::Size, ocvrs_return: *mut Result<()>); + pub fn cv_va_intel_ocl_initializeContextFromVA_VADisplay(display: core::VADisplay, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_va_intel_ocl_initializeContextFromVA_VADisplay_bool(display: core::VADisplay, try_interop: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_vconcat_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_vconcat_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_writeScalar_FileStorageR_const_StringR(fs: *mut c_void, value: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_writeScalar_FileStorageR_double(fs: *mut c_void, value: f64, ocvrs_return: *mut Result<()>); + pub fn cv_writeScalar_FileStorageR_float(fs: *mut c_void, value: f32, ocvrs_return: *mut Result<()>); + pub fn cv_writeScalar_FileStorageR_int(fs: *mut c_void, value: i32, ocvrs_return: *mut Result<()>); + pub fn cv_writeScalar_FileStorageR_int64_t(fs: *mut c_void, value: i64, ocvrs_return: *mut Result<()>); + pub fn cv_write_FileStorageR_const_StringR_const_MatR(fs: *mut c_void, name: *const c_char, value: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_write_FileStorageR_const_StringR_const_SparseMatR(fs: *mut c_void, name: *const c_char, value: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_write_FileStorageR_const_StringR_const_StringR(fs: *mut c_void, name: *const c_char, value: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_write_FileStorageR_const_StringR_const_vectorLDMatchGR(fs: *mut c_void, name: *const c_char, value: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_write_FileStorageR_const_StringR_const_vectorLKeyPointGR(fs: *mut c_void, name: *const c_char, value: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_write_FileStorageR_const_StringR_double(fs: *mut c_void, name: *const c_char, value: f64, ocvrs_return: *mut Result<()>); + pub fn cv_write_FileStorageR_const_StringR_float(fs: *mut c_void, name: *const c_char, value: f32, ocvrs_return: *mut Result<()>); + pub fn cv_write_FileStorageR_const_StringR_int(fs: *mut c_void, name: *const c_char, value: i32, ocvrs_return: *mut Result<()>); + pub fn cv_write_FileStorageR_const_StringR_int64_t(fs: *mut c_void, name: *const c_char, value: i64, ocvrs_return: *mut Result<()>); + pub fn cv_Algorithm_clear(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Algorithm_write_const_FileStorageR(instance: *const c_void, fs: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Algorithm_write_const_FileStorageR_const_StringR(instance: *const c_void, fs: *mut c_void, name: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_Algorithm_write_const_const_PtrLFileStorageGR_const_StringR(instance: *const c_void, fs: *const c_void, name: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_Algorithm_write_const_const_PtrLFileStorageGR(instance: *const c_void, fs: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Algorithm_read_const_FileNodeR(instance: *mut c_void, fn_: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Algorithm_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_Algorithm_save_const_const_StringR(instance: *const c_void, filename: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_Algorithm_getDefaultName_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Algorithm_to_ConjGradSolver(instance: *mut c_void) -> *mut c_void; + pub fn cv_Algorithm_to_DownhillSolver(instance: *mut c_void) -> *mut c_void; + pub fn cv_Algorithm_to_MinProblemSolver(instance: *mut c_void) -> *mut c_void; + pub fn cv_Algorithm_delete(instance: *mut c_void); + pub fn cv_AsyncArray_AsyncArray() -> *mut c_void; + pub fn cv_AsyncArray_AsyncArray_const_AsyncArrayR(o: *const c_void) -> *mut c_void; + pub fn cv_AsyncArray_operatorST_const_AsyncArrayR(instance: *mut c_void, o: *const c_void); + pub fn cv_AsyncArray_release(instance: *mut c_void); + pub fn cv_AsyncArray_get_const_const__OutputArrayR(instance: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_AsyncArray_get_const_const__OutputArrayR_int64_t(instance: *const c_void, dst: *const c_void, timeout_ns: i64, ocvrs_return: *mut Result); + pub fn cv_AsyncArray_get_const_const__OutputArrayR_double(instance: *const c_void, dst: *const c_void, timeout_ns: f64, ocvrs_return: *mut Result); + pub fn cv_AsyncArray_wait_for_const_int64_t(instance: *const c_void, timeout_ns: i64, ocvrs_return: *mut Result); + pub fn cv_AsyncArray_wait_for_const_double(instance: *const c_void, timeout_ns: f64, ocvrs_return: *mut Result); + pub fn cv_AsyncArray_valid_const(instance: *const c_void) -> bool; + pub fn cv_AsyncArray_AsyncArray_AsyncArrayRR(o: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_AsyncArray_operatorST_AsyncArrayRR(instance: *mut c_void, o: *mut c_void); + pub fn cv_AsyncArray_delete(instance: *mut c_void); + pub fn cv_AsyncPromise_AsyncPromise() -> *mut c_void; + pub fn cv_AsyncPromise_AsyncPromise_const_AsyncPromiseR(o: *const c_void) -> *mut c_void; + pub fn cv_AsyncPromise_operatorST_const_AsyncPromiseR(instance: *mut c_void, o: *const c_void); + pub fn cv_AsyncPromise_release(instance: *mut c_void); + pub fn cv_AsyncPromise_getArrayResult(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_AsyncPromise_setValue_const__InputArrayR(instance: *mut c_void, value: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_AsyncPromise_setException_const_ExceptionR(instance: *mut c_void, exception: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_AsyncPromise_AsyncPromise_AsyncPromiseRR(o: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_AsyncPromise_operatorST_AsyncPromiseRR(instance: *mut c_void, o: *mut c_void); + pub fn cv_AsyncPromise__getImpl_const(instance: *const c_void) -> *mut c_void; + pub fn cv_AsyncPromise_delete(instance: *mut c_void); + pub fn cv_CommandLineParser_CommandLineParser_int_const_charXX_const_StringR(argc: i32, argv: *const *const c_char, keys: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_CommandLineParser_CommandLineParser_const_CommandLineParserR(parser: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_CommandLineParser_operatorST_const_CommandLineParserR(instance: *mut c_void, parser: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_CommandLineParser_getPathToApplication_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_CommandLineParser_get_bool_const_const_StringR_bool(instance: *const c_void, name: *const c_char, space_delete: bool, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_bool_const_const_StringR(instance: *const c_void, name: *const c_char, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_int_const_const_StringR_bool(instance: *const c_void, name: *const c_char, space_delete: bool, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_int_const_const_StringR(instance: *const c_void, name: *const c_char, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_double_const_const_StringR_bool(instance: *const c_void, name: *const c_char, space_delete: bool, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_double_const_const_StringR(instance: *const c_void, name: *const c_char, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_cv_String_const_const_StringR_bool(instance: *const c_void, name: *const c_char, space_delete: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_CommandLineParser_get_cv_String_const_const_StringR(instance: *const c_void, name: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_CommandLineParser_get_uint64_t_const_const_StringR_bool(instance: *const c_void, name: *const c_char, space_delete: bool, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_uint64_t_const_const_StringR(instance: *const c_void, name: *const c_char, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_bool_const_int_bool(instance: *const c_void, index: i32, space_delete: bool, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_bool_const_int(instance: *const c_void, index: i32, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_int_const_int_bool(instance: *const c_void, index: i32, space_delete: bool, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_int_const_int(instance: *const c_void, index: i32, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_double_const_int_bool(instance: *const c_void, index: i32, space_delete: bool, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_double_const_int(instance: *const c_void, index: i32, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_cv_String_const_int_bool(instance: *const c_void, index: i32, space_delete: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_CommandLineParser_get_cv_String_const_int(instance: *const c_void, index: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_CommandLineParser_get_uint64_t_const_int_bool(instance: *const c_void, index: i32, space_delete: bool, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_get_uint64_t_const_int(instance: *const c_void, index: i32, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_has_const_const_StringR(instance: *const c_void, name: *const c_char, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_check_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_CommandLineParser_about_const_StringR(instance: *mut c_void, message: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_CommandLineParser_printMessage_const(instance: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_CommandLineParser_printErrors_const(instance: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_CommandLineParser_delete(instance: *mut c_void); + pub fn cv_ConjGradSolver_create_const_PtrLFunctionGR_TermCriteria(f: *const c_void, termcrit: *const core::TermCriteria, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ConjGradSolver_create(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ConjGradSolver_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_ConjGradSolver_to_MinProblemSolver(instance: *mut c_void) -> *mut c_void; + pub fn cv_ConjGradSolver_delete(instance: *mut c_void); + pub fn cv_DMatch_operatorL_const_const_DMatchR(instance: *const c_void, m: *const core::DMatch, ocvrs_return: *mut Result); + pub fn cv_DownhillSolver_getInitStep_const_const__OutputArrayR(instance: *const c_void, step: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_DownhillSolver_setInitStep_const__InputArrayR(instance: *mut c_void, step: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_DownhillSolver_create_const_PtrLFunctionGR_const__InputArrayR_TermCriteria(f: *const c_void, init_step: *const c_void, termcrit: *const core::TermCriteria, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_DownhillSolver_create(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_DownhillSolver_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_DownhillSolver_to_MinProblemSolver(instance: *mut c_void) -> *mut c_void; + pub fn cv_DownhillSolver_delete(instance: *mut c_void); + pub fn cv_Exception_Exception(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Exception_Exception_int_const_StringR_const_StringR_const_StringR_int(_code: i32, _err: *const c_char, _func: *const c_char, _file: *const c_char, _line: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Exception_what_const(instance: *const c_void) -> *mut c_void; + pub fn cv_Exception_formatMessage(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Exception_propMsg_const(instance: *const c_void) -> *mut c_void; + pub fn cv_Exception_propMsg_const_String(instance: *mut c_void, val: *const c_char); + pub fn cv_Exception_propCode_const(instance: *const c_void) -> i32; + pub fn cv_Exception_propCode_const_int(instance: *mut c_void, val: i32); + pub fn cv_Exception_propErr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_Exception_propErr_const_String(instance: *mut c_void, val: *const c_char); + pub fn cv_Exception_propFunc_const(instance: *const c_void) -> *mut c_void; + pub fn cv_Exception_propFunc_const_String(instance: *mut c_void, val: *const c_char); + pub fn cv_Exception_propFile_const(instance: *const c_void) -> *mut c_void; + pub fn cv_Exception_propFile_const_String(instance: *mut c_void, val: *const c_char); + pub fn cv_Exception_propLine_const(instance: *const c_void) -> i32; + pub fn cv_Exception_propLine_const_int(instance: *mut c_void, val: i32); + pub fn cv_Exception_delete(instance: *mut c_void); + pub fn cv_FileNode_operatorST_const_FileNodeR(instance: *mut c_void, node: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_FileNode_operator___const_const_StringR(instance: *const c_void, nodename: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileNode_operator___const_const_charX(instance: *const c_void, nodename: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileNode_operator___const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileNode_keys_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileNode_type_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_isNone_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_isSeq_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_isMap_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_isInt_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_isReal_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_isString_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_isNamed_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_name_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileNode_size_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_rawSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_operator_int_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_operator_int64_t_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_operator_float_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_operator_double_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_operator_std_string_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileNode_isMap_int(flags: i32, ocvrs_return: *mut Result); + pub fn cv_FileNode_isSeq_int(flags: i32, ocvrs_return: *mut Result); + pub fn cv_FileNode_isCollection_int(flags: i32, ocvrs_return: *mut Result); + pub fn cv_FileNode_isEmptyCollection_int(flags: i32, ocvrs_return: *mut Result); + pub fn cv_FileNode_isFlow_int(flags: i32, ocvrs_return: *mut Result); + pub fn cv_FileNode_ptr(instance: *mut c_void, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_FileNode_ptr_const(instance: *const c_void, ocvrs_return: *mut Result<*const u8>); + pub fn cv_FileNode_readRaw_const_const_StringR_voidX_size_t(instance: *const c_void, fmt: *const c_char, vec: *mut c_void, len: size_t, ocvrs_return: *mut Result<()>); + pub fn cv_FileNode_setValue_int_const_voidX_int(instance: *mut c_void, typ: i32, value: *const c_void, len: i32, ocvrs_return: *mut Result<()>); + pub fn cv_FileNode_real_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNode_string_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileNode_mat_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileNode_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_FileNode_propBlockIdx_const(instance: *const c_void) -> size_t; + pub fn cv_FileNode_propBlockIdx_const_size_t(instance: *mut c_void, val: size_t); + pub fn cv_FileNode_propOfs_const(instance: *const c_void) -> size_t; + pub fn cv_FileNode_propOfs_const_size_t(instance: *mut c_void, val: size_t); + pub fn cv_FileNode_delete(instance: *mut c_void); + pub fn cv_FileNodeIterator_operatorST_const_FileNodeIteratorR(instance: *mut c_void, it: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_FileNodeIterator_operatorX_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileNodeIterator_operatorAA(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileNodeIterator_readRaw_const_StringR_voidX_size_t(instance: *mut c_void, fmt: *const c_char, vec: *mut c_void, len: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileNodeIterator_remaining_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNodeIterator_equalTo_const_const_FileNodeIteratorR(instance: *const c_void, it: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileNodeIterator_delete(instance: *mut c_void); + pub fn cv_FileStorage_FileStorage(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileStorage_FileStorage_const_StringR_int_const_StringR(filename: *const c_char, flags: i32, encoding: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileStorage_FileStorage_const_StringR_int(filename: *const c_char, flags: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileStorage_open_const_StringR_int_const_StringR(instance: *mut c_void, filename: *const c_char, flags: i32, encoding: *const c_char, ocvrs_return: *mut Result); + pub fn cv_FileStorage_open_const_StringR_int(instance: *mut c_void, filename: *const c_char, flags: i32, ocvrs_return: *mut Result); + pub fn cv_FileStorage_isOpened_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileStorage_release(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_FileStorage_releaseAndGetString(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileStorage_write_const_StringR_int(instance: *mut c_void, name: *const c_char, val: i32, ocvrs_return: *mut Result<()>); + pub fn cv_FileStorage_write_const_StringR_int64_t(instance: *mut c_void, name: *const c_char, val: i64, ocvrs_return: *mut Result<()>); + pub fn cv_FileStorage_write_const_StringR_double(instance: *mut c_void, name: *const c_char, val: f64, ocvrs_return: *mut Result<()>); + pub fn cv_FileStorage_write_const_StringR_const_StringR(instance: *mut c_void, name: *const c_char, val: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_FileStorage_write_const_StringR_const_MatR(instance: *mut c_void, name: *const c_char, val: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_FileStorage_write_const_StringR_const_vectorLStringGR(instance: *mut c_void, name: *const c_char, val: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_FileStorage_writeRaw_const_StringR_const_voidX_size_t(instance: *mut c_void, fmt: *const c_char, vec: *const c_void, len: size_t, ocvrs_return: *mut Result<()>); + pub fn cv_FileStorage_writeComment_const_StringR_bool(instance: *mut c_void, comment: *const c_char, append: bool, ocvrs_return: *mut Result<()>); + pub fn cv_FileStorage_writeComment_const_StringR(instance: *mut c_void, comment: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_FileStorage_startWriteStruct_const_StringR_int_const_StringR(instance: *mut c_void, name: *const c_char, flags: i32, type_name: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_FileStorage_startWriteStruct_const_StringR_int(instance: *mut c_void, name: *const c_char, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_FileStorage_endWriteStruct(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_FileStorage_getDefaultObjectName_const_StringR(filename: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_FileStorage_getFormat_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_FileStorage_propState_const(instance: *const c_void) -> i32; + pub fn cv_FileStorage_propState_const_int(instance: *mut c_void, val: i32); + pub fn cv_FileStorage_propElname_const(instance: *const c_void) -> *mut c_void; + pub fn cv_FileStorage_propElname_const_string(instance: *mut c_void, val: *const c_char); + pub fn cv_FileStorage_delete(instance: *mut c_void); + pub fn cv_Formatted_next(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Formatted_reset(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Formatted_delete(instance: *mut c_void); + pub fn cv_Formatter_format_const_const_MatR(instance: *const c_void, mtx: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Formatter_set16fPrecision_int(instance: *mut c_void, p: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Formatter_set16fPrecision(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Formatter_set32fPrecision_int(instance: *mut c_void, p: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Formatter_set32fPrecision(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Formatter_set64fPrecision_int(instance: *mut c_void, p: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Formatter_set64fPrecision(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Formatter_setMultiline_bool(instance: *mut c_void, ml: bool, ocvrs_return: *mut Result<()>); + pub fn cv_Formatter_setMultiline(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Formatter_get_FormatType(fmt: core::Formatter_FormatType, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Formatter_get(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Formatter_delete(instance: *mut c_void); + pub fn cv_Hamming_operator___const_const_unsigned_charX_const_unsigned_charX_int(instance: *const c_void, a: *const u8, b: *const u8, size: i32, ocvrs_return: *mut Result); + pub fn cv_Hamming_defaultNew_const() -> *mut c_void; + pub fn cv_Hamming_delete(instance: *mut c_void); + pub fn cv_KeyPoint_hash_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_KeyPoint_convert_const_vectorLKeyPointGR_vectorLPoint2fGR_const_vectorLintGR(keypoints: *const c_void, points2f: *mut c_void, keypoint_indexes: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_KeyPoint_convert_const_vectorLKeyPointGR_vectorLPoint2fGR(keypoints: *const c_void, points2f: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_KeyPoint_convert_const_vectorLPoint2fGR_vectorLKeyPointGR_float_float_int_int(points2f: *const c_void, keypoints: *mut c_void, size: f32, response: f32, octave: i32, class_id: i32, ocvrs_return: *mut Result<()>); + pub fn cv_KeyPoint_convert_const_vectorLPoint2fGR_vectorLKeyPointGR(points2f: *const c_void, keypoints: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_KeyPoint_overlap_const_KeyPointR_const_KeyPointR(kp1: *const c_void, kp2: *const c_void, ocvrs_return: *mut Result); + pub fn cv_KeyPoint_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_KeyPoint_propPt_const(instance: *const c_void, ocvrs_return: *mut core::Point2f); + pub fn cv_KeyPoint_propPt_const_Point2f(instance: *mut c_void, val: *const core::Point2f); + pub fn cv_KeyPoint_propSize_const(instance: *const c_void) -> f32; + pub fn cv_KeyPoint_propSize_const_float(instance: *mut c_void, val: f32); + pub fn cv_KeyPoint_propAngle_const(instance: *const c_void) -> f32; + pub fn cv_KeyPoint_propAngle_const_float(instance: *mut c_void, val: f32); + pub fn cv_KeyPoint_propResponse_const(instance: *const c_void) -> f32; + pub fn cv_KeyPoint_propResponse_const_float(instance: *mut c_void, val: f32); + pub fn cv_KeyPoint_propOctave_const(instance: *const c_void) -> i32; + pub fn cv_KeyPoint_propOctave_const_int(instance: *mut c_void, val: i32); + pub fn cv_KeyPoint_propClass_id_const(instance: *const c_void) -> i32; + pub fn cv_KeyPoint_propClass_id_const_int(instance: *mut c_void, val: i32); + pub fn cv_KeyPoint_delete(instance: *mut c_void); + pub fn cv_LDA_LDA_int(num_components: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LDA_LDA(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LDA_LDA_const__InputArrayR_const__InputArrayR_int(src: *const c_void, labels: *const c_void, num_components: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LDA_LDA_const__InputArrayR_const__InputArrayR(src: *const c_void, labels: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LDA_save_const_const_StringR(instance: *const c_void, filename: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_LDA_load_const_StringR(instance: *mut c_void, filename: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_LDA_save_const_FileStorageR(instance: *const c_void, fs: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_LDA_load_const_FileStorageR(instance: *mut c_void, node: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_LDA_compute_const__InputArrayR_const__InputArrayR(instance: *mut c_void, src: *const c_void, labels: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_LDA_project_const__InputArrayR(instance: *mut c_void, src: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LDA_reconstruct_const__InputArrayR(instance: *mut c_void, src: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LDA_eigenvectors_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LDA_eigenvalues_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LDA_subspaceProject_const__InputArrayR_const__InputArrayR_const__InputArrayR(w: *const c_void, mean: *const c_void, src: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LDA_subspaceReconstruct_const__InputArrayR_const__InputArrayR_const__InputArrayR(w: *const c_void, mean: *const c_void, src: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LDA_delete(instance: *mut c_void); + pub fn cv_Mat_row_const_int(instance: *const c_void, y: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_row_int(instance: *mut c_void, y: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_col_const_int(instance: *const c_void, x: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_col_int(instance: *mut c_void, x: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_rowRange_const_int_int(instance: *const c_void, startrow: i32, endrow: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_rowRange_int_int(instance: *mut c_void, startrow: i32, endrow: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_rowRange_const_const_RangeR(instance: *const c_void, r: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_rowRange_const_RangeR(instance: *mut c_void, r: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_colRange_const_int_int(instance: *const c_void, startcol: i32, endcol: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_colRange_int_int(instance: *mut c_void, startcol: i32, endcol: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_colRange_const_const_RangeR(instance: *const c_void, r: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_colRange_const_RangeR(instance: *mut c_void, r: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_diag_const_int(instance: *const c_void, d: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_diag(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_diag_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_diag_int(instance: *mut c_void, d: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_diag_const_MatR(d: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_clone_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_copyTo_const_const__OutputArrayR(instance: *const c_void, m: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_copyTo_const_const__OutputArrayR_const__InputArrayR(instance: *const c_void, m: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_copyAt_const_const__OutputArrayR(instance: *const c_void, m: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_copyAt_const_const__OutputArrayR_const__InputArrayR(instance: *const c_void, m: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_convertTo_const_const__OutputArrayR_int_double_double(instance: *const c_void, m: *const c_void, rtype: i32, alpha: f64, beta: f64, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_convertTo_const_const__OutputArrayR_int(instance: *const c_void, m: *const c_void, rtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_assignTo_const_MatR_int(instance: *const c_void, m: *mut c_void, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_assignTo_const_MatR(instance: *const c_void, m: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_operatorST_const_ScalarR(instance: *mut c_void, s: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_setTo_const__InputArrayR_const__InputArrayR(instance: *mut c_void, value: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_setTo_const__InputArrayR(instance: *mut c_void, value: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_reshape_const_int_int(instance: *const c_void, cn: i32, rows: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_reshape_int(instance: *mut c_void, cn: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_reshape_const_int(instance: *const c_void, cn: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_reshape_int_int(instance: *mut c_void, cn: i32, rows: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_reshape_const_int_int_const_intX(instance: *const c_void, cn: i32, newndims: i32, newsz: *const i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_reshape_int_int_const_intX(instance: *mut c_void, cn: i32, newndims: i32, newsz: *const i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_reshape_const_int_const_vectorLintGR(instance: *const c_void, cn: i32, newshape: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_reshape_int_const_vectorLintGR(instance: *mut c_void, cn: i32, newshape: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_reinterpret_const_int(instance: *const c_void, typ: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_cross_const_const__InputArrayR(instance: *const c_void, m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_dot_const_const__InputArrayR(instance: *const c_void, m: *const c_void, ocvrs_return: *mut Result); + pub fn cv_Mat_create_int_int_int(instance: *mut c_void, rows: i32, cols: i32, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_create_Size_int(instance: *mut c_void, size: *const core::Size, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_create_int_const_intX_int(instance: *mut c_void, ndims: i32, sizes: *const i32, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_create_const_vectorLintGR_int(instance: *mut c_void, sizes: *const c_void, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_addref(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_release(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_deallocate(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_reserve_size_t(instance: *mut c_void, sz: size_t, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_reserveBuffer_size_t(instance: *mut c_void, sz: size_t, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_resize_size_t(instance: *mut c_void, sz: size_t, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_resize_size_t_const_ScalarR(instance: *mut c_void, sz: size_t, s: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_push_back_const_MatR(instance: *mut c_void, m: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_pop_back_size_t(instance: *mut c_void, nelems: size_t, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_pop_back(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_locateROI_const_SizeR_PointR(instance: *const c_void, whole_size: *mut core::Size, ofs: *mut core::Point, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_adjustROI_int_int_int_int(instance: *mut c_void, dtop: i32, dbottom: i32, dleft: i32, dright: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_operator___const_Range_Range(instance: *const c_void, row_range: *mut c_void, col_range: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_operator___Range_Range(instance: *mut c_void, row_range: *mut c_void, col_range: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_operator___const_const_RectR(instance: *const c_void, roi: *const core::Rect, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_operator___const_RectR(instance: *mut c_void, roi: *const core::Rect, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_operator___const_const_vectorLRangeGR(instance: *const c_void, ranges: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_operator___const_vectorLRangeGR(instance: *mut c_void, ranges: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_isContinuous_const(instance: *const c_void) -> bool; + pub fn cv_Mat_isSubmatrix_const(instance: *const c_void) -> bool; + pub fn cv_Mat_elemSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_Mat_elemSize1_const(instance: *const c_void) -> size_t; + pub fn cv_Mat_type_const(instance: *const c_void) -> i32; + pub fn cv_Mat_depth_const(instance: *const c_void) -> i32; + pub fn cv_Mat_channels_const(instance: *const c_void) -> i32; + pub fn cv_Mat_step1_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv_Mat_step1_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_Mat_empty_const(instance: *const c_void) -> bool; + pub fn cv_Mat_total_const(instance: *const c_void) -> size_t; + pub fn cv_Mat_total_const_int_int(instance: *const c_void, start_dim: i32, end_dim: i32, ocvrs_return: *mut Result); + pub fn cv_Mat_total_const_int(instance: *const c_void, start_dim: i32, ocvrs_return: *mut Result); + pub fn cv_Mat_checkVector_const_int_int_bool(instance: *const c_void, elem_channels: i32, depth: i32, require_continuous: bool, ocvrs_return: *mut Result); + pub fn cv_Mat_checkVector_const_int(instance: *const c_void, elem_channels: i32, ocvrs_return: *mut Result); + pub fn cv_Mat_ptr_int(instance: *mut c_void, i0: i32, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_Mat_ptr(instance: *mut c_void, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_Mat_ptr_const_int(instance: *const c_void, i0: i32, ocvrs_return: *mut Result<*const u8>); + pub fn cv_Mat_ptr_const(instance: *const c_void, ocvrs_return: *mut Result<*const u8>); + pub fn cv_Mat_ptr_int_int(instance: *mut c_void, row: i32, col: i32, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_Mat_ptr_const_int_int(instance: *const c_void, row: i32, col: i32, ocvrs_return: *mut Result<*const u8>); + pub fn cv_Mat_ptr_int_int_int(instance: *mut c_void, i0: i32, i1: i32, i2: i32, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_Mat_ptr_const_int_int_int(instance: *const c_void, i0: i32, i1: i32, i2: i32, ocvrs_return: *mut Result<*const u8>); + pub fn cv_Mat_ptr_const_intX(instance: *mut c_void, idx: *const i32, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_Mat_ptr_const_const_intX(instance: *const c_void, idx: *const i32, ocvrs_return: *mut Result<*const u8>); + pub fn cv_Mat_operatorST_MatRR(instance: *mut c_void, m: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_updateContinuityFlag(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Mat_size_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_Mat_getDataDump_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Mat_propFlags_const(instance: *const c_void) -> i32; + pub fn cv_Mat_propFlags_const_int(instance: *mut c_void, val: i32); + pub fn cv_Mat_propDims_const(instance: *const c_void) -> i32; + pub fn cv_Mat_propDims_const_int(instance: *mut c_void, val: i32); + pub fn cv_Mat_propRows_const(instance: *const c_void) -> i32; + pub fn cv_Mat_propRows_const_int(instance: *mut c_void, val: i32); + pub fn cv_Mat_propCols_const(instance: *const c_void) -> i32; + pub fn cv_Mat_propCols_const_int(instance: *mut c_void, val: i32); + pub fn cv_Mat_propData_const(instance: *const c_void) -> *const u8; + pub fn cv_Mat_propData(instance: *mut c_void) -> *mut u8; + pub fn cv_Mat_propData_unsigned_charX(instance: *mut c_void, val: *const u8); + pub fn cv_Mat_propDatastart_const(instance: *const c_void) -> *const u8; + pub fn cv_Mat_propDataend_const(instance: *const c_void) -> *const u8; + pub fn cv_Mat_propDatalimit_const(instance: *const c_void) -> *const u8; + pub fn cv_Mat_propU(instance: *mut c_void) -> *mut c_void; + pub fn cv_Mat_propU_UMatDataX(instance: *mut c_void, val: *const c_void); + pub fn cv_Mat_propSize_const(instance: *const c_void) -> *mut c_void; + pub fn cv_Mat_propSize_const_MatSize(instance: *mut c_void, val: *const c_void); + pub fn cv_Mat_propStep_const(instance: *const c_void) -> *mut c_void; + pub fn cv_Mat_delete(instance: *mut c_void); + pub fn cv_MatConstIterator_operatorST_const_MatConstIteratorR(instance: *mut c_void, it: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_MatConstIterator_operatorX_const(instance: *const c_void, ocvrs_return: *mut Result<*const u8>); + pub fn cv_MatConstIterator_operator___const_ptrdiff_t(instance: *const c_void, i: ptrdiff_t, ocvrs_return: *mut Result<*const u8>); + pub fn cv_MatConstIterator_operatorSS(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatConstIterator_operatorAA(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatConstIterator_pos_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_MatConstIterator_pos_const_intX(instance: *const c_void, _idx: *mut i32, ocvrs_return: *mut Result<()>); + pub fn cv_MatConstIterator_lpos_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_MatConstIterator_seek_ptrdiff_t_bool(instance: *mut c_void, ofs: ptrdiff_t, relative: bool, ocvrs_return: *mut Result<()>); + pub fn cv_MatConstIterator_seek_ptrdiff_t(instance: *mut c_void, ofs: ptrdiff_t, ocvrs_return: *mut Result<()>); + pub fn cv_MatConstIterator_seek_const_intX_bool(instance: *mut c_void, _idx: *const i32, relative: bool, ocvrs_return: *mut Result<()>); + pub fn cv_MatConstIterator_seek_const_intX(instance: *mut c_void, _idx: *const i32, ocvrs_return: *mut Result<()>); + pub fn cv_MatConstIterator_type_const(instance: *const c_void) -> i32; + pub fn cv_MatConstIterator_propM_const(instance: *const c_void) -> *mut c_void; + pub fn cv_MatConstIterator_propElemSize_const(instance: *const c_void) -> size_t; + pub fn cv_MatConstIterator_propElemSize_const_size_t(instance: *mut c_void, val: size_t); + pub fn cv_MatConstIterator_propPtr_const(instance: *const c_void) -> *const u8; + pub fn cv_MatConstIterator_propSliceStart_const(instance: *const c_void) -> *const u8; + pub fn cv_MatConstIterator_propSliceEnd_const(instance: *const c_void) -> *const u8; + pub fn cv_MatConstIterator_delete(instance: *mut c_void); + pub fn cv_MatExpr_operator_cv_Mat_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_size_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_MatExpr_type_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_MatExpr_row_const_int(instance: *const c_void, y: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_col_const_int(instance: *const c_void, x: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_diag_const_int(instance: *const c_void, d: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_diag_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_operator___const_const_RangeR_const_RangeR(instance: *const c_void, row_range: *const c_void, col_range: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_operator___const_const_RectR(instance: *const c_void, roi: *const core::Rect, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_t_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_inv_const_int(instance: *const c_void, method: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_inv_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_mul_const_const_MatExprR_double(instance: *const c_void, e: *const c_void, scale: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_mul_const_const_MatExprR(instance: *const c_void, e: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_mul_const_const_MatR_double(instance: *const c_void, m: *const c_void, scale: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_mul_const_const_MatR(instance: *const c_void, m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_cross_const_const_MatR(instance: *const c_void, m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MatExpr_dot_const_const_MatR(instance: *const c_void, m: *const c_void, ocvrs_return: *mut Result); + pub fn cv_MatExpr_swap_MatExprR(instance: *mut c_void, b: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_MatExpr_propFlags_const(instance: *const c_void) -> i32; + pub fn cv_MatExpr_propFlags_const_int(instance: *mut c_void, val: i32); + pub fn cv_MatExpr_propA_const(instance: *const c_void) -> *mut c_void; + pub fn cv_MatExpr_propA_const_Mat(instance: *mut c_void, val: *const c_void); + pub fn cv_MatExpr_propB_const(instance: *const c_void) -> *mut c_void; + pub fn cv_MatExpr_propB_const_Mat(instance: *mut c_void, val: *const c_void); + pub fn cv_MatExpr_propC_const(instance: *const c_void) -> *mut c_void; + pub fn cv_MatExpr_propC_const_Mat(instance: *mut c_void, val: *const c_void); + pub fn cv_MatExpr_propAlpha_const(instance: *const c_void) -> f64; + pub fn cv_MatExpr_propAlpha_const_double(instance: *mut c_void, val: f64); + pub fn cv_MatExpr_propBeta_const(instance: *const c_void) -> f64; + pub fn cv_MatExpr_propBeta_const_double(instance: *mut c_void, val: f64); + pub fn cv_MatExpr_propS_const(instance: *const c_void, ocvrs_return: *mut core::Scalar); + pub fn cv_MatExpr_propS_const_Scalar(instance: *mut c_void, val: *const core::Scalar); + pub fn cv_MatExpr_delete(instance: *mut c_void); + pub fn cv_MatOp_delete(instance: *mut c_void); + pub fn cv_MatSize_MatSize_intX(_p: *mut i32) -> *mut c_void; + pub fn cv_MatSize_dims_const(instance: *const c_void) -> i32; + pub fn cv_MatSize_operator___const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_MatSize_operator___const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv_MatSize_operator___int(instance: *mut c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv_MatSize_operator_const_intX_const(instance: *const c_void) -> *const i32; + pub fn cv_MatSize_operatorEQ_const_const_MatSizeR(instance: *const c_void, sz: *const c_void) -> bool; + pub fn cv_MatSize_operatorNE_const_const_MatSizeR(instance: *const c_void, sz: *const c_void) -> bool; + pub fn cv_MatSize_propP_const(instance: *const c_void) -> *const i32; + pub fn cv_MatSize_propP(instance: *mut c_void) -> *mut i32; + pub fn cv_MatSize_propP_intX(instance: *mut c_void, val: *const i32); + pub fn cv_MatSize_delete(instance: *mut c_void); + pub fn cv_MatStep_MatStep() -> *mut c_void; + pub fn cv_MatStep_MatStep_size_t(s: size_t) -> *mut c_void; + pub fn cv_MatStep_operator___const_int(instance: *const c_void, i: i32) -> size_t; + pub fn cv_MatStep_operator___int(instance: *mut c_void, i: i32) -> size_t; + pub fn cv_MatStep_operator_size_t_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_MatStep_operatorST_size_t(instance: *mut c_void, s: size_t, ocvrs_return: *mut Result<()>); + pub fn cv_MatStep_propP_const(instance: *const c_void) -> *const size_t; + pub fn cv_MatStep_propP(instance: *mut c_void) -> *mut size_t; + pub fn cv_MatStep_propP_size_tX(instance: *mut c_void, val: *const size_t); + pub fn cv_MatStep_propBuf_const(instance: *const c_void) -> *const [size_t; 2]; + pub fn cv_MatStep_propBuf(instance: *mut c_void) -> *mut [size_t; 2]; + pub fn cv_MatStep_delete(instance: *mut c_void); + pub fn cv_Matx_AddOp_Matx_AddOp(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_AddOp_Matx_AddOp_const_Matx_AddOpR(unnamed: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_AddOp_delete(instance: *mut c_void); + pub fn cv_Matx_DivOp_Matx_DivOp(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_DivOp_Matx_DivOp_const_Matx_DivOpR(unnamed: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_DivOp_delete(instance: *mut c_void); + pub fn cv_Matx_MatMulOp_Matx_MatMulOp(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_MatMulOp_Matx_MatMulOp_const_Matx_MatMulOpR(unnamed: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_MatMulOp_delete(instance: *mut c_void); + pub fn cv_Matx_MulOp_Matx_MulOp(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_MulOp_Matx_MulOp_const_Matx_MulOpR(unnamed: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_MulOp_delete(instance: *mut c_void); + pub fn cv_Matx_ScaleOp_Matx_ScaleOp(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_ScaleOp_Matx_ScaleOp_const_Matx_ScaleOpR(unnamed: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_ScaleOp_delete(instance: *mut c_void); + pub fn cv_Matx_SubOp_Matx_SubOp(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_SubOp_Matx_SubOp_const_Matx_SubOpR(unnamed: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_SubOp_delete(instance: *mut c_void); + pub fn cv_Matx_TOp_Matx_TOp(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_TOp_Matx_TOp_const_Matx_TOpR(unnamed: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Matx_TOp_delete(instance: *mut c_void); + pub fn cv_MinProblemSolver_getFunction_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_MinProblemSolver_setFunction_const_PtrLFunctionGR(instance: *mut c_void, f: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_MinProblemSolver_getTermCriteria_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_MinProblemSolver_setTermCriteria_const_TermCriteriaR(instance: *mut c_void, termcrit: *const core::TermCriteria, ocvrs_return: *mut Result<()>); + pub fn cv_MinProblemSolver_minimize_const__InputOutputArrayR(instance: *mut c_void, x: *const c_void, ocvrs_return: *mut Result); + pub fn cv_MinProblemSolver_to_ConjGradSolver(instance: *mut c_void) -> *mut c_void; + pub fn cv_MinProblemSolver_to_DownhillSolver(instance: *mut c_void) -> *mut c_void; + pub fn cv_MinProblemSolver_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_MinProblemSolver_delete(instance: *mut c_void); + pub fn cv_MinProblemSolver_Function_getDims_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_MinProblemSolver_Function_getGradientEps_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_MinProblemSolver_Function_calc_const_const_doubleX(instance: *const c_void, x: *const f64, ocvrs_return: *mut Result); + pub fn cv_MinProblemSolver_Function_getGradient_const_doubleX_doubleX(instance: *mut c_void, x: *const f64, grad: *mut f64, ocvrs_return: *mut Result<()>); + pub fn cv_MinProblemSolver_Function_delete(instance: *mut c_void); + pub fn cv_Moments_Moments(ocvrs_return: *mut Result); + pub fn cv_Moments_Moments_double_double_double_double_double_double_double_double_double_double(m00: f64, m10: f64, m01: f64, m20: f64, m11: f64, m02: f64, m30: f64, m21: f64, m12: f64, m03: f64, ocvrs_return: *mut Result); + pub fn cv_PCA_PCA(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_PCA_PCA_const__InputArrayR_const__InputArrayR_int_int(data: *const c_void, mean: *const c_void, flags: i32, max_components: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_PCA_PCA_const__InputArrayR_const__InputArrayR_int(data: *const c_void, mean: *const c_void, flags: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_PCA_PCA_const__InputArrayR_const__InputArrayR_int_double(data: *const c_void, mean: *const c_void, flags: i32, retained_variance: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_PCA_operator___const__InputArrayR_const__InputArrayR_int_int(instance: *mut c_void, data: *const c_void, mean: *const c_void, flags: i32, max_components: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_PCA_operator___const__InputArrayR_const__InputArrayR_int(instance: *mut c_void, data: *const c_void, mean: *const c_void, flags: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_PCA_operator___const__InputArrayR_const__InputArrayR_int_double(instance: *mut c_void, data: *const c_void, mean: *const c_void, flags: i32, retained_variance: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_PCA_project_const_const__InputArrayR(instance: *const c_void, vec: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_PCA_project_const_const__InputArrayR_const__OutputArrayR(instance: *const c_void, vec: *const c_void, result: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_PCA_backProject_const_const__InputArrayR(instance: *const c_void, vec: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_PCA_backProject_const_const__InputArrayR_const__OutputArrayR(instance: *const c_void, vec: *const c_void, result: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_PCA_write_const_FileStorageR(instance: *const c_void, fs: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_PCA_read_const_FileNodeR(instance: *mut c_void, fn_: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_PCA_propEigenvectors_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PCA_propEigenvectors_const_Mat(instance: *mut c_void, val: *const c_void); + pub fn cv_PCA_propEigenvalues_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PCA_propEigenvalues_const_Mat(instance: *mut c_void, val: *const c_void); + pub fn cv_PCA_propMean_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PCA_propMean_const_Mat(instance: *mut c_void, val: *const c_void); + pub fn cv_PCA_delete(instance: *mut c_void); + pub fn cv_ParallelLoopBody_operator___const_const_RangeR(instance: *const c_void, range: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ParallelLoopBody_delete(instance: *mut c_void); + pub fn cv_RNG_next(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_operator_unsigned_char(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_operator_signed_char(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_operator_unsigned_short(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_operator_short(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_operator_unsigned_int(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_operator_int(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_operator_float(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_operator_double(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_operator__(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_operator___unsigned_int(instance: *mut c_void, n: u32, ocvrs_return: *mut Result); + pub fn cv_RNG_uniform_int_int(instance: *mut c_void, a: i32, b: i32, ocvrs_return: *mut Result); + pub fn cv_RNG_uniform_float_float(instance: *mut c_void, a: f32, b: f32, ocvrs_return: *mut Result); + pub fn cv_RNG_uniform_double_double(instance: *mut c_void, a: f64, b: f64, ocvrs_return: *mut Result); + pub fn cv_RNG_fill_const__InputOutputArrayR_int_const__InputArrayR_const__InputArrayR_bool(instance: *mut c_void, mat: *const c_void, dist_type: i32, a: *const c_void, b: *const c_void, saturate_range: bool, ocvrs_return: *mut Result<()>); + pub fn cv_RNG_fill_const__InputOutputArrayR_int_const__InputArrayR_const__InputArrayR(instance: *mut c_void, mat: *const c_void, dist_type: i32, a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_RNG_gaussian_double(instance: *mut c_void, sigma: f64, ocvrs_return: *mut Result); + pub fn cv_RNG_operatorEQ_const_const_RNGR(instance: *const c_void, other: *const c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_propState_const(instance: *const c_void) -> u64; + pub fn cv_RNG_propState_const_uint64_t(instance: *mut c_void, val: u64); + pub fn cv_RNG_delete(instance: *mut c_void); + pub fn cv_RNG_MT19937_RNG_MT19937(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_RNG_MT19937_RNG_MT19937_unsigned_int(s: u32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_RNG_MT19937_seed_unsigned_int(instance: *mut c_void, s: u32, ocvrs_return: *mut Result<()>); + pub fn cv_RNG_MT19937_next(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_MT19937_operator_int(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_MT19937_operator_unsigned_int(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_MT19937_operator_float(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_MT19937_operator_double(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_RNG_MT19937_operator___unsigned_int(instance: *mut c_void, n: u32, ocvrs_return: *mut Result); + pub fn cv_RNG_MT19937_uniform_int_int(instance: *mut c_void, a: i32, b: i32, ocvrs_return: *mut Result); + pub fn cv_RNG_MT19937_uniform_float_float(instance: *mut c_void, a: f32, b: f32, ocvrs_return: *mut Result); + pub fn cv_RNG_MT19937_uniform_double_double(instance: *mut c_void, a: f64, b: f64, ocvrs_return: *mut Result); + pub fn cv_RNG_MT19937_delete(instance: *mut c_void); + pub fn cv_Range_size_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_Range_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_Range_all(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Range_propStart_const(instance: *const c_void) -> i32; + pub fn cv_Range_propStart_const_int(instance: *mut c_void, val: i32); + pub fn cv_Range_propEnd_const(instance: *const c_void) -> i32; + pub fn cv_Range_propEnd_const_int(instance: *mut c_void, val: i32); + pub fn cv_Range_delete(instance: *mut c_void); + pub fn cv_RotatedRect_points_const_Point2fXX(instance: *const c_void, pts: *mut [core::Point2f; 4], ocvrs_return: *mut Result<()>); + pub fn cv_RotatedRect_points_const_vectorLPoint2fGR(instance: *const c_void, pts: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_RotatedRect_boundingRect_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_RotatedRect_boundingRect2f_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_SVD_SVD(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_SVD_SVD_const__InputArrayR_int(src: *const c_void, flags: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_SVD_SVD_const__InputArrayR(src: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_SVD_operator___const__InputArrayR_int(instance: *mut c_void, src: *const c_void, flags: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_SVD_operator___const__InputArrayR(instance: *mut c_void, src: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_SVD_compute_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int(src: *const c_void, w: *const c_void, u: *const c_void, vt: *const c_void, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_SVD_compute_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(src: *const c_void, w: *const c_void, u: *const c_void, vt: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SVD_compute_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, w: *const c_void, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_SVD_compute_const__InputArrayR_const__OutputArrayR(src: *const c_void, w: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SVD_backSubst_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(w: *const c_void, u: *const c_void, vt: *const c_void, rhs: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SVD_solveZ_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SVD_backSubst_const_const__InputArrayR_const__OutputArrayR(instance: *const c_void, rhs: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SVD_propU_const(instance: *const c_void) -> *mut c_void; + pub fn cv_SVD_propU_const_Mat(instance: *mut c_void, val: *const c_void); + pub fn cv_SVD_propW_const(instance: *const c_void) -> *mut c_void; + pub fn cv_SVD_propW_const_Mat(instance: *mut c_void, val: *const c_void); + pub fn cv_SVD_propVt_const(instance: *const c_void) -> *mut c_void; + pub fn cv_SVD_propVt_const_Mat(instance: *mut c_void, val: *const c_void); + pub fn cv_SVD_delete(instance: *mut c_void); + pub fn cv_SparseMat_operatorST_const_SparseMatR(instance: *mut c_void, m: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_operatorST_const_MatR(instance: *mut c_void, m: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_clone_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_SparseMat_copyTo_const_SparseMatR(instance: *const c_void, m: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_copyTo_const_MatR(instance: *const c_void, m: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_convertTo_const_SparseMatR_int_double(instance: *const c_void, m: *mut c_void, rtype: i32, alpha: f64, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_convertTo_const_SparseMatR_int(instance: *const c_void, m: *mut c_void, rtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_convertTo_const_MatR_int_double_double(instance: *const c_void, m: *mut c_void, rtype: i32, alpha: f64, beta: f64, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_convertTo_const_MatR_int(instance: *const c_void, m: *mut c_void, rtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_assignTo_const_SparseMatR_int(instance: *const c_void, m: *mut c_void, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_assignTo_const_SparseMatR(instance: *const c_void, m: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_create_int_const_intX_int(instance: *mut c_void, dims: i32, _sizes: *const i32, _type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_clear(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_addref(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_release(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_elemSize_const(instance: *const c_void) -> size_t; + pub fn cv_SparseMat_elemSize1_const(instance: *const c_void) -> size_t; + pub fn cv_SparseMat_type_const(instance: *const c_void) -> i32; + pub fn cv_SparseMat_depth_const(instance: *const c_void) -> i32; + pub fn cv_SparseMat_channels_const(instance: *const c_void) -> i32; + pub fn cv_SparseMat_size_const(instance: *const c_void, ocvrs_return: *mut Result<*const i32>); + pub fn cv_SparseMat_size_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv_SparseMat_dims_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_SparseMat_nzcount_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_SparseMat_hash_const_int(instance: *const c_void, i0: i32, ocvrs_return: *mut Result); + pub fn cv_SparseMat_hash_const_int_int(instance: *const c_void, i0: i32, i1: i32, ocvrs_return: *mut Result); + pub fn cv_SparseMat_hash_const_int_int_int(instance: *const c_void, i0: i32, i1: i32, i2: i32, ocvrs_return: *mut Result); + pub fn cv_SparseMat_hash_const_const_intX(instance: *const c_void, idx: *const i32, ocvrs_return: *mut Result); + pub fn cv_SparseMat_ptr_int_bool_size_tX(instance: *mut c_void, i0: i32, create_missing: bool, hashval: *mut size_t, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_SparseMat_ptr_int_bool(instance: *mut c_void, i0: i32, create_missing: bool, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_SparseMat_ptr_int_int_bool_size_tX(instance: *mut c_void, i0: i32, i1: i32, create_missing: bool, hashval: *mut size_t, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_SparseMat_ptr_int_int_bool(instance: *mut c_void, i0: i32, i1: i32, create_missing: bool, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_SparseMat_ptr_int_int_int_bool_size_tX(instance: *mut c_void, i0: i32, i1: i32, i2: i32, create_missing: bool, hashval: *mut size_t, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_SparseMat_ptr_int_int_int_bool(instance: *mut c_void, i0: i32, i1: i32, i2: i32, create_missing: bool, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_SparseMat_ptr_const_intX_bool_size_tX(instance: *mut c_void, idx: *const i32, create_missing: bool, hashval: *mut size_t, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_SparseMat_ptr_const_intX_bool(instance: *mut c_void, idx: *const i32, create_missing: bool, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_SparseMat_erase_int_int_size_tX(instance: *mut c_void, i0: i32, i1: i32, hashval: *mut size_t, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_erase_int_int(instance: *mut c_void, i0: i32, i1: i32, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_erase_int_int_int_size_tX(instance: *mut c_void, i0: i32, i1: i32, i2: i32, hashval: *mut size_t, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_erase_int_int_int(instance: *mut c_void, i0: i32, i1: i32, i2: i32, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_erase_const_intX_size_tX(instance: *mut c_void, idx: *const i32, hashval: *mut size_t, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_erase_const_intX(instance: *mut c_void, idx: *const i32, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_node_size_t(instance: *mut c_void, nidx: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_SparseMat_node_const_size_t(instance: *const c_void, nidx: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_SparseMat_newNode_const_intX_size_t(instance: *mut c_void, idx: *const i32, hashval: size_t, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_SparseMat_removeNode_size_t_size_t_size_t(instance: *mut c_void, hidx: size_t, nidx: size_t, previdx: size_t, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_resizeHashTab_size_t(instance: *mut c_void, newsize: size_t, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_propFlags_const(instance: *const c_void) -> i32; + pub fn cv_SparseMat_propFlags_const_int(instance: *mut c_void, val: i32); + pub fn cv_SparseMat_propHdr(instance: *mut c_void) -> *mut c_void; + pub fn cv_SparseMat_propHdr_HdrX(instance: *mut c_void, val: *const c_void); + pub fn cv_SparseMat_delete(instance: *mut c_void); + pub fn cv_SparseMat_Hdr_Hdr_int_const_intX_int(_dims: i32, _sizes: *const i32, _type: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_SparseMat_Hdr_clear(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMat_Hdr_propRefcount_const(instance: *const c_void) -> i32; + pub fn cv_SparseMat_Hdr_propRefcount_const_int(instance: *mut c_void, val: i32); + pub fn cv_SparseMat_Hdr_propDims_const(instance: *const c_void) -> i32; + pub fn cv_SparseMat_Hdr_propDims_const_int(instance: *mut c_void, val: i32); + pub fn cv_SparseMat_Hdr_propValueOffset_const(instance: *const c_void) -> i32; + pub fn cv_SparseMat_Hdr_propValueOffset_const_int(instance: *mut c_void, val: i32); + pub fn cv_SparseMat_Hdr_propNodeSize_const(instance: *const c_void) -> size_t; + pub fn cv_SparseMat_Hdr_propNodeSize_const_size_t(instance: *mut c_void, val: size_t); + pub fn cv_SparseMat_Hdr_propNodeCount_const(instance: *const c_void) -> size_t; + pub fn cv_SparseMat_Hdr_propNodeCount_const_size_t(instance: *mut c_void, val: size_t); + pub fn cv_SparseMat_Hdr_propFreeList_const(instance: *const c_void) -> size_t; + pub fn cv_SparseMat_Hdr_propFreeList_const_size_t(instance: *mut c_void, val: size_t); + pub fn cv_SparseMat_Hdr_propPool_const(instance: *const c_void) -> *mut c_void; + pub fn cv_SparseMat_Hdr_propPool_const_vectorLunsigned_charG(instance: *mut c_void, val: *const c_void); + pub fn cv_SparseMat_Hdr_propHashtab_const(instance: *const c_void) -> *mut c_void; + pub fn cv_SparseMat_Hdr_propHashtab_const_vectorLsize_tG(instance: *mut c_void, val: *const c_void); + pub fn cv_SparseMat_Hdr_propSize_const(instance: *const c_void) -> *const [i32; 32]; + pub fn cv_SparseMat_Hdr_propSize(instance: *mut c_void) -> *mut [i32; 32]; + pub fn cv_SparseMat_Hdr_delete(instance: *mut c_void); + pub fn cv_SparseMat_Node_defaultNew_const() -> *mut c_void; + pub fn cv_SparseMat_Node_propHashval_const(instance: *const c_void) -> size_t; + pub fn cv_SparseMat_Node_propHashval_const_size_t(instance: *mut c_void, val: size_t); + pub fn cv_SparseMat_Node_propNext_const(instance: *const c_void) -> size_t; + pub fn cv_SparseMat_Node_propNext_const_size_t(instance: *mut c_void, val: size_t); + pub fn cv_SparseMat_Node_propIdx_const(instance: *const c_void) -> *const [i32; 32]; + pub fn cv_SparseMat_Node_propIdx(instance: *mut c_void) -> *mut [i32; 32]; + pub fn cv_SparseMat_Node_delete(instance: *mut c_void); + pub fn cv_SparseMatConstIterator_operatorST_const_SparseMatConstIteratorR(instance: *mut c_void, it: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMatConstIterator_node_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_SparseMatConstIterator_operatorAA(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_SparseMatConstIterator_seekEnd(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMatConstIterator_propM_const(instance: *const c_void) -> *mut c_void; + pub fn cv_SparseMatConstIterator_propHashidx_const(instance: *const c_void) -> size_t; + pub fn cv_SparseMatConstIterator_propHashidx_const_size_t(instance: *mut c_void, val: size_t); + pub fn cv_SparseMatConstIterator_propPtr_const(instance: *const c_void) -> *const u8; + pub fn cv_SparseMatConstIterator_propPtr(instance: *mut c_void) -> *mut u8; + pub fn cv_SparseMatConstIterator_propPtr_unsigned_charX(instance: *mut c_void, val: *const u8); + pub fn cv_SparseMatConstIterator_delete(instance: *mut c_void); + pub fn cv_SparseMatIterator_operatorST_const_SparseMatIteratorR(instance: *mut c_void, it: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_SparseMatIterator_node_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_SparseMatIterator_operatorAA(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_SparseMatIterator_to_SparseMatConstIterator(instance: *mut c_void) -> *mut c_void; + pub fn cv_SparseMatIterator_delete(instance: *mut c_void); + pub fn cv_TermCriteria_isValid_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_TickMeter_TickMeter(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_TickMeter_start(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_TickMeter_stop(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_TickMeter_getTimeTicks_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_TickMeter_getTimeMicro_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_TickMeter_getTimeMilli_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_TickMeter_getTimeSec_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_TickMeter_getLastTimeTicks_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_TickMeter_getLastTimeMicro_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_TickMeter_getLastTimeMilli_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_TickMeter_getLastTimeSec_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_TickMeter_getCounter_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_TickMeter_getFPS_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_TickMeter_getAvgTimeSec_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_TickMeter_getAvgTimeMilli_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_TickMeter_reset(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_TickMeter_delete(instance: *mut c_void); + pub fn cv_UMat_operatorST_const_UMatR(instance: *mut c_void, m: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_getMat_const_AccessFlag(instance: *const c_void, flags: core::AccessFlag, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_row_const_int(instance: *const c_void, y: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_row_int(instance: *mut c_void, y: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_col_const_int(instance: *const c_void, x: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_col_int(instance: *mut c_void, x: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_rowRange_const_int_int(instance: *const c_void, startrow: i32, endrow: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_rowRange_int_int(instance: *mut c_void, startrow: i32, endrow: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_rowRange_const_const_RangeR(instance: *const c_void, r: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_rowRange_const_RangeR(instance: *mut c_void, r: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_colRange_const_int_int(instance: *const c_void, startcol: i32, endcol: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_colRange_int_int(instance: *mut c_void, startcol: i32, endcol: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_colRange_const_const_RangeR(instance: *const c_void, r: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_colRange_const_RangeR(instance: *mut c_void, r: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_diag_const_int(instance: *const c_void, d: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_diag(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_diag_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_diag_int(instance: *mut c_void, d: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_diag_const_UMatR_UMatUsageFlags(d: *const c_void, usage_flags: core::UMatUsageFlags, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_diag_const_UMatR(d: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_clone_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_copyTo_const_const__OutputArrayR(instance: *const c_void, m: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_copyTo_const_const__OutputArrayR_const__InputArrayR(instance: *const c_void, m: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_convertTo_const_const__OutputArrayR_int_double_double(instance: *const c_void, m: *const c_void, rtype: i32, alpha: f64, beta: f64, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_convertTo_const_const__OutputArrayR_int(instance: *const c_void, m: *const c_void, rtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_assignTo_const_UMatR_int(instance: *const c_void, m: *mut c_void, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_assignTo_const_UMatR(instance: *const c_void, m: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_operatorST_const_ScalarR(instance: *mut c_void, s: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_setTo_const__InputArrayR_const__InputArrayR(instance: *mut c_void, value: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_setTo_const__InputArrayR(instance: *mut c_void, value: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_reshape_const_int_int(instance: *const c_void, cn: i32, rows: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_reshape_int(instance: *mut c_void, cn: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_reshape_const_int(instance: *const c_void, cn: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_reshape_int_int(instance: *mut c_void, cn: i32, rows: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_reshape_const_int_int_const_intX(instance: *const c_void, cn: i32, newndims: i32, newsz: *const i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_reshape_int_int_const_intX(instance: *mut c_void, cn: i32, newndims: i32, newsz: *const i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_t_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_inv_const_int(instance: *const c_void, method: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_inv_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_mul_const_const__InputArrayR_double(instance: *const c_void, m: *const c_void, scale: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_mul_const_const__InputArrayR(instance: *const c_void, m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_dot_const_const__InputArrayR(instance: *const c_void, m: *const c_void, ocvrs_return: *mut Result); + pub fn cv_UMat_zeros_int_int_int_UMatUsageFlags(rows: i32, cols: i32, typ: i32, usage_flags: core::UMatUsageFlags, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_zeros_Size_int_UMatUsageFlags(size: *const core::Size, typ: i32, usage_flags: core::UMatUsageFlags, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_zeros_int_const_intX_int_UMatUsageFlags(ndims: i32, sz: *const i32, typ: i32, usage_flags: core::UMatUsageFlags, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_zeros_int_int_int(rows: i32, cols: i32, typ: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_zeros_Size_int(size: *const core::Size, typ: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_zeros_int_const_intX_int(ndims: i32, sz: *const i32, typ: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_ones_int_int_int_UMatUsageFlags(rows: i32, cols: i32, typ: i32, usage_flags: core::UMatUsageFlags, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_ones_Size_int_UMatUsageFlags(size: *const core::Size, typ: i32, usage_flags: core::UMatUsageFlags, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_ones_int_const_intX_int_UMatUsageFlags(ndims: i32, sz: *const i32, typ: i32, usage_flags: core::UMatUsageFlags, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_ones_int_int_int(rows: i32, cols: i32, typ: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_ones_Size_int(size: *const core::Size, typ: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_ones_int_const_intX_int(ndims: i32, sz: *const i32, typ: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_eye_int_int_int_UMatUsageFlags(rows: i32, cols: i32, typ: i32, usage_flags: core::UMatUsageFlags, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_eye_Size_int_UMatUsageFlags(size: *const core::Size, typ: i32, usage_flags: core::UMatUsageFlags, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_eye_int_int_int(rows: i32, cols: i32, typ: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_eye_Size_int(size: *const core::Size, typ: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_create_int_int_int_UMatUsageFlags(instance: *mut c_void, rows: i32, cols: i32, typ: i32, usage_flags: core::UMatUsageFlags, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_create_int_int_int(instance: *mut c_void, rows: i32, cols: i32, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_create_Size_int_UMatUsageFlags(instance: *mut c_void, size: *const core::Size, typ: i32, usage_flags: core::UMatUsageFlags, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_create_Size_int(instance: *mut c_void, size: *const core::Size, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_create_int_const_intX_int_UMatUsageFlags(instance: *mut c_void, ndims: i32, sizes: *const i32, typ: i32, usage_flags: core::UMatUsageFlags, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_create_int_const_intX_int(instance: *mut c_void, ndims: i32, sizes: *const i32, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_create_const_vectorLintGR_int_UMatUsageFlags(instance: *mut c_void, sizes: *const c_void, typ: i32, usage_flags: core::UMatUsageFlags, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_create_const_vectorLintGR_int(instance: *mut c_void, sizes: *const c_void, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_addref(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_release(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_deallocate(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_locateROI_const_SizeR_PointR(instance: *const c_void, whole_size: *mut core::Size, ofs: *mut core::Point, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_adjustROI_int_int_int_int(instance: *mut c_void, dtop: i32, dbottom: i32, dleft: i32, dright: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_operator___const_Range_Range(instance: *const c_void, row_range: *mut c_void, col_range: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_operator___Range_Range(instance: *mut c_void, row_range: *mut c_void, col_range: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_operator___const_const_RectR(instance: *const c_void, roi: *const core::Rect, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_operator___const_RectR(instance: *mut c_void, roi: *const core::Rect, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_operator___const_const_vectorLRangeGR(instance: *const c_void, ranges: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_operator___const_vectorLRangeGR(instance: *mut c_void, ranges: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_isContinuous_const(instance: *const c_void) -> bool; + pub fn cv_UMat_isSubmatrix_const(instance: *const c_void) -> bool; + pub fn cv_UMat_elemSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_UMat_elemSize1_const(instance: *const c_void) -> size_t; + pub fn cv_UMat_type_const(instance: *const c_void) -> i32; + pub fn cv_UMat_depth_const(instance: *const c_void) -> i32; + pub fn cv_UMat_channels_const(instance: *const c_void) -> i32; + pub fn cv_UMat_step1_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv_UMat_step1_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_UMat_empty_const(instance: *const c_void) -> bool; + pub fn cv_UMat_total_const(instance: *const c_void) -> size_t; + pub fn cv_UMat_checkVector_const_int_int_bool(instance: *const c_void, elem_channels: i32, depth: i32, require_continuous: bool, ocvrs_return: *mut Result); + pub fn cv_UMat_checkVector_const_int(instance: *const c_void, elem_channels: i32, ocvrs_return: *mut Result); + pub fn cv_UMat_operatorST_UMatRR(instance: *mut c_void, m: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_handle_const_AccessFlag(instance: *const c_void, access_flags: core::AccessFlag, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_UMat_ndoffset_const_size_tX(instance: *const c_void, ofs: *mut size_t, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_updateContinuityFlag(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_UMat_size_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_UMat_propFlags_const(instance: *const c_void) -> i32; + pub fn cv_UMat_propFlags_const_int(instance: *mut c_void, val: i32); + pub fn cv_UMat_propDims_const(instance: *const c_void) -> i32; + pub fn cv_UMat_propDims_const_int(instance: *mut c_void, val: i32); + pub fn cv_UMat_propRows_const(instance: *const c_void) -> i32; + pub fn cv_UMat_propRows_const_int(instance: *mut c_void, val: i32); + pub fn cv_UMat_propCols_const(instance: *const c_void) -> i32; + pub fn cv_UMat_propCols_const_int(instance: *mut c_void, val: i32); + pub fn cv_UMat_propUsageFlags_const(instance: *const c_void, ocvrs_return: *mut core::UMatUsageFlags); + pub fn cv_UMat_propUsageFlags_const_UMatUsageFlags(instance: *mut c_void, val: core::UMatUsageFlags); + pub fn cv_UMat_propU(instance: *mut c_void) -> *mut c_void; + pub fn cv_UMat_propU_UMatDataX(instance: *mut c_void, val: *const c_void); + pub fn cv_UMat_propOffset_const(instance: *const c_void) -> size_t; + pub fn cv_UMat_propOffset_const_size_t(instance: *mut c_void, val: size_t); + pub fn cv_UMat_propSize_const(instance: *const c_void) -> *mut c_void; + pub fn cv_UMat_propSize_const_MatSize(instance: *mut c_void, val: *const c_void); + pub fn cv_UMat_propStep_const(instance: *const c_void) -> *mut c_void; + pub fn cv_UMat_delete(instance: *mut c_void); + pub fn cv_UMatData_lock(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_UMatData_unlock(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_UMatData_hostCopyObsolete_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_UMatData_deviceCopyObsolete_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_UMatData_deviceMemMapped_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_UMatData_copyOnMap_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_UMatData_tempUMat_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_UMatData_tempCopiedUMat_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_UMatData_markHostCopyObsolete_bool(instance: *mut c_void, flag: bool, ocvrs_return: *mut Result<()>); + pub fn cv_UMatData_markDeviceCopyObsolete_bool(instance: *mut c_void, flag: bool, ocvrs_return: *mut Result<()>); + pub fn cv_UMatData_markDeviceMemMapped_bool(instance: *mut c_void, flag: bool, ocvrs_return: *mut Result<()>); + pub fn cv_UMatData_propUrefcount_const(instance: *const c_void) -> i32; + pub fn cv_UMatData_propUrefcount_const_int(instance: *mut c_void, val: i32); + pub fn cv_UMatData_propRefcount_const(instance: *const c_void) -> i32; + pub fn cv_UMatData_propRefcount_const_int(instance: *mut c_void, val: i32); + pub fn cv_UMatData_propData_const(instance: *const c_void) -> *const u8; + pub fn cv_UMatData_propData(instance: *mut c_void) -> *mut u8; + pub fn cv_UMatData_propData_unsigned_charX(instance: *mut c_void, val: *const u8); + pub fn cv_UMatData_propOrigdata_const(instance: *const c_void) -> *const u8; + pub fn cv_UMatData_propOrigdata(instance: *mut c_void) -> *mut u8; + pub fn cv_UMatData_propOrigdata_unsigned_charX(instance: *mut c_void, val: *const u8); + pub fn cv_UMatData_propSize_const(instance: *const c_void) -> size_t; + pub fn cv_UMatData_propSize_const_size_t(instance: *mut c_void, val: size_t); + pub fn cv_UMatData_propFlags_const(instance: *const c_void, ocvrs_return: *mut core::UMatData_MemoryFlag); + pub fn cv_UMatData_propFlags_const_MemoryFlag(instance: *mut c_void, val: core::UMatData_MemoryFlag); + pub fn cv_UMatData_propHandle(instance: *mut c_void) -> *mut c_void; + pub fn cv_UMatData_propHandle_voidX(instance: *mut c_void, val: *const c_void); + pub fn cv_UMatData_propUserdata(instance: *mut c_void) -> *mut c_void; + pub fn cv_UMatData_propUserdata_voidX(instance: *mut c_void, val: *const c_void); + pub fn cv_UMatData_propAllocatorFlags__const(instance: *const c_void) -> i32; + pub fn cv_UMatData_propAllocatorFlags__const_int(instance: *mut c_void, val: i32); + pub fn cv_UMatData_propMapcount_const(instance: *const c_void) -> i32; + pub fn cv_UMatData_propMapcount_const_int(instance: *mut c_void, val: i32); + pub fn cv_UMatData_propOriginalUMatData(instance: *mut c_void) -> *mut c_void; + pub fn cv_UMatData_propOriginalUMatData_UMatDataX(instance: *mut c_void, val: *const c_void); + pub fn cv_UMatData_delete(instance: *mut c_void); + pub fn cv__InputArray__InputArray(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv__InputArray__InputArray_int_voidX(_flags: i32, _obj: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv__InputArray__InputArray_const_vectorLboolGR(vec: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv__InputArray__InputArray_const_doubleR(val: *const f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv__InputArray_getFlags_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_getObj_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv__InputArray_getSz_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_kind_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_dims_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_dims_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_cols_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_cols_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_rows_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_rows_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_size_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_size_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_sizend_const_intX_int(instance: *const c_void, sz: *mut i32, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_sizend_const_intX(instance: *const c_void, sz: *mut i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_sameSize_const_const__InputArrayR(instance: *const c_void, arr: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_total_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_total_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_type_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_type_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_depth_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_depth_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_channels_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_channels_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_isContinuous_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_isContinuous_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_isSubmatrix_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_isSubmatrix_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_empty_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_offset_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_offset_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_step_const_int(instance: *const c_void, i: i32, ocvrs_return: *mut Result); + pub fn cv__InputArray_step_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_isMat_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_isUMat_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_isMatVector_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_isUMatVector_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_isMatx_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_isVector_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_isGpuMat_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_isGpuMatVector_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray_isGpuMatND_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__InputArray__InputArray_const_unsigned_charX_int(vec: *const u8, n: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv__InputArray_delete(instance: *mut c_void); + pub fn cv__InputOutputArray__InputOutputArray(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv__InputOutputArray__InputOutputArray_int_voidX(_flags: i32, _obj: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv__InputOutputArray_to__InputArray(instance: *mut c_void) -> *mut c_void; + pub fn cv__InputOutputArray_to__OutputArray(instance: *mut c_void) -> *mut c_void; + pub fn cv__InputOutputArray_delete(instance: *mut c_void); + pub fn cv__OutputArray_fixedSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__OutputArray_fixedType_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__OutputArray_needed_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv__OutputArray_create_const_Size_int_int_bool_DepthMask(instance: *const c_void, sz: *const core::Size, typ: i32, i: i32, allow_transposed: bool, fixed_depth_mask: core::_OutputArray_DepthMask, ocvrs_return: *mut Result<()>); + pub fn cv__OutputArray_create_const_Size_int(instance: *const c_void, sz: *const core::Size, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv__OutputArray_create_const_int_int_int_int_bool_DepthMask(instance: *const c_void, rows: i32, cols: i32, typ: i32, i: i32, allow_transposed: bool, fixed_depth_mask: core::_OutputArray_DepthMask, ocvrs_return: *mut Result<()>); + pub fn cv__OutputArray_create_const_int_int_int(instance: *const c_void, rows: i32, cols: i32, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv__OutputArray_create_const_int_const_intX_int_int_bool_DepthMask(instance: *const c_void, dims: i32, size: *const i32, typ: i32, i: i32, allow_transposed: bool, fixed_depth_mask: core::_OutputArray_DepthMask, ocvrs_return: *mut Result<()>); + pub fn cv__OutputArray_create_const_int_const_intX_int(instance: *const c_void, dims: i32, size: *const i32, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv__OutputArray_createSameSize_const_const__InputArrayR_int(instance: *const c_void, arr: *const c_void, mtype: i32, ocvrs_return: *mut Result<()>); + pub fn cv__OutputArray_release_const(instance: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv__OutputArray_clear_const(instance: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv__OutputArray_setTo_const_const__InputArrayR_const__InputArrayR(instance: *const c_void, value: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv__OutputArray_setTo_const_const__InputArrayR(instance: *const c_void, value: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv__OutputArray_to__InputArray(instance: *mut c_void) -> *mut c_void; + pub fn cv__OutputArray_delete(instance: *mut c_void); + pub fn cv_detail_CheckContext_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_detail_CheckContext_defaultNew_const() -> *mut c_void; + pub fn cv_detail_CheckContext_propFunc_const(instance: *const c_void) -> *mut c_void; + pub fn cv_detail_CheckContext_propFile_const(instance: *const c_void) -> *mut c_void; + pub fn cv_detail_CheckContext_propLine_const(instance: *const c_void) -> i32; + pub fn cv_detail_CheckContext_propLine_const_int(instance: *mut c_void, val: i32); + pub fn cv_detail_CheckContext_propTestOp_const(instance: *const c_void, ocvrs_return: *mut core::Detail_TestOp); + pub fn cv_detail_CheckContext_propTestOp_const_TestOp(instance: *mut c_void, val: core::Detail_TestOp); + pub fn cv_detail_CheckContext_propMessage_const(instance: *const c_void) -> *mut c_void; + pub fn cv_detail_CheckContext_propP1_str_const(instance: *const c_void) -> *mut c_void; + pub fn cv_detail_CheckContext_propP2_str_const(instance: *const c_void) -> *mut c_void; + pub fn cv_detail_CheckContext_delete(instance: *mut c_void); + pub fn cv_hfloat_hfloat(ocvrs_return: *mut Result); + pub fn cv_hfloat_hfloat_float(x: f32, ocvrs_return: *mut Result); + pub fn cv_hfloat_operator_float_const(instance: *const core::hfloat, ocvrs_return: *mut Result); + pub fn cv_instr_NodeData_NodeData_const_charX_const_charX_int_voidX_bool_TYPE_IMPL(fun_name: *const c_char, file_name: *const c_char, line_num: i32, ret_address: *mut c_void, always_expand: bool, instr_type: core::TYPE, impl_type: core::IMPL, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_instr_NodeData_NodeData(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_instr_NodeData_NodeData_NodeDataR(ref_: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_instr_NodeData_operatorST_const_NodeDataR(instance: *mut c_void, unnamed: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_instr_NodeData_getTotalMs_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_instr_NodeData_getMeanMs_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_instr_NodeData_propM_funName_const(instance: *const c_void) -> *mut c_void; + pub fn cv_instr_NodeData_propM_funName_const_String(instance: *mut c_void, val: *const c_char); + pub fn cv_instr_NodeData_propM_instrType_const(instance: *const c_void, ocvrs_return: *mut core::TYPE); + pub fn cv_instr_NodeData_propM_instrType_const_TYPE(instance: *mut c_void, val: core::TYPE); + pub fn cv_instr_NodeData_propM_implType_const(instance: *const c_void, ocvrs_return: *mut core::IMPL); + pub fn cv_instr_NodeData_propM_implType_const_IMPL(instance: *mut c_void, val: core::IMPL); + pub fn cv_instr_NodeData_propM_fileName_const(instance: *const c_void) -> *mut c_void; + pub fn cv_instr_NodeData_propM_lineNum_const(instance: *const c_void) -> i32; + pub fn cv_instr_NodeData_propM_lineNum_const_int(instance: *mut c_void, val: i32); + pub fn cv_instr_NodeData_propM_retAddress(instance: *mut c_void) -> *mut c_void; + pub fn cv_instr_NodeData_propM_retAddress_voidX(instance: *mut c_void, val: *const c_void); + pub fn cv_instr_NodeData_propM_alwaysExpand_const(instance: *const c_void) -> bool; + pub fn cv_instr_NodeData_propM_alwaysExpand_const_bool(instance: *mut c_void, val: bool); + pub fn cv_instr_NodeData_propM_funError_const(instance: *const c_void) -> bool; + pub fn cv_instr_NodeData_propM_funError_const_bool(instance: *mut c_void, val: bool); + pub fn cv_instr_NodeData_propM_counter_const(instance: *const c_void) -> i32; + pub fn cv_instr_NodeData_propM_counter_const_int(instance: *mut c_void, val: i32); + pub fn cv_instr_NodeData_propM_ticksTotal_const(instance: *const c_void) -> u64; + pub fn cv_instr_NodeData_propM_ticksTotal_const_uint64_t(instance: *mut c_void, val: u64); + pub fn cv_instr_NodeData_propM_threads_const(instance: *const c_void) -> i32; + pub fn cv_instr_NodeData_propM_threads_const_int(instance: *mut c_void, val: i32); + pub fn cv_instr_NodeData_delete(instance: *mut c_void); + pub fn cv_internal_WriteStructContext_WriteStructContext_FileStorageR_const_StringR_int_const_StringR(_fs: *mut c_void, name: *const c_char, flags: i32, type_name: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_internal_WriteStructContext_WriteStructContext_FileStorageR_const_StringR_int(_fs: *mut c_void, name: *const c_char, flags: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_internal_WriteStructContext_delete(instance: *mut c_void); + pub fn cv_ocl_Context_operatorST_const_ContextR(instance: *mut c_void, c: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Context_operatorST_ContextRR(instance: *mut c_void, c: *mut c_void); + pub fn cv_ocl_Context_create(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Context_create_int(instance: *mut c_void, dtype: i32, ocvrs_return: *mut Result); + pub fn cv_ocl_Context_ndevices_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Context_device_const_size_t(instance: *const c_void, idx: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Context_getDefault_bool(initialize: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Context_getDefault(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Context_ptr_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Context_getOpenCLContextProperty_const_int(instance: *const c_void, property_id: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Context_useSVM_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Context_setUseSVM_bool(instance: *mut c_void, enabled: bool, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Context_fromHandle_voidX(context: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Context_fromDevice_const_DeviceR(device: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Context_create_const_stringR(configuration: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Context_release(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Context_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Context_delete(instance: *mut c_void); + pub fn cv_ocl_Context_UserContext_defaultNew_const() -> *mut c_void; + pub fn cv_ocl_Context_UserContext_delete(instance: *mut c_void); + pub fn cv_ocl_Device_operatorST_const_DeviceR(instance: *mut c_void, d: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Device_operatorST_DeviceRR(instance: *mut c_void, d: *mut c_void); + pub fn cv_ocl_Device_set_voidX(instance: *mut c_void, d: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Device_name_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Device_extensions_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Device_isExtensionSupported_const_const_StringR(instance: *const c_void, extension_name: *const c_char, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_version_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Device_vendorName_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Device_OpenCL_C_Version_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Device_OpenCLVersion_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Device_deviceVersionMajor_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_deviceVersionMinor_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_driverVersion_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Device_ptr_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Device_type_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_addressBits_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_available_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_compilerAvailable_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_linkerAvailable_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_doubleFPConfig_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_singleFPConfig_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_halfFPConfig_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_hasFP64_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_hasFP16_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_endianLittle_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_errorCorrectionSupport_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_executionCapabilities_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_globalMemCacheSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_globalMemCacheType_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_globalMemCacheLineSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_globalMemSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_localMemSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_localMemType_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_hostUnifiedMemory_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_imageSupport_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_imageFromBufferSupport_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_imagePitchAlignment_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_imageBaseAddressAlignment_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_intelSubgroupsSupport_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_image2DMaxWidth_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_image2DMaxHeight_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_image3DMaxWidth_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_image3DMaxHeight_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_image3DMaxDepth_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_imageMaxBufferSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_imageMaxArraySize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_vendorID_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_isAMD_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_isIntel_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_isNVidia_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_maxClockFrequency_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_maxComputeUnits_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_maxConstantArgs_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_maxConstantBufferSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_maxMemAllocSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_maxParameterSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_maxReadImageArgs_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_maxWriteImageArgs_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_maxSamplers_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_maxWorkGroupSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_maxWorkItemDims_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_maxWorkItemSizes_const_size_tX(instance: *const c_void, unnamed: *mut size_t, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Device_memBaseAddrAlign_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_nativeVectorWidthChar_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_nativeVectorWidthShort_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_nativeVectorWidthInt_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_nativeVectorWidthLong_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_nativeVectorWidthFloat_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_nativeVectorWidthDouble_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_nativeVectorWidthHalf_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_preferredVectorWidthChar_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_preferredVectorWidthShort_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_preferredVectorWidthInt_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_preferredVectorWidthLong_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_preferredVectorWidthFloat_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_preferredVectorWidthDouble_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_preferredVectorWidthHalf_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_printfBufferSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_profilingTimerResolution_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_getDefault(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Device_fromHandle_voidX(d: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Device_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Device_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_ocl_Device_delete(instance: *mut c_void); + pub fn cv_ocl_Image2D_operatorST_const_Image2DR(instance: *mut c_void, i: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Image2D_operatorST_Image2DRR(instance: *mut c_void, unnamed: *mut c_void); + pub fn cv_ocl_Image2D_canCreateAlias_const_UMatR(u: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Image2D_isFormatSupported_int_int_bool(depth: i32, cn: i32, norm: bool, ocvrs_return: *mut Result); + pub fn cv_ocl_Image2D_ptr_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Image2D_delete(instance: *mut c_void); + pub fn cv_ocl_Kernel_operatorST_const_KernelR(instance: *mut c_void, k: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Kernel_operatorST_KernelRR(instance: *mut c_void, k: *mut c_void); + pub fn cv_ocl_Kernel_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_set_int_const_voidX_size_t(instance: *mut c_void, i: i32, value: *const c_void, sz: size_t, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_set_int_const_UMatR(instance: *mut c_void, i: i32, m: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_set_int_const_KernelArgR(instance: *mut c_void, i: i32, arg: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_run_int_size_tX_size_tX_bool_const_QueueR(instance: *mut c_void, dims: i32, globalsize: *mut size_t, localsize: *mut size_t, sync: bool, q: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_run_int_size_tX_size_tX_bool(instance: *mut c_void, dims: i32, globalsize: *mut size_t, localsize: *mut size_t, sync: bool, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_run__int_size_tX_size_tX_bool_const_QueueR(instance: *mut c_void, dims: i32, globalsize: *mut size_t, localsize: *mut size_t, sync: bool, q: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_run__int_size_tX_size_tX_bool(instance: *mut c_void, dims: i32, globalsize: *mut size_t, localsize: *mut size_t, sync: bool, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_runTask_bool_const_QueueR(instance: *mut c_void, sync: bool, q: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_runTask_bool(instance: *mut c_void, sync: bool, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_runProfiling_int_size_tX_size_tX_const_QueueR(instance: *mut c_void, dims: i32, globalsize: *mut size_t, localsize: *mut size_t, q: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_runProfiling_int_size_tX_size_tX(instance: *mut c_void, dims: i32, globalsize: *mut size_t, localsize: *mut size_t, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_workGroupSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_preferedWorkGroupSizeMultiple_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_compileWorkGroupSize_const_size_tX(instance: *const c_void, wsz: *mut size_t, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_localMemSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Kernel_ptr_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Kernel_delete(instance: *mut c_void); + pub fn cv_ocl_KernelArg_KernelArg_int_UMatX_int_int_const_voidX_size_t(_flags: i32, _m: *mut c_void, wscale: i32, iwscale: i32, _obj: *const c_void, _sz: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_KernelArg_int_UMatX(_flags: i32, _m: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_KernelArg() -> *mut c_void; + pub fn cv_ocl_KernelArg_Local_size_t(local_mem_size: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_PtrWriteOnly_const_UMatR(m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_PtrReadOnly_const_UMatR(m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_PtrReadWrite_const_UMatR(m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_ReadWrite_const_UMatR_int_int(m: *const c_void, wscale: i32, iwscale: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_ReadWrite_const_UMatR(m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_ReadWriteNoSize_const_UMatR_int_int(m: *const c_void, wscale: i32, iwscale: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_ReadWriteNoSize_const_UMatR(m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_ReadOnly_const_UMatR_int_int(m: *const c_void, wscale: i32, iwscale: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_ReadOnly_const_UMatR(m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_WriteOnly_const_UMatR_int_int(m: *const c_void, wscale: i32, iwscale: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_WriteOnly_const_UMatR(m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_ReadOnlyNoSize_const_UMatR_int_int(m: *const c_void, wscale: i32, iwscale: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_ReadOnlyNoSize_const_UMatR(m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_WriteOnlyNoSize_const_UMatR_int_int(m: *const c_void, wscale: i32, iwscale: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_WriteOnlyNoSize_const_UMatR(m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_Constant_const_MatR(m: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_KernelArg_propFlags_const(instance: *const c_void) -> i32; + pub fn cv_ocl_KernelArg_propFlags_const_int(instance: *mut c_void, val: i32); + pub fn cv_ocl_KernelArg_propM(instance: *mut c_void) -> *mut c_void; + pub fn cv_ocl_KernelArg_propM_UMatX(instance: *mut c_void, val: *const c_void); + pub fn cv_ocl_KernelArg_propObj_const(instance: *const c_void) -> *const c_void; + pub fn cv_ocl_KernelArg_propSz_const(instance: *const c_void) -> size_t; + pub fn cv_ocl_KernelArg_propSz_const_size_t(instance: *mut c_void, val: size_t); + pub fn cv_ocl_KernelArg_propWscale_const(instance: *const c_void) -> i32; + pub fn cv_ocl_KernelArg_propWscale_const_int(instance: *mut c_void, val: i32); + pub fn cv_ocl_KernelArg_propIwscale_const(instance: *const c_void) -> i32; + pub fn cv_ocl_KernelArg_propIwscale_const_int(instance: *mut c_void, val: i32); + pub fn cv_ocl_KernelArg_delete(instance: *mut c_void); + pub fn cv_ocl_OpenCLExecutionContext_OpenCLExecutionContext() -> *mut c_void; + pub fn cv_ocl_OpenCLExecutionContext_OpenCLExecutionContext_const_OpenCLExecutionContextR(unnamed: *const c_void) -> *mut c_void; + pub fn cv_ocl_OpenCLExecutionContext_OpenCLExecutionContext_OpenCLExecutionContextRR(unnamed: *mut c_void) -> *mut c_void; + pub fn cv_ocl_OpenCLExecutionContext_operatorST_const_OpenCLExecutionContextR(instance: *mut c_void, unnamed: *const c_void); + pub fn cv_ocl_OpenCLExecutionContext_operatorST_OpenCLExecutionContextRR(instance: *mut c_void, unnamed: *mut c_void); + pub fn cv_ocl_OpenCLExecutionContext_getContext_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_OpenCLExecutionContext_getDevice_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_OpenCLExecutionContext_getQueue_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_OpenCLExecutionContext_useOpenCL_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_OpenCLExecutionContext_setUseOpenCL_bool(instance: *mut c_void, flag: bool, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_OpenCLExecutionContext_getCurrent(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_OpenCLExecutionContext_getCurrentRef(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_OpenCLExecutionContext_bind_const(instance: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_OpenCLExecutionContext_cloneWithNewQueue_const_const_QueueR(instance: *const c_void, q: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_OpenCLExecutionContext_cloneWithNewQueue_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_OpenCLExecutionContext_create_const_stringR_voidX_voidX_voidX(platform_name: *const c_char, platform_id: *mut c_void, context: *mut c_void, device_id: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_OpenCLExecutionContext_create_const_ContextR_const_DeviceR_const_QueueR(context: *const c_void, device: *const c_void, queue: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_OpenCLExecutionContext_create_const_ContextR_const_DeviceR(context: *const c_void, device: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_OpenCLExecutionContext_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_OpenCLExecutionContext_release(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_OpenCLExecutionContext_delete(instance: *mut c_void); + pub fn cv_ocl_Platform_Platform() -> *mut c_void; + pub fn cv_ocl_Platform_Platform_const_PlatformR(p: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Platform_operatorST_const_PlatformR(instance: *mut c_void, p: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Platform_Platform_PlatformRR(p: *mut c_void) -> *mut c_void; + pub fn cv_ocl_Platform_operatorST_PlatformRR(instance: *mut c_void, p: *mut c_void); + pub fn cv_ocl_Platform_ptr_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Platform_getDefault(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Platform_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Platform_delete(instance: *mut c_void); + pub fn cv_ocl_PlatformInfo_operatorST_const_PlatformInfoR(instance: *mut c_void, i: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_PlatformInfo_operatorST_PlatformInfoRR(instance: *mut c_void, i: *mut c_void); + pub fn cv_ocl_PlatformInfo_name_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_PlatformInfo_vendor_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_PlatformInfo_version_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_PlatformInfo_versionMajor_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_PlatformInfo_versionMinor_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_PlatformInfo_deviceNumber_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_PlatformInfo_getDevice_const_DeviceR_int(instance: *const c_void, device: *mut c_void, d: i32, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_PlatformInfo_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_PlatformInfo_delete(instance: *mut c_void); + pub fn cv_ocl_Program_operatorST_const_ProgramR(instance: *mut c_void, prog: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Program_operatorST_ProgramRR(instance: *mut c_void, prog: *mut c_void); + pub fn cv_ocl_Program_ptr_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Program_getBinary_const_vectorLcharGR(instance: *const c_void, binary: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Program_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Program_read_const_StringR_const_StringR(instance: *mut c_void, buf: *const c_char, buildflags: *const c_char, ocvrs_return: *mut Result); + pub fn cv_ocl_Program_write_const_StringR(instance: *const c_void, buf: *mut *mut c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Program_getPrefix_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Program_getPrefix_const_StringR(buildflags: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Program_delete(instance: *mut c_void); + pub fn cv_ocl_ProgramSource_operatorST_const_ProgramSourceR(instance: *mut c_void, prog: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_ProgramSource_operatorST_ProgramSourceRR(instance: *mut c_void, prog: *mut c_void); + pub fn cv_ocl_ProgramSource_source_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_ProgramSource_hash_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_ProgramSource_fromBinary_const_StringR_const_StringR_const_unsigned_charX_const_size_t_const_StringR(module: *const c_char, name: *const c_char, binary: *const u8, size: size_t, build_options: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_ProgramSource_fromBinary_const_StringR_const_StringR_const_unsigned_charX_const_size_t(module: *const c_char, name: *const c_char, binary: *const u8, size: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_ProgramSource_fromSPIR_const_StringR_const_StringR_const_unsigned_charX_const_size_t_const_StringR(module: *const c_char, name: *const c_char, binary: *const u8, size: size_t, build_options: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_ProgramSource_fromSPIR_const_StringR_const_StringR_const_unsigned_charX_const_size_t(module: *const c_char, name: *const c_char, binary: *const u8, size: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_ProgramSource_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_ProgramSource_delete(instance: *mut c_void); + pub fn cv_ocl_Queue_operatorST_const_QueueR(instance: *mut c_void, q: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Queue_operatorST_QueueRR(instance: *mut c_void, q: *mut c_void); + pub fn cv_ocl_Queue_create_const_ContextR_const_DeviceR(instance: *mut c_void, c: *const c_void, d: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Queue_create(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Queue_finish(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Queue_ptr_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Queue_getDefault(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Queue_getProfilingQueue_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Queue_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Queue_delete(instance: *mut c_void); + pub fn cv_ocl_Timer_Timer_const_QueueR(q: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_ocl_Timer_start(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Timer_stop(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ocl_Timer_durationNS_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_ocl_Timer_delete(instance: *mut c_void); + pub fn cv_utils_ClassWithKeywordProperties_ClassWithKeywordProperties_int_int(lambda_arg: i32, except_arg: i32, ocvrs_return: *mut Result); + pub fn cv_utils_ClassWithKeywordProperties_ClassWithKeywordProperties(ocvrs_return: *mut Result); + pub fn cv_utils_FunctionParams_setLambda_int(instance: *const core::FunctionParams, value: i32, ocvrs_return: *mut core::FunctionParams); + pub fn cv_utils_FunctionParams_setSigma_float(instance: *const core::FunctionParams, value: f32, ocvrs_return: *mut core::FunctionParams); + pub fn cv_utils_logging_LogTag_LogTag_const_charX_LogLevel(_name: *const c_char, _level: core::LogLevel, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_logging_LogTag_propName_const(instance: *const c_void) -> *mut c_void; + pub fn cv_utils_logging_LogTag_propLevel_const(instance: *const c_void, ocvrs_return: *mut core::LogLevel); + pub fn cv_utils_logging_LogTag_propLevel_const_LogLevel(instance: *mut c_void, val: core::LogLevel); + pub fn cv_utils_logging_LogTag_delete(instance: *mut c_void); + pub fn cv_utils_nested_OriginalClassName_OriginalClassName_const_ParamsR(params: *const core::OriginalClassName_Params, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_nested_OriginalClassName_OriginalClassName(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_nested_OriginalClassName_getIntParam_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_utils_nested_OriginalClassName_getFloatParam_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_utils_nested_OriginalClassName_originalName(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_nested_OriginalClassName_create_const_ParamsR(params: *const core::OriginalClassName_Params, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_nested_OriginalClassName_create(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_utils_nested_OriginalClassName_delete(instance: *mut c_void); + pub fn cv_utils_nested_OriginalClassName_Params_Params_int_float(int_param: i32, float_param: f32, ocvrs_return: *mut Result); + pub fn cv_utils_nested_OriginalClassName_Params_Params(ocvrs_return: *mut Result); + pub fn cv_PtrLcv_AlgorithmG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_AlgorithmG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_AlgorithmG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_AlgorithmG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_AlgorithmG_new_const_Algorithm(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_ConjGradSolverG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_ConjGradSolverG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_ConjGradSolverG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_ConjGradSolverG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_ConjGradSolverG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_ConjGradSolverG_to_PtrOfMinProblemSolver(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_DownhillSolverG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_DownhillSolverG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_DownhillSolverG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_DownhillSolverG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_DownhillSolverG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_DownhillSolverG_to_PtrOfMinProblemSolver(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_FileStorageG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_FileStorageG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_FileStorageG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_FileStorageG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_FileStorageG_new_const_FileStorage(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_FormattedG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_FormattedG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_FormattedG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_FormattedG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_FormatterG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_FormatterG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_FormatterG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_FormatterG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_KeyPointG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_KeyPointG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_KeyPointG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_KeyPointG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_KeyPointG_new_const_KeyPoint(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_MinProblemSolverG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_MinProblemSolverG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_MinProblemSolverG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_MinProblemSolverG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_MinProblemSolverG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_MinProblemSolver_FunctionG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_MinProblemSolver_FunctionG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_MinProblemSolver_FunctionG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_MinProblemSolver_FunctionG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_utils_nested_OriginalClassNameG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_utils_nested_OriginalClassNameG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_utils_nested_OriginalClassNameG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_utils_nested_OriginalClassNameG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_utils_nested_OriginalClassNameG_new_const_OriginalClassName(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLfloatG_getInnerPtr_const(instance: *const c_void) -> *const f32; + pub fn cv_PtrLfloatG_getInnerPtrMut(instance: *mut c_void) -> *mut f32; + pub fn cv_PtrLfloatG_new_null_const() -> *mut c_void; + pub fn cv_PtrLfloatG_delete(instance: *mut c_void); + pub fn cv_PtrLfloatG_new_const_float(val: f32) -> *mut c_void; + pub fn std_pairLint__floatG_new_const_int_float(arg: i32, arg_1: f32) -> *mut c_void; + pub fn std_pairLint__floatG_get_0_const(instance: *const c_void, ocvrs_return: *mut i32); + pub fn std_pairLint__floatG_get_1_const(instance: *const c_void, ocvrs_return: *mut f32); + pub fn std_pairLint__floatG_delete(instance: *mut c_void); + pub fn std_vectorLcv_DMatchG_new_const() -> *mut c_void; + pub fn std_vectorLcv_DMatchG_delete(instance: *mut c_void); + pub fn std_vectorLcv_DMatchG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_DMatchG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_DMatchG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_DMatchG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_DMatchG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_DMatchG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_DMatchG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_DMatchG_clear(instance: *mut c_void); + pub fn std_vectorLcv_DMatchG_push_const_DMatch(instance: *mut c_void, val: *const core::DMatch); + pub fn std_vectorLcv_DMatchG_insert_size_t_const_DMatch(instance: *mut c_void, index: size_t, val: *const core::DMatch); + pub fn std_vectorLcv_DMatchG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut core::DMatch); + pub fn std_vectorLcv_DMatchG_set_size_t_const_DMatch(instance: *mut c_void, index: size_t, val: *const core::DMatch); + pub fn std_vectorLcv_DMatchG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_DMatchG_data_const(instance: *const c_void) -> *const core::DMatch; + pub fn std_vectorLcv_DMatchG_dataMut(instance: *mut c_void) -> *mut core::DMatch; + pub fn cv_fromSlice_const_const_DMatchX_size_t(data: *const core::DMatch, len: size_t) -> *mut c_void; + pub fn std_vectorLcv_KeyPointG_new_const() -> *mut c_void; + pub fn std_vectorLcv_KeyPointG_delete(instance: *mut c_void); + pub fn std_vectorLcv_KeyPointG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_KeyPointG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_KeyPointG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_KeyPointG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_KeyPointG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_KeyPointG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_KeyPointG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_KeyPointG_clear(instance: *mut c_void); + pub fn std_vectorLcv_KeyPointG_push_const_KeyPoint(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLcv_KeyPointG_insert_size_t_const_KeyPoint(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_KeyPointG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLcv_KeyPointG_set_size_t_const_KeyPoint(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_MatG_new_const() -> *mut c_void; + pub fn std_vectorLcv_MatG_delete(instance: *mut c_void); + pub fn std_vectorLcv_MatG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_MatG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_MatG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_MatG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_MatG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_MatG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_MatG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_MatG_clear(instance: *mut c_void); + pub fn std_vectorLcv_MatG_push_const_Mat(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLcv_MatG_insert_size_t_const_Mat(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_MatG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLcv_MatG_set_size_t_const_Mat(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_ocl_PlatformInfoG_new_const() -> *mut c_void; + pub fn std_vectorLcv_ocl_PlatformInfoG_delete(instance: *mut c_void); + pub fn std_vectorLcv_ocl_PlatformInfoG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_ocl_PlatformInfoG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_ocl_PlatformInfoG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_ocl_PlatformInfoG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_ocl_PlatformInfoG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_ocl_PlatformInfoG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_ocl_PlatformInfoG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_ocl_PlatformInfoG_clear(instance: *mut c_void); + pub fn std_vectorLcv_ocl_PlatformInfoG_push_const_PlatformInfo(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLcv_ocl_PlatformInfoG_insert_size_t_const_PlatformInfo(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_ocl_PlatformInfoG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLcv_ocl_PlatformInfoG_set_size_t_const_PlatformInfo(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_PointG_new_const() -> *mut c_void; + pub fn std_vectorLcv_PointG_delete(instance: *mut c_void); + pub fn std_vectorLcv_PointG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_PointG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_PointG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_PointG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_PointG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_PointG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_PointG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_PointG_clear(instance: *mut c_void); + pub fn std_vectorLcv_PointG_push_const_Point(instance: *mut c_void, val: *const core::Point); + pub fn std_vectorLcv_PointG_insert_size_t_const_Point(instance: *mut c_void, index: size_t, val: *const core::Point); + pub fn std_vectorLcv_PointG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut core::Point); + pub fn std_vectorLcv_PointG_set_size_t_const_Point(instance: *mut c_void, index: size_t, val: *const core::Point); + pub fn std_vectorLcv_PointG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_PointG_data_const(instance: *const c_void) -> *const core::Point; + pub fn std_vectorLcv_PointG_dataMut(instance: *mut c_void) -> *mut core::Point; + pub fn cv_fromSlice_const_const_PointX_size_t(data: *const core::Point, len: size_t) -> *mut c_void; + pub fn std_vectorLcv_PointG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_PointG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_PointG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Point2dG_new_const() -> *mut c_void; + pub fn std_vectorLcv_Point2dG_delete(instance: *mut c_void); + pub fn std_vectorLcv_Point2dG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Point2dG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_Point2dG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Point2dG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_Point2dG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_Point2dG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_Point2dG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_Point2dG_clear(instance: *mut c_void); + pub fn std_vectorLcv_Point2dG_push_const_Point2d(instance: *mut c_void, val: *const core::Point2d); + pub fn std_vectorLcv_Point2dG_insert_size_t_const_Point2d(instance: *mut c_void, index: size_t, val: *const core::Point2d); + pub fn std_vectorLcv_Point2dG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut core::Point2d); + pub fn std_vectorLcv_Point2dG_set_size_t_const_Point2d(instance: *mut c_void, index: size_t, val: *const core::Point2d); + pub fn std_vectorLcv_Point2dG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_Point2dG_data_const(instance: *const c_void) -> *const core::Point2d; + pub fn std_vectorLcv_Point2dG_dataMut(instance: *mut c_void) -> *mut core::Point2d; + pub fn cv_fromSlice_const_const_Point2dX_size_t(data: *const core::Point2d, len: size_t) -> *mut c_void; + pub fn std_vectorLcv_Point2dG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Point2dG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Point2dG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Point2fG_new_const() -> *mut c_void; + pub fn std_vectorLcv_Point2fG_delete(instance: *mut c_void); + pub fn std_vectorLcv_Point2fG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Point2fG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_Point2fG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Point2fG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_Point2fG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_Point2fG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_Point2fG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_Point2fG_clear(instance: *mut c_void); + pub fn std_vectorLcv_Point2fG_push_const_Point2f(instance: *mut c_void, val: *const core::Point2f); + pub fn std_vectorLcv_Point2fG_insert_size_t_const_Point2f(instance: *mut c_void, index: size_t, val: *const core::Point2f); + pub fn std_vectorLcv_Point2fG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut core::Point2f); + pub fn std_vectorLcv_Point2fG_set_size_t_const_Point2f(instance: *mut c_void, index: size_t, val: *const core::Point2f); + pub fn std_vectorLcv_Point2fG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_Point2fG_data_const(instance: *const c_void) -> *const core::Point2f; + pub fn std_vectorLcv_Point2fG_dataMut(instance: *mut c_void) -> *mut core::Point2f; + pub fn cv_fromSlice_const_const_Point2fX_size_t(data: *const core::Point2f, len: size_t) -> *mut c_void; + pub fn std_vectorLcv_Point2fG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Point2fG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Point2fG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_RangeG_new_const() -> *mut c_void; + pub fn std_vectorLcv_RangeG_delete(instance: *mut c_void); + pub fn std_vectorLcv_RangeG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_RangeG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_RangeG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_RangeG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_RangeG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_RangeG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_RangeG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_RangeG_clear(instance: *mut c_void); + pub fn std_vectorLcv_RangeG_push_const_Range(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLcv_RangeG_insert_size_t_const_Range(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_RangeG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLcv_RangeG_set_size_t_const_Range(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_RectG_new_const() -> *mut c_void; + pub fn std_vectorLcv_RectG_delete(instance: *mut c_void); + pub fn std_vectorLcv_RectG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_RectG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_RectG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_RectG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_RectG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_RectG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_RectG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_RectG_clear(instance: *mut c_void); + pub fn std_vectorLcv_RectG_push_const_Rect(instance: *mut c_void, val: *const core::Rect); + pub fn std_vectorLcv_RectG_insert_size_t_const_Rect(instance: *mut c_void, index: size_t, val: *const core::Rect); + pub fn std_vectorLcv_RectG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut core::Rect); + pub fn std_vectorLcv_RectG_set_size_t_const_Rect(instance: *mut c_void, index: size_t, val: *const core::Rect); + pub fn std_vectorLcv_RectG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_RectG_data_const(instance: *const c_void) -> *const core::Rect; + pub fn std_vectorLcv_RectG_dataMut(instance: *mut c_void) -> *mut core::Rect; + pub fn cv_fromSlice_const_const_RectX_size_t(data: *const core::Rect, len: size_t) -> *mut c_void; + pub fn std_vectorLcv_RectG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_RectG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_RectG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_RotatedRectG_new_const() -> *mut c_void; + pub fn std_vectorLcv_RotatedRectG_delete(instance: *mut c_void); + pub fn std_vectorLcv_RotatedRectG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_RotatedRectG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_RotatedRectG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_RotatedRectG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_RotatedRectG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_RotatedRectG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_RotatedRectG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_RotatedRectG_clear(instance: *mut c_void); + pub fn std_vectorLcv_RotatedRectG_push_const_RotatedRect(instance: *mut c_void, val: *const core::RotatedRect); + pub fn std_vectorLcv_RotatedRectG_insert_size_t_const_RotatedRect(instance: *mut c_void, index: size_t, val: *const core::RotatedRect); + pub fn std_vectorLcv_RotatedRectG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut core::RotatedRect); + pub fn std_vectorLcv_RotatedRectG_set_size_t_const_RotatedRect(instance: *mut c_void, index: size_t, val: *const core::RotatedRect); + pub fn std_vectorLcv_RotatedRectG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_RotatedRectG_data_const(instance: *const c_void) -> *const core::RotatedRect; + pub fn std_vectorLcv_RotatedRectG_dataMut(instance: *mut c_void) -> *mut core::RotatedRect; + pub fn cv_fromSlice_const_const_RotatedRectX_size_t(data: *const core::RotatedRect, len: size_t) -> *mut c_void; + pub fn std_vectorLcv_StringG_new_const() -> *mut c_void; + pub fn std_vectorLcv_StringG_delete(instance: *mut c_void); + pub fn std_vectorLcv_StringG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_StringG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_StringG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_StringG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_StringG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_StringG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_StringG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_StringG_clear(instance: *mut c_void); + pub fn std_vectorLcv_StringG_push_const_String(instance: *mut c_void, val: *const c_char); + pub fn std_vectorLcv_StringG_insert_size_t_const_String(instance: *mut c_void, index: size_t, val: *const c_char); + pub fn std_vectorLcv_StringG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLcv_StringG_set_size_t_const_String(instance: *mut c_void, index: size_t, val: *const c_char); + pub fn std_vectorLcv_Vec2dG_new_const() -> *mut c_void; + pub fn std_vectorLcv_Vec2dG_delete(instance: *mut c_void); + pub fn std_vectorLcv_Vec2dG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec2dG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_Vec2dG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec2dG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_Vec2dG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_Vec2dG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_Vec2dG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_Vec2dG_clear(instance: *mut c_void); + pub fn std_vectorLcv_Vec2dG_push_const_Vec2d(instance: *mut c_void, val: *const core::Vec2d); + pub fn std_vectorLcv_Vec2dG_insert_size_t_const_Vec2d(instance: *mut c_void, index: size_t, val: *const core::Vec2d); + pub fn std_vectorLcv_Vec2dG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut core::Vec2d); + pub fn std_vectorLcv_Vec2dG_set_size_t_const_Vec2d(instance: *mut c_void, index: size_t, val: *const core::Vec2d); + pub fn std_vectorLcv_Vec2dG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_Vec2dG_data_const(instance: *const c_void) -> *const core::Vec2d; + pub fn std_vectorLcv_Vec2dG_dataMut(instance: *mut c_void) -> *mut core::Vec2d; + pub fn cv_fromSlice_const_const_Vec2dX_size_t(data: *const core::Vec2d, len: size_t) -> *mut c_void; + pub fn std_vectorLcv_Vec2dG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec2dG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec2dG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec2fG_new_const() -> *mut c_void; + pub fn std_vectorLcv_Vec2fG_delete(instance: *mut c_void); + pub fn std_vectorLcv_Vec2fG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec2fG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_Vec2fG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec2fG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_Vec2fG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_Vec2fG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_Vec2fG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_Vec2fG_clear(instance: *mut c_void); + pub fn std_vectorLcv_Vec2fG_push_const_Vec2f(instance: *mut c_void, val: *const core::Vec2f); + pub fn std_vectorLcv_Vec2fG_insert_size_t_const_Vec2f(instance: *mut c_void, index: size_t, val: *const core::Vec2f); + pub fn std_vectorLcv_Vec2fG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut core::Vec2f); + pub fn std_vectorLcv_Vec2fG_set_size_t_const_Vec2f(instance: *mut c_void, index: size_t, val: *const core::Vec2f); + pub fn std_vectorLcv_Vec2fG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_Vec2fG_data_const(instance: *const c_void) -> *const core::Vec2f; + pub fn std_vectorLcv_Vec2fG_dataMut(instance: *mut c_void) -> *mut core::Vec2f; + pub fn cv_fromSlice_const_const_Vec2fX_size_t(data: *const core::Vec2f, len: size_t) -> *mut c_void; + pub fn std_vectorLcv_Vec2fG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec2fG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec2fG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec3dG_new_const() -> *mut c_void; + pub fn std_vectorLcv_Vec3dG_delete(instance: *mut c_void); + pub fn std_vectorLcv_Vec3dG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec3dG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_Vec3dG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec3dG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_Vec3dG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_Vec3dG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_Vec3dG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_Vec3dG_clear(instance: *mut c_void); + pub fn std_vectorLcv_Vec3dG_push_const_Vec3d(instance: *mut c_void, val: *const core::Vec3d); + pub fn std_vectorLcv_Vec3dG_insert_size_t_const_Vec3d(instance: *mut c_void, index: size_t, val: *const core::Vec3d); + pub fn std_vectorLcv_Vec3dG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut core::Vec3d); + pub fn std_vectorLcv_Vec3dG_set_size_t_const_Vec3d(instance: *mut c_void, index: size_t, val: *const core::Vec3d); + pub fn std_vectorLcv_Vec3dG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_Vec3dG_data_const(instance: *const c_void) -> *const core::Vec3d; + pub fn std_vectorLcv_Vec3dG_dataMut(instance: *mut c_void) -> *mut core::Vec3d; + pub fn cv_fromSlice_const_const_Vec3dX_size_t(data: *const core::Vec3d, len: size_t) -> *mut c_void; + pub fn std_vectorLcv_Vec3dG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec3dG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec3dG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec3fG_new_const() -> *mut c_void; + pub fn std_vectorLcv_Vec3fG_delete(instance: *mut c_void); + pub fn std_vectorLcv_Vec3fG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec3fG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_Vec3fG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec3fG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_Vec3fG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_Vec3fG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_Vec3fG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_Vec3fG_clear(instance: *mut c_void); + pub fn std_vectorLcv_Vec3fG_push_const_Vec3f(instance: *mut c_void, val: *const core::Vec3f); + pub fn std_vectorLcv_Vec3fG_insert_size_t_const_Vec3f(instance: *mut c_void, index: size_t, val: *const core::Vec3f); + pub fn std_vectorLcv_Vec3fG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut core::Vec3f); + pub fn std_vectorLcv_Vec3fG_set_size_t_const_Vec3f(instance: *mut c_void, index: size_t, val: *const core::Vec3f); + pub fn std_vectorLcv_Vec3fG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_Vec3fG_data_const(instance: *const c_void) -> *const core::Vec3f; + pub fn std_vectorLcv_Vec3fG_dataMut(instance: *mut c_void) -> *mut core::Vec3f; + pub fn cv_fromSlice_const_const_Vec3fX_size_t(data: *const core::Vec3f, len: size_t) -> *mut c_void; + pub fn std_vectorLcv_Vec3fG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec3fG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec3fG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec4fG_new_const() -> *mut c_void; + pub fn std_vectorLcv_Vec4fG_delete(instance: *mut c_void); + pub fn std_vectorLcv_Vec4fG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec4fG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_Vec4fG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec4fG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_Vec4fG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_Vec4fG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_Vec4fG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_Vec4fG_clear(instance: *mut c_void); + pub fn std_vectorLcv_Vec4fG_push_const_Vec4f(instance: *mut c_void, val: *const core::Vec4f); + pub fn std_vectorLcv_Vec4fG_insert_size_t_const_Vec4f(instance: *mut c_void, index: size_t, val: *const core::Vec4f); + pub fn std_vectorLcv_Vec4fG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut core::Vec4f); + pub fn std_vectorLcv_Vec4fG_set_size_t_const_Vec4f(instance: *mut c_void, index: size_t, val: *const core::Vec4f); + pub fn std_vectorLcv_Vec4fG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_Vec4fG_data_const(instance: *const c_void) -> *const core::Vec4f; + pub fn std_vectorLcv_Vec4fG_dataMut(instance: *mut c_void) -> *mut core::Vec4f; + pub fn cv_fromSlice_const_const_Vec4fX_size_t(data: *const core::Vec4f, len: size_t) -> *mut c_void; + pub fn std_vectorLcv_Vec4fG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec4fG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec4fG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec4iG_new_const() -> *mut c_void; + pub fn std_vectorLcv_Vec4iG_delete(instance: *mut c_void); + pub fn std_vectorLcv_Vec4iG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec4iG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_Vec4iG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec4iG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_Vec4iG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_Vec4iG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_Vec4iG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_Vec4iG_clear(instance: *mut c_void); + pub fn std_vectorLcv_Vec4iG_push_const_Vec4i(instance: *mut c_void, val: *const core::Vec4i); + pub fn std_vectorLcv_Vec4iG_insert_size_t_const_Vec4i(instance: *mut c_void, index: size_t, val: *const core::Vec4i); + pub fn std_vectorLcv_Vec4iG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut core::Vec4i); + pub fn std_vectorLcv_Vec4iG_set_size_t_const_Vec4i(instance: *mut c_void, index: size_t, val: *const core::Vec4i); + pub fn std_vectorLcv_Vec4iG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_Vec4iG_data_const(instance: *const c_void) -> *const core::Vec4i; + pub fn std_vectorLcv_Vec4iG_dataMut(instance: *mut c_void) -> *mut core::Vec4i; + pub fn cv_fromSlice_const_const_Vec4iX_size_t(data: *const core::Vec4i, len: size_t) -> *mut c_void; + pub fn std_vectorLcv_Vec4iG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec4iG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec4iG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec6fG_new_const() -> *mut c_void; + pub fn std_vectorLcv_Vec6fG_delete(instance: *mut c_void); + pub fn std_vectorLcv_Vec6fG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec6fG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_Vec6fG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_Vec6fG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_Vec6fG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_Vec6fG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_Vec6fG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_Vec6fG_clear(instance: *mut c_void); + pub fn std_vectorLcv_Vec6fG_push_const_Vec6f(instance: *mut c_void, val: *const core::Vec6f); + pub fn std_vectorLcv_Vec6fG_insert_size_t_const_Vec6f(instance: *mut c_void, index: size_t, val: *const core::Vec6f); + pub fn std_vectorLcv_Vec6fG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut core::Vec6f); + pub fn std_vectorLcv_Vec6fG_set_size_t_const_Vec6f(instance: *mut c_void, index: size_t, val: *const core::Vec6f); + pub fn std_vectorLcv_Vec6fG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_Vec6fG_data_const(instance: *const c_void) -> *const core::Vec6f; + pub fn std_vectorLcv_Vec6fG_dataMut(instance: *mut c_void) -> *mut core::Vec6f; + pub fn cv_fromSlice_const_const_Vec6fX_size_t(data: *const core::Vec6f, len: size_t) -> *mut c_void; + pub fn std_vectorLcv_Vec6fG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec6fG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_Vec6fG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLstd_vectorLcv_MatGG_new_const() -> *mut c_void; + pub fn std_vectorLstd_vectorLcv_MatGG_delete(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_MatGG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_vectorLcv_MatGG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLstd_vectorLcv_MatGG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_vectorLcv_MatGG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_MatGG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLstd_vectorLcv_MatGG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLstd_vectorLcv_MatGG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLstd_vectorLcv_MatGG_clear(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_MatGG_push_const_vectorLMatG(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_MatGG_insert_size_t_const_vectorLMatG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_MatGG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLstd_vectorLcv_MatGG_set_size_t_const_vectorLMatG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_PointGG_new_const() -> *mut c_void; + pub fn std_vectorLstd_vectorLcv_PointGG_delete(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_PointGG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_vectorLcv_PointGG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLstd_vectorLcv_PointGG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_vectorLcv_PointGG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_PointGG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLstd_vectorLcv_PointGG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLstd_vectorLcv_PointGG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLstd_vectorLcv_PointGG_clear(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_PointGG_push_const_vectorLPointG(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_PointGG_insert_size_t_const_vectorLPointG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_PointGG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLstd_vectorLcv_PointGG_set_size_t_const_vectorLPointG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_PointGG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLstd_vectorLcv_PointGG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLstd_vectorLcv_PointGG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLstd_vectorLcv_Point2fGG_new_const() -> *mut c_void; + pub fn std_vectorLstd_vectorLcv_Point2fGG_delete(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_Point2fGG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_vectorLcv_Point2fGG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLstd_vectorLcv_Point2fGG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_vectorLcv_Point2fGG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_Point2fGG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLstd_vectorLcv_Point2fGG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLstd_vectorLcv_Point2fGG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLstd_vectorLcv_Point2fGG_clear(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_Point2fGG_push_const_vectorLPoint2fG(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_Point2fGG_insert_size_t_const_vectorLPoint2fG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_Point2fGG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLstd_vectorLcv_Point2fGG_set_size_t_const_vectorLPoint2fG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_Point2fGG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLstd_vectorLcv_Point2fGG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLstd_vectorLcv_Point2fGG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLstd_vectorLcv_RangeGG_new_const() -> *mut c_void; + pub fn std_vectorLstd_vectorLcv_RangeGG_delete(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_RangeGG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_vectorLcv_RangeGG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLstd_vectorLcv_RangeGG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_vectorLcv_RangeGG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_RangeGG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLstd_vectorLcv_RangeGG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLstd_vectorLcv_RangeGG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLstd_vectorLcv_RangeGG_clear(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_RangeGG_push_const_vectorLRangeG(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_RangeGG_insert_size_t_const_vectorLRangeG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_RangeGG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLstd_vectorLcv_RangeGG_set_size_t_const_vectorLRangeG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLstd_vectorLintGG_new_const() -> *mut c_void; + pub fn std_vectorLstd_vectorLintGG_delete(instance: *mut c_void); + pub fn std_vectorLstd_vectorLintGG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_vectorLintGG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLstd_vectorLintGG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_vectorLintGG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLstd_vectorLintGG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLstd_vectorLintGG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLstd_vectorLintGG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLstd_vectorLintGG_clear(instance: *mut c_void); + pub fn std_vectorLstd_vectorLintGG_push_const_vectorLintG(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLstd_vectorLintGG_insert_size_t_const_vectorLintG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLstd_vectorLintGG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLstd_vectorLintGG_set_size_t_const_vectorLintG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLstd_vectorLintGG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLstd_vectorLintGG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLstd_vectorLintGG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLboolG_new_const() -> *mut c_void; + pub fn std_vectorLboolG_delete(instance: *mut c_void); + pub fn std_vectorLboolG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLboolG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLboolG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLboolG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLboolG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLboolG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLboolG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLboolG_clear(instance: *mut c_void); + pub fn std_vectorLboolG_push_const_bool(instance: *mut c_void, val: bool); + pub fn std_vectorLboolG_insert_size_t_const_bool(instance: *mut c_void, index: size_t, val: bool); + pub fn std_vectorLboolG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut bool); + pub fn std_vectorLboolG_set_size_t_const_bool(instance: *mut c_void, index: size_t, val: bool); + pub fn std_vectorLboolG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLfloatG_new_const() -> *mut c_void; + pub fn std_vectorLfloatG_delete(instance: *mut c_void); + pub fn std_vectorLfloatG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLfloatG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLfloatG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLfloatG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLfloatG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLfloatG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLfloatG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLfloatG_clear(instance: *mut c_void); + pub fn std_vectorLfloatG_push_const_float(instance: *mut c_void, val: f32); + pub fn std_vectorLfloatG_insert_size_t_const_float(instance: *mut c_void, index: size_t, val: f32); + pub fn std_vectorLfloatG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut f32); + pub fn std_vectorLfloatG_set_size_t_const_float(instance: *mut c_void, index: size_t, val: f32); + pub fn std_vectorLfloatG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLfloatG_data_const(instance: *const c_void) -> *const f32; + pub fn std_vectorLfloatG_dataMut(instance: *mut c_void) -> *mut f32; + pub fn cv_fromSlice_const_const_floatX_size_t(data: *const f32, len: size_t) -> *mut c_void; + pub fn std_vectorLfloatG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLfloatG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLfloatG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLdoubleG_new_const() -> *mut c_void; + pub fn std_vectorLdoubleG_delete(instance: *mut c_void); + pub fn std_vectorLdoubleG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLdoubleG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLdoubleG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLdoubleG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLdoubleG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLdoubleG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLdoubleG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLdoubleG_clear(instance: *mut c_void); + pub fn std_vectorLdoubleG_push_const_double(instance: *mut c_void, val: f64); + pub fn std_vectorLdoubleG_insert_size_t_const_double(instance: *mut c_void, index: size_t, val: f64); + pub fn std_vectorLdoubleG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut f64); + pub fn std_vectorLdoubleG_set_size_t_const_double(instance: *mut c_void, index: size_t, val: f64); + pub fn std_vectorLdoubleG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLdoubleG_data_const(instance: *const c_void) -> *const f64; + pub fn std_vectorLdoubleG_dataMut(instance: *mut c_void) -> *mut f64; + pub fn cv_fromSlice_const_const_doubleX_size_t(data: *const f64, len: size_t) -> *mut c_void; + pub fn std_vectorLdoubleG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLdoubleG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLdoubleG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLintG_new_const() -> *mut c_void; + pub fn std_vectorLintG_delete(instance: *mut c_void); + pub fn std_vectorLintG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLintG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLintG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLintG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLintG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLintG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLintG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLintG_clear(instance: *mut c_void); + pub fn std_vectorLintG_push_const_int(instance: *mut c_void, val: i32); + pub fn std_vectorLintG_insert_size_t_const_int(instance: *mut c_void, index: size_t, val: i32); + pub fn std_vectorLintG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut i32); + pub fn std_vectorLintG_set_size_t_const_int(instance: *mut c_void, index: size_t, val: i32); + pub fn std_vectorLintG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLintG_data_const(instance: *const c_void) -> *const i32; + pub fn std_vectorLintG_dataMut(instance: *mut c_void) -> *mut i32; + pub fn cv_fromSlice_const_const_intX_size_t(data: *const i32, len: size_t) -> *mut c_void; + pub fn std_vectorLintG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLintG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLintG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLsigned_charG_new_const() -> *mut c_void; + pub fn std_vectorLsigned_charG_delete(instance: *mut c_void); + pub fn std_vectorLsigned_charG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLsigned_charG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLsigned_charG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLsigned_charG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLsigned_charG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLsigned_charG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLsigned_charG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLsigned_charG_clear(instance: *mut c_void); + pub fn std_vectorLsigned_charG_push_const_signed_char(instance: *mut c_void, val: i8); + pub fn std_vectorLsigned_charG_insert_size_t_const_signed_char(instance: *mut c_void, index: size_t, val: i8); + pub fn std_vectorLsigned_charG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut i8); + pub fn std_vectorLsigned_charG_set_size_t_const_signed_char(instance: *mut c_void, index: size_t, val: i8); + pub fn std_vectorLsigned_charG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLsigned_charG_data_const(instance: *const c_void) -> *const i8; + pub fn std_vectorLsigned_charG_dataMut(instance: *mut c_void) -> *mut i8; + pub fn cv_fromSlice_const_const_signed_charX_size_t(data: *const i8, len: size_t) -> *mut c_void; + pub fn std_vectorLsize_tG_new_const() -> *mut c_void; + pub fn std_vectorLsize_tG_delete(instance: *mut c_void); + pub fn std_vectorLsize_tG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLsize_tG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLsize_tG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLsize_tG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLsize_tG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLsize_tG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLsize_tG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLsize_tG_clear(instance: *mut c_void); + pub fn std_vectorLsize_tG_push_const_size_t(instance: *mut c_void, val: size_t); + pub fn std_vectorLsize_tG_insert_size_t_const_size_t(instance: *mut c_void, index: size_t, val: size_t); + pub fn std_vectorLsize_tG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut size_t); + pub fn std_vectorLsize_tG_set_size_t_const_size_t(instance: *mut c_void, index: size_t, val: size_t); + pub fn std_vectorLsize_tG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLsize_tG_data_const(instance: *const c_void) -> *const size_t; + pub fn std_vectorLsize_tG_dataMut(instance: *mut c_void) -> *mut size_t; + pub fn cv_fromSlice_const_const_size_tX_size_t(data: *const size_t, len: size_t) -> *mut c_void; + pub fn std_vectorLunsigned_charG_new_const() -> *mut c_void; + pub fn std_vectorLunsigned_charG_delete(instance: *mut c_void); + pub fn std_vectorLunsigned_charG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLunsigned_charG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLunsigned_charG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLunsigned_charG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLunsigned_charG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLunsigned_charG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLunsigned_charG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLunsigned_charG_clear(instance: *mut c_void); + pub fn std_vectorLunsigned_charG_push_const_unsigned_char(instance: *mut c_void, val: u8); + pub fn std_vectorLunsigned_charG_insert_size_t_const_unsigned_char(instance: *mut c_void, index: size_t, val: u8); + pub fn std_vectorLunsigned_charG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut u8); + pub fn std_vectorLunsigned_charG_set_size_t_const_unsigned_char(instance: *mut c_void, index: size_t, val: u8); + pub fn std_vectorLunsigned_charG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLunsigned_charG_data_const(instance: *const c_void) -> *const u8; + pub fn std_vectorLunsigned_charG_dataMut(instance: *mut c_void) -> *mut u8; + pub fn cv_fromSlice_const_const_unsigned_charX_size_t(data: *const u8, len: size_t) -> *mut c_void; + pub fn std_vectorLunsigned_charG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLunsigned_charG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLunsigned_charG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + } +} +pub use core_sys::*; + +mod dnn_sys { + use super::*; + + unsafe extern "C" { + pub fn cv_dnn_NMSBoxesBatched_const_vectorLRect2dGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR(bboxes: *const c_void, scores: *const c_void, class_ids: *const c_void, score_threshold: f32, nms_threshold: f32, indices: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_NMSBoxesBatched_const_vectorLRect2dGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR_const_float_const_int(bboxes: *const c_void, scores: *const c_void, class_ids: *const c_void, score_threshold: f32, nms_threshold: f32, indices: *mut c_void, eta: f32, top_k: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_NMSBoxesBatched_const_vectorLRectGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR(bboxes: *const c_void, scores: *const c_void, class_ids: *const c_void, score_threshold: f32, nms_threshold: f32, indices: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_NMSBoxesBatched_const_vectorLRectGR_const_vectorLfloatGR_const_vectorLintGR_const_float_const_float_vectorLintGR_const_float_const_int(bboxes: *const c_void, scores: *const c_void, class_ids: *const c_void, score_threshold: f32, nms_threshold: f32, indices: *mut c_void, eta: f32, top_k: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_NMSBoxes_const_vectorLRect2dGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR(bboxes: *const c_void, scores: *const c_void, score_threshold: f32, nms_threshold: f32, indices: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_NMSBoxes_const_vectorLRect2dGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR_const_float_const_int(bboxes: *const c_void, scores: *const c_void, score_threshold: f32, nms_threshold: f32, indices: *mut c_void, eta: f32, top_k: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_NMSBoxes_const_vectorLRectGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR(bboxes: *const c_void, scores: *const c_void, score_threshold: f32, nms_threshold: f32, indices: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_NMSBoxes_const_vectorLRectGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR_const_float_const_int(bboxes: *const c_void, scores: *const c_void, score_threshold: f32, nms_threshold: f32, indices: *mut c_void, eta: f32, top_k: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_NMSBoxes_const_vectorLRotatedRectGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR(bboxes: *const c_void, scores: *const c_void, score_threshold: f32, nms_threshold: f32, indices: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_NMSBoxes_const_vectorLRotatedRectGR_const_vectorLfloatGR_const_float_const_float_vectorLintGR_const_float_const_int(bboxes: *const c_void, scores: *const c_void, score_threshold: f32, nms_threshold: f32, indices: *mut c_void, eta: f32, top_k: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_blobFromImageWithParams_const__InputArrayR(image: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_blobFromImageWithParams_const__InputArrayR_const_Image2BlobParamsR(image: *const c_void, param: *const crate::dnn::Image2BlobParams, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_blobFromImageWithParams_const__InputArrayR_const__OutputArrayR(image: *const c_void, blob: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_blobFromImageWithParams_const__InputArrayR_const__OutputArrayR_const_Image2BlobParamsR(image: *const c_void, blob: *const c_void, param: *const crate::dnn::Image2BlobParams, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_blobFromImage_const__InputArrayR(image: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_blobFromImage_const__InputArrayR_const__OutputArrayR(image: *const c_void, blob: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_blobFromImage_const__InputArrayR_const__OutputArrayR_double_const_SizeR_const_ScalarR_bool_bool_int(image: *const c_void, blob: *const c_void, scalefactor: f64, size: *const core::Size, mean: *const core::Scalar, swap_rb: bool, crop: bool, ddepth: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_blobFromImage_const__InputArrayR_double_const_SizeR_const_ScalarR_bool_bool_int(image: *const c_void, scalefactor: f64, size: *const core::Size, mean: *const core::Scalar, swap_rb: bool, crop: bool, ddepth: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_blobFromImagesWithParams_const__InputArrayR(images: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_blobFromImagesWithParams_const__InputArrayR_const_Image2BlobParamsR(images: *const c_void, param: *const crate::dnn::Image2BlobParams, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_blobFromImagesWithParams_const__InputArrayR_const__OutputArrayR(images: *const c_void, blob: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_blobFromImagesWithParams_const__InputArrayR_const__OutputArrayR_const_Image2BlobParamsR(images: *const c_void, blob: *const c_void, param: *const crate::dnn::Image2BlobParams, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_blobFromImages_const__InputArrayR(images: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_blobFromImages_const__InputArrayR_const__OutputArrayR(images: *const c_void, blob: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_blobFromImages_const__InputArrayR_const__OutputArrayR_double_Size_const_ScalarR_bool_bool_int(images: *const c_void, blob: *const c_void, scalefactor: f64, size: *const core::Size, mean: *const core::Scalar, swap_rb: bool, crop: bool, ddepth: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_blobFromImages_const__InputArrayR_double_Size_const_ScalarR_bool_bool_int(images: *const c_void, scalefactor: f64, size: *const core::Size, mean: *const core::Scalar, swap_rb: bool, crop: bool, ddepth: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_concat_const_MatShapeR_const_MatShapeR(a: *const c_void, b: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_enableModelDiagnostics_bool(is_diagnostics_mode: bool, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_getAvailableBackends(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_getAvailableTargets_Backend(be: crate::dnn::Backend, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_getInferenceEngineBackendType(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_getInferenceEngineCPUType(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_getInferenceEngineVPUType(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_getPlane_const_MatR_int_int(m: *const c_void, n: i32, cn: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_imagesFromBlob_const_MatR_const__OutputArrayR(blob_: *const c_void, images_: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_readNetFromCaffe_const_StringR(prototxt: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromCaffe_const_StringR_const_StringR(prototxt: *const c_char, caffe_model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromCaffe_const_charX_size_t(buffer_proto: *const c_char, len_proto: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromCaffe_const_charX_size_t_const_charX_size_t(buffer_proto: *const c_char, len_proto: size_t, buffer_model: *const c_char, len_model: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromCaffe_const_vectorLunsigned_charGR(buffer_proto: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromCaffe_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(buffer_proto: *const c_void, buffer_model: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromDarknet_const_StringR(cfg_file: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromDarknet_const_StringR_const_StringR(cfg_file: *const c_char, darknet_model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromDarknet_const_charX_size_t(buffer_cfg: *const c_char, len_cfg: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromDarknet_const_charX_size_t_const_charX_size_t(buffer_cfg: *const c_char, len_cfg: size_t, buffer_model: *const c_char, len_model: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromDarknet_const_vectorLunsigned_charGR(buffer_cfg: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromDarknet_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(buffer_cfg: *const c_void, buffer_model: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromModelOptimizer_const_StringR(xml: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromModelOptimizer_const_StringR_const_StringR(xml: *const c_char, bin: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromModelOptimizer_const_unsigned_charX_size_t_const_unsigned_charX_size_t(buffer_model_config_ptr: *const u8, buffer_model_config_size: size_t, buffer_weights_ptr: *const u8, buffer_weights_size: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromModelOptimizer_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(buffer_model_config: *const c_void, buffer_weights: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromONNX_const_StringR(onnx_file: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromONNX_const_charX_size_t(buffer: *const c_char, size_buffer: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromONNX_const_vectorLunsigned_charGR(buffer: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromTFLite_const_StringR(model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromTFLite_const_charX_size_t(buffer_model: *const c_char, len_model: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromTFLite_const_vectorLunsigned_charGR(buffer_model: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromTensorflow_const_StringR(model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromTensorflow_const_StringR_const_StringR(model: *const c_char, config: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromTensorflow_const_charX_size_t(buffer_model: *const c_char, len_model: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromTensorflow_const_charX_size_t_const_charX_size_t(buffer_model: *const c_char, len_model: size_t, buffer_config: *const c_char, len_config: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromTensorflow_const_vectorLunsigned_charGR(buffer_model: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromTensorflow_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(buffer_model: *const c_void, buffer_config: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromTorch_const_StringR(model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNetFromTorch_const_StringR_bool_bool(model: *const c_char, is_binary: bool, evaluate: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNet_const_StringR(model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNet_const_StringR_const_StringR_const_StringR(model: *const c_char, config: *const c_char, framework: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNet_const_StringR_const_vectorLunsigned_charGR(framework: *const c_char, buffer_model: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readNet_const_StringR_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(framework: *const c_char, buffer_model: *const c_void, buffer_config: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readTensorFromONNX_const_StringR(path: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readTorchBlob_const_StringR(filename: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_readTorchBlob_const_StringR_bool(filename: *const c_char, is_binary: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_releaseHDDLPlugin(ocvrs_return: *mut Result<()>); + pub fn cv_dnn_resetMyriadDevice(ocvrs_return: *mut Result<()>); + pub fn cv_dnn_setInferenceEngineBackendType_const_StringR(new_backend_type: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_shape_const_MatR(mat: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_shape_const_MatSizeR(sz: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_shape_const_UMatR(mat: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_shape_const_intX_const_int(dims: *const i32, n: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_shape_int(a0: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_shape_int_int_int_int(a0: i32, a1: i32, a2: i32, a3: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_shrinkCaffeModel_const_StringR_const_StringR(src: *const c_char, dst: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_shrinkCaffeModel_const_StringR_const_StringR_const_vectorLStringGR(src: *const c_char, dst: *const c_char, layers_types: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_slice_const_MatR_const__RangeR(m: *const c_void, r0: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_slice_const_MatR_const__RangeR_const__RangeR(m: *const c_void, r0: *const c_void, r1: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_slice_const_MatR_const__RangeR_const__RangeR_const__RangeR(m: *const c_void, r0: *const c_void, r1: *const c_void, r2: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_slice_const_MatR_const__RangeR_const__RangeR_const__RangeR_const__RangeR(m: *const c_void, r0: *const c_void, r1: *const c_void, r2: *const c_void, r3: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_softNMSBoxes_const_vectorLRectGR_const_vectorLfloatGR_vectorLfloatGR_const_float_const_float_vectorLintGR(bboxes: *const c_void, scores: *const c_void, updated_scores: *mut c_void, score_threshold: f32, nms_threshold: f32, indices: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_softNMSBoxes_const_vectorLRectGR_const_vectorLfloatGR_vectorLfloatGR_const_float_const_float_vectorLintGR_size_t_const_float_SoftNMSMethod(bboxes: *const c_void, scores: *const c_void, updated_scores: *mut c_void, score_threshold: f32, nms_threshold: f32, indices: *mut c_void, top_k: size_t, sigma: f32, method: crate::dnn::SoftNMSMethod, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_total_const_MatR(mat: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_total_const_MatR_int_int(mat: *const c_void, start: i32, end: i32, ocvrs_return: *mut Result); + pub fn cv_dnn_total_const_MatShapeR(shape: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_total_const_MatShapeR_int_int(shape: *const c_void, start: i32, end: i32, ocvrs_return: *mut Result); + pub fn cv_dnn_writeTextGraph_const_StringR_const_StringR(model: *const c_char, output: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_AbsLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_AbsLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_AbsLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AbsLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AbsLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AbsLayer_delete(instance: *mut c_void); + pub fn cv_dnn_AccumLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_AccumLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_AccumLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AccumLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AccumLayer_delete(instance: *mut c_void); + pub fn cv_dnn_AcosLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_AcosLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_AcosLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AcosLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AcosLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AcosLayer_delete(instance: *mut c_void); + pub fn cv_dnn_AcoshLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_AcoshLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_AcoshLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AcoshLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AcoshLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AcoshLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ActivationLayer_forwardSlice_const_const_floatX_floatX_int_size_t_int_int(instance: *const c_void, src: *const f32, dst: *mut f32, len: i32, out_plane_size: size_t, cn0: i32, cn1: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_ActivationLayer_forwardSlice_const_const_intX_const_intX_intX_int_size_t_int_int(instance: *const c_void, src: *const i32, lut: *const i32, dst: *mut i32, len: i32, out_plane_size: size_t, cn0: i32, cn1: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_ActivationLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_AbsLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_AcosLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_AcoshLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_ActivationLayerInt8(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_AsinLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_AsinhLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_AtanLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_AtanhLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_BNLLLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_BatchNormLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_BatchNormLayerInt8(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_CeilLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_CeluLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_ChannelsPReLULayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_CosLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_CoshLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_ELULayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_ErfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_ExpLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_FloorLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_GeluApproximationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_GeluLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_HardSigmoidLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_HardSwishLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_LogLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_MishLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_NotLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_PowerLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_ReLU6Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_ReLULayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_ReciprocalLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_RoundLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_SeluLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_ShrinkLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_SigmoidLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_SignLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_SinLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_SinhLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_SoftplusLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_SoftsignLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_SqrtLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_SwishLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_TanHLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_TanLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_ThresholdedReluLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ActivationLayerInt8_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ActivationLayerInt8_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ActivationLayerInt8_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayerInt8_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayerInt8_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ActivationLayerInt8_delete(instance: *mut c_void); + pub fn cv_dnn_ArgLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ArgLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ArgLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ArgLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ArgLayer_delete(instance: *mut c_void); + pub fn cv_dnn_AsinLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_AsinLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_AsinLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AsinLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AsinLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AsinLayer_delete(instance: *mut c_void); + pub fn cv_dnn_AsinhLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_AsinhLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_AsinhLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AsinhLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AsinhLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AsinhLayer_delete(instance: *mut c_void); + pub fn cv_dnn_AtanLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_AtanLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_AtanLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AtanLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AtanLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AtanLayer_delete(instance: *mut c_void); + pub fn cv_dnn_AtanhLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_AtanhLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_AtanhLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AtanhLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AtanhLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AtanhLayer_delete(instance: *mut c_void); + pub fn cv_dnn_AttentionLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_AttentionLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_AttentionLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AttentionLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_AttentionLayer_delete(instance: *mut c_void); + pub fn cv_dnn_BNLLLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_BNLLLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_BNLLLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BNLLLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BNLLLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BNLLLayer_delete(instance: *mut c_void); + pub fn cv_dnn_BackendNode_propBackendId_const(instance: *const c_void) -> i32; + pub fn cv_dnn_BackendNode_propBackendId_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_BackendNode_delete(instance: *mut c_void); + pub fn cv_dnn_BackendWrapper_copyToHost(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_BackendWrapper_setHostDirty(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_BackendWrapper_propBackendId_const(instance: *const c_void) -> i32; + pub fn cv_dnn_BackendWrapper_propBackendId_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_BackendWrapper_propTargetId_const(instance: *const c_void) -> i32; + pub fn cv_dnn_BackendWrapper_propTargetId_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_BackendWrapper_delete(instance: *mut c_void); + pub fn cv_dnn_BaseConvolutionLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_BaseConvolutionLayer_propKernel_const(instance: *const c_void, ocvrs_return: *mut core::Size); + pub fn cv_dnn_BaseConvolutionLayer_propKernel_const_Size(instance: *mut c_void, val: *const core::Size); + pub fn cv_dnn_BaseConvolutionLayer_propStride_const(instance: *const c_void, ocvrs_return: *mut core::Size); + pub fn cv_dnn_BaseConvolutionLayer_propStride_const_Size(instance: *mut c_void, val: *const core::Size); + pub fn cv_dnn_BaseConvolutionLayer_propPad_const(instance: *const c_void, ocvrs_return: *mut core::Size); + pub fn cv_dnn_BaseConvolutionLayer_propPad_const_Size(instance: *mut c_void, val: *const core::Size); + pub fn cv_dnn_BaseConvolutionLayer_propDilation_const(instance: *const c_void, ocvrs_return: *mut core::Size); + pub fn cv_dnn_BaseConvolutionLayer_propDilation_const_Size(instance: *mut c_void, val: *const core::Size); + pub fn cv_dnn_BaseConvolutionLayer_propAdjustPad_const(instance: *const c_void, ocvrs_return: *mut core::Size); + pub fn cv_dnn_BaseConvolutionLayer_propAdjustPad_const_Size(instance: *mut c_void, val: *const core::Size); + pub fn cv_dnn_BaseConvolutionLayer_propAdjust_pads_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_BaseConvolutionLayer_propAdjust_pads_const_vectorLsize_tG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_BaseConvolutionLayer_propKernel_size_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_BaseConvolutionLayer_propKernel_size_const_vectorLsize_tG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_BaseConvolutionLayer_propStrides_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_BaseConvolutionLayer_propStrides_const_vectorLsize_tG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_BaseConvolutionLayer_propDilations_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_BaseConvolutionLayer_propDilations_const_vectorLsize_tG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_BaseConvolutionLayer_propPads_begin_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_BaseConvolutionLayer_propPads_begin_const_vectorLsize_tG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_BaseConvolutionLayer_propPads_end_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_BaseConvolutionLayer_propPads_end_const_vectorLsize_tG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_BaseConvolutionLayer_propPadMode_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_BaseConvolutionLayer_propPadMode_const_String(instance: *mut c_void, val: *const c_char); + pub fn cv_dnn_BaseConvolutionLayer_propNumOutput_const(instance: *const c_void) -> i32; + pub fn cv_dnn_BaseConvolutionLayer_propNumOutput_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_BaseConvolutionLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BaseConvolutionLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BaseConvolutionLayer_delete(instance: *mut c_void); + pub fn cv_dnn_BatchNormLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_BatchNormLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_BatchNormLayer_propHasWeights_const(instance: *const c_void) -> bool; + pub fn cv_dnn_BatchNormLayer_propHasWeights_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_BatchNormLayer_propHasBias_const(instance: *const c_void) -> bool; + pub fn cv_dnn_BatchNormLayer_propHasBias_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_BatchNormLayer_propEpsilon_const(instance: *const c_void) -> f32; + pub fn cv_dnn_BatchNormLayer_propEpsilon_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_BatchNormLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BatchNormLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BatchNormLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BatchNormLayer_delete(instance: *mut c_void); + pub fn cv_dnn_BatchNormLayerInt8_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_BatchNormLayerInt8_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_BatchNormLayerInt8_propInput_sc_const(instance: *const c_void) -> f32; + pub fn cv_dnn_BatchNormLayerInt8_propInput_sc_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_BatchNormLayerInt8_propOutput_sc_const(instance: *const c_void) -> f32; + pub fn cv_dnn_BatchNormLayerInt8_propOutput_sc_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_BatchNormLayerInt8_propInput_zp_const(instance: *const c_void) -> i32; + pub fn cv_dnn_BatchNormLayerInt8_propInput_zp_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_BatchNormLayerInt8_propOutput_zp_const(instance: *const c_void) -> i32; + pub fn cv_dnn_BatchNormLayerInt8_propOutput_zp_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_BatchNormLayerInt8_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BatchNormLayerInt8_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BatchNormLayerInt8_to_BatchNormLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BatchNormLayerInt8_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BatchNormLayerInt8_delete(instance: *mut c_void); + pub fn cv_dnn_BlankLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_BlankLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_BlankLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BlankLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_BlankLayer_delete(instance: *mut c_void); + pub fn cv_dnn_CeilLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_CeilLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_CeilLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CeilLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CeilLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CeilLayer_delete(instance: *mut c_void); + pub fn cv_dnn_CeluLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_CeluLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_CeluLayer_propAlpha_const(instance: *const c_void) -> f32; + pub fn cv_dnn_CeluLayer_propAlpha_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_CeluLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CeluLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CeluLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CeluLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ChannelsPReLULayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ChannelsPReLULayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ChannelsPReLULayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ChannelsPReLULayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ChannelsPReLULayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ChannelsPReLULayer_delete(instance: *mut c_void); + pub fn cv_dnn_ClassificationModel_ClassificationModel(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ClassificationModel_ClassificationModel_const_StringR_const_StringR(model: *const c_char, config: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ClassificationModel_ClassificationModel_const_StringR(model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ClassificationModel_ClassificationModel_const_NetR(network: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ClassificationModel_setEnableSoftmaxPostProcessing_bool(instance: *mut c_void, enable: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ClassificationModel_getEnableSoftmaxPostProcessing_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_ClassificationModel_classify_const__InputArrayR(instance: *mut c_void, frame: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ClassificationModel_classify_const__InputArrayR_intR_floatR(instance: *mut c_void, frame: *const c_void, class_id: *mut i32, conf: *mut f32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_ClassificationModel_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_ClassificationModel_to_Model(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ClassificationModel_delete(instance: *mut c_void); + pub fn cv_dnn_CompareLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_CompareLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_CompareLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CompareLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CompareLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ConcatLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ConcatLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ConcatLayer_propAxis_const(instance: *const c_void) -> i32; + pub fn cv_dnn_ConcatLayer_propAxis_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_ConcatLayer_propPadding_const(instance: *const c_void) -> bool; + pub fn cv_dnn_ConcatLayer_propPadding_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_ConcatLayer_propPaddingValue_const(instance: *const c_void) -> i32; + pub fn cv_dnn_ConcatLayer_propPaddingValue_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_ConcatLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ConcatLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ConcatLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ConstLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ConstLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ConstLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ConstLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ConstLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ConvolutionLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ConvolutionLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ConvolutionLayer_propFusedActivation_const(instance: *const c_void) -> bool; + pub fn cv_dnn_ConvolutionLayer_propFusedActivation_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_ConvolutionLayer_propFusedAdd_const(instance: *const c_void) -> bool; + pub fn cv_dnn_ConvolutionLayer_propFusedAdd_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_ConvolutionLayer_propUseWinograd_const(instance: *const c_void) -> bool; + pub fn cv_dnn_ConvolutionLayer_propUseWinograd_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_ConvolutionLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ConvolutionLayer_to_BaseConvolutionLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ConvolutionLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ConvolutionLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ConvolutionLayerInt8_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ConvolutionLayerInt8_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ConvolutionLayerInt8_propInput_zp_const(instance: *const c_void) -> i32; + pub fn cv_dnn_ConvolutionLayerInt8_propInput_zp_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_ConvolutionLayerInt8_propOutput_zp_const(instance: *const c_void) -> i32; + pub fn cv_dnn_ConvolutionLayerInt8_propOutput_zp_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_ConvolutionLayerInt8_propInput_sc_const(instance: *const c_void) -> f32; + pub fn cv_dnn_ConvolutionLayerInt8_propInput_sc_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_ConvolutionLayerInt8_propOutput_sc_const(instance: *const c_void) -> f32; + pub fn cv_dnn_ConvolutionLayerInt8_propOutput_sc_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_ConvolutionLayerInt8_propPer_channel_const(instance: *const c_void) -> bool; + pub fn cv_dnn_ConvolutionLayerInt8_propPer_channel_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_ConvolutionLayerInt8_propUseWinograd_const(instance: *const c_void) -> bool; + pub fn cv_dnn_ConvolutionLayerInt8_propUseWinograd_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_ConvolutionLayerInt8_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ConvolutionLayerInt8_to_BaseConvolutionLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ConvolutionLayerInt8_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ConvolutionLayerInt8_delete(instance: *mut c_void); + pub fn cv_dnn_CorrelationLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_CorrelationLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_CorrelationLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CorrelationLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CorrelationLayer_delete(instance: *mut c_void); + pub fn cv_dnn_CosLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_CosLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_CosLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CosLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CosLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CosLayer_delete(instance: *mut c_void); + pub fn cv_dnn_CoshLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_CoshLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_CoshLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CoshLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CoshLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CoshLayer_delete(instance: *mut c_void); + pub fn cv_dnn_CropAndResizeLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_CropAndResizeLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_CropAndResizeLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CropAndResizeLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CropAndResizeLayer_delete(instance: *mut c_void); + pub fn cv_dnn_CropLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_CropLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_CropLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CropLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CropLayer_delete(instance: *mut c_void); + pub fn cv_dnn_CumSumLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_CumSumLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_CumSumLayer_propExclusive_const(instance: *const c_void) -> i32; + pub fn cv_dnn_CumSumLayer_propExclusive_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_CumSumLayer_propReverse_const(instance: *const c_void) -> i32; + pub fn cv_dnn_CumSumLayer_propReverse_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_CumSumLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CumSumLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_CumSumLayer_delete(instance: *mut c_void); + pub fn cv_dnn_DataAugmentationLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DataAugmentationLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_DataAugmentationLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_DataAugmentationLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_DataAugmentationLayer_delete(instance: *mut c_void); + pub fn cv_dnn_DeconvolutionLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DeconvolutionLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_DeconvolutionLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_DeconvolutionLayer_to_BaseConvolutionLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_DeconvolutionLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_DeconvolutionLayer_delete(instance: *mut c_void); + pub fn cv_dnn_DepthToSpaceLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DepthToSpaceLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_DepthToSpaceLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_DepthToSpaceLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_DepthToSpaceLayer_delete(instance: *mut c_void); + pub fn cv_dnn_DequantizeLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DequantizeLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_DequantizeLayer_propScales_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_DequantizeLayer_propScales_const_vectorLfloatG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_DequantizeLayer_propZeropoints_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_DequantizeLayer_propZeropoints_const_vectorLintG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_DequantizeLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_DequantizeLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_DequantizeLayer_delete(instance: *mut c_void); + pub fn cv_dnn_DetectionModel_DetectionModel_const_StringR_const_StringR(model: *const c_char, config: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DetectionModel_DetectionModel_const_StringR(model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DetectionModel_DetectionModel_const_NetR(network: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DetectionModel_DetectionModel(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DetectionModel_setNmsAcrossClasses_bool(instance: *mut c_void, value: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DetectionModel_getNmsAcrossClasses(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_DetectionModel_detect_const__InputArrayR_vectorLintGR_vectorLfloatGR_vectorLRectGR_float_float(instance: *mut c_void, frame: *const c_void, class_ids: *mut c_void, confidences: *mut c_void, boxes: *mut c_void, conf_threshold: f32, nms_threshold: f32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_DetectionModel_detect_const__InputArrayR_vectorLintGR_vectorLfloatGR_vectorLRectGR(instance: *mut c_void, frame: *const c_void, class_ids: *mut c_void, confidences: *mut c_void, boxes: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_DetectionModel_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_DetectionModel_to_Model(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_DetectionModel_delete(instance: *mut c_void); + pub fn cv_dnn_DetectionOutputLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DetectionOutputLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_DetectionOutputLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_DetectionOutputLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_DetectionOutputLayer_delete(instance: *mut c_void); + pub fn cv_dnn_Dict_has_const_const_StringR(instance: *const c_void, key: *const c_char, ocvrs_return: *mut Result); + pub fn cv_dnn_Dict_ptr_const_StringR(instance: *mut c_void, key: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Dict_ptr_const_const_StringR(instance: *const c_void, key: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Dict_get_const_const_StringR(instance: *const c_void, key: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Dict_set_const_cv_String_const_StringR_const_StringR(instance: *mut c_void, key: *const c_char, value: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Dict_set_const_cv_dnn_DictValue_const_StringR_const_DictValueR(instance: *mut c_void, key: *const c_char, value: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Dict_set_const_double_const_StringR_const_doubleR(instance: *mut c_void, key: *const c_char, value: *const f64, ocvrs_return: *mut Result); + pub fn cv_dnn_Dict_set_const_int64_t_const_StringR_const_int64_tR(instance: *mut c_void, key: *const c_char, value: *const i64, ocvrs_return: *mut Result); + pub fn cv_dnn_Dict_erase_const_StringR(instance: *mut c_void, key: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Dict_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_Dict_delete(instance: *mut c_void); + pub fn cv_dnn_DictValue_DictValue_const_DictValueR(r: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DictValue_DictValue_bool(i: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DictValue_DictValue_int64_t(i: i64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DictValue_DictValue(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DictValue_DictValue_int(i: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DictValue_DictValue_unsigned_int(p: u32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DictValue_DictValue_double(p: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DictValue_DictValue_const_charX(s: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DictValue_get_cv_String_const_int(instance: *const c_void, idx: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DictValue_get_cv_String_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DictValue_get_double_const_int(instance: *const c_void, idx: i32, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_get_double_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_get_int_const_int(instance: *const c_void, idx: i32, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_get_int_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_get_int64_t_const_int(instance: *const c_void, idx: i32, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_get_int64_t_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_size_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_isInt_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_isString_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_isReal_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_getIntValue_const_int(instance: *const c_void, idx: i32, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_getIntValue_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_getRealValue_const_int(instance: *const c_void, idx: i32, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_getRealValue_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_DictValue_getStringValue_const_int(instance: *const c_void, idx: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DictValue_getStringValue_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_DictValue_operatorST_const_DictValueR(instance: *mut c_void, r: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_DictValue_delete(instance: *mut c_void); + pub fn cv_dnn_ELULayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ELULayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ELULayer_propAlpha_const(instance: *const c_void) -> f32; + pub fn cv_dnn_ELULayer_propAlpha_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_ELULayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ELULayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ELULayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ELULayer_delete(instance: *mut c_void); + pub fn cv_dnn_EinsumLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_EinsumLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_EinsumLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_EinsumLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_EinsumLayer_delete(instance: *mut c_void); + pub fn cv_dnn_EltwiseLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_EltwiseLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_EltwiseLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_EltwiseLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_EltwiseLayer_delete(instance: *mut c_void); + pub fn cv_dnn_EltwiseLayerInt8_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_EltwiseLayerInt8_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_EltwiseLayerInt8_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_EltwiseLayerInt8_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_EltwiseLayerInt8_delete(instance: *mut c_void); + pub fn cv_dnn_ErfLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ErfLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ErfLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ErfLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ErfLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ErfLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ExpLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ExpLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ExpLayer_propBase_const(instance: *const c_void) -> f32; + pub fn cv_dnn_ExpLayer_propBase_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_ExpLayer_propScale_const(instance: *const c_void) -> f32; + pub fn cv_dnn_ExpLayer_propScale_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_ExpLayer_propShift_const(instance: *const c_void) -> f32; + pub fn cv_dnn_ExpLayer_propShift_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_ExpLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ExpLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ExpLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ExpLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ExpandLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ExpandLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ExpandLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ExpandLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ExpandLayer_delete(instance: *mut c_void); + pub fn cv_dnn_FlattenLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_FlattenLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_FlattenLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_FlattenLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_FlattenLayer_delete(instance: *mut c_void); + pub fn cv_dnn_FloorLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_FloorLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_FloorLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_FloorLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_FloorLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_FloorLayer_delete(instance: *mut c_void); + pub fn cv_dnn_FlowWarpLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_FlowWarpLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_FlowWarpLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_FlowWarpLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_FlowWarpLayer_delete(instance: *mut c_void); + pub fn cv_dnn_GRULayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_GRULayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_GRULayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GRULayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GRULayer_delete(instance: *mut c_void); + pub fn cv_dnn_GatherElementsLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_GatherElementsLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_GatherElementsLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GatherElementsLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GatherElementsLayer_delete(instance: *mut c_void); + pub fn cv_dnn_GatherLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_GatherLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_GatherLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GatherLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GatherLayer_delete(instance: *mut c_void); + pub fn cv_dnn_GeluApproximationLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_GeluApproximationLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_GeluApproximationLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GeluApproximationLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GeluApproximationLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GeluApproximationLayer_delete(instance: *mut c_void); + pub fn cv_dnn_GeluLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_GeluLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_GeluLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GeluLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GeluLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GeluLayer_delete(instance: *mut c_void); + pub fn cv_dnn_GemmLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_GemmLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_GemmLayer_propTrans_a_const(instance: *const c_void) -> bool; + pub fn cv_dnn_GemmLayer_propTrans_a_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_GemmLayer_propTrans_b_const(instance: *const c_void) -> bool; + pub fn cv_dnn_GemmLayer_propTrans_b_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_GemmLayer_propAlpha_const(instance: *const c_void) -> f32; + pub fn cv_dnn_GemmLayer_propAlpha_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_GemmLayer_propBeta_const(instance: *const c_void) -> f32; + pub fn cv_dnn_GemmLayer_propBeta_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_GemmLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GemmLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GemmLayer_delete(instance: *mut c_void); + pub fn cv_dnn_GroupNormLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_GroupNormLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_GroupNormLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GroupNormLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_GroupNormLayer_delete(instance: *mut c_void); + pub fn cv_dnn_HardSigmoidLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_HardSigmoidLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_HardSigmoidLayer_propAlpha_const(instance: *const c_void) -> f32; + pub fn cv_dnn_HardSigmoidLayer_propAlpha_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_HardSigmoidLayer_propBeta_const(instance: *const c_void) -> f32; + pub fn cv_dnn_HardSigmoidLayer_propBeta_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_HardSigmoidLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_HardSigmoidLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_HardSigmoidLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_HardSigmoidLayer_delete(instance: *mut c_void); + pub fn cv_dnn_HardSwishLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_HardSwishLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_HardSwishLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_HardSwishLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_HardSwishLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_HardSwishLayer_delete(instance: *mut c_void); + pub fn cv_dnn_Image2BlobParams_Image2BlobParams(ocvrs_return: *mut Result); + pub fn cv_dnn_Image2BlobParams_Image2BlobParams_const_ScalarR_const_SizeR_const_ScalarR_bool_int_DataLayout_ImagePaddingMode_Scalar(scalefactor: *const core::Scalar, size: *const core::Size, mean: *const core::Scalar, swap_rb: bool, ddepth: i32, datalayout: crate::dnn::DataLayout, mode: crate::dnn::ImagePaddingMode, border_value: *const core::Scalar, ocvrs_return: *mut Result); + pub fn cv_dnn_Image2BlobParams_Image2BlobParams_const_ScalarR(scalefactor: *const core::Scalar, ocvrs_return: *mut Result); + pub fn cv_dnn_Image2BlobParams_blobRectToImageRect_const_RectR_const_SizeR(instance: *const crate::dnn::Image2BlobParams, r_blob: *const core::Rect, size: *const core::Size, ocvrs_return: *mut Result); + pub fn cv_dnn_Image2BlobParams_blobRectsToImageRects_const_vectorLRectGR_vectorLRectGR_const_SizeR(instance: *const crate::dnn::Image2BlobParams, r_blob: *const c_void, r_img: *mut c_void, size: *const core::Size, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_InnerProductLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_InnerProductLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_InnerProductLayer_propAxis_const(instance: *const c_void) -> i32; + pub fn cv_dnn_InnerProductLayer_propAxis_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_InnerProductLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_InnerProductLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_InnerProductLayer_delete(instance: *mut c_void); + pub fn cv_dnn_InnerProductLayerInt8_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_InnerProductLayerInt8_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_InnerProductLayerInt8_propInput_zp_const(instance: *const c_void) -> i32; + pub fn cv_dnn_InnerProductLayerInt8_propInput_zp_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_InnerProductLayerInt8_propOutput_zp_const(instance: *const c_void) -> i32; + pub fn cv_dnn_InnerProductLayerInt8_propOutput_zp_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_InnerProductLayerInt8_propInput_sc_const(instance: *const c_void) -> f32; + pub fn cv_dnn_InnerProductLayerInt8_propInput_sc_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_InnerProductLayerInt8_propOutput_sc_const(instance: *const c_void) -> f32; + pub fn cv_dnn_InnerProductLayerInt8_propOutput_sc_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_InnerProductLayerInt8_propPer_channel_const(instance: *const c_void) -> bool; + pub fn cv_dnn_InnerProductLayerInt8_propPer_channel_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_InnerProductLayerInt8_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_InnerProductLayerInt8_to_InnerProductLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_InnerProductLayerInt8_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_InnerProductLayerInt8_delete(instance: *mut c_void); + pub fn cv_dnn_InstanceNormLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_InstanceNormLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_InstanceNormLayer_propEpsilon_const(instance: *const c_void) -> f32; + pub fn cv_dnn_InstanceNormLayer_propEpsilon_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_InstanceNormLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_InstanceNormLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_InstanceNormLayer_delete(instance: *mut c_void); + pub fn cv_dnn_InterpLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_InterpLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_InterpLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_InterpLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_InterpLayer_delete(instance: *mut c_void); + pub fn cv_dnn_KeypointsModel_KeypointsModel_const_StringR_const_StringR(model: *const c_char, config: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_KeypointsModel_KeypointsModel_const_StringR(model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_KeypointsModel_KeypointsModel_const_NetR(network: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_KeypointsModel_estimate_const__InputArrayR_float(instance: *mut c_void, frame: *const c_void, thresh: f32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_KeypointsModel_estimate_const__InputArrayR(instance: *mut c_void, frame: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_KeypointsModel_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_KeypointsModel_to_Model(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_KeypointsModel_delete(instance: *mut c_void); + pub fn cv_dnn_LRNLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_LRNLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_LRNLayer_propType_const(instance: *const c_void) -> i32; + pub fn cv_dnn_LRNLayer_propType_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_LRNLayer_propSize_const(instance: *const c_void) -> i32; + pub fn cv_dnn_LRNLayer_propSize_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_LRNLayer_propAlpha_const(instance: *const c_void) -> f32; + pub fn cv_dnn_LRNLayer_propAlpha_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_LRNLayer_propBeta_const(instance: *const c_void) -> f32; + pub fn cv_dnn_LRNLayer_propBeta_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_LRNLayer_propBias_const(instance: *const c_void) -> f32; + pub fn cv_dnn_LRNLayer_propBias_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_LRNLayer_propNormBySize_const(instance: *const c_void) -> bool; + pub fn cv_dnn_LRNLayer_propNormBySize_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_LRNLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_LRNLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_LRNLayer_delete(instance: *mut c_void); + pub fn cv_dnn_LSTMLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_LSTMLayer_setWeights_const_MatR_const_MatR_const_MatR(instance: *mut c_void, wh: *const c_void, wx: *const c_void, b: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_LSTMLayer_setOutShape_const_MatShapeR(instance: *mut c_void, out_tail_shape: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_LSTMLayer_setOutShape(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_LSTMLayer_setUseTimstampsDim_bool(instance: *mut c_void, use_: bool, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_LSTMLayer_setUseTimstampsDim(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_LSTMLayer_setProduceCellOutput_bool(instance: *mut c_void, produce: bool, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_LSTMLayer_setProduceCellOutput(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_LSTMLayer_inputNameToIndex_String(instance: *mut c_void, input_name: *const c_char, ocvrs_return: *mut Result); + pub fn cv_dnn_LSTMLayer_outputNameToIndex_const_StringR(instance: *mut c_void, output_name: *const c_char, ocvrs_return: *mut Result); + pub fn cv_dnn_LSTMLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_LSTMLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_LSTMLayer_delete(instance: *mut c_void); + pub fn cv_dnn_Layer_finalize_const__InputArrayR_const__OutputArrayR(instance: *mut c_void, inputs: *const c_void, outputs: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Layer_forward_vectorLMatXGR_vectorLMatGR_vectorLMatGR(instance: *mut c_void, input: *mut c_void, output: *mut c_void, internals: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Layer_forward_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(instance: *mut c_void, inputs: *const c_void, outputs: *const c_void, internals: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Layer_tryQuantize_const_vectorLvectorLfloatGGR_const_vectorLvectorLintGGR_LayerParamsR(instance: *mut c_void, scales: *const c_void, zeropoints: *const c_void, params: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Layer_forward_fallback_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(instance: *mut c_void, inputs: *const c_void, outputs: *const c_void, internals: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Layer_finalize_const_vectorLMatGR_vectorLMatGR(instance: *mut c_void, inputs: *const c_void, outputs: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Layer_finalize_const_vectorLMatGR(instance: *mut c_void, inputs: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Layer_run_const_vectorLMatGR_vectorLMatGR_vectorLMatGR(instance: *mut c_void, inputs: *const c_void, outputs: *mut c_void, internals: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Layer_inputNameToIndex_String(instance: *mut c_void, input_name: *const c_char, ocvrs_return: *mut Result); + pub fn cv_dnn_Layer_outputNameToIndex_const_StringR(instance: *mut c_void, output_name: *const c_char, ocvrs_return: *mut Result); + pub fn cv_dnn_Layer_supportBackend_int(instance: *mut c_void, backend_id: i32, ocvrs_return: *mut Result); + pub fn cv_dnn_Layer_initHalide_const_vectorLPtrLBackendWrapperGGR(instance: *mut c_void, inputs: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Layer_initNgraph_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendNodeGGR(instance: *mut c_void, inputs: *const c_void, nodes: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Layer_initVkCom_const_vectorLPtrLBackendWrapperGGR_vectorLPtrLBackendWrapperGGR(instance: *mut c_void, inputs: *const c_void, outputs: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Layer_initWebnn_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendNodeGGR(instance: *mut c_void, inputs: *const c_void, nodes: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Layer_initCUDA_voidX_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendWrapperGGR(instance: *mut c_void, context: *mut c_void, inputs: *const c_void, outputs: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Layer_initTimVX_voidX_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendWrapperGGR_bool(instance: *mut c_void, tim_vx_info: *mut c_void, inputs_wrapper: *const c_void, outputs_wrapper: *const c_void, is_last: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Layer_initCann_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendWrapperGGR_const_vectorLPtrLBackendNodeGGR(instance: *mut c_void, inputs: *const c_void, outputs: *const c_void, nodes: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Layer_applyHalideScheduler_const_PtrLBackendNodeGR_const_vectorLMatXGR_const_vectorLMatGR_int(instance: *const c_void, node: *mut c_void, inputs: *const c_void, outputs: *const c_void, target_id: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Layer_tryAttach_const_PtrLBackendNodeGR(instance: *mut c_void, node: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Layer_tryFuse_PtrLLayerGR(instance: *mut c_void, top: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Layer_getScaleShift_const_MatR_MatR(instance: *const c_void, scale: *mut c_void, shift: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Layer_getScaleZeropoint_const_floatR_intR(instance: *const c_void, scale: *mut f32, zeropoint: *mut i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Layer_unsetAttached(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Layer_getMemoryShapes_const_const_vectorLMatShapeGR_const_int_vectorLMatShapeGR_vectorLMatShapeGR(instance: *const c_void, inputs: *const c_void, required_outputs: i32, outputs: *mut c_void, internals: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Layer_getFLOPS_const_const_vectorLMatShapeGR_const_vectorLMatShapeGR(instance: *const c_void, inputs: *const c_void, outputs: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Layer_updateMemoryShapes_const_vectorLMatShapeGR(instance: *mut c_void, inputs: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Layer_Layer(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Layer_Layer_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Layer_setParamsFrom_const_LayerParamsR(instance: *mut c_void, params: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Layer_propBlobs_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_Layer_propBlobs_const_vectorLMatG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_Layer_propName_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_Layer_propName_const_String(instance: *mut c_void, val: *const c_char); + pub fn cv_dnn_Layer_propType_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_Layer_propType_const_String(instance: *mut c_void, val: *const c_char); + pub fn cv_dnn_Layer_propPreferableTarget_const(instance: *const c_void) -> i32; + pub fn cv_dnn_Layer_propPreferableTarget_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_Layer_to_AbsLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_AccumLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_AcosLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_AcoshLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ActivationLayerInt8(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ArgLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_AsinLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_AsinhLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_AtanLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_AtanhLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_AttentionLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_BNLLLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_BaseConvolutionLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_BatchNormLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_BatchNormLayerInt8(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_BlankLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_CeilLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_CeluLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ChannelsPReLULayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_CompareLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ConcatLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ConstLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ConvolutionLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ConvolutionLayerInt8(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_CorrelationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_CosLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_CoshLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_CropAndResizeLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_CropLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_CumSumLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_DataAugmentationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_DeconvolutionLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_DepthToSpaceLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_DequantizeLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_DetectionOutputLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ELULayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_EinsumLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_EltwiseLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_EltwiseLayerInt8(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ErfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ExpLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ExpandLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_FlattenLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_FloorLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_FlowWarpLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_GRULayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_GatherElementsLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_GatherLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_GeluApproximationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_GeluLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_GemmLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_GroupNormLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_HardSigmoidLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_HardSwishLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_InnerProductLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_InnerProductLayerInt8(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_InstanceNormLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_InterpLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_LRNLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_LSTMLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_LayerNormLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_LogLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_MVNLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_MatMulLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_MaxUnpoolLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_MishLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_NaryEltwiseLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_NormalizeBBoxLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_NotLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_PaddingLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_PermuteLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_PoolingLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_PoolingLayerInt8(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_PowerLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_PriorBoxLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ProposalLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_QuantizeLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_RNNLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ReLU6Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ReLULayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ReciprocalLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ReduceLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_RegionLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ReorgLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_RequantizeLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ReshapeLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ResizeLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_RoundLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ScaleLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ScaleLayerInt8(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ScatterLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ScatterNDLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SeluLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ShiftLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ShiftLayerInt8(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ShrinkLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ShuffleChannelLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SigmoidLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SignLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SinLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SinhLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SliceLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SoftmaxLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SoftmaxLayerInt8(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SoftplusLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SoftsignLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SpaceToDepthLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SplitLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SqrtLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_SwishLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_TanHLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_TanLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_ThresholdedReluLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_TileLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_TopKLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Layer_delete(instance: *mut c_void); + pub fn cv_dnn_LayerFactory_registerLayer_const_StringR_Constructor(typ: *const c_char, constructor: Option *mut c_void>, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_LayerFactory_unregisterLayer_const_StringR(typ: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_LayerFactory_isLayerRegistered_const_stringR(typ: *const c_char, ocvrs_return: *mut Result); + pub fn cv_dnn_LayerFactory_createLayerInstance_const_StringR_LayerParamsR(typ: *const c_char, params: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_LayerFactory_delete(instance: *mut c_void); + pub fn cv_dnn_LayerNormLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_LayerNormLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_LayerNormLayer_propHasBias_const(instance: *const c_void) -> bool; + pub fn cv_dnn_LayerNormLayer_propHasBias_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_LayerNormLayer_propAxis_const(instance: *const c_void) -> i32; + pub fn cv_dnn_LayerNormLayer_propAxis_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_LayerNormLayer_propEpsilon_const(instance: *const c_void) -> f32; + pub fn cv_dnn_LayerNormLayer_propEpsilon_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_LayerNormLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_LayerNormLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_LayerNormLayer_delete(instance: *mut c_void); + pub fn cv_dnn_LayerParams_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_LayerParams_propBlobs_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_LayerParams_propBlobs_const_vectorLMatG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_LayerParams_propName_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_LayerParams_propName_const_String(instance: *mut c_void, val: *const c_char); + pub fn cv_dnn_LayerParams_propType_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_LayerParams_propType_const_String(instance: *mut c_void, val: *const c_char); + pub fn cv_dnn_LayerParams_to_Dict(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_LayerParams_delete(instance: *mut c_void); + pub fn cv_dnn_LogLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_LogLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_LogLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_LogLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_LogLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_LogLayer_delete(instance: *mut c_void); + pub fn cv_dnn_MVNLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_MVNLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_MVNLayer_propEps_const(instance: *const c_void) -> f32; + pub fn cv_dnn_MVNLayer_propEps_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_MVNLayer_propNormVariance_const(instance: *const c_void) -> bool; + pub fn cv_dnn_MVNLayer_propNormVariance_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_MVNLayer_propAcrossChannels_const(instance: *const c_void) -> bool; + pub fn cv_dnn_MVNLayer_propAcrossChannels_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_MVNLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_MVNLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_MVNLayer_delete(instance: *mut c_void); + pub fn cv_dnn_MatMulLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_MatMulLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_MatMulLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_MatMulLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_MatMulLayer_delete(instance: *mut c_void); + pub fn cv_dnn_MaxUnpoolLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_MaxUnpoolLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_MaxUnpoolLayer_propPoolKernel_const(instance: *const c_void, ocvrs_return: *mut core::Size); + pub fn cv_dnn_MaxUnpoolLayer_propPoolKernel_const_Size(instance: *mut c_void, val: *const core::Size); + pub fn cv_dnn_MaxUnpoolLayer_propPoolPad_const(instance: *const c_void, ocvrs_return: *mut core::Size); + pub fn cv_dnn_MaxUnpoolLayer_propPoolPad_const_Size(instance: *mut c_void, val: *const core::Size); + pub fn cv_dnn_MaxUnpoolLayer_propPoolStride_const(instance: *const c_void, ocvrs_return: *mut core::Size); + pub fn cv_dnn_MaxUnpoolLayer_propPoolStride_const_Size(instance: *mut c_void, val: *const core::Size); + pub fn cv_dnn_MaxUnpoolLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_MaxUnpoolLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_MaxUnpoolLayer_delete(instance: *mut c_void); + pub fn cv_dnn_MishLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_MishLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_MishLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_MishLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_MishLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_MishLayer_delete(instance: *mut c_void); + pub fn cv_dnn_Model_Model(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_Model_const_ModelR(unnamed: *const c_void) -> *mut c_void; + pub fn cv_dnn_Model_Model_ModelRR(unnamed: *mut c_void) -> *mut c_void; + pub fn cv_dnn_Model_operatorST_const_ModelR(instance: *mut c_void, unnamed: *const c_void); + pub fn cv_dnn_Model_operatorST_ModelRR(instance: *mut c_void, unnamed: *mut c_void); + pub fn cv_dnn_Model_Model_const_StringR_const_StringR(model: *const c_char, config: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_Model_const_StringR(model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_Model_const_NetR(network: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_setInputSize_const_SizeR(instance: *mut c_void, size: *const core::Size, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_setInputSize_int_int(instance: *mut c_void, width: i32, height: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_setInputMean_const_ScalarR(instance: *mut c_void, mean: *const core::Scalar, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_setInputScale_const_ScalarR(instance: *mut c_void, scale: *const core::Scalar, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_setInputCrop_bool(instance: *mut c_void, crop: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_setInputSwapRB_bool(instance: *mut c_void, swap_rb: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_setOutputNames_const_vectorLStringGR(instance: *mut c_void, out_names: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_setInputParams_double_const_SizeR_const_ScalarR_bool_bool(instance: *mut c_void, scale: f64, size: *const core::Size, mean: *const core::Scalar, swap_rb: bool, crop: bool, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Model_setInputParams(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Model_predict_const_const__InputArrayR_const__OutputArrayR(instance: *const c_void, frame: *const c_void, outs: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Model_setPreferableBackend_Backend(instance: *mut c_void, backend_id: crate::dnn::Backend, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_setPreferableTarget_Target(instance: *mut c_void, target_id: crate::dnn::Target, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_enableWinograd_bool(instance: *mut c_void, use_winograd: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_getNetwork__const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_getNetwork_(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Model_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_Model_delete(instance: *mut c_void); + pub fn cv_dnn_NaryEltwiseLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_NaryEltwiseLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_NaryEltwiseLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_NaryEltwiseLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_NaryEltwiseLayer_delete(instance: *mut c_void); + pub fn cv_dnn_Net_Net(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_readFromModelOptimizer_const_StringR_const_StringR(xml: *const c_char, bin: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_readFromModelOptimizer_const_vectorLunsigned_charGR_const_vectorLunsigned_charGR(buffer_model_config: *const c_void, buffer_weights: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_readFromModelOptimizer_const_unsigned_charX_size_t_const_unsigned_charX_size_t(buffer_model_config_ptr: *const u8, buffer_model_config_size: size_t, buffer_weights_ptr: *const u8, buffer_weights_size: size_t, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_empty_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Net_dump(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_dumpToFile_const_StringR(instance: *mut c_void, path: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_dumpToPbtxt_const_StringR(instance: *mut c_void, path: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_addLayer_const_StringR_const_StringR_const_intR_LayerParamsR(instance: *mut c_void, name: *const c_char, typ: *const c_char, dtype: *const i32, params: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Net_addLayer_const_StringR_const_StringR_LayerParamsR(instance: *mut c_void, name: *const c_char, typ: *const c_char, params: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Net_addLayerToPrev_const_StringR_const_StringR_const_intR_LayerParamsR(instance: *mut c_void, name: *const c_char, typ: *const c_char, dtype: *const i32, params: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Net_addLayerToPrev_const_StringR_const_StringR_LayerParamsR(instance: *mut c_void, name: *const c_char, typ: *const c_char, params: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Net_getLayerId_const_const_StringR(instance: *const c_void, layer: *const c_char, ocvrs_return: *mut Result); + pub fn cv_dnn_Net_getLayerNames_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_getLayer_const_int(instance: *const c_void, layer_id: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_getLayer_const_const_StringR(instance: *const c_void, layer_name: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_getLayer_const_const_LayerIdR(instance: *const c_void, layer_id: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_getLayerInputs_const_int(instance: *const c_void, layer_id: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_connect_String_String(instance: *mut c_void, out_pin: *const c_char, inp_pin: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_connect_int_int_int_int(instance: *mut c_void, out_layer_id: i32, out_num: i32, inp_layer_id: i32, inp_num: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_registerOutput_const_stringR_int_int(instance: *mut c_void, output_name: *const c_char, layer_id: i32, output_port: i32, ocvrs_return: *mut Result); + pub fn cv_dnn_Net_setInputsNames_const_vectorLStringGR(instance: *mut c_void, input_blob_names: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_setInputShape_const_StringR_const_MatShapeR(instance: *mut c_void, input_name: *const c_char, shape: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_forward_const_StringR(instance: *mut c_void, output_name: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_forward(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_forwardAsync_const_StringR(instance: *mut c_void, output_name: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_forwardAsync(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_forward_const__OutputArrayR_const_StringR(instance: *mut c_void, output_blobs: *const c_void, output_name: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_forward_const__OutputArrayR(instance: *mut c_void, output_blobs: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_forward_const__OutputArrayR_const_vectorLStringGR(instance: *mut c_void, output_blobs: *const c_void, out_blob_names: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_forward_vectorLvectorLMatGGR_const_vectorLStringGR(instance: *mut c_void, output_blobs: *mut c_void, out_blob_names: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_quantize_const__InputArrayR_int_int_bool(instance: *mut c_void, calib_data: *const c_void, inputs_dtype: i32, outputs_dtype: i32, per_channel: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_quantize_const__InputArrayR_int_int(instance: *mut c_void, calib_data: *const c_void, inputs_dtype: i32, outputs_dtype: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_getInputDetails_const_vectorLfloatGR_vectorLintGR(instance: *const c_void, scales: *mut c_void, zeropoints: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_getOutputDetails_const_vectorLfloatGR_vectorLintGR(instance: *const c_void, scales: *mut c_void, zeropoints: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_setHalideScheduler_const_StringR(instance: *mut c_void, scheduler: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_setPreferableBackend_int(instance: *mut c_void, backend_id: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_setPreferableTarget_int(instance: *mut c_void, target_id: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_setInput_const__InputArrayR_const_StringR_double_const_ScalarR(instance: *mut c_void, blob: *const c_void, name: *const c_char, scalefactor: f64, mean: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_setInput_const__InputArrayR(instance: *mut c_void, blob: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_setParam_int_int_const_MatR(instance: *mut c_void, layer: i32, num_param: i32, blob: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_setParam_const_StringR_int_const_MatR(instance: *mut c_void, layer_name: *const c_char, num_param: i32, blob: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_getParam_const_int_int(instance: *const c_void, layer: i32, num_param: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_getParam_const_int(instance: *const c_void, layer: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_getParam_const_const_StringR_int(instance: *const c_void, layer_name: *const c_char, num_param: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_getParam_const_const_StringR(instance: *const c_void, layer_name: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_getUnconnectedOutLayers_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_getUnconnectedOutLayersNames_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_Net_getLayersShapes_const_const_vectorLMatShapeGR_vectorLintGR_vectorLvectorLMatShapeGGR_vectorLvectorLMatShapeGGR(instance: *const c_void, net_input_shapes: *const c_void, layers_ids: *mut c_void, in_layers_shapes: *mut c_void, out_layers_shapes: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_getLayersShapes_const_const_MatShapeR_vectorLintGR_vectorLvectorLMatShapeGGR_vectorLvectorLMatShapeGGR(instance: *const c_void, net_input_shape: *const c_void, layers_ids: *mut c_void, in_layers_shapes: *mut c_void, out_layers_shapes: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_getLayerShapes_const_const_MatShapeR_const_int_vectorLMatShapeGR_vectorLMatShapeGR(instance: *const c_void, net_input_shape: *const c_void, layer_id: i32, in_layer_shapes: *mut c_void, out_layer_shapes: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_getLayerShapes_const_const_vectorLMatShapeGR_const_int_vectorLMatShapeGR_vectorLMatShapeGR(instance: *const c_void, net_input_shapes: *const c_void, layer_id: i32, in_layer_shapes: *mut c_void, out_layer_shapes: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_getFLOPS_const_const_vectorLMatShapeGR(instance: *const c_void, net_input_shapes: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Net_getFLOPS_const_const_MatShapeR(instance: *const c_void, net_input_shape: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Net_getFLOPS_const_const_int_const_vectorLMatShapeGR(instance: *const c_void, layer_id: i32, net_input_shapes: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Net_getFLOPS_const_const_int_const_MatShapeR(instance: *const c_void, layer_id: i32, net_input_shape: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Net_getLayerTypes_const_vectorLStringGR(instance: *const c_void, layers_types: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_getLayersCount_const_const_StringR(instance: *const c_void, layer_type: *const c_char, ocvrs_return: *mut Result); + pub fn cv_dnn_Net_getMemoryConsumption_const_const_vectorLMatShapeGR_size_tR_size_tR(instance: *const c_void, net_input_shapes: *const c_void, weights: *mut size_t, blobs: *mut size_t, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_getMemoryConsumption_const_const_MatShapeR_size_tR_size_tR(instance: *const c_void, net_input_shape: *const c_void, weights: *mut size_t, blobs: *mut size_t, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_getMemoryConsumption_const_const_int_const_vectorLMatShapeGR_size_tR_size_tR(instance: *const c_void, layer_id: i32, net_input_shapes: *const c_void, weights: *mut size_t, blobs: *mut size_t, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_getMemoryConsumption_const_const_int_const_MatShapeR_size_tR_size_tR(instance: *const c_void, layer_id: i32, net_input_shape: *const c_void, weights: *mut size_t, blobs: *mut size_t, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_getMemoryConsumption_const_const_vectorLMatShapeGR_vectorLintGR_vectorLsize_tGR_vectorLsize_tGR(instance: *const c_void, net_input_shapes: *const c_void, layer_ids: *mut c_void, weights: *mut c_void, blobs: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_getMemoryConsumption_const_const_MatShapeR_vectorLintGR_vectorLsize_tGR_vectorLsize_tGR(instance: *const c_void, net_input_shape: *const c_void, layer_ids: *mut c_void, weights: *mut c_void, blobs: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_enableFusion_bool(instance: *mut c_void, fusion: bool, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_enableWinograd_bool(instance: *mut c_void, use_winograd: bool, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_Net_getPerfProfile_vectorLdoubleGR(instance: *mut c_void, timings: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_Net_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_Net_delete(instance: *mut c_void); + pub fn cv_dnn_NormalizeBBoxLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_NormalizeBBoxLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_NormalizeBBoxLayer_propPnorm_const(instance: *const c_void) -> f32; + pub fn cv_dnn_NormalizeBBoxLayer_propPnorm_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_NormalizeBBoxLayer_propEpsilon_const(instance: *const c_void) -> f32; + pub fn cv_dnn_NormalizeBBoxLayer_propEpsilon_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_NormalizeBBoxLayer_propAcrossSpatial_const(instance: *const c_void) -> bool; + pub fn cv_dnn_NormalizeBBoxLayer_propAcrossSpatial_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_NormalizeBBoxLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_NormalizeBBoxLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_NormalizeBBoxLayer_delete(instance: *mut c_void); + pub fn cv_dnn_NotLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_NotLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_NotLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_NotLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_NotLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_NotLayer_delete(instance: *mut c_void); + pub fn cv_dnn_PaddingLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_PaddingLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_PaddingLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PaddingLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PaddingLayer_delete(instance: *mut c_void); + pub fn cv_dnn_PermuteLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_PermuteLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_PermuteLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PermuteLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PermuteLayer_delete(instance: *mut c_void); + pub fn cv_dnn_PoolingLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_PoolingLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_PoolingLayer_propType_const(instance: *const c_void) -> i32; + pub fn cv_dnn_PoolingLayer_propType_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_PoolingLayer_propKernel_size_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_PoolingLayer_propKernel_size_const_vectorLsize_tG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_PoolingLayer_propStrides_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_PoolingLayer_propStrides_const_vectorLsize_tG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_PoolingLayer_propPads_begin_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_PoolingLayer_propPads_begin_const_vectorLsize_tG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_PoolingLayer_propPads_end_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_PoolingLayer_propPads_end_const_vectorLsize_tG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_PoolingLayer_propGlobalPooling_const(instance: *const c_void) -> bool; + pub fn cv_dnn_PoolingLayer_propGlobalPooling_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_PoolingLayer_propIsGlobalPooling_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_PoolingLayer_propIsGlobalPooling_const_vectorLboolG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_PoolingLayer_propComputeMaxIdx_const(instance: *const c_void) -> bool; + pub fn cv_dnn_PoolingLayer_propComputeMaxIdx_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_PoolingLayer_propPadMode_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_PoolingLayer_propPadMode_const_String(instance: *mut c_void, val: *const c_char); + pub fn cv_dnn_PoolingLayer_propCeilMode_const(instance: *const c_void) -> bool; + pub fn cv_dnn_PoolingLayer_propCeilMode_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_PoolingLayer_propAvePoolPaddedArea_const(instance: *const c_void) -> bool; + pub fn cv_dnn_PoolingLayer_propAvePoolPaddedArea_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_PoolingLayer_propPooledSize_const(instance: *const c_void, ocvrs_return: *mut core::Size); + pub fn cv_dnn_PoolingLayer_propPooledSize_const_Size(instance: *mut c_void, val: *const core::Size); + pub fn cv_dnn_PoolingLayer_propSpatialScale_const(instance: *const c_void) -> f32; + pub fn cv_dnn_PoolingLayer_propSpatialScale_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_PoolingLayer_propPsRoiOutChannels_const(instance: *const c_void) -> i32; + pub fn cv_dnn_PoolingLayer_propPsRoiOutChannels_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_PoolingLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PoolingLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PoolingLayer_delete(instance: *mut c_void); + pub fn cv_dnn_PoolingLayerInt8_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_PoolingLayerInt8_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_PoolingLayerInt8_propInput_zp_const(instance: *const c_void) -> i32; + pub fn cv_dnn_PoolingLayerInt8_propInput_zp_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_PoolingLayerInt8_propOutput_zp_const(instance: *const c_void) -> i32; + pub fn cv_dnn_PoolingLayerInt8_propOutput_zp_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_PoolingLayerInt8_propInput_sc_const(instance: *const c_void) -> f32; + pub fn cv_dnn_PoolingLayerInt8_propInput_sc_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_PoolingLayerInt8_propOutput_sc_const(instance: *const c_void) -> f32; + pub fn cv_dnn_PoolingLayerInt8_propOutput_sc_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_PoolingLayerInt8_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PoolingLayerInt8_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PoolingLayerInt8_to_PoolingLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PoolingLayerInt8_delete(instance: *mut c_void); + pub fn cv_dnn_PowerLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_PowerLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_PowerLayer_propPower_const(instance: *const c_void) -> f32; + pub fn cv_dnn_PowerLayer_propPower_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_PowerLayer_propScale_const(instance: *const c_void) -> f32; + pub fn cv_dnn_PowerLayer_propScale_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_PowerLayer_propShift_const(instance: *const c_void) -> f32; + pub fn cv_dnn_PowerLayer_propShift_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_PowerLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PowerLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PowerLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PowerLayer_delete(instance: *mut c_void); + pub fn cv_dnn_PriorBoxLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_PriorBoxLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_PriorBoxLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PriorBoxLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_PriorBoxLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ProposalLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ProposalLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ProposalLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ProposalLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ProposalLayer_delete(instance: *mut c_void); + pub fn cv_dnn_QuantizeLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_QuantizeLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_QuantizeLayer_propScales_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_QuantizeLayer_propScales_const_vectorLfloatG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_QuantizeLayer_propZeropoints_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_QuantizeLayer_propZeropoints_const_vectorLintG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_QuantizeLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_QuantizeLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_QuantizeLayer_delete(instance: *mut c_void); + pub fn cv_dnn_RNNLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_RNNLayer_setWeights_const_MatR_const_MatR_const_MatR_const_MatR_const_MatR(instance: *mut c_void, wxh: *const c_void, bh: *const c_void, whh: *const c_void, who: *const c_void, bo: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_RNNLayer_setProduceHiddenOutput_bool(instance: *mut c_void, produce: bool, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_RNNLayer_setProduceHiddenOutput(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_RNNLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_RNNLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_RNNLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ReLU6Layer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ReLU6Layer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ReLU6Layer_propMinValue_const(instance: *const c_void) -> f32; + pub fn cv_dnn_ReLU6Layer_propMinValue_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_ReLU6Layer_propMaxValue_const(instance: *const c_void) -> f32; + pub fn cv_dnn_ReLU6Layer_propMaxValue_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_ReLU6Layer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReLU6Layer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReLU6Layer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReLU6Layer_delete(instance: *mut c_void); + pub fn cv_dnn_ReLULayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ReLULayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ReLULayer_propNegativeSlope_const(instance: *const c_void) -> f32; + pub fn cv_dnn_ReLULayer_propNegativeSlope_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_ReLULayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReLULayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReLULayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReLULayer_delete(instance: *mut c_void); + pub fn cv_dnn_ReciprocalLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ReciprocalLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ReciprocalLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReciprocalLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReciprocalLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReciprocalLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ReduceLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ReduceLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ReduceLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReduceLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReduceLayer_delete(instance: *mut c_void); + pub fn cv_dnn_RegionLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_RegionLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_RegionLayer_propNmsThreshold_const(instance: *const c_void) -> f32; + pub fn cv_dnn_RegionLayer_propNmsThreshold_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_RegionLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_RegionLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_RegionLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ReorgLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ReorgLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ReorgLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReorgLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReorgLayer_delete(instance: *mut c_void); + pub fn cv_dnn_RequantizeLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_RequantizeLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_RequantizeLayer_propScale_const(instance: *const c_void) -> f32; + pub fn cv_dnn_RequantizeLayer_propScale_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_RequantizeLayer_propShift_const(instance: *const c_void) -> f32; + pub fn cv_dnn_RequantizeLayer_propShift_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_RequantizeLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_RequantizeLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_RequantizeLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ReshapeLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ReshapeLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ReshapeLayer_propNewShapeDesc_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_ReshapeLayer_propNewShapeDesc_const_MatShape(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_ReshapeLayer_propNewShapeRange_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_ReshapeLayer_propNewShapeRange_const_Range(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_ReshapeLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReshapeLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ReshapeLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ResizeLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ResizeLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ResizeLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ResizeLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ResizeLayer_delete(instance: *mut c_void); + pub fn cv_dnn_RoundLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_RoundLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_RoundLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_RoundLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_RoundLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_RoundLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ScaleLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ScaleLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ScaleLayer_propHasBias_const(instance: *const c_void) -> bool; + pub fn cv_dnn_ScaleLayer_propHasBias_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_ScaleLayer_propAxis_const(instance: *const c_void) -> i32; + pub fn cv_dnn_ScaleLayer_propAxis_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_ScaleLayer_propMode_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_ScaleLayer_propMode_const_String(instance: *mut c_void, val: *const c_char); + pub fn cv_dnn_ScaleLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ScaleLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ScaleLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ScaleLayerInt8_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ScaleLayerInt8_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ScaleLayerInt8_propOutput_sc_const(instance: *const c_void) -> f32; + pub fn cv_dnn_ScaleLayerInt8_propOutput_sc_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_ScaleLayerInt8_propOutput_zp_const(instance: *const c_void) -> i32; + pub fn cv_dnn_ScaleLayerInt8_propOutput_zp_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_ScaleLayerInt8_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ScaleLayerInt8_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ScaleLayerInt8_to_ScaleLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ScaleLayerInt8_delete(instance: *mut c_void); + pub fn cv_dnn_ScatterLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ScatterLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ScatterLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ScatterLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ScatterLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ScatterNDLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ScatterNDLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ScatterNDLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ScatterNDLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ScatterNDLayer_delete(instance: *mut c_void); + pub fn cv_dnn_SegmentationModel_SegmentationModel_const_StringR_const_StringR(model: *const c_char, config: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SegmentationModel_SegmentationModel_const_StringR(model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SegmentationModel_SegmentationModel_const_NetR(network: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SegmentationModel_segment_const__InputArrayR_const__OutputArrayR(instance: *mut c_void, frame: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_SegmentationModel_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_SegmentationModel_to_Model(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SegmentationModel_delete(instance: *mut c_void); + pub fn cv_dnn_SeluLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SeluLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SeluLayer_propAlpha_const(instance: *const c_void) -> f32; + pub fn cv_dnn_SeluLayer_propAlpha_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_SeluLayer_propGamma_const(instance: *const c_void) -> f32; + pub fn cv_dnn_SeluLayer_propGamma_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_SeluLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SeluLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SeluLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SeluLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ShiftLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ShiftLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ShiftLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ShiftLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ShiftLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ShiftLayerInt8_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ShiftLayerInt8_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ShiftLayerInt8_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ShiftLayerInt8_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ShiftLayerInt8_delete(instance: *mut c_void); + pub fn cv_dnn_ShrinkLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ShrinkLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ShrinkLayer_propBias_const(instance: *const c_void) -> f32; + pub fn cv_dnn_ShrinkLayer_propBias_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_ShrinkLayer_propLambd_const(instance: *const c_void) -> f32; + pub fn cv_dnn_ShrinkLayer_propLambd_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_ShrinkLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ShrinkLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ShrinkLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ShrinkLayer_delete(instance: *mut c_void); + pub fn cv_dnn_ShuffleChannelLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ShuffleChannelLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ShuffleChannelLayer_propGroup_const(instance: *const c_void) -> i32; + pub fn cv_dnn_ShuffleChannelLayer_propGroup_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_ShuffleChannelLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ShuffleChannelLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ShuffleChannelLayer_delete(instance: *mut c_void); + pub fn cv_dnn_SigmoidLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SigmoidLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SigmoidLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SigmoidLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SigmoidLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SigmoidLayer_delete(instance: *mut c_void); + pub fn cv_dnn_SignLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SignLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SignLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SignLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SignLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SignLayer_delete(instance: *mut c_void); + pub fn cv_dnn_SinLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SinLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SinLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SinLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SinLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SinLayer_delete(instance: *mut c_void); + pub fn cv_dnn_SinhLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SinhLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SinhLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SinhLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SinhLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SinhLayer_delete(instance: *mut c_void); + pub fn cv_dnn_SliceLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SliceLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SliceLayer_propSliceRanges_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_SliceLayer_propSliceRanges_const_vectorLvectorLRangeGG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_SliceLayer_propSliceSteps_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_SliceLayer_propSliceSteps_const_vectorLvectorLintGG(instance: *mut c_void, val: *const c_void); + pub fn cv_dnn_SliceLayer_propAxis_const(instance: *const c_void) -> i32; + pub fn cv_dnn_SliceLayer_propAxis_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_SliceLayer_propNum_split_const(instance: *const c_void) -> i32; + pub fn cv_dnn_SliceLayer_propNum_split_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_SliceLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SliceLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SliceLayer_delete(instance: *mut c_void); + pub fn cv_dnn_SoftmaxLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SoftmaxLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SoftmaxLayer_propLogSoftMax_const(instance: *const c_void) -> bool; + pub fn cv_dnn_SoftmaxLayer_propLogSoftMax_const_bool(instance: *mut c_void, val: bool); + pub fn cv_dnn_SoftmaxLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SoftmaxLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SoftmaxLayer_delete(instance: *mut c_void); + pub fn cv_dnn_SoftmaxLayerInt8_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SoftmaxLayerInt8_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SoftmaxLayerInt8_propOutput_sc_const(instance: *const c_void) -> f32; + pub fn cv_dnn_SoftmaxLayerInt8_propOutput_sc_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_SoftmaxLayerInt8_propOutput_zp_const(instance: *const c_void) -> i32; + pub fn cv_dnn_SoftmaxLayerInt8_propOutput_zp_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_SoftmaxLayerInt8_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SoftmaxLayerInt8_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SoftmaxLayerInt8_to_SoftmaxLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SoftmaxLayerInt8_delete(instance: *mut c_void); + pub fn cv_dnn_SoftplusLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SoftplusLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SoftplusLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SoftplusLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SoftplusLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SoftplusLayer_delete(instance: *mut c_void); + pub fn cv_dnn_SoftsignLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SoftsignLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SoftsignLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SoftsignLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SoftsignLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SoftsignLayer_delete(instance: *mut c_void); + pub fn cv_dnn_SpaceToDepthLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SpaceToDepthLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SpaceToDepthLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SpaceToDepthLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SpaceToDepthLayer_delete(instance: *mut c_void); + pub fn cv_dnn_SplitLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SplitLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SplitLayer_propOutputsCount_const(instance: *const c_void) -> i32; + pub fn cv_dnn_SplitLayer_propOutputsCount_const_int(instance: *mut c_void, val: i32); + pub fn cv_dnn_SplitLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SplitLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SplitLayer_delete(instance: *mut c_void); + pub fn cv_dnn_SqrtLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SqrtLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SqrtLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SqrtLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SqrtLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SqrtLayer_delete(instance: *mut c_void); + pub fn cv_dnn_SwishLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_SwishLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_SwishLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SwishLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SwishLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_SwishLayer_delete(instance: *mut c_void); + pub fn cv_dnn_TanHLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TanHLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_TanHLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TanHLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TanHLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TanHLayer_delete(instance: *mut c_void); + pub fn cv_dnn_TanLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TanLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_TanLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TanLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TanLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TanLayer_delete(instance: *mut c_void); + pub fn cv_dnn_TextDetectionModel_detect_const_const__InputArrayR_vectorLvectorLPointGGR_vectorLfloatGR(instance: *const c_void, frame: *const c_void, detections: *mut c_void, confidences: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_TextDetectionModel_detect_const_const__InputArrayR_vectorLvectorLPointGGR(instance: *const c_void, frame: *const c_void, detections: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_TextDetectionModel_detectTextRectangles_const_const__InputArrayR_vectorLRotatedRectGR_vectorLfloatGR(instance: *const c_void, frame: *const c_void, detections: *mut c_void, confidences: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_TextDetectionModel_detectTextRectangles_const_const__InputArrayR_vectorLRotatedRectGR(instance: *const c_void, frame: *const c_void, detections: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_TextDetectionModel_to_Model(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TextDetectionModel_delete(instance: *mut c_void); + pub fn cv_dnn_TextDetectionModel_DB_TextDetectionModel_DB(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_DB_TextDetectionModel_DB_const_NetR(network: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_DB_TextDetectionModel_DB_const_stringR_const_stringR(model: *const c_char, config: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_DB_TextDetectionModel_DB_const_stringR(model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_DB_setBinaryThreshold_float(instance: *mut c_void, binary_threshold: f32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_DB_getBinaryThreshold_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_TextDetectionModel_DB_setPolygonThreshold_float(instance: *mut c_void, polygon_threshold: f32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_DB_getPolygonThreshold_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_TextDetectionModel_DB_setUnclipRatio_double(instance: *mut c_void, unclip_ratio: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_DB_getUnclipRatio_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_TextDetectionModel_DB_setMaxCandidates_int(instance: *mut c_void, max_candidates: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_DB_getMaxCandidates_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_TextDetectionModel_DB_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_TextDetectionModel_DB_to_Model(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TextDetectionModel_DB_to_TextDetectionModel(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TextDetectionModel_DB_delete(instance: *mut c_void); + pub fn cv_dnn_TextDetectionModel_EAST_TextDetectionModel_EAST(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_EAST_TextDetectionModel_EAST_const_NetR(network: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_EAST_TextDetectionModel_EAST_const_stringR_const_stringR(model: *const c_char, config: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_EAST_TextDetectionModel_EAST_const_stringR(model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_EAST_setConfidenceThreshold_float(instance: *mut c_void, conf_threshold: f32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_EAST_getConfidenceThreshold_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_TextDetectionModel_EAST_setNMSThreshold_float(instance: *mut c_void, nms_threshold: f32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextDetectionModel_EAST_getNMSThreshold_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_dnn_TextDetectionModel_EAST_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_TextDetectionModel_EAST_to_Model(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TextDetectionModel_EAST_to_TextDetectionModel(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TextDetectionModel_EAST_delete(instance: *mut c_void); + pub fn cv_dnn_TextRecognitionModel_TextRecognitionModel(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextRecognitionModel_TextRecognitionModel_const_NetR(network: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextRecognitionModel_TextRecognitionModel_const_stringR_const_stringR(model: *const c_char, config: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextRecognitionModel_TextRecognitionModel_const_stringR(model: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextRecognitionModel_setDecodeType_const_stringR(instance: *mut c_void, decode_type: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextRecognitionModel_getDecodeType_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextRecognitionModel_setDecodeOptsCTCPrefixBeamSearch_int_int(instance: *mut c_void, beam_size: i32, voc_prune_size: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextRecognitionModel_setDecodeOptsCTCPrefixBeamSearch_int(instance: *mut c_void, beam_size: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextRecognitionModel_setVocabulary_const_vectorLstringGR(instance: *mut c_void, vocabulary: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextRecognitionModel_getVocabulary_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextRecognitionModel_recognize_const_const__InputArrayR(instance: *const c_void, frame: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TextRecognitionModel_recognize_const_const__InputArrayR_const__InputArrayR_vectorLstringGR(instance: *const c_void, frame: *const c_void, roi_rects: *const c_void, results: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dnn_TextRecognitionModel_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_dnn_TextRecognitionModel_to_Model(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TextRecognitionModel_delete(instance: *mut c_void); + pub fn cv_dnn_ThresholdedReluLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_ThresholdedReluLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_ThresholdedReluLayer_propAlpha_const(instance: *const c_void) -> f32; + pub fn cv_dnn_ThresholdedReluLayer_propAlpha_const_float(instance: *mut c_void, val: f32); + pub fn cv_dnn_ThresholdedReluLayer_to_ActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ThresholdedReluLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ThresholdedReluLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_ThresholdedReluLayer_delete(instance: *mut c_void); + pub fn cv_dnn_TileLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TileLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_TileLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TileLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TileLayer_delete(instance: *mut c_void); + pub fn cv_dnn_TopKLayer_create_const_LayerParamsR(params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn_TopKLayer_defaultNew_const() -> *mut c_void; + pub fn cv_dnn_TopKLayer_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TopKLayer_to_Layer(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn_TopKLayer_delete(instance: *mut c_void); + pub fn cv_dnn__Range__Range_const_RangeR(r: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn__Range__Range_int_int(start_: i32, size_: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn__Range__Range_int(start_: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_dnn__Range_to_Range(instance: *mut c_void) -> *mut c_void; + pub fn cv_dnn__Range_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_AbsLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AbsLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AbsLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_AbsLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_AbsLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AbsLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AbsLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AbsLayerG_new_const_AbsLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AccumLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AccumLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AccumLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_AccumLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_AccumLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AccumLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AccumLayerG_new_const_AccumLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcosLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcosLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcosLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcosLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_AcosLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcosLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcosLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcosLayerG_new_const_AcosLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcoshLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcoshLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcoshLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcoshLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_AcoshLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcoshLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcoshLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AcoshLayerG_new_const_AcoshLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ActivationLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ActivationLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ActivationLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ActivationLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ActivationLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ActivationLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ActivationLayerG_new_const_ActivationLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ActivationLayerInt8G_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ActivationLayerInt8G_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ActivationLayerInt8G_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ActivationLayerInt8G_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ActivationLayerInt8G_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ActivationLayerInt8G_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ActivationLayerInt8G_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ActivationLayerInt8G_new_const_ActivationLayerInt8(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ArgLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ArgLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ArgLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ArgLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ArgLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ArgLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ArgLayerG_new_const_ArgLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_AsinLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinLayerG_new_const_AsinLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinhLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinhLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinhLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinhLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_AsinhLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinhLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinhLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AsinhLayerG_new_const_AsinhLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_AtanLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanLayerG_new_const_AtanLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanhLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanhLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanhLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanhLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_AtanhLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanhLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanhLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AtanhLayerG_new_const_AtanhLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AttentionLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AttentionLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AttentionLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_AttentionLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_AttentionLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AttentionLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_AttentionLayerG_new_const_AttentionLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BNLLLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BNLLLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BNLLLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_BNLLLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_BNLLLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BNLLLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BNLLLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BNLLLayerG_new_const_BNLLLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BackendNodeG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BackendNodeG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BackendNodeG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_BackendNodeG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_BackendNodeG_new_const_BackendNode(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BackendWrapperG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BackendWrapperG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BackendWrapperG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_BackendWrapperG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_BaseConvolutionLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BaseConvolutionLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BaseConvolutionLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_BaseConvolutionLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_BaseConvolutionLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BaseConvolutionLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BaseConvolutionLayerG_new_const_BaseConvolutionLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_BatchNormLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerG_new_const_BatchNormLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerInt8G_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerInt8G_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerInt8G_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerInt8G_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_BatchNormLayerInt8G_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerInt8G_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerInt8G_to_PtrOfBatchNormLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerInt8G_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BatchNormLayerInt8G_new_const_BatchNormLayerInt8(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BlankLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BlankLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BlankLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_BlankLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_BlankLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BlankLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_BlankLayerG_new_const_BlankLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeilLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeilLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeilLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeilLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_CeilLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeilLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeilLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeilLayerG_new_const_CeilLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeluLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeluLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeluLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeluLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_CeluLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeluLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeluLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CeluLayerG_new_const_CeluLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ChannelsPReLULayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ChannelsPReLULayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ChannelsPReLULayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ChannelsPReLULayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ChannelsPReLULayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ChannelsPReLULayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ChannelsPReLULayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ChannelsPReLULayerG_new_const_ChannelsPReLULayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CompareLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CompareLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CompareLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_CompareLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_CompareLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CompareLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CompareLayerG_new_const_CompareLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConcatLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConcatLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConcatLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConcatLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ConcatLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConcatLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConcatLayerG_new_const_ConcatLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConstLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConstLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConstLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConstLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ConstLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConstLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConstLayerG_new_const_ConstLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ConvolutionLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerG_to_PtrOfBaseConvolutionLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerG_new_const_ConvolutionLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerInt8G_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerInt8G_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerInt8G_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerInt8G_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ConvolutionLayerInt8G_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerInt8G_to_PtrOfBaseConvolutionLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerInt8G_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ConvolutionLayerInt8G_new_const_ConvolutionLayerInt8(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CorrelationLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CorrelationLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CorrelationLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_CorrelationLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_CorrelationLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CorrelationLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CorrelationLayerG_new_const_CorrelationLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CosLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CosLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CosLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_CosLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_CosLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CosLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CosLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CosLayerG_new_const_CosLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CoshLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CoshLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CoshLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_CoshLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_CoshLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CoshLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CoshLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CoshLayerG_new_const_CoshLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CropAndResizeLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CropAndResizeLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CropAndResizeLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_CropAndResizeLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_CropAndResizeLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CropAndResizeLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CropAndResizeLayerG_new_const_CropAndResizeLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CropLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CropLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CropLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_CropLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_CropLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CropLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CropLayerG_new_const_CropLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CumSumLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CumSumLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CumSumLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_CumSumLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_CumSumLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CumSumLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_CumSumLayerG_new_const_CumSumLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DataAugmentationLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DataAugmentationLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DataAugmentationLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_DataAugmentationLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_DataAugmentationLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DataAugmentationLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DataAugmentationLayerG_new_const_DataAugmentationLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DeconvolutionLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DeconvolutionLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DeconvolutionLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_DeconvolutionLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_DeconvolutionLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DeconvolutionLayerG_to_PtrOfBaseConvolutionLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DeconvolutionLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DeconvolutionLayerG_new_const_DeconvolutionLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DepthToSpaceLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DepthToSpaceLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DepthToSpaceLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_DepthToSpaceLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_DepthToSpaceLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DepthToSpaceLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DepthToSpaceLayerG_new_const_DepthToSpaceLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DequantizeLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DequantizeLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DequantizeLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_DequantizeLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_DequantizeLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DequantizeLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DequantizeLayerG_new_const_DequantizeLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DetectionOutputLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DetectionOutputLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DetectionOutputLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_DetectionOutputLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_DetectionOutputLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DetectionOutputLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_DetectionOutputLayerG_new_const_DetectionOutputLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ELULayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ELULayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ELULayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ELULayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ELULayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ELULayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ELULayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ELULayerG_new_const_ELULayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EinsumLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EinsumLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EinsumLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_EinsumLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_EinsumLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EinsumLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EinsumLayerG_new_const_EinsumLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EltwiseLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EltwiseLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EltwiseLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_EltwiseLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_EltwiseLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EltwiseLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EltwiseLayerG_new_const_EltwiseLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EltwiseLayerInt8G_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EltwiseLayerInt8G_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EltwiseLayerInt8G_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_EltwiseLayerInt8G_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_EltwiseLayerInt8G_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EltwiseLayerInt8G_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_EltwiseLayerInt8G_new_const_EltwiseLayerInt8(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ErfLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ErfLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ErfLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ErfLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ErfLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ErfLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ErfLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ErfLayerG_new_const_ErfLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ExpLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ExpLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ExpLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ExpLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ExpLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ExpLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ExpLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ExpLayerG_new_const_ExpLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ExpandLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ExpandLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ExpandLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ExpandLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ExpandLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ExpandLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ExpandLayerG_new_const_ExpandLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FlattenLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FlattenLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FlattenLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_FlattenLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_FlattenLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FlattenLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FlattenLayerG_new_const_FlattenLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FloorLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FloorLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FloorLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_FloorLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_FloorLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FloorLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FloorLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FloorLayerG_new_const_FloorLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FlowWarpLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FlowWarpLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FlowWarpLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_FlowWarpLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_FlowWarpLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FlowWarpLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_FlowWarpLayerG_new_const_FlowWarpLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GRULayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GRULayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GRULayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_GRULayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_GRULayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GRULayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GRULayerG_new_const_GRULayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GatherElementsLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GatherElementsLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GatherElementsLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_GatherElementsLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_GatherElementsLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GatherElementsLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GatherElementsLayerG_new_const_GatherElementsLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GatherLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GatherLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GatherLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_GatherLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_GatherLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GatherLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GatherLayerG_new_const_GatherLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluApproximationLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluApproximationLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluApproximationLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluApproximationLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_GeluApproximationLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluApproximationLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluApproximationLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluApproximationLayerG_new_const_GeluApproximationLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_GeluLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GeluLayerG_new_const_GeluLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GemmLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GemmLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GemmLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_GemmLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_GemmLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GemmLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GemmLayerG_new_const_GemmLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GroupNormLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GroupNormLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GroupNormLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_GroupNormLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_GroupNormLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GroupNormLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_GroupNormLayerG_new_const_GroupNormLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSigmoidLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSigmoidLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSigmoidLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSigmoidLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_HardSigmoidLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSigmoidLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSigmoidLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSigmoidLayerG_new_const_HardSigmoidLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSwishLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSwishLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSwishLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSwishLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_HardSwishLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSwishLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSwishLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_HardSwishLayerG_new_const_HardSwishLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InnerProductLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InnerProductLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InnerProductLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_InnerProductLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_InnerProductLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InnerProductLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InnerProductLayerG_new_const_InnerProductLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InnerProductLayerInt8G_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InnerProductLayerInt8G_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InnerProductLayerInt8G_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_InnerProductLayerInt8G_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_InnerProductLayerInt8G_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InnerProductLayerInt8G_to_PtrOfInnerProductLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InnerProductLayerInt8G_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InnerProductLayerInt8G_new_const_InnerProductLayerInt8(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InstanceNormLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InstanceNormLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InstanceNormLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_InstanceNormLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_InstanceNormLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InstanceNormLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InstanceNormLayerG_new_const_InstanceNormLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InterpLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InterpLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InterpLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_InterpLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_InterpLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InterpLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_InterpLayerG_new_const_InterpLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LRNLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LRNLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LRNLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_LRNLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_LRNLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LRNLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LRNLayerG_new_const_LRNLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LSTMLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LSTMLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LSTMLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_LSTMLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_LSTMLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LSTMLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_LayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_LayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LayerG_new_const_Layer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LayerNormLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LayerNormLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LayerNormLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_LayerNormLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_LayerNormLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LayerNormLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LayerNormLayerG_new_const_LayerNormLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LogLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LogLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LogLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_LogLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_LogLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LogLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LogLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_LogLayerG_new_const_LogLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MVNLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MVNLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MVNLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_MVNLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_MVNLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MVNLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MVNLayerG_new_const_MVNLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MatMulLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MatMulLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MatMulLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_MatMulLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_MatMulLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MatMulLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MatMulLayerG_new_const_MatMulLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MaxUnpoolLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MaxUnpoolLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MaxUnpoolLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_MaxUnpoolLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_MaxUnpoolLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MaxUnpoolLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MaxUnpoolLayerG_new_const_MaxUnpoolLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MishLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MishLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MishLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_MishLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_MishLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MishLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MishLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_MishLayerG_new_const_MishLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NaryEltwiseLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NaryEltwiseLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NaryEltwiseLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_NaryEltwiseLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_NaryEltwiseLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NaryEltwiseLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NaryEltwiseLayerG_new_const_NaryEltwiseLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NormalizeBBoxLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NormalizeBBoxLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NormalizeBBoxLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_NormalizeBBoxLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_NormalizeBBoxLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NormalizeBBoxLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NormalizeBBoxLayerG_new_const_NormalizeBBoxLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NotLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NotLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NotLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_NotLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_NotLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NotLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NotLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_NotLayerG_new_const_NotLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PaddingLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PaddingLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PaddingLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_PaddingLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_PaddingLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PaddingLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PaddingLayerG_new_const_PaddingLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PermuteLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PermuteLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PermuteLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_PermuteLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_PermuteLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PermuteLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PermuteLayerG_new_const_PermuteLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PoolingLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PoolingLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PoolingLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_PoolingLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_PoolingLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PoolingLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PoolingLayerG_new_const_PoolingLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PoolingLayerInt8G_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PoolingLayerInt8G_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PoolingLayerInt8G_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_PoolingLayerInt8G_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_PoolingLayerInt8G_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PoolingLayerInt8G_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PoolingLayerInt8G_to_PtrOfPoolingLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PoolingLayerInt8G_new_const_PoolingLayerInt8(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PowerLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PowerLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PowerLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_PowerLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_PowerLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PowerLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PowerLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PowerLayerG_new_const_PowerLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PriorBoxLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PriorBoxLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PriorBoxLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_PriorBoxLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_PriorBoxLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PriorBoxLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_PriorBoxLayerG_new_const_PriorBoxLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ProposalLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ProposalLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ProposalLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ProposalLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ProposalLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ProposalLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ProposalLayerG_new_const_ProposalLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_QuantizeLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_QuantizeLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_QuantizeLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_QuantizeLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_QuantizeLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_QuantizeLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_QuantizeLayerG_new_const_QuantizeLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RNNLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RNNLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RNNLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_RNNLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_RNNLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RNNLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLU6LayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLU6LayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLU6LayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLU6LayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ReLU6LayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLU6LayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLU6LayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLU6LayerG_new_const_ReLU6Layer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLULayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLULayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLULayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLULayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ReLULayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLULayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLULayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReLULayerG_new_const_ReLULayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReciprocalLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReciprocalLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReciprocalLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReciprocalLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ReciprocalLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReciprocalLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReciprocalLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReciprocalLayerG_new_const_ReciprocalLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReduceLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReduceLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReduceLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReduceLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ReduceLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReduceLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReduceLayerG_new_const_ReduceLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RegionLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RegionLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RegionLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_RegionLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_RegionLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RegionLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RegionLayerG_new_const_RegionLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReorgLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReorgLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReorgLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReorgLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ReorgLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReorgLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReorgLayerG_new_const_ReorgLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RequantizeLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RequantizeLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RequantizeLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_RequantizeLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_RequantizeLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RequantizeLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RequantizeLayerG_new_const_RequantizeLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReshapeLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReshapeLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReshapeLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReshapeLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ReshapeLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReshapeLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ReshapeLayerG_new_const_ReshapeLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ResizeLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ResizeLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ResizeLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ResizeLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ResizeLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ResizeLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ResizeLayerG_new_const_ResizeLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RoundLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RoundLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RoundLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_RoundLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_RoundLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RoundLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RoundLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_RoundLayerG_new_const_RoundLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScaleLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScaleLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScaleLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScaleLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ScaleLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScaleLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScaleLayerG_new_const_ScaleLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScaleLayerInt8G_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScaleLayerInt8G_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScaleLayerInt8G_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScaleLayerInt8G_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ScaleLayerInt8G_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScaleLayerInt8G_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScaleLayerInt8G_to_PtrOfScaleLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScaleLayerInt8G_new_const_ScaleLayerInt8(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScatterLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScatterLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScatterLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScatterLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ScatterLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScatterLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScatterLayerG_new_const_ScatterLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScatterNDLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScatterNDLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScatterNDLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScatterNDLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ScatterNDLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScatterNDLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ScatterNDLayerG_new_const_ScatterNDLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SeluLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SeluLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SeluLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SeluLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SeluLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SeluLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SeluLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SeluLayerG_new_const_SeluLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShiftLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShiftLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShiftLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShiftLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ShiftLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShiftLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShiftLayerG_new_const_ShiftLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShiftLayerInt8G_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShiftLayerInt8G_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShiftLayerInt8G_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShiftLayerInt8G_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ShiftLayerInt8G_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShiftLayerInt8G_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShiftLayerInt8G_new_const_ShiftLayerInt8(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShrinkLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShrinkLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShrinkLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShrinkLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ShrinkLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShrinkLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShrinkLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShrinkLayerG_new_const_ShrinkLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShuffleChannelLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShuffleChannelLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShuffleChannelLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShuffleChannelLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ShuffleChannelLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShuffleChannelLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ShuffleChannelLayerG_new_const_ShuffleChannelLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SigmoidLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SigmoidLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SigmoidLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SigmoidLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SigmoidLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SigmoidLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SigmoidLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SigmoidLayerG_new_const_SigmoidLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SignLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SignLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SignLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SignLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SignLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SignLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SignLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SignLayerG_new_const_SignLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SinLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinLayerG_new_const_SinLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinhLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinhLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinhLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinhLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SinhLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinhLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinhLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SinhLayerG_new_const_SinhLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SliceLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SliceLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SliceLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SliceLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SliceLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SliceLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SliceLayerG_new_const_SliceLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftmaxLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftmaxLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftmaxLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftmaxLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SoftmaxLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftmaxLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftmaxLayerG_new_const_SoftmaxLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftmaxLayerInt8G_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftmaxLayerInt8G_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftmaxLayerInt8G_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftmaxLayerInt8G_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SoftmaxLayerInt8G_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftmaxLayerInt8G_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftmaxLayerInt8G_to_PtrOfSoftmaxLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftmaxLayerInt8G_new_const_SoftmaxLayerInt8(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftplusLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftplusLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftplusLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftplusLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SoftplusLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftplusLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftplusLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftplusLayerG_new_const_SoftplusLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftsignLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftsignLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftsignLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftsignLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SoftsignLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftsignLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftsignLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SoftsignLayerG_new_const_SoftsignLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SpaceToDepthLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SpaceToDepthLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SpaceToDepthLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SpaceToDepthLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SpaceToDepthLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SpaceToDepthLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SpaceToDepthLayerG_new_const_SpaceToDepthLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SplitLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SplitLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SplitLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SplitLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SplitLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SplitLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SplitLayerG_new_const_SplitLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SqrtLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SqrtLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SqrtLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SqrtLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SqrtLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SqrtLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SqrtLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SqrtLayerG_new_const_SqrtLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SwishLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SwishLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SwishLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_SwishLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_SwishLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SwishLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SwishLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_SwishLayerG_new_const_SwishLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanHLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanHLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanHLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanHLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_TanHLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanHLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanHLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanHLayerG_new_const_TanHLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_TanLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TanLayerG_new_const_TanLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ThresholdedReluLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ThresholdedReluLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ThresholdedReluLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_ThresholdedReluLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_ThresholdedReluLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ThresholdedReluLayerG_to_PtrOfActivationLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ThresholdedReluLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_ThresholdedReluLayerG_new_const_ThresholdedReluLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TileLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TileLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TileLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_TileLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_TileLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TileLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TileLayerG_new_const_TileLayer(val: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TopKLayerG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TopKLayerG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TopKLayerG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_dnn_TopKLayerG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_dnn_TopKLayerG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TopKLayerG_to_PtrOfLayer(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_dnn_TopKLayerG_new_const_TopKLayer(val: *mut c_void) -> *mut c_void; + pub fn std_pairLcv_dnn_Backend__cv_dnn_TargetG_new_const_Backend_Target(arg: crate::dnn::Backend, arg_1: crate::dnn::Target) -> *mut c_void; + pub fn std_pairLcv_dnn_Backend__cv_dnn_TargetG_get_0_const(instance: *const c_void, ocvrs_return: *mut crate::dnn::Backend); + pub fn std_pairLcv_dnn_Backend__cv_dnn_TargetG_get_1_const(instance: *const c_void, ocvrs_return: *mut crate::dnn::Target); + pub fn std_pairLcv_dnn_Backend__cv_dnn_TargetG_delete(instance: *mut c_void); + pub fn std_vectorLcv_dnn_MatShapeG_new_const() -> *mut c_void; + pub fn std_vectorLcv_dnn_MatShapeG_delete(instance: *mut c_void); + pub fn std_vectorLcv_dnn_MatShapeG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_dnn_MatShapeG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_dnn_MatShapeG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_dnn_MatShapeG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_dnn_MatShapeG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_dnn_MatShapeG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_dnn_MatShapeG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_dnn_MatShapeG_clear(instance: *mut c_void); + pub fn std_vectorLcv_dnn_MatShapeG_push_const_MatShape(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLcv_dnn_MatShapeG_insert_size_t_const_MatShape(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_dnn_MatShapeG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLcv_dnn_MatShapeG_set_size_t_const_MatShape(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_dnn_MatShapeG_inputArray_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_dnn_MatShapeG_outputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_dnn_MatShapeG_inputOutputArray(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_new_const() -> *mut c_void; + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_delete(instance: *mut c_void); + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_clear(instance: *mut c_void); + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_push_const_PtrLBackendWrapperG(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_insert_size_t_const_PtrLBackendWrapperG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_set_size_t_const_PtrLBackendWrapperG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_new_const() -> *mut c_void; + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_delete(instance: *mut c_void); + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_clear(instance: *mut c_void); + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_push_const_PtrLLayerG(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_insert_size_t_const_PtrLLayerG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLcv_PtrLcv_dnn_LayerGG_set_size_t_const_PtrLLayerG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLcv_dnn_TargetG_new_const() -> *mut c_void; + pub fn std_vectorLcv_dnn_TargetG_delete(instance: *mut c_void); + pub fn std_vectorLcv_dnn_TargetG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_dnn_TargetG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_dnn_TargetG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_dnn_TargetG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_dnn_TargetG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_dnn_TargetG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_dnn_TargetG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_dnn_TargetG_clear(instance: *mut c_void); + pub fn std_vectorLcv_dnn_TargetG_push_const_Target(instance: *mut c_void, val: crate::dnn::Target); + pub fn std_vectorLcv_dnn_TargetG_insert_size_t_const_Target(instance: *mut c_void, index: size_t, val: crate::dnn::Target); + pub fn std_vectorLcv_dnn_TargetG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut crate::dnn::Target); + pub fn std_vectorLcv_dnn_TargetG_set_size_t_const_Target(instance: *mut c_void, index: size_t, val: crate::dnn::Target); + pub fn std_vectorLcv_dnn_TargetG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_dnn_TargetG_data_const(instance: *const c_void) -> *const crate::dnn::Target; + pub fn std_vectorLcv_dnn_TargetG_dataMut(instance: *mut c_void) -> *mut crate::dnn::Target; + pub fn cv_fromSlice_const_const_TargetX_size_t(data: *const crate::dnn::Target, len: size_t) -> *mut c_void; + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_new_const() -> *mut c_void; + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_delete(instance: *mut c_void); + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_clear(instance: *mut c_void); + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_push_const_pairLcv_dnn_Backend__cv_dnn_TargetG(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_insert_size_t_const_pairLcv_dnn_Backend__cv_dnn_TargetG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_set_size_t_const_pairLcv_dnn_Backend__cv_dnn_TargetG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_new_const() -> *mut c_void; + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_delete(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_clear(instance: *mut c_void); + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_push_const_vectorLMatShapeG(instance: *mut c_void, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_insert_size_t_const_vectorLMatShapeG(instance: *mut c_void, index: size_t, val: *const c_void); + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut *mut c_void); + pub fn std_vectorLstd_vectorLcv_dnn_MatShapeGG_set_size_t_const_vectorLMatShapeG(instance: *mut c_void, index: size_t, val: *const c_void); + } +} +pub use dnn_sys::*; + +mod highgui_sys { + use super::*; + + unsafe extern "C" { + pub fn cv_addText_const_MatR_const_StringR_Point_const_QtFontR(img: *const c_void, text: *const c_char, org: *const core::Point, font: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_addText_const_MatR_const_StringR_Point_const_StringR(img: *const c_void, text: *const c_char, org: *const core::Point, name_font: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_addText_const_MatR_const_StringR_Point_const_StringR_int_Scalar_int_int_int(img: *const c_void, text: *const c_char, org: *const core::Point, name_font: *const c_char, point_size: i32, color: *const core::Scalar, weight: i32, style: i32, spacing: i32, ocvrs_return: *mut Result<()>); + pub fn cv_createButton_const_StringR_ButtonCallback_voidX(bar_name: *const c_char, on_change: Option ()>, userdata: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_createButton_const_StringR_ButtonCallback_voidX_int_bool(bar_name: *const c_char, on_change: Option ()>, userdata: *mut c_void, typ: i32, initial_button_state: bool, ocvrs_return: *mut Result); + pub fn cv_createTrackbar_const_StringR_const_StringR_intX_int_TrackbarCallback_voidX(trackbarname: *const c_char, winname: *const c_char, value: *mut i32, count: i32, on_change: Option ()>, userdata: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_currentUIFramework(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_destroyAllWindows(ocvrs_return: *mut Result<()>); + pub fn cv_destroyWindow_const_StringR(winname: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_displayOverlay_const_StringR_const_StringR(winname: *const c_char, text: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_displayOverlay_const_StringR_const_StringR_int(winname: *const c_char, text: *const c_char, delayms: i32, ocvrs_return: *mut Result<()>); + pub fn cv_displayStatusBar_const_StringR_const_StringR(winname: *const c_char, text: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_displayStatusBar_const_StringR_const_StringR_int(winname: *const c_char, text: *const c_char, delayms: i32, ocvrs_return: *mut Result<()>); + pub fn cv_fontQt_const_StringR(name_font: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_fontQt_const_StringR_int_Scalar_int_int_int(name_font: *const c_char, point_size: i32, color: *const core::Scalar, weight: i32, style: i32, spacing: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getMouseWheelDelta_int(flags: i32, ocvrs_return: *mut Result); + pub fn cv_getTrackbarPos_const_StringR_const_StringR(trackbarname: *const c_char, winname: *const c_char, ocvrs_return: *mut Result); + pub fn cv_getWindowImageRect_const_StringR(winname: *const c_char, ocvrs_return: *mut Result); + pub fn cv_getWindowProperty_const_StringR_int(winname: *const c_char, prop_id: i32, ocvrs_return: *mut Result); + pub fn cv_imshow_const_StringR_const__InputArrayR(winname: *const c_char, mat: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_loadWindowParameters_const_StringR(window_name: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_moveWindow_const_StringR_int_int(winname: *const c_char, x: i32, y: i32, ocvrs_return: *mut Result<()>); + pub fn cv_namedWindow_const_StringR(winname: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_namedWindow_const_StringR_int(winname: *const c_char, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_pollKey(ocvrs_return: *mut Result); + pub fn cv_resizeWindow_const_StringR_const_SizeR(winname: *const c_char, size: *const core::Size, ocvrs_return: *mut Result<()>); + pub fn cv_resizeWindow_const_StringR_int_int(winname: *const c_char, width: i32, height: i32, ocvrs_return: *mut Result<()>); + pub fn cv_saveWindowParameters_const_StringR(window_name: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_selectROI_const_StringR_const__InputArrayR(window_name: *const c_char, img: *const c_void, ocvrs_return: *mut Result); + pub fn cv_selectROI_const_StringR_const__InputArrayR_bool_bool_bool(window_name: *const c_char, img: *const c_void, show_crosshair: bool, from_center: bool, print_notice: bool, ocvrs_return: *mut Result); + pub fn cv_selectROI_const__InputArrayR(img: *const c_void, ocvrs_return: *mut Result); + pub fn cv_selectROI_const__InputArrayR_bool_bool_bool(img: *const c_void, show_crosshair: bool, from_center: bool, print_notice: bool, ocvrs_return: *mut Result); + pub fn cv_selectROIs_const_StringR_const__InputArrayR_vectorLRectGR(window_name: *const c_char, img: *const c_void, bounding_boxes: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_selectROIs_const_StringR_const__InputArrayR_vectorLRectGR_bool_bool_bool(window_name: *const c_char, img: *const c_void, bounding_boxes: *mut c_void, show_crosshair: bool, from_center: bool, print_notice: bool, ocvrs_return: *mut Result<()>); + pub fn cv_setMouseCallback_const_StringR_MouseCallback_voidX(winname: *const c_char, on_mouse: Option ()>, userdata: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_setOpenGlContext_const_StringR(winname: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_setOpenGlDrawCallback_const_StringR_OpenGlDrawCallback_voidX(winname: *const c_char, on_opengl_draw: Option ()>, userdata: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_setTrackbarMax_const_StringR_const_StringR_int(trackbarname: *const c_char, winname: *const c_char, maxval: i32, ocvrs_return: *mut Result<()>); + pub fn cv_setTrackbarMin_const_StringR_const_StringR_int(trackbarname: *const c_char, winname: *const c_char, minval: i32, ocvrs_return: *mut Result<()>); + pub fn cv_setTrackbarPos_const_StringR_const_StringR_int(trackbarname: *const c_char, winname: *const c_char, pos: i32, ocvrs_return: *mut Result<()>); + pub fn cv_setWindowProperty_const_StringR_int_double(winname: *const c_char, prop_id: i32, prop_value: f64, ocvrs_return: *mut Result<()>); + pub fn cv_setWindowTitle_const_StringR_const_StringR(winname: *const c_char, title: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_startLoop_int__X__int__charXX__int_charXX(pt2_func: Option i32>, argc: i32, argv: *mut *mut c_char, ocvrs_return: *mut Result); + pub fn cv_startWindowThread(ocvrs_return: *mut Result); + pub fn cv_stopLoop(ocvrs_return: *mut Result<()>); + pub fn cv_updateWindow_const_StringR(winname: *const c_char, ocvrs_return: *mut Result<()>); + pub fn cv_waitKey(ocvrs_return: *mut Result); + pub fn cv_waitKeyEx(ocvrs_return: *mut Result); + pub fn cv_waitKeyEx_int(delay: i32, ocvrs_return: *mut Result); + pub fn cv_waitKey_int(delay: i32, ocvrs_return: *mut Result); + pub fn cv_QtFont_defaultNew_const() -> *mut c_void; + pub fn cv_QtFont_propNameFont_const(instance: *const c_void) -> *mut c_void; + pub fn cv_QtFont_propColor_const(instance: *const c_void, ocvrs_return: *mut core::Scalar); + pub fn cv_QtFont_propColor_const_Scalar(instance: *mut c_void, val: *const core::Scalar); + pub fn cv_QtFont_propFont_face_const(instance: *const c_void) -> i32; + pub fn cv_QtFont_propFont_face_const_int(instance: *mut c_void, val: i32); + pub fn cv_QtFont_propAscii_const(instance: *const c_void) -> *const i32; + pub fn cv_QtFont_propGreek_const(instance: *const c_void) -> *const i32; + pub fn cv_QtFont_propCyrillic_const(instance: *const c_void) -> *const i32; + pub fn cv_QtFont_propHscale_const(instance: *const c_void) -> f32; + pub fn cv_QtFont_propHscale_const_float(instance: *mut c_void, val: f32); + pub fn cv_QtFont_propVscale_const(instance: *const c_void) -> f32; + pub fn cv_QtFont_propVscale_const_float(instance: *mut c_void, val: f32); + pub fn cv_QtFont_propShear_const(instance: *const c_void) -> f32; + pub fn cv_QtFont_propShear_const_float(instance: *mut c_void, val: f32); + pub fn cv_QtFont_propThickness_const(instance: *const c_void) -> i32; + pub fn cv_QtFont_propThickness_const_int(instance: *mut c_void, val: i32); + pub fn cv_QtFont_propDx_const(instance: *const c_void) -> f32; + pub fn cv_QtFont_propDx_const_float(instance: *mut c_void, val: f32); + pub fn cv_QtFont_propLine_type_const(instance: *const c_void) -> i32; + pub fn cv_QtFont_propLine_type_const_int(instance: *mut c_void, val: i32); + pub fn cv_QtFont_delete(instance: *mut c_void); + } +} +pub use highgui_sys::*; + +mod imgproc_sys { + use super::*; + + unsafe extern "C" { + pub fn cv_Canny_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double_double(dx: *const c_void, dy: *const c_void, edges: *const c_void, threshold1: f64, threshold2: f64, ocvrs_return: *mut Result<()>); + pub fn cv_Canny_const__InputArrayR_const__InputArrayR_const__OutputArrayR_double_double_bool(dx: *const c_void, dy: *const c_void, edges: *const c_void, threshold1: f64, threshold2: f64, l2gradient: bool, ocvrs_return: *mut Result<()>); + pub fn cv_Canny_const__InputArrayR_const__OutputArrayR_double_double(image: *const c_void, edges: *const c_void, threshold1: f64, threshold2: f64, ocvrs_return: *mut Result<()>); + pub fn cv_Canny_const__InputArrayR_const__OutputArrayR_double_double_int_bool(image: *const c_void, edges: *const c_void, threshold1: f64, threshold2: f64, aperture_size: i32, l2gradient: bool, ocvrs_return: *mut Result<()>); + pub fn cv_EMD_const__InputArrayR_const__InputArrayR_int(signature1: *const c_void, signature2: *const c_void, dist_type: i32, ocvrs_return: *mut Result); + pub fn cv_EMD_const__InputArrayR_const__InputArrayR_int_const__InputArrayR_floatX_const__OutputArrayR(signature1: *const c_void, signature2: *const c_void, dist_type: i32, cost: *const c_void, lower_bound: *mut f32, flow: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GaussianBlur_const__InputArrayR_const__OutputArrayR_Size_double(src: *const c_void, dst: *const c_void, ksize: *const core::Size, sigma_x: f64, ocvrs_return: *mut Result<()>); + pub fn cv_GaussianBlur_const__InputArrayR_const__OutputArrayR_Size_double_double_int_AlgorithmHint(src: *const c_void, dst: *const c_void, ksize: *const core::Size, sigma_x: f64, sigma_y: f64, border_type: i32, hint: core::AlgorithmHint, ocvrs_return: *mut Result<()>); + pub fn cv_HoughCircles_const__InputArrayR_const__OutputArrayR_int_double_double(image: *const c_void, circles: *const c_void, method: i32, dp: f64, min_dist: f64, ocvrs_return: *mut Result<()>); + pub fn cv_HoughCircles_const__InputArrayR_const__OutputArrayR_int_double_double_double_double_int_int(image: *const c_void, circles: *const c_void, method: i32, dp: f64, min_dist: f64, param1: f64, param2: f64, min_radius: i32, max_radius: i32, ocvrs_return: *mut Result<()>); + pub fn cv_HoughLinesP_const__InputArrayR_const__OutputArrayR_double_double_int(image: *const c_void, lines: *const c_void, rho: f64, theta: f64, threshold: i32, ocvrs_return: *mut Result<()>); + pub fn cv_HoughLinesP_const__InputArrayR_const__OutputArrayR_double_double_int_double_double(image: *const c_void, lines: *const c_void, rho: f64, theta: f64, threshold: i32, min_line_length: f64, max_line_gap: f64, ocvrs_return: *mut Result<()>); + pub fn cv_HoughLinesPointSet_const__InputArrayR_const__OutputArrayR_int_int_double_double_double_double_double_double(point: *const c_void, lines: *const c_void, lines_max: i32, threshold: i32, min_rho: f64, max_rho: f64, rho_step: f64, min_theta: f64, max_theta: f64, theta_step: f64, ocvrs_return: *mut Result<()>); + pub fn cv_HoughLines_const__InputArrayR_const__OutputArrayR_double_double_int(image: *const c_void, lines: *const c_void, rho: f64, theta: f64, threshold: i32, ocvrs_return: *mut Result<()>); + pub fn cv_HoughLines_const__InputArrayR_const__OutputArrayR_double_double_int_double_double_double_double_bool(image: *const c_void, lines: *const c_void, rho: f64, theta: f64, threshold: i32, srn: f64, stn: f64, min_theta: f64, max_theta: f64, use_edgeval: bool, ocvrs_return: *mut Result<()>); + pub fn cv_HuMoments_const_MomentsR_const__OutputArrayR(m: *const core::Moments, hu: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_HuMoments_const_MomentsR_doubleXX(moments: *const core::Moments, hu: *mut [f64; 7], ocvrs_return: *mut Result<()>); + pub fn cv_Laplacian_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, ddepth: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Laplacian_const__InputArrayR_const__OutputArrayR_int_int_double_double_int(src: *const c_void, dst: *const c_void, ddepth: i32, ksize: i32, scale: f64, delta: f64, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Scharr_const__InputArrayR_const__OutputArrayR_int_int_int(src: *const c_void, dst: *const c_void, ddepth: i32, dx: i32, dy: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Scharr_const__InputArrayR_const__OutputArrayR_int_int_int_double_double_int(src: *const c_void, dst: *const c_void, ddepth: i32, dx: i32, dy: i32, scale: f64, delta: f64, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Sobel_const__InputArrayR_const__OutputArrayR_int_int_int(src: *const c_void, dst: *const c_void, ddepth: i32, dx: i32, dy: i32, ocvrs_return: *mut Result<()>); + pub fn cv_Sobel_const__InputArrayR_const__OutputArrayR_int_int_int_int_double_double_int(src: *const c_void, dst: *const c_void, ddepth: i32, dx: i32, dy: i32, ksize: i32, scale: f64, delta: f64, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_accumulateProduct_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_accumulateProduct_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR(src1: *const c_void, src2: *const c_void, dst: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_accumulateSquare_const__InputArrayR_const__InputOutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_accumulateSquare_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR(src: *const c_void, dst: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_accumulateWeighted_const__InputArrayR_const__InputOutputArrayR_double(src: *const c_void, dst: *const c_void, alpha: f64, ocvrs_return: *mut Result<()>); + pub fn cv_accumulateWeighted_const__InputArrayR_const__InputOutputArrayR_double_const__InputArrayR(src: *const c_void, dst: *const c_void, alpha: f64, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_accumulate_const__InputArrayR_const__InputOutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_accumulate_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR(src: *const c_void, dst: *const c_void, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_adaptiveThreshold_const__InputArrayR_const__OutputArrayR_double_int_int_int_double(src: *const c_void, dst: *const c_void, max_value: f64, adaptive_method: i32, threshold_type: i32, block_size: i32, c: f64, ocvrs_return: *mut Result<()>); + pub fn cv_applyColorMap_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src: *const c_void, dst: *const c_void, user_color: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_applyColorMap_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, colormap: i32, ocvrs_return: *mut Result<()>); + pub fn cv_approxPolyDP_const__InputArrayR_const__OutputArrayR_double_bool(curve: *const c_void, approx_curve: *const c_void, epsilon: f64, closed: bool, ocvrs_return: *mut Result<()>); + pub fn cv_approxPolyN_const__InputArrayR_const__OutputArrayR_int(curve: *const c_void, approx_curve: *const c_void, nsides: i32, ocvrs_return: *mut Result<()>); + pub fn cv_approxPolyN_const__InputArrayR_const__OutputArrayR_int_float_bool(curve: *const c_void, approx_curve: *const c_void, nsides: i32, epsilon_percentage: f32, ensure_convex: bool, ocvrs_return: *mut Result<()>); + pub fn cv_arcLength_const__InputArrayR_bool(curve: *const c_void, closed: bool, ocvrs_return: *mut Result); + pub fn cv_arrowedLine_const__InputOutputArrayR_Point_Point_const_ScalarR(img: *const c_void, pt1: *const core::Point, pt2: *const core::Point, color: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_arrowedLine_const__InputOutputArrayR_Point_Point_const_ScalarR_int_int_int_double(img: *const c_void, pt1: *const core::Point, pt2: *const core::Point, color: *const core::Scalar, thickness: i32, line_type: i32, shift: i32, tip_length: f64, ocvrs_return: *mut Result<()>); + pub fn cv_bilateralFilter_const__InputArrayR_const__OutputArrayR_int_double_double(src: *const c_void, dst: *const c_void, d: i32, sigma_color: f64, sigma_space: f64, ocvrs_return: *mut Result<()>); + pub fn cv_bilateralFilter_const__InputArrayR_const__OutputArrayR_int_double_double_int(src: *const c_void, dst: *const c_void, d: i32, sigma_color: f64, sigma_space: f64, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_blendLinear_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(src1: *const c_void, src2: *const c_void, weights1: *const c_void, weights2: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_blur_const__InputArrayR_const__OutputArrayR_Size(src: *const c_void, dst: *const c_void, ksize: *const core::Size, ocvrs_return: *mut Result<()>); + pub fn cv_blur_const__InputArrayR_const__OutputArrayR_Size_Point_int(src: *const c_void, dst: *const c_void, ksize: *const core::Size, anchor: *const core::Point, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_boundingRect_const__InputArrayR(array: *const c_void, ocvrs_return: *mut Result); + pub fn cv_boxFilter_const__InputArrayR_const__OutputArrayR_int_Size(src: *const c_void, dst: *const c_void, ddepth: i32, ksize: *const core::Size, ocvrs_return: *mut Result<()>); + pub fn cv_boxFilter_const__InputArrayR_const__OutputArrayR_int_Size_Point_bool_int(src: *const c_void, dst: *const c_void, ddepth: i32, ksize: *const core::Size, anchor: *const core::Point, normalize: bool, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_boxPoints_RotatedRect_const__OutputArrayR(box_: *const core::RotatedRect, points: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_buildPyramid_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, maxlevel: i32, ocvrs_return: *mut Result<()>); + pub fn cv_buildPyramid_const__InputArrayR_const__OutputArrayR_int_int(src: *const c_void, dst: *const c_void, maxlevel: i32, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_calcBackProject_const__InputArrayR_const_vectorLintGR_const__InputArrayR_const__OutputArrayR_const_vectorLfloatGR_double(images: *const c_void, channels: *const c_void, hist: *const c_void, dst: *const c_void, ranges: *const c_void, scale: f64, ocvrs_return: *mut Result<()>); + pub fn cv_calcHist_const__InputArrayR_const_vectorLintGR_const__InputArrayR_const__OutputArrayR_const_vectorLintGR_const_vectorLfloatGR(images: *const c_void, channels: *const c_void, mask: *const c_void, hist: *const c_void, hist_size: *const c_void, ranges: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_calcHist_const__InputArrayR_const_vectorLintGR_const__InputArrayR_const__OutputArrayR_const_vectorLintGR_const_vectorLfloatGR_bool(images: *const c_void, channels: *const c_void, mask: *const c_void, hist: *const c_void, hist_size: *const c_void, ranges: *const c_void, accumulate: bool, ocvrs_return: *mut Result<()>); + pub fn cv_circle_const__InputOutputArrayR_Point_int_const_ScalarR(img: *const c_void, center: *const core::Point, radius: i32, color: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_circle_const__InputOutputArrayR_Point_int_const_ScalarR_int_int_int(img: *const c_void, center: *const core::Point, radius: i32, color: *const core::Scalar, thickness: i32, line_type: i32, shift: i32, ocvrs_return: *mut Result<()>); + pub fn cv_clipLine_Rect_PointR_PointR(img_rect: *const core::Rect, pt1: *mut core::Point, pt2: *mut core::Point, ocvrs_return: *mut Result); + pub fn cv_clipLine_Size2l_Point2lR_Point2lR(img_size: *const core::Size2l, pt1: *mut core::Point2l, pt2: *mut core::Point2l, ocvrs_return: *mut Result); + pub fn cv_clipLine_Size_PointR_PointR(img_size: *const core::Size, pt1: *mut core::Point, pt2: *mut core::Point, ocvrs_return: *mut Result); + pub fn cv_compareHist_const_SparseMatR_const_SparseMatR_int(h1: *const c_void, h2: *const c_void, method: i32, ocvrs_return: *mut Result); + pub fn cv_compareHist_const__InputArrayR_const__InputArrayR_int(h1: *const c_void, h2: *const c_void, method: i32, ocvrs_return: *mut Result); + pub fn cv_connectedComponentsWithStats_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(image: *const c_void, labels: *const c_void, stats: *const c_void, centroids: *const c_void, ocvrs_return: *mut Result); + pub fn cv_connectedComponentsWithStats_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(image: *const c_void, labels: *const c_void, stats: *const c_void, centroids: *const c_void, connectivity: i32, ltype: i32, ocvrs_return: *mut Result); + pub fn cv_connectedComponentsWithStats_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_int_int(image: *const c_void, labels: *const c_void, stats: *const c_void, centroids: *const c_void, connectivity: i32, ltype: i32, ccltype: i32, ocvrs_return: *mut Result); + pub fn cv_connectedComponents_const__InputArrayR_const__OutputArrayR(image: *const c_void, labels: *const c_void, ocvrs_return: *mut Result); + pub fn cv_connectedComponents_const__InputArrayR_const__OutputArrayR_int_int(image: *const c_void, labels: *const c_void, connectivity: i32, ltype: i32, ocvrs_return: *mut Result); + pub fn cv_connectedComponents_const__InputArrayR_const__OutputArrayR_int_int_int(image: *const c_void, labels: *const c_void, connectivity: i32, ltype: i32, ccltype: i32, ocvrs_return: *mut Result); + pub fn cv_contourArea_const__InputArrayR(contour: *const c_void, ocvrs_return: *mut Result); + pub fn cv_contourArea_const__InputArrayR_bool(contour: *const c_void, oriented: bool, ocvrs_return: *mut Result); + pub fn cv_convertMaps_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int(map1: *const c_void, map2: *const c_void, dstmap1: *const c_void, dstmap2: *const c_void, dstmap1type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_convertMaps_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_bool(map1: *const c_void, map2: *const c_void, dstmap1: *const c_void, dstmap2: *const c_void, dstmap1type: i32, nninterpolation: bool, ocvrs_return: *mut Result<()>); + pub fn cv_convexHull_const__InputArrayR_const__OutputArrayR(points: *const c_void, hull: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_convexHull_const__InputArrayR_const__OutputArrayR_bool_bool(points: *const c_void, hull: *const c_void, clockwise: bool, return_points: bool, ocvrs_return: *mut Result<()>); + pub fn cv_convexityDefects_const__InputArrayR_const__InputArrayR_const__OutputArrayR(contour: *const c_void, convexhull: *const c_void, convexity_defects: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_cornerEigenValsAndVecs_const__InputArrayR_const__OutputArrayR_int_int(src: *const c_void, dst: *const c_void, block_size: i32, ksize: i32, ocvrs_return: *mut Result<()>); + pub fn cv_cornerEigenValsAndVecs_const__InputArrayR_const__OutputArrayR_int_int_int(src: *const c_void, dst: *const c_void, block_size: i32, ksize: i32, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_cornerHarris_const__InputArrayR_const__OutputArrayR_int_int_double(src: *const c_void, dst: *const c_void, block_size: i32, ksize: i32, k: f64, ocvrs_return: *mut Result<()>); + pub fn cv_cornerHarris_const__InputArrayR_const__OutputArrayR_int_int_double_int(src: *const c_void, dst: *const c_void, block_size: i32, ksize: i32, k: f64, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_cornerMinEigenVal_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, block_size: i32, ocvrs_return: *mut Result<()>); + pub fn cv_cornerMinEigenVal_const__InputArrayR_const__OutputArrayR_int_int_int(src: *const c_void, dst: *const c_void, block_size: i32, ksize: i32, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_cornerSubPix_const__InputArrayR_const__InputOutputArrayR_Size_Size_TermCriteria(image: *const c_void, corners: *const c_void, win_size: *const core::Size, zero_zone: *const core::Size, criteria: *const core::TermCriteria, ocvrs_return: *mut Result<()>); + pub fn cv_createCLAHE(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_createCLAHE_double_Size(clip_limit: f64, tile_grid_size: *const core::Size, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_createGeneralizedHoughBallard(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_createGeneralizedHoughGuil(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_createHanningWindow_const__OutputArrayR_Size_int(dst: *const c_void, win_size: *const core::Size, typ: i32, ocvrs_return: *mut Result<()>); + pub fn cv_createLineSegmentDetector(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_createLineSegmentDetector_int_double_double_double_double_double_double_int(refine: i32, scale: f64, sigma_scale: f64, quant: f64, ang_th: f64, log_eps: f64, density_th: f64, n_bins: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_cvtColorTwoPlane_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(src1: *const c_void, src2: *const c_void, dst: *const c_void, code: i32, ocvrs_return: *mut Result<()>); + pub fn cv_cvtColorTwoPlane_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_AlgorithmHint(src1: *const c_void, src2: *const c_void, dst: *const c_void, code: i32, hint: core::AlgorithmHint, ocvrs_return: *mut Result<()>); + pub fn cv_cvtColor_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, code: i32, ocvrs_return: *mut Result<()>); + pub fn cv_cvtColor_const__InputArrayR_const__OutputArrayR_int_int_AlgorithmHint(src: *const c_void, dst: *const c_void, code: i32, dst_cn: i32, hint: core::AlgorithmHint, ocvrs_return: *mut Result<()>); + pub fn cv_demosaicing_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, code: i32, ocvrs_return: *mut Result<()>); + pub fn cv_demosaicing_const__InputArrayR_const__OutputArrayR_int_int(src: *const c_void, dst: *const c_void, code: i32, dst_cn: i32, ocvrs_return: *mut Result<()>); + pub fn cv_dilate_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src: *const c_void, dst: *const c_void, kernel: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_dilate_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Point_int_int_const_ScalarR(src: *const c_void, dst: *const c_void, kernel: *const c_void, anchor: *const core::Point, iterations: i32, border_type: i32, border_value: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_distanceTransform_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(src: *const c_void, dst: *const c_void, labels: *const c_void, distance_type: i32, mask_size: i32, ocvrs_return: *mut Result<()>); + pub fn cv_distanceTransform_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int_int(src: *const c_void, dst: *const c_void, labels: *const c_void, distance_type: i32, mask_size: i32, label_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_distanceTransform_const__InputArrayR_const__OutputArrayR_int_int(src: *const c_void, dst: *const c_void, distance_type: i32, mask_size: i32, ocvrs_return: *mut Result<()>); + pub fn cv_distanceTransform_const__InputArrayR_const__OutputArrayR_int_int_int(src: *const c_void, dst: *const c_void, distance_type: i32, mask_size: i32, dst_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_divSpectrums_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(a: *const c_void, b: *const c_void, c: *const c_void, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_divSpectrums_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_bool(a: *const c_void, b: *const c_void, c: *const c_void, flags: i32, conj_b: bool, ocvrs_return: *mut Result<()>); + pub fn cv_drawContours_const__InputOutputArrayR_const__InputArrayR_int_const_ScalarR(image: *const c_void, contours: *const c_void, contour_idx: i32, color: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_drawContours_const__InputOutputArrayR_const__InputArrayR_int_const_ScalarR_int_int_const__InputArrayR_int_Point(image: *const c_void, contours: *const c_void, contour_idx: i32, color: *const core::Scalar, thickness: i32, line_type: i32, hierarchy: *const c_void, max_level: i32, offset: *const core::Point, ocvrs_return: *mut Result<()>); + pub fn cv_drawMarker_const__InputOutputArrayR_Point_const_ScalarR(img: *const c_void, position: *const core::Point, color: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_drawMarker_const__InputOutputArrayR_Point_const_ScalarR_int_int_int_int(img: *const c_void, position: *const core::Point, color: *const core::Scalar, marker_type: i32, marker_size: i32, thickness: i32, line_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_ellipse2Poly_Point2d_Size2d_int_int_int_int_vectorLPoint2dGR(center: *const core::Point2d, axes: *const core::Size2d, angle: i32, arc_start: i32, arc_end: i32, delta: i32, pts: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ellipse2Poly_Point_Size_int_int_int_int_vectorLPointGR(center: *const core::Point, axes: *const core::Size, angle: i32, arc_start: i32, arc_end: i32, delta: i32, pts: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_ellipse_const__InputOutputArrayR_Point_Size_double_double_double_const_ScalarR(img: *const c_void, center: *const core::Point, axes: *const core::Size, angle: f64, start_angle: f64, end_angle: f64, color: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_ellipse_const__InputOutputArrayR_Point_Size_double_double_double_const_ScalarR_int_int_int(img: *const c_void, center: *const core::Point, axes: *const core::Size, angle: f64, start_angle: f64, end_angle: f64, color: *const core::Scalar, thickness: i32, line_type: i32, shift: i32, ocvrs_return: *mut Result<()>); + pub fn cv_ellipse_const__InputOutputArrayR_const_RotatedRectR_const_ScalarR(img: *const c_void, box_: *const core::RotatedRect, color: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_ellipse_const__InputOutputArrayR_const_RotatedRectR_const_ScalarR_int_int(img: *const c_void, box_: *const core::RotatedRect, color: *const core::Scalar, thickness: i32, line_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_equalizeHist_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_erode_const__InputArrayR_const__OutputArrayR_const__InputArrayR(src: *const c_void, dst: *const c_void, kernel: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_erode_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Point_int_int_const_ScalarR(src: *const c_void, dst: *const c_void, kernel: *const c_void, anchor: *const core::Point, iterations: i32, border_type: i32, border_value: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_fillConvexPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR(img: *const c_void, points: *const c_void, color: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_fillConvexPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR_int_int(img: *const c_void, points: *const c_void, color: *const core::Scalar, line_type: i32, shift: i32, ocvrs_return: *mut Result<()>); + pub fn cv_fillPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR(img: *const c_void, pts: *const c_void, color: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_fillPoly_const__InputOutputArrayR_const__InputArrayR_const_ScalarR_int_int_Point(img: *const c_void, pts: *const c_void, color: *const core::Scalar, line_type: i32, shift: i32, offset: *const core::Point, ocvrs_return: *mut Result<()>); + pub fn cv_filter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR(src: *const c_void, dst: *const c_void, ddepth: i32, kernel: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_filter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_Point_double_int(src: *const c_void, dst: *const c_void, ddepth: i32, kernel: *const c_void, anchor: *const core::Point, delta: f64, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_findContoursLinkRuns_const__InputArrayR_const__OutputArrayR(image: *const c_void, contours: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_findContoursLinkRuns_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(image: *const c_void, contours: *const c_void, hierarchy: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_findContours_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(image: *const c_void, contours: *const c_void, hierarchy: *const c_void, mode: i32, method: i32, ocvrs_return: *mut Result<()>); + pub fn cv_findContours_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int_Point(image: *const c_void, contours: *const c_void, hierarchy: *const c_void, mode: i32, method: i32, offset: *const core::Point, ocvrs_return: *mut Result<()>); + pub fn cv_findContours_const__InputArrayR_const__OutputArrayR_int_int(image: *const c_void, contours: *const c_void, mode: i32, method: i32, ocvrs_return: *mut Result<()>); + pub fn cv_findContours_const__InputArrayR_const__OutputArrayR_int_int_Point(image: *const c_void, contours: *const c_void, mode: i32, method: i32, offset: *const core::Point, ocvrs_return: *mut Result<()>); + pub fn cv_fitEllipseAMS_const__InputArrayR(points: *const c_void, ocvrs_return: *mut Result); + pub fn cv_fitEllipseDirect_const__InputArrayR(points: *const c_void, ocvrs_return: *mut Result); + pub fn cv_fitEllipse_const__InputArrayR(points: *const c_void, ocvrs_return: *mut Result); + pub fn cv_fitLine_const__InputArrayR_const__OutputArrayR_int_double_double_double(points: *const c_void, line: *const c_void, dist_type: i32, param: f64, reps: f64, aeps: f64, ocvrs_return: *mut Result<()>); + pub fn cv_floodFill_const__InputOutputArrayR_Point_Scalar(image: *const c_void, seed_point: *const core::Point, new_val: *const core::Scalar, ocvrs_return: *mut Result); + pub fn cv_floodFill_const__InputOutputArrayR_Point_Scalar_RectX_Scalar_Scalar_int(image: *const c_void, seed_point: *const core::Point, new_val: *const core::Scalar, rect: *mut core::Rect, lo_diff: *const core::Scalar, up_diff: *const core::Scalar, flags: i32, ocvrs_return: *mut Result); + pub fn cv_floodFill_const__InputOutputArrayR_const__InputOutputArrayR_Point_Scalar(image: *const c_void, mask: *const c_void, seed_point: *const core::Point, new_val: *const core::Scalar, ocvrs_return: *mut Result); + pub fn cv_floodFill_const__InputOutputArrayR_const__InputOutputArrayR_Point_Scalar_RectX_Scalar_Scalar_int(image: *const c_void, mask: *const c_void, seed_point: *const core::Point, new_val: *const core::Scalar, rect: *mut core::Rect, lo_diff: *const core::Scalar, up_diff: *const core::Scalar, flags: i32, ocvrs_return: *mut Result); + pub fn cv_getAffineTransform_const_Point2fXX_const_Point2fXX(src: *const [core::Point2f; 3], dst: *const [core::Point2f; 3], ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getAffineTransform_const__InputArrayR_const__InputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getClosestEllipsePoints_const_RotatedRectR_const__InputArrayR_const__OutputArrayR(ellipse_params: *const core::RotatedRect, points: *const c_void, closest_pts: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_getDerivKernels_const__OutputArrayR_const__OutputArrayR_int_int_int(kx: *const c_void, ky: *const c_void, dx: i32, dy: i32, ksize: i32, ocvrs_return: *mut Result<()>); + pub fn cv_getDerivKernels_const__OutputArrayR_const__OutputArrayR_int_int_int_bool_int(kx: *const c_void, ky: *const c_void, dx: i32, dy: i32, ksize: i32, normalize: bool, ktype: i32, ocvrs_return: *mut Result<()>); + pub fn cv_getFontScaleFromHeight_const_int_const_int(font_face: i32, pixel_height: i32, ocvrs_return: *mut Result); + pub fn cv_getFontScaleFromHeight_const_int_const_int_const_int(font_face: i32, pixel_height: i32, thickness: i32, ocvrs_return: *mut Result); + pub fn cv_getGaborKernel_Size_double_double_double_double(ksize: *const core::Size, sigma: f64, theta: f64, lambd: f64, gamma: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getGaborKernel_Size_double_double_double_double_double_int(ksize: *const core::Size, sigma: f64, theta: f64, lambd: f64, gamma: f64, psi: f64, ktype: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getGaussianKernel_int_double(ksize: i32, sigma: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getGaussianKernel_int_double_int(ksize: i32, sigma: f64, ktype: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getPerspectiveTransform_const_Point2fXX_const_Point2fXX(src: *const [core::Point2f; 4], dst: *const [core::Point2f; 4], ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getPerspectiveTransform_const_Point2fXX_const_Point2fXX_int(src: *const [core::Point2f; 4], dst: *const [core::Point2f; 4], solve_method: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getPerspectiveTransform_const__InputArrayR_const__InputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getPerspectiveTransform_const__InputArrayR_const__InputArrayR_int(src: *const c_void, dst: *const c_void, solve_method: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getRectSubPix_const__InputArrayR_Size_Point2f_const__OutputArrayR(image: *const c_void, patch_size: *const core::Size, center: *const core::Point2f, patch: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_getRectSubPix_const__InputArrayR_Size_Point2f_const__OutputArrayR_int(image: *const c_void, patch_size: *const core::Size, center: *const core::Point2f, patch: *const c_void, patch_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_getRotationMatrix2D_Point2f_double_double(center: *const core::Point2f, angle: f64, scale: f64, ocvrs_return: *mut Result<*mut c_void>); + #[cfg(not(target_os = "windows"))] + pub fn cv_getRotationMatrix2D__Point2f_double_double(center: *const core::Point2f, angle: f64, scale: f64, ocvrs_return: *mut Result); + pub fn cv_getStructuringElement_int_Size(shape: i32, ksize: *const core::Size, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getStructuringElement_int_Size_Point(shape: i32, ksize: *const core::Size, anchor: *const core::Point, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_getTextSize_const_StringR_int_double_int_intX(text: *const c_char, font_face: i32, font_scale: f64, thickness: i32, base_line: *mut i32, ocvrs_return: *mut Result); + pub fn cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double(image: *const c_void, corners: *const c_void, max_corners: i32, quality_level: f64, min_distance: f64, ocvrs_return: *mut Result<()>); + pub fn cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_const__OutputArrayR(image: *const c_void, corners: *const c_void, max_corners: i32, quality_level: f64, min_distance: f64, mask: *const c_void, corners_quality: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_const__OutputArrayR_int_int_bool_double(image: *const c_void, corners: *const c_void, max_corners: i32, quality_level: f64, min_distance: f64, mask: *const c_void, corners_quality: *const c_void, block_size: i32, gradient_size: i32, use_harris_detector: bool, k: f64, ocvrs_return: *mut Result<()>); + pub fn cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_int_bool_double(image: *const c_void, corners: *const c_void, max_corners: i32, quality_level: f64, min_distance: f64, mask: *const c_void, block_size: i32, use_harris_detector: bool, k: f64, ocvrs_return: *mut Result<()>); + pub fn cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_int_int(image: *const c_void, corners: *const c_void, max_corners: i32, quality_level: f64, min_distance: f64, mask: *const c_void, block_size: i32, gradient_size: i32, ocvrs_return: *mut Result<()>); + pub fn cv_goodFeaturesToTrack_const__InputArrayR_const__OutputArrayR_int_double_double_const__InputArrayR_int_int_bool_double(image: *const c_void, corners: *const c_void, max_corners: i32, quality_level: f64, min_distance: f64, mask: *const c_void, block_size: i32, gradient_size: i32, use_harris_detector: bool, k: f64, ocvrs_return: *mut Result<()>); + pub fn cv_grabCut_const__InputArrayR_const__InputOutputArrayR_Rect_const__InputOutputArrayR_const__InputOutputArrayR_int(img: *const c_void, mask: *const c_void, rect: *const core::Rect, bgd_model: *const c_void, fgd_model: *const c_void, iter_count: i32, ocvrs_return: *mut Result<()>); + pub fn cv_grabCut_const__InputArrayR_const__InputOutputArrayR_Rect_const__InputOutputArrayR_const__InputOutputArrayR_int_int(img: *const c_void, mask: *const c_void, rect: *const core::Rect, bgd_model: *const c_void, fgd_model: *const c_void, iter_count: i32, mode: i32, ocvrs_return: *mut Result<()>); + pub fn cv_integral_const__InputArrayR_const__OutputArrayR(src: *const c_void, sum: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src: *const c_void, sum: *const c_void, sqsum: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(src: *const c_void, sum: *const c_void, sqsum: *const c_void, tilted: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(src: *const c_void, sum: *const c_void, sqsum: *const c_void, tilted: *const c_void, sdepth: i32, sqdepth: i32, ocvrs_return: *mut Result<()>); + pub fn cv_integral_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(src: *const c_void, sum: *const c_void, sqsum: *const c_void, sdepth: i32, sqdepth: i32, ocvrs_return: *mut Result<()>); + pub fn cv_integral_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, sum: *const c_void, sdepth: i32, ocvrs_return: *mut Result<()>); + pub fn cv_intersectConvexConvex_const__InputArrayR_const__InputArrayR_const__OutputArrayR(p1: *const c_void, p2: *const c_void, p12: *const c_void, ocvrs_return: *mut Result); + pub fn cv_intersectConvexConvex_const__InputArrayR_const__InputArrayR_const__OutputArrayR_bool(p1: *const c_void, p2: *const c_void, p12: *const c_void, handle_nested: bool, ocvrs_return: *mut Result); + pub fn cv_invertAffineTransform_const__InputArrayR_const__OutputArrayR(m: *const c_void, i_m: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_isContourConvex_const__InputArrayR(contour: *const c_void, ocvrs_return: *mut Result); + pub fn cv_line_const__InputOutputArrayR_Point_Point_const_ScalarR(img: *const c_void, pt1: *const core::Point, pt2: *const core::Point, color: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_line_const__InputOutputArrayR_Point_Point_const_ScalarR_int_int_int(img: *const c_void, pt1: *const core::Point, pt2: *const core::Point, color: *const core::Scalar, thickness: i32, line_type: i32, shift: i32, ocvrs_return: *mut Result<()>); + pub fn cv_linearPolar_const__InputArrayR_const__OutputArrayR_Point2f_double_int(src: *const c_void, dst: *const c_void, center: *const core::Point2f, max_radius: f64, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_logPolar_const__InputArrayR_const__OutputArrayR_Point2f_double_int(src: *const c_void, dst: *const c_void, center: *const core::Point2f, m: f64, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_matchShapes_const__InputArrayR_const__InputArrayR_int_double(contour1: *const c_void, contour2: *const c_void, method: i32, parameter: f64, ocvrs_return: *mut Result); + pub fn cv_matchTemplate_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int(image: *const c_void, templ: *const c_void, result: *const c_void, method: i32, ocvrs_return: *mut Result<()>); + pub fn cv_matchTemplate_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR(image: *const c_void, templ: *const c_void, result: *const c_void, method: i32, mask: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_medianBlur_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, ksize: i32, ocvrs_return: *mut Result<()>); + pub fn cv_minAreaRect_const__InputArrayR(points: *const c_void, ocvrs_return: *mut Result); + pub fn cv_minEnclosingCircle_const__InputArrayR_Point2fR_floatR(points: *const c_void, center: *mut core::Point2f, radius: *mut f32, ocvrs_return: *mut Result<()>); + pub fn cv_minEnclosingConvexPolygon_const__InputArrayR_const__OutputArrayR_int(points: *const c_void, polygon: *const c_void, k: i32, ocvrs_return: *mut Result); + pub fn cv_minEnclosingTriangle_const__InputArrayR_const__OutputArrayR(points: *const c_void, triangle: *const c_void, ocvrs_return: *mut Result); + pub fn cv_moments_const__InputArrayR(array: *const c_void, ocvrs_return: *mut Result); + pub fn cv_moments_const__InputArrayR_bool(array: *const c_void, binary_image: bool, ocvrs_return: *mut Result); + pub fn cv_morphologyDefaultBorderValue(ocvrs_return: *mut Result); + pub fn cv_morphologyEx_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR(src: *const c_void, dst: *const c_void, op: i32, kernel: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_morphologyEx_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_Point_int_int_const_ScalarR(src: *const c_void, dst: *const c_void, op: i32, kernel: *const c_void, anchor: *const core::Point, iterations: i32, border_type: i32, border_value: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_phaseCorrelateIterative_const__InputArrayR_const__InputArrayR(src1: *const c_void, src2: *const c_void, ocvrs_return: *mut Result); + pub fn cv_phaseCorrelateIterative_const__InputArrayR_const__InputArrayR_int_int(src1: *const c_void, src2: *const c_void, l2size: i32, max_iters: i32, ocvrs_return: *mut Result); + pub fn cv_phaseCorrelate_const__InputArrayR_const__InputArrayR(src1: *const c_void, src2: *const c_void, ocvrs_return: *mut Result); + pub fn cv_phaseCorrelate_const__InputArrayR_const__InputArrayR_const__InputArrayR_doubleX(src1: *const c_void, src2: *const c_void, window: *const c_void, response: *mut f64, ocvrs_return: *mut Result); + pub fn cv_pointPolygonTest_const__InputArrayR_Point2f_bool(contour: *const c_void, pt: *const core::Point2f, measure_dist: bool, ocvrs_return: *mut Result); + pub fn cv_polylines_const__InputOutputArrayR_const__InputArrayR_bool_const_ScalarR(img: *const c_void, pts: *const c_void, is_closed: bool, color: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_polylines_const__InputOutputArrayR_const__InputArrayR_bool_const_ScalarR_int_int_int(img: *const c_void, pts: *const c_void, is_closed: bool, color: *const core::Scalar, thickness: i32, line_type: i32, shift: i32, ocvrs_return: *mut Result<()>); + pub fn cv_preCornerDetect_const__InputArrayR_const__OutputArrayR_int(src: *const c_void, dst: *const c_void, ksize: i32, ocvrs_return: *mut Result<()>); + pub fn cv_preCornerDetect_const__InputArrayR_const__OutputArrayR_int_int(src: *const c_void, dst: *const c_void, ksize: i32, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_putText_const__InputOutputArrayR_const_StringR_Point_int_double_Scalar(img: *const c_void, text: *const c_char, org: *const core::Point, font_face: i32, font_scale: f64, color: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_putText_const__InputOutputArrayR_const_StringR_Point_int_double_Scalar_int_int_bool(img: *const c_void, text: *const c_char, org: *const core::Point, font_face: i32, font_scale: f64, color: *const core::Scalar, thickness: i32, line_type: i32, bottom_left_origin: bool, ocvrs_return: *mut Result<()>); + pub fn cv_pyrDown_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_pyrDown_const__InputArrayR_const__OutputArrayR_const_SizeR_int(src: *const c_void, dst: *const c_void, dstsize: *const core::Size, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_pyrMeanShiftFiltering_const__InputArrayR_const__OutputArrayR_double_double(src: *const c_void, dst: *const c_void, sp: f64, sr: f64, ocvrs_return: *mut Result<()>); + pub fn cv_pyrMeanShiftFiltering_const__InputArrayR_const__OutputArrayR_double_double_int_TermCriteria(src: *const c_void, dst: *const c_void, sp: f64, sr: f64, max_level: i32, termcrit: *const core::TermCriteria, ocvrs_return: *mut Result<()>); + pub fn cv_pyrUp_const__InputArrayR_const__OutputArrayR(src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_pyrUp_const__InputArrayR_const__OutputArrayR_const_SizeR_int(src: *const c_void, dst: *const c_void, dstsize: *const core::Size, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_rectangle_const__InputOutputArrayR_Point_Point_const_ScalarR(img: *const c_void, pt1: *const core::Point, pt2: *const core::Point, color: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_rectangle_const__InputOutputArrayR_Point_Point_const_ScalarR_int_int_int(img: *const c_void, pt1: *const core::Point, pt2: *const core::Point, color: *const core::Scalar, thickness: i32, line_type: i32, shift: i32, ocvrs_return: *mut Result<()>); + pub fn cv_rectangle_const__InputOutputArrayR_Rect_const_ScalarR(img: *const c_void, rec: *const core::Rect, color: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_rectangle_const__InputOutputArrayR_Rect_const_ScalarR_int_int_int(img: *const c_void, rec: *const core::Rect, color: *const core::Scalar, thickness: i32, line_type: i32, shift: i32, ocvrs_return: *mut Result<()>); + pub fn cv_remap_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_int(src: *const c_void, dst: *const c_void, map1: *const c_void, map2: *const c_void, interpolation: i32, ocvrs_return: *mut Result<()>); + pub fn cv_remap_const__InputArrayR_const__OutputArrayR_const__InputArrayR_const__InputArrayR_int_int_const_ScalarR(src: *const c_void, dst: *const c_void, map1: *const c_void, map2: *const c_void, interpolation: i32, border_mode: i32, border_value: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_resize_const__InputArrayR_const__OutputArrayR_Size(src: *const c_void, dst: *const c_void, dsize: *const core::Size, ocvrs_return: *mut Result<()>); + pub fn cv_resize_const__InputArrayR_const__OutputArrayR_Size_double_double_int(src: *const c_void, dst: *const c_void, dsize: *const core::Size, fx: f64, fy: f64, interpolation: i32, ocvrs_return: *mut Result<()>); + pub fn cv_rotatedRectangleIntersection_const_RotatedRectR_const_RotatedRectR_const__OutputArrayR(rect1: *const core::RotatedRect, rect2: *const core::RotatedRect, intersecting_region: *const c_void, ocvrs_return: *mut Result); + pub fn cv_sepFilter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_const__InputArrayR(src: *const c_void, dst: *const c_void, ddepth: i32, kernel_x: *const c_void, kernel_y: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_sepFilter2D_const__InputArrayR_const__OutputArrayR_int_const__InputArrayR_const__InputArrayR_Point_double_int(src: *const c_void, dst: *const c_void, ddepth: i32, kernel_x: *const c_void, kernel_y: *const c_void, anchor: *const core::Point, delta: f64, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_spatialGradient_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(src: *const c_void, dx: *const c_void, dy: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_spatialGradient_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_int_int(src: *const c_void, dx: *const c_void, dy: *const c_void, ksize: i32, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_sqrBoxFilter_const__InputArrayR_const__OutputArrayR_int_Size(src: *const c_void, dst: *const c_void, ddepth: i32, ksize: *const core::Size, ocvrs_return: *mut Result<()>); + pub fn cv_sqrBoxFilter_const__InputArrayR_const__OutputArrayR_int_Size_Point_bool_int(src: *const c_void, dst: *const c_void, ddepth: i32, ksize: *const core::Size, anchor: *const core::Point, normalize: bool, border_type: i32, ocvrs_return: *mut Result<()>); + pub fn cv_stackBlur_const__InputArrayR_const__OutputArrayR_Size(src: *const c_void, dst: *const c_void, ksize: *const core::Size, ocvrs_return: *mut Result<()>); + pub fn cv_thresholdWithMask_const__InputArrayR_const__InputOutputArrayR_const__InputArrayR_double_double_int(src: *const c_void, dst: *const c_void, mask: *const c_void, thresh: f64, maxval: f64, typ: i32, ocvrs_return: *mut Result); + pub fn cv_threshold_const__InputArrayR_const__OutputArrayR_double_double_int(src: *const c_void, dst: *const c_void, thresh: f64, maxval: f64, typ: i32, ocvrs_return: *mut Result); + pub fn cv_warpAffine_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size(src: *const c_void, dst: *const c_void, m: *const c_void, dsize: *const core::Size, ocvrs_return: *mut Result<()>); + pub fn cv_warpAffine_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size_int_int_const_ScalarR(src: *const c_void, dst: *const c_void, m: *const c_void, dsize: *const core::Size, flags: i32, border_mode: i32, border_value: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_warpPerspective_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size(src: *const c_void, dst: *const c_void, m: *const c_void, dsize: *const core::Size, ocvrs_return: *mut Result<()>); + pub fn cv_warpPerspective_const__InputArrayR_const__OutputArrayR_const__InputArrayR_Size_int_int_const_ScalarR(src: *const c_void, dst: *const c_void, m: *const c_void, dsize: *const core::Size, flags: i32, border_mode: i32, border_value: *const core::Scalar, ocvrs_return: *mut Result<()>); + pub fn cv_warpPolar_const__InputArrayR_const__OutputArrayR_Size_Point2f_double_int(src: *const c_void, dst: *const c_void, dsize: *const core::Size, center: *const core::Point2f, max_radius: f64, flags: i32, ocvrs_return: *mut Result<()>); + pub fn cv_watershed_const__InputArrayR_const__InputOutputArrayR(image: *const c_void, markers: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_wrapperEMD_const__InputArrayR_const__InputArrayR_int(signature1: *const c_void, signature2: *const c_void, dist_type: i32, ocvrs_return: *mut Result); + pub fn cv_wrapperEMD_const__InputArrayR_const__InputArrayR_int_const__InputArrayR_PtrLfloatG_const__OutputArrayR(signature1: *const c_void, signature2: *const c_void, dist_type: i32, cost: *const c_void, lower_bound: *mut c_void, flow: *const c_void, ocvrs_return: *mut Result); + pub fn cv_CLAHE_apply_const__InputArrayR_const__OutputArrayR(instance: *mut c_void, src: *const c_void, dst: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_CLAHE_setClipLimit_double(instance: *mut c_void, clip_limit: f64, ocvrs_return: *mut Result<()>); + pub fn cv_CLAHE_getClipLimit_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_CLAHE_setTilesGridSize_Size(instance: *mut c_void, tile_grid_size: *const core::Size, ocvrs_return: *mut Result<()>); + pub fn cv_CLAHE_getTilesGridSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_CLAHE_setBitShift_int(instance: *mut c_void, bit_shift: i32, ocvrs_return: *mut Result<()>); + pub fn cv_CLAHE_getBitShift_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_CLAHE_collectGarbage(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_CLAHE_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_CLAHE_delete(instance: *mut c_void); + pub fn cv_GeneralizedHough_setTemplate_const__InputArrayR_Point(instance: *mut c_void, templ: *const c_void, templ_center: *const core::Point, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHough_setTemplate_const__InputArrayR(instance: *mut c_void, templ: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHough_setTemplate_const__InputArrayR_const__InputArrayR_const__InputArrayR_Point(instance: *mut c_void, edges: *const c_void, dx: *const c_void, dy: *const c_void, templ_center: *const core::Point, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHough_setTemplate_const__InputArrayR_const__InputArrayR_const__InputArrayR(instance: *mut c_void, edges: *const c_void, dx: *const c_void, dy: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHough_detect_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(instance: *mut c_void, image: *const c_void, positions: *const c_void, votes: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHough_detect_const__InputArrayR_const__OutputArrayR(instance: *mut c_void, image: *const c_void, positions: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHough_detect_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR_const__OutputArrayR(instance: *mut c_void, edges: *const c_void, dx: *const c_void, dy: *const c_void, positions: *const c_void, votes: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHough_detect_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__OutputArrayR(instance: *mut c_void, edges: *const c_void, dx: *const c_void, dy: *const c_void, positions: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHough_setCannyLowThresh_int(instance: *mut c_void, canny_low_thresh: i32, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHough_getCannyLowThresh_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHough_setCannyHighThresh_int(instance: *mut c_void, canny_high_thresh: i32, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHough_getCannyHighThresh_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHough_setMinDist_double(instance: *mut c_void, min_dist: f64, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHough_getMinDist_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHough_setDp_double(instance: *mut c_void, dp: f64, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHough_getDp_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHough_setMaxBufferSize_int(instance: *mut c_void, max_buffer_size: i32, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHough_getMaxBufferSize_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHough_to_GeneralizedHoughBallard(instance: *mut c_void) -> *mut c_void; + pub fn cv_GeneralizedHough_to_GeneralizedHoughGuil(instance: *mut c_void) -> *mut c_void; + pub fn cv_GeneralizedHough_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_GeneralizedHough_delete(instance: *mut c_void); + pub fn cv_GeneralizedHoughBallard_setLevels_int(instance: *mut c_void, levels: i32, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughBallard_getLevels_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughBallard_setVotesThreshold_int(instance: *mut c_void, votes_threshold: i32, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughBallard_getVotesThreshold_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughBallard_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_GeneralizedHoughBallard_to_GeneralizedHough(instance: *mut c_void) -> *mut c_void; + pub fn cv_GeneralizedHoughBallard_delete(instance: *mut c_void); + pub fn cv_GeneralizedHoughGuil_setXi_double(instance: *mut c_void, xi: f64, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughGuil_getXi_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughGuil_setLevels_int(instance: *mut c_void, levels: i32, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughGuil_getLevels_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughGuil_setAngleEpsilon_double(instance: *mut c_void, angle_epsilon: f64, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughGuil_getAngleEpsilon_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughGuil_setMinAngle_double(instance: *mut c_void, min_angle: f64, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughGuil_getMinAngle_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughGuil_setMaxAngle_double(instance: *mut c_void, max_angle: f64, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughGuil_getMaxAngle_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughGuil_setAngleStep_double(instance: *mut c_void, angle_step: f64, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughGuil_getAngleStep_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughGuil_setAngleThresh_int(instance: *mut c_void, angle_thresh: i32, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughGuil_getAngleThresh_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughGuil_setMinScale_double(instance: *mut c_void, min_scale: f64, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughGuil_getMinScale_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughGuil_setMaxScale_double(instance: *mut c_void, max_scale: f64, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughGuil_getMaxScale_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughGuil_setScaleStep_double(instance: *mut c_void, scale_step: f64, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughGuil_getScaleStep_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughGuil_setScaleThresh_int(instance: *mut c_void, scale_thresh: i32, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughGuil_getScaleThresh_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughGuil_setPosThresh_int(instance: *mut c_void, pos_thresh: i32, ocvrs_return: *mut Result<()>); + pub fn cv_GeneralizedHoughGuil_getPosThresh_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_GeneralizedHoughGuil_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_GeneralizedHoughGuil_to_GeneralizedHough(instance: *mut c_void) -> *mut c_void; + pub fn cv_GeneralizedHoughGuil_delete(instance: *mut c_void); + pub fn cv_LineIterator_LineIterator_const_MatR_Point_Point_int_bool(img: *const c_void, pt1: *const core::Point, pt2: *const core::Point, connectivity: i32, left_to_right: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LineIterator_LineIterator_const_MatR_Point_Point(img: *const c_void, pt1: *const core::Point, pt2: *const core::Point, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LineIterator_LineIterator_Point_Point_int_bool(pt1: *const core::Point, pt2: *const core::Point, connectivity: i32, left_to_right: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LineIterator_LineIterator_Point_Point(pt1: *const core::Point, pt2: *const core::Point, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LineIterator_LineIterator_Size_Point_Point_int_bool(bounding_area_size: *const core::Size, pt1: *const core::Point, pt2: *const core::Point, connectivity: i32, left_to_right: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LineIterator_LineIterator_Size_Point_Point(bounding_area_size: *const core::Size, pt1: *const core::Point, pt2: *const core::Point, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LineIterator_LineIterator_Rect_Point_Point_int_bool(bounding_area_rect: *const core::Rect, pt1: *const core::Point, pt2: *const core::Point, connectivity: i32, left_to_right: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LineIterator_LineIterator_Rect_Point_Point(bounding_area_rect: *const core::Rect, pt1: *const core::Point, pt2: *const core::Point, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LineIterator_init_const_MatX_Rect_Point_Point_int_bool(instance: *mut c_void, img: *const c_void, bounding_area_rect: *const core::Rect, pt1: *const core::Point, pt2: *const core::Point, connectivity: i32, left_to_right: bool, ocvrs_return: *mut Result<()>); + pub fn cv_LineIterator_operatorX(instance: *mut c_void, ocvrs_return: *mut Result<*mut u8>); + pub fn cv_LineIterator_operatorAA(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_LineIterator_pos_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_LineIterator_propPtr_const(instance: *const c_void) -> *const u8; + pub fn cv_LineIterator_propPtr(instance: *mut c_void) -> *mut u8; + pub fn cv_LineIterator_propPtr_unsigned_charX(instance: *mut c_void, val: *const u8); + pub fn cv_LineIterator_propPtr0_const(instance: *const c_void) -> *const u8; + pub fn cv_LineIterator_propStep_const(instance: *const c_void) -> i32; + pub fn cv_LineIterator_propStep_const_int(instance: *mut c_void, val: i32); + pub fn cv_LineIterator_propElemSize_const(instance: *const c_void) -> i32; + pub fn cv_LineIterator_propElemSize_const_int(instance: *mut c_void, val: i32); + pub fn cv_LineIterator_propErr_const(instance: *const c_void) -> i32; + pub fn cv_LineIterator_propErr_const_int(instance: *mut c_void, val: i32); + pub fn cv_LineIterator_propCount_const(instance: *const c_void) -> i32; + pub fn cv_LineIterator_propCount_const_int(instance: *mut c_void, val: i32); + pub fn cv_LineIterator_propMinusDelta_const(instance: *const c_void) -> i32; + pub fn cv_LineIterator_propMinusDelta_const_int(instance: *mut c_void, val: i32); + pub fn cv_LineIterator_propPlusDelta_const(instance: *const c_void) -> i32; + pub fn cv_LineIterator_propPlusDelta_const_int(instance: *mut c_void, val: i32); + pub fn cv_LineIterator_propMinusStep_const(instance: *const c_void) -> i32; + pub fn cv_LineIterator_propMinusStep_const_int(instance: *mut c_void, val: i32); + pub fn cv_LineIterator_propPlusStep_const(instance: *const c_void) -> i32; + pub fn cv_LineIterator_propPlusStep_const_int(instance: *mut c_void, val: i32); + pub fn cv_LineIterator_propMinusShift_const(instance: *const c_void) -> i32; + pub fn cv_LineIterator_propMinusShift_const_int(instance: *mut c_void, val: i32); + pub fn cv_LineIterator_propPlusShift_const(instance: *const c_void) -> i32; + pub fn cv_LineIterator_propPlusShift_const_int(instance: *mut c_void, val: i32); + pub fn cv_LineIterator_propP_const(instance: *const c_void, ocvrs_return: *mut core::Point); + pub fn cv_LineIterator_propP_const_Point(instance: *mut c_void, val: *const core::Point); + pub fn cv_LineIterator_propPtmode_const(instance: *const c_void) -> bool; + pub fn cv_LineIterator_propPtmode_const_bool(instance: *mut c_void, val: bool); + pub fn cv_LineIterator_delete(instance: *mut c_void); + pub fn cv_LineSegmentDetector_detect_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR_const__OutputArrayR(instance: *mut c_void, image: *const c_void, lines: *const c_void, width: *const c_void, prec: *const c_void, nfa: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_LineSegmentDetector_detect_const__InputArrayR_const__OutputArrayR(instance: *mut c_void, image: *const c_void, lines: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_LineSegmentDetector_drawSegments_const__InputOutputArrayR_const__InputArrayR(instance: *mut c_void, image: *const c_void, lines: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_LineSegmentDetector_compareSegments_const_SizeR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR(instance: *mut c_void, size: *const core::Size, lines1: *const c_void, lines2: *const c_void, image: *const c_void, ocvrs_return: *mut Result); + pub fn cv_LineSegmentDetector_compareSegments_const_SizeR_const__InputArrayR_const__InputArrayR(instance: *mut c_void, size: *const core::Size, lines1: *const c_void, lines2: *const c_void, ocvrs_return: *mut Result); + pub fn cv_LineSegmentDetector_to_Algorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_LineSegmentDetector_delete(instance: *mut c_void); + pub fn cv_Subdiv2D_Subdiv2D(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Subdiv2D_Subdiv2D_Rect(rect: *const core::Rect, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Subdiv2D_Subdiv2D_Rect2f(rect2f: *const core::Rect2f, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_Subdiv2D_initDelaunay_Rect(instance: *mut c_void, rect: *const core::Rect, ocvrs_return: *mut Result<()>); + pub fn cv_Subdiv2D_initDelaunay_Rect2f(instance: *mut c_void, rect: *const core::Rect2f, ocvrs_return: *mut Result<()>); + pub fn cv_Subdiv2D_insert_Point2f(instance: *mut c_void, pt: *const core::Point2f, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_insert_const_vectorLPoint2fGR(instance: *mut c_void, ptvec: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Subdiv2D_locate_Point2f_intR_intR(instance: *mut c_void, pt: *const core::Point2f, edge: *mut i32, vertex: *mut i32, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_findNearest_Point2f_Point2fX(instance: *mut c_void, pt: *const core::Point2f, nearest_pt: *mut core::Point2f, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_findNearest_Point2f(instance: *mut c_void, pt: *const core::Point2f, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_getEdgeList_const_vectorLVec4fGR(instance: *const c_void, edge_list: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Subdiv2D_getLeadingEdgeList_const_vectorLintGR(instance: *const c_void, leading_edge_list: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Subdiv2D_getTriangleList_const_vectorLVec6fGR(instance: *const c_void, triangle_list: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Subdiv2D_getVoronoiFacetList_const_vectorLintGR_vectorLvectorLPoint2fGGR_vectorLPoint2fGR(instance: *mut c_void, idx: *const c_void, facet_list: *mut c_void, facet_centers: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_Subdiv2D_getVertex_const_int_intX(instance: *const c_void, vertex: i32, first_edge: *mut i32, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_getVertex_const_int(instance: *const c_void, vertex: i32, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_getEdge_const_int_int(instance: *const c_void, edge: i32, next_edge_type: i32, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_nextEdge_const_int(instance: *const c_void, edge: i32, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_rotateEdge_const_int_int(instance: *const c_void, edge: i32, rotate: i32, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_symEdge_const_int(instance: *const c_void, edge: i32, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_edgeOrg_const_int_Point2fX(instance: *const c_void, edge: i32, orgpt: *mut core::Point2f, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_edgeOrg_const_int(instance: *const c_void, edge: i32, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_edgeDst_const_int_Point2fX(instance: *const c_void, edge: i32, dstpt: *mut core::Point2f, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_edgeDst_const_int(instance: *const c_void, edge: i32, ocvrs_return: *mut Result); + pub fn cv_Subdiv2D_delete(instance: *mut c_void); + pub fn cv_segmentation_IntelligentScissorsMB_IntelligentScissorsMB(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_segmentation_IntelligentScissorsMB_setWeights_float_float_float(instance: *mut c_void, weight_non_edge: f32, weight_gradient_direction: f32, weight_gradient_magnitude: f32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_segmentation_IntelligentScissorsMB_setGradientMagnitudeMaxLimit_float(instance: *mut c_void, gradient_magnitude_threshold_max: f32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_segmentation_IntelligentScissorsMB_setGradientMagnitudeMaxLimit(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_segmentation_IntelligentScissorsMB_setEdgeFeatureZeroCrossingParameters_float(instance: *mut c_void, gradient_magnitude_min_value: f32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_segmentation_IntelligentScissorsMB_setEdgeFeatureZeroCrossingParameters(instance: *mut c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_segmentation_IntelligentScissorsMB_setEdgeFeatureCannyParameters_double_double_int_bool(instance: *mut c_void, threshold1: f64, threshold2: f64, aperture_size: i32, l2gradient: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_segmentation_IntelligentScissorsMB_setEdgeFeatureCannyParameters_double_double(instance: *mut c_void, threshold1: f64, threshold2: f64, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_segmentation_IntelligentScissorsMB_applyImage_const__InputArrayR(instance: *mut c_void, image: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_segmentation_IntelligentScissorsMB_applyImageFeatures_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputArrayR(instance: *mut c_void, non_edge: *const c_void, gradient_direction: *const c_void, gradient_magnitude: *const c_void, image: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_segmentation_IntelligentScissorsMB_applyImageFeatures_const__InputArrayR_const__InputArrayR_const__InputArrayR(instance: *mut c_void, non_edge: *const c_void, gradient_direction: *const c_void, gradient_magnitude: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_segmentation_IntelligentScissorsMB_buildMap_const_PointR(instance: *mut c_void, source_pt: *const core::Point, ocvrs_return: *mut Result<()>); + pub fn cv_segmentation_IntelligentScissorsMB_getContour_const_const_PointR_const__OutputArrayR_bool(instance: *const c_void, target_pt: *const core::Point, contour: *const c_void, backward: bool, ocvrs_return: *mut Result<()>); + pub fn cv_segmentation_IntelligentScissorsMB_getContour_const_const_PointR_const__OutputArrayR(instance: *const c_void, target_pt: *const core::Point, contour: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_segmentation_IntelligentScissorsMB_implicitClone_const(instance: *const c_void) -> *mut c_void; + pub fn cv_segmentation_IntelligentScissorsMB_delete(instance: *mut c_void); + pub fn cv_PtrLcv_CLAHEG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_CLAHEG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_CLAHEG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_CLAHEG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_CLAHEG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_GeneralizedHoughG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughBallardG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughBallardG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughBallardG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughBallardG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_GeneralizedHoughBallardG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughBallardG_to_PtrOfGeneralizedHough(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughGuilG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughGuilG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughGuilG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughGuilG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_GeneralizedHoughGuilG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_GeneralizedHoughGuilG_to_PtrOfGeneralizedHough(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_LineSegmentDetectorG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_LineSegmentDetectorG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_LineSegmentDetectorG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_LineSegmentDetectorG_delete(instance: *mut c_void); + pub fn cv_PtrLcv_LineSegmentDetectorG_to_PtrOfAlgorithm(instance: *mut c_void) -> *mut c_void; + } +} +pub use imgproc_sys::*; + +mod videoio_sys { + use super::*; + + unsafe extern "C" { + pub fn cv_videoio_registry_getBackendName_VideoCaptureAPIs(api: crate::videoio::VideoCaptureAPIs, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_videoio_registry_getBackends(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_videoio_registry_getCameraBackendPluginVersion_VideoCaptureAPIs_intR_intR(api: crate::videoio::VideoCaptureAPIs, version_abi: *mut i32, version_api: *mut i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_videoio_registry_getCameraBackends(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_videoio_registry_getStreamBackendPluginVersion_VideoCaptureAPIs_intR_intR(api: crate::videoio::VideoCaptureAPIs, version_abi: *mut i32, version_api: *mut i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_videoio_registry_getStreamBackends(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_videoio_registry_getStreamBufferedBackendPluginVersion_VideoCaptureAPIs_intR_intR(api: crate::videoio::VideoCaptureAPIs, version_abi: *mut i32, version_api: *mut i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_videoio_registry_getStreamBufferedBackends(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_videoio_registry_getWriterBackendPluginVersion_VideoCaptureAPIs_intR_intR(api: crate::videoio::VideoCaptureAPIs, version_abi: *mut i32, version_api: *mut i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_videoio_registry_getWriterBackends(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_videoio_registry_hasBackend_VideoCaptureAPIs(api: crate::videoio::VideoCaptureAPIs, ocvrs_return: *mut Result); + pub fn cv_videoio_registry_isBackendBuiltIn_VideoCaptureAPIs(api: crate::videoio::VideoCaptureAPIs, ocvrs_return: *mut Result); + pub fn cv_IStreamReader_read_charX_long_long(instance: *mut c_void, buffer: *mut *mut c_void, size: i64, ocvrs_return: *mut Result); + pub fn cv_IStreamReader_seek_long_long_int(instance: *mut c_void, offset: i64, origin: i32, ocvrs_return: *mut Result); + pub fn cv_IStreamReader_delete(instance: *mut c_void); + pub fn cv_VideoCapture_VideoCapture(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoCapture_VideoCapture_const_StringR_int(filename: *const c_char, api_preference: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoCapture_VideoCapture_const_StringR(filename: *const c_char, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoCapture_VideoCapture_const_StringR_int_const_vectorLintGR(filename: *const c_char, api_preference: i32, params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoCapture_VideoCapture_int_int(index: i32, api_preference: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoCapture_VideoCapture_int(index: i32, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoCapture_VideoCapture_int_int_const_vectorLintGR(index: i32, api_preference: i32, params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoCapture_VideoCapture_const_PtrLIStreamReaderGR_int_const_vectorLintGR(source: *const c_void, api_preference: i32, params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoCapture_open_const_StringR_int(instance: *mut c_void, filename: *const c_char, api_preference: i32, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_open_const_StringR(instance: *mut c_void, filename: *const c_char, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_open_const_StringR_int_const_vectorLintGR(instance: *mut c_void, filename: *const c_char, api_preference: i32, params: *const c_void, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_open_int_int(instance: *mut c_void, index: i32, api_preference: i32, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_open_int(instance: *mut c_void, index: i32, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_open_int_int_const_vectorLintGR(instance: *mut c_void, index: i32, api_preference: i32, params: *const c_void, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_open_const_PtrLIStreamReaderGR_int_const_vectorLintGR(instance: *mut c_void, source: *const c_void, api_preference: i32, params: *const c_void, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_isOpened_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_release(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_VideoCapture_grab(instance: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_retrieve_const__OutputArrayR_int(instance: *mut c_void, image: *const c_void, flag: i32, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_retrieve_const__OutputArrayR(instance: *mut c_void, image: *const c_void, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_read_const__OutputArrayR(instance: *mut c_void, image: *const c_void, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_set_int_double(instance: *mut c_void, prop_id: i32, value: f64, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_get_const_int(instance: *const c_void, prop_id: i32, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_getBackendName_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoCapture_setExceptionMode_bool(instance: *mut c_void, enable: bool, ocvrs_return: *mut Result<()>); + pub fn cv_VideoCapture_getExceptionMode_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_waitAny_const_vectorLVideoCaptureGR_vectorLintGR_int64_t(streams: *const c_void, ready_index: *mut c_void, timeout_ns: i64, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_waitAny_const_vectorLVideoCaptureGR_vectorLintGR(streams: *const c_void, ready_index: *mut c_void, ocvrs_return: *mut Result); + pub fn cv_VideoCapture_delete(instance: *mut c_void); + pub fn cv_VideoWriter_VideoWriter(ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoWriter_VideoWriter_const_StringR_int_double_Size_bool(filename: *const c_char, fourcc: i32, fps: f64, frame_size: *const core::Size, is_color: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoWriter_VideoWriter_const_StringR_int_double_Size(filename: *const c_char, fourcc: i32, fps: f64, frame_size: *const core::Size, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoWriter_VideoWriter_const_StringR_int_int_double_Size_bool(filename: *const c_char, api_preference: i32, fourcc: i32, fps: f64, frame_size: *const core::Size, is_color: bool, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoWriter_VideoWriter_const_StringR_int_int_double_Size(filename: *const c_char, api_preference: i32, fourcc: i32, fps: f64, frame_size: *const core::Size, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoWriter_VideoWriter_const_StringR_int_double_const_SizeR_const_vectorLintGR(filename: *const c_char, fourcc: i32, fps: f64, frame_size: *const core::Size, params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoWriter_VideoWriter_const_StringR_int_int_double_const_SizeR_const_vectorLintGR(filename: *const c_char, api_preference: i32, fourcc: i32, fps: f64, frame_size: *const core::Size, params: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoWriter_open_const_StringR_int_double_Size_bool(instance: *mut c_void, filename: *const c_char, fourcc: i32, fps: f64, frame_size: *const core::Size, is_color: bool, ocvrs_return: *mut Result); + pub fn cv_VideoWriter_open_const_StringR_int_double_Size(instance: *mut c_void, filename: *const c_char, fourcc: i32, fps: f64, frame_size: *const core::Size, ocvrs_return: *mut Result); + pub fn cv_VideoWriter_open_const_StringR_int_int_double_Size_bool(instance: *mut c_void, filename: *const c_char, api_preference: i32, fourcc: i32, fps: f64, frame_size: *const core::Size, is_color: bool, ocvrs_return: *mut Result); + pub fn cv_VideoWriter_open_const_StringR_int_int_double_Size(instance: *mut c_void, filename: *const c_char, api_preference: i32, fourcc: i32, fps: f64, frame_size: *const core::Size, ocvrs_return: *mut Result); + pub fn cv_VideoWriter_open_const_StringR_int_double_const_SizeR_const_vectorLintGR(instance: *mut c_void, filename: *const c_char, fourcc: i32, fps: f64, frame_size: *const core::Size, params: *const c_void, ocvrs_return: *mut Result); + pub fn cv_VideoWriter_open_const_StringR_int_int_double_const_SizeR_const_vectorLintGR(instance: *mut c_void, filename: *const c_char, api_preference: i32, fourcc: i32, fps: f64, frame_size: *const core::Size, params: *const c_void, ocvrs_return: *mut Result); + pub fn cv_VideoWriter_isOpened_const(instance: *const c_void, ocvrs_return: *mut Result); + pub fn cv_VideoWriter_release(instance: *mut c_void, ocvrs_return: *mut Result<()>); + pub fn cv_VideoWriter_write_const__InputArrayR(instance: *mut c_void, image: *const c_void, ocvrs_return: *mut Result<()>); + pub fn cv_VideoWriter_set_int_double(instance: *mut c_void, prop_id: i32, value: f64, ocvrs_return: *mut Result); + pub fn cv_VideoWriter_get_const_int(instance: *const c_void, prop_id: i32, ocvrs_return: *mut Result); + pub fn cv_VideoWriter_fourcc_char_char_char_char(c1: c_char, c2: c_char, c3: c_char, c4: c_char, ocvrs_return: *mut Result); + pub fn cv_VideoWriter_getBackendName_const(instance: *const c_void, ocvrs_return: *mut Result<*mut c_void>); + pub fn cv_VideoWriter_delete(instance: *mut c_void); + pub fn cv_PtrLcv_IStreamReaderG_getInnerPtr_const(instance: *const c_void) -> *mut c_void; + pub fn cv_PtrLcv_IStreamReaderG_getInnerPtrMut(instance: *mut c_void) -> *mut c_void; + pub fn cv_PtrLcv_IStreamReaderG_new_null_const() -> *mut c_void; + pub fn cv_PtrLcv_IStreamReaderG_delete(instance: *mut c_void); + pub fn std_vectorLcv_VideoCaptureAPIsG_new_const() -> *mut c_void; + pub fn std_vectorLcv_VideoCaptureAPIsG_delete(instance: *mut c_void); + pub fn std_vectorLcv_VideoCaptureAPIsG_len_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_VideoCaptureAPIsG_isEmpty_const(instance: *const c_void) -> bool; + pub fn std_vectorLcv_VideoCaptureAPIsG_capacity_const(instance: *const c_void) -> size_t; + pub fn std_vectorLcv_VideoCaptureAPIsG_shrinkToFit(instance: *mut c_void); + pub fn std_vectorLcv_VideoCaptureAPIsG_reserve_size_t(instance: *mut c_void, additional: size_t); + pub fn std_vectorLcv_VideoCaptureAPIsG_remove_size_t(instance: *mut c_void, index: size_t); + pub fn std_vectorLcv_VideoCaptureAPIsG_swap_size_t_size_t(instance: *mut c_void, index1: size_t, index2: size_t); + pub fn std_vectorLcv_VideoCaptureAPIsG_clear(instance: *mut c_void); + pub fn std_vectorLcv_VideoCaptureAPIsG_push_const_VideoCaptureAPIs(instance: *mut c_void, val: crate::videoio::VideoCaptureAPIs); + pub fn std_vectorLcv_VideoCaptureAPIsG_insert_size_t_const_VideoCaptureAPIs(instance: *mut c_void, index: size_t, val: crate::videoio::VideoCaptureAPIs); + pub fn std_vectorLcv_VideoCaptureAPIsG_get_const_size_t(instance: *const c_void, index: size_t, ocvrs_return: *mut crate::videoio::VideoCaptureAPIs); + pub fn std_vectorLcv_VideoCaptureAPIsG_set_size_t_const_VideoCaptureAPIs(instance: *mut c_void, index: size_t, val: crate::videoio::VideoCaptureAPIs); + pub fn std_vectorLcv_VideoCaptureAPIsG_clone_const(instance: *const c_void) -> *mut c_void; + pub fn std_vectorLcv_VideoCaptureAPIsG_data_const(instance: *const c_void) -> *const crate::videoio::VideoCaptureAPIs; + pub fn std_vectorLcv_VideoCaptureAPIsG_dataMut(instance: *mut c_void) -> *mut crate::videoio::VideoCaptureAPIs; + pub fn cv_fromSlice_const_const_VideoCaptureAPIsX_size_t(data: *const crate::videoio::VideoCaptureAPIs, len: size_t) -> *mut c_void; + } +} +pub use videoio_sys::*; + +pub use crate::manual::sys::*; diff --git a/target/debug/build/opencv-4e105b5546afc119/out/opencv/types.rs b/target/debug/build/opencv-4e105b5546afc119/out/opencv/types.rs new file mode 100644 index 0000000..02329ff --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/opencv/types.rs @@ -0,0 +1,8728 @@ + +mod core_types { + use crate::{mod_prelude::*, core, types, sys}; + + ptr_extern! { core::Algorithm, + cv_PtrLcv_AlgorithmG_new_null_const, cv_PtrLcv_AlgorithmG_delete, cv_PtrLcv_AlgorithmG_getInnerPtr_const, cv_PtrLcv_AlgorithmG_getInnerPtrMut + } + + ptr_extern_ctor! { core::Algorithm, cv_PtrLcv_AlgorithmG_new_const_Algorithm } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfAlgorithm(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfAlgorithm(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfAlgorithm") + .finish() + } + } + + ptr_extern! { core::ConjGradSolver, + cv_PtrLcv_ConjGradSolverG_new_null_const, cv_PtrLcv_ConjGradSolverG_delete, cv_PtrLcv_ConjGradSolverG_getInnerPtr_const, cv_PtrLcv_ConjGradSolverG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfConjGradSolver(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfConjGradSolver(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl core::ConjGradSolverTraitConst for core::Ptr { + #[inline] fn as_raw_ConjGradSolver(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::ConjGradSolverTrait for core::Ptr { + #[inline] fn as_raw_mut_ConjGradSolver(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_ConjGradSolverG_to_PtrOfAlgorithm } + + impl core::MinProblemSolverTraitConst for core::Ptr { + #[inline] fn as_raw_MinProblemSolver(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::MinProblemSolverTrait for core::Ptr { + #[inline] fn as_raw_mut_MinProblemSolver(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_ConjGradSolverG_to_PtrOfMinProblemSolver } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfConjGradSolver") + .finish() + } + } + + ptr_extern! { core::DownhillSolver, + cv_PtrLcv_DownhillSolverG_new_null_const, cv_PtrLcv_DownhillSolverG_delete, cv_PtrLcv_DownhillSolverG_getInnerPtr_const, cv_PtrLcv_DownhillSolverG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfDownhillSolver(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfDownhillSolver(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl core::DownhillSolverTraitConst for core::Ptr { + #[inline] fn as_raw_DownhillSolver(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::DownhillSolverTrait for core::Ptr { + #[inline] fn as_raw_mut_DownhillSolver(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_DownhillSolverG_to_PtrOfAlgorithm } + + impl core::MinProblemSolverTraitConst for core::Ptr { + #[inline] fn as_raw_MinProblemSolver(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::MinProblemSolverTrait for core::Ptr { + #[inline] fn as_raw_mut_MinProblemSolver(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_DownhillSolverG_to_PtrOfMinProblemSolver } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfDownhillSolver") + .finish() + } + } + + ptr_extern! { core::FileStorage, + cv_PtrLcv_FileStorageG_new_null_const, cv_PtrLcv_FileStorageG_delete, cv_PtrLcv_FileStorageG_getInnerPtr_const, cv_PtrLcv_FileStorageG_getInnerPtrMut + } + + ptr_extern_ctor! { core::FileStorage, cv_PtrLcv_FileStorageG_new_const_FileStorage } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfFileStorage(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfFileStorage(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl core::FileStorageTraitConst for core::Ptr { + #[inline] fn as_raw_FileStorage(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::FileStorageTrait for core::Ptr { + #[inline] fn as_raw_mut_FileStorage(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfFileStorage") + .field("state", &core::FileStorageTraitConst::state(self)) + .field("elname", &core::FileStorageTraitConst::elname(self)) + .finish() + } + } + + ptr_extern! { core::Formatted, + cv_PtrLcv_FormattedG_new_null_const, cv_PtrLcv_FormattedG_delete, cv_PtrLcv_FormattedG_getInnerPtr_const, cv_PtrLcv_FormattedG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfFormatted(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfFormatted(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl core::FormattedTraitConst for core::Ptr { + #[inline] fn as_raw_Formatted(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::FormattedTrait for core::Ptr { + #[inline] fn as_raw_mut_Formatted(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfFormatted") + .finish() + } + } + + ptr_extern! { core::Formatter, + cv_PtrLcv_FormatterG_new_null_const, cv_PtrLcv_FormatterG_delete, cv_PtrLcv_FormatterG_getInnerPtr_const, cv_PtrLcv_FormatterG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfFormatter(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfFormatter(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl core::FormatterTraitConst for core::Ptr { + #[inline] fn as_raw_Formatter(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::FormatterTrait for core::Ptr { + #[inline] fn as_raw_mut_Formatter(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfFormatter") + .finish() + } + } + + ptr_extern! { core::KeyPoint, + cv_PtrLcv_KeyPointG_new_null_const, cv_PtrLcv_KeyPointG_delete, cv_PtrLcv_KeyPointG_getInnerPtr_const, cv_PtrLcv_KeyPointG_getInnerPtrMut + } + + ptr_extern_ctor! { core::KeyPoint, cv_PtrLcv_KeyPointG_new_const_KeyPoint } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfKeyPoint(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfKeyPoint(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl core::KeyPointTraitConst for core::Ptr { + #[inline] fn as_raw_KeyPoint(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::KeyPointTrait for core::Ptr { + #[inline] fn as_raw_mut_KeyPoint(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfKeyPoint") + .finish() + } + } + + ptr_extern! { core::MinProblemSolver, + cv_PtrLcv_MinProblemSolverG_new_null_const, cv_PtrLcv_MinProblemSolverG_delete, cv_PtrLcv_MinProblemSolverG_getInnerPtr_const, cv_PtrLcv_MinProblemSolverG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfMinProblemSolver(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfMinProblemSolver(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl core::MinProblemSolverTraitConst for core::Ptr { + #[inline] fn as_raw_MinProblemSolver(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::MinProblemSolverTrait for core::Ptr { + #[inline] fn as_raw_mut_MinProblemSolver(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_MinProblemSolverG_to_PtrOfAlgorithm } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfMinProblemSolver") + .finish() + } + } + + ptr_extern! { core::MinProblemSolver_Function, + cv_PtrLcv_MinProblemSolver_FunctionG_new_null_const, cv_PtrLcv_MinProblemSolver_FunctionG_delete, cv_PtrLcv_MinProblemSolver_FunctionG_getInnerPtr_const, cv_PtrLcv_MinProblemSolver_FunctionG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfMinProblemSolver_Function(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfMinProblemSolver_Function(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl core::MinProblemSolver_FunctionTraitConst for core::Ptr { + #[inline] fn as_raw_MinProblemSolver_Function(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::MinProblemSolver_FunctionTrait for core::Ptr { + #[inline] fn as_raw_mut_MinProblemSolver_Function(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfMinProblemSolver_Function") + .finish() + } + } + + ptr_extern! { core::OriginalClassName, + cv_PtrLcv_utils_nested_OriginalClassNameG_new_null_const, cv_PtrLcv_utils_nested_OriginalClassNameG_delete, cv_PtrLcv_utils_nested_OriginalClassNameG_getInnerPtr_const, cv_PtrLcv_utils_nested_OriginalClassNameG_getInnerPtrMut + } + + ptr_extern_ctor! { core::OriginalClassName, cv_PtrLcv_utils_nested_OriginalClassNameG_new_const_OriginalClassName } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfOriginalClassName(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfOriginalClassName(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl core::OriginalClassNameTraitConst for core::Ptr { + #[inline] fn as_raw_OriginalClassName(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::OriginalClassNameTrait for core::Ptr { + #[inline] fn as_raw_mut_OriginalClassName(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfOriginalClassName") + .finish() + } + } + + ptr_extern! { f32, + cv_PtrLfloatG_new_null_const, cv_PtrLfloatG_delete, cv_PtrLfloatG_getInnerPtr_const, cv_PtrLfloatG_getInnerPtrMut + } + + ptr_extern_ctor! { f32, cv_PtrLfloatG_new_const_float } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOff32(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOff32(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl core::Tuple<(i32, f32)> { + pub fn as_raw_TupleOfi32_f32(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_TupleOfi32_f32(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + tuple_extern! { (i32, f32), + std_pairLint__floatG_new_const_int_float, std_pairLint__floatG_delete, + 0 = arg: i32, get_0 via std_pairLint__floatG_get_0_const, + 1 = arg_1: f32, get_1 via std_pairLint__floatG_get_1_const + } + + impl core::Vector { + pub fn as_raw_VectorOfDMatch(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfDMatch(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::DMatch, + std_vectorLcv_DMatchG_new_const, std_vectorLcv_DMatchG_delete, + std_vectorLcv_DMatchG_len_const, std_vectorLcv_DMatchG_isEmpty_const, + std_vectorLcv_DMatchG_capacity_const, std_vectorLcv_DMatchG_shrinkToFit, + std_vectorLcv_DMatchG_reserve_size_t, std_vectorLcv_DMatchG_remove_size_t, + std_vectorLcv_DMatchG_swap_size_t_size_t, std_vectorLcv_DMatchG_clear, + std_vectorLcv_DMatchG_get_const_size_t, std_vectorLcv_DMatchG_set_size_t_const_DMatch, + std_vectorLcv_DMatchG_push_const_DMatch, std_vectorLcv_DMatchG_insert_size_t_const_DMatch, + } + + vector_copy_non_bool! { core::DMatch, + std_vectorLcv_DMatchG_data_const, std_vectorLcv_DMatchG_dataMut, cv_fromSlice_const_const_DMatchX_size_t, + std_vectorLcv_DMatchG_clone_const, + } + + + impl core::Vector { + pub fn as_raw_VectorOfKeyPoint(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfKeyPoint(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::KeyPoint, + std_vectorLcv_KeyPointG_new_const, std_vectorLcv_KeyPointG_delete, + std_vectorLcv_KeyPointG_len_const, std_vectorLcv_KeyPointG_isEmpty_const, + std_vectorLcv_KeyPointG_capacity_const, std_vectorLcv_KeyPointG_shrinkToFit, + std_vectorLcv_KeyPointG_reserve_size_t, std_vectorLcv_KeyPointG_remove_size_t, + std_vectorLcv_KeyPointG_swap_size_t_size_t, std_vectorLcv_KeyPointG_clear, + std_vectorLcv_KeyPointG_get_const_size_t, std_vectorLcv_KeyPointG_set_size_t_const_KeyPoint, + std_vectorLcv_KeyPointG_push_const_KeyPoint, std_vectorLcv_KeyPointG_insert_size_t_const_KeyPoint, + } + + vector_non_copy_or_bool! { clone core::KeyPoint } + + vector_boxed_ref! { core::KeyPoint } + + vector_extern! { BoxedRef<'t, core::KeyPoint>, + std_vectorLcv_KeyPointG_new_const, std_vectorLcv_KeyPointG_delete, + std_vectorLcv_KeyPointG_len_const, std_vectorLcv_KeyPointG_isEmpty_const, + std_vectorLcv_KeyPointG_capacity_const, std_vectorLcv_KeyPointG_shrinkToFit, + std_vectorLcv_KeyPointG_reserve_size_t, std_vectorLcv_KeyPointG_remove_size_t, + std_vectorLcv_KeyPointG_swap_size_t_size_t, std_vectorLcv_KeyPointG_clear, + std_vectorLcv_KeyPointG_get_const_size_t, std_vectorLcv_KeyPointG_set_size_t_const_KeyPoint, + std_vectorLcv_KeyPointG_push_const_KeyPoint, std_vectorLcv_KeyPointG_insert_size_t_const_KeyPoint, + } + + + impl core::Vector { + pub fn as_raw_VectorOfMat(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfMat(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Mat, + std_vectorLcv_MatG_new_const, std_vectorLcv_MatG_delete, + std_vectorLcv_MatG_len_const, std_vectorLcv_MatG_isEmpty_const, + std_vectorLcv_MatG_capacity_const, std_vectorLcv_MatG_shrinkToFit, + std_vectorLcv_MatG_reserve_size_t, std_vectorLcv_MatG_remove_size_t, + std_vectorLcv_MatG_swap_size_t_size_t, std_vectorLcv_MatG_clear, + std_vectorLcv_MatG_get_const_size_t, std_vectorLcv_MatG_set_size_t_const_Mat, + std_vectorLcv_MatG_push_const_Mat, std_vectorLcv_MatG_insert_size_t_const_Mat, + } + + vector_non_copy_or_bool! { clone core::Mat } + + vector_boxed_ref! { core::Mat } + + vector_extern! { BoxedRef<'t, core::Mat>, + std_vectorLcv_MatG_new_const, std_vectorLcv_MatG_delete, + std_vectorLcv_MatG_len_const, std_vectorLcv_MatG_isEmpty_const, + std_vectorLcv_MatG_capacity_const, std_vectorLcv_MatG_shrinkToFit, + std_vectorLcv_MatG_reserve_size_t, std_vectorLcv_MatG_remove_size_t, + std_vectorLcv_MatG_swap_size_t_size_t, std_vectorLcv_MatG_clear, + std_vectorLcv_MatG_get_const_size_t, std_vectorLcv_MatG_set_size_t_const_Mat, + std_vectorLcv_MatG_push_const_Mat, std_vectorLcv_MatG_insert_size_t_const_Mat, + } + + + impl core::Vector { + pub fn as_raw_VectorOfPlatformInfo(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfPlatformInfo(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::PlatformInfo, + std_vectorLcv_ocl_PlatformInfoG_new_const, std_vectorLcv_ocl_PlatformInfoG_delete, + std_vectorLcv_ocl_PlatformInfoG_len_const, std_vectorLcv_ocl_PlatformInfoG_isEmpty_const, + std_vectorLcv_ocl_PlatformInfoG_capacity_const, std_vectorLcv_ocl_PlatformInfoG_shrinkToFit, + std_vectorLcv_ocl_PlatformInfoG_reserve_size_t, std_vectorLcv_ocl_PlatformInfoG_remove_size_t, + std_vectorLcv_ocl_PlatformInfoG_swap_size_t_size_t, std_vectorLcv_ocl_PlatformInfoG_clear, + std_vectorLcv_ocl_PlatformInfoG_get_const_size_t, std_vectorLcv_ocl_PlatformInfoG_set_size_t_const_PlatformInfo, + std_vectorLcv_ocl_PlatformInfoG_push_const_PlatformInfo, std_vectorLcv_ocl_PlatformInfoG_insert_size_t_const_PlatformInfo, + } + + vector_non_copy_or_bool! { core::PlatformInfo } + + vector_boxed_ref! { core::PlatformInfo } + + vector_extern! { BoxedRef<'t, core::PlatformInfo>, + std_vectorLcv_ocl_PlatformInfoG_new_const, std_vectorLcv_ocl_PlatformInfoG_delete, + std_vectorLcv_ocl_PlatformInfoG_len_const, std_vectorLcv_ocl_PlatformInfoG_isEmpty_const, + std_vectorLcv_ocl_PlatformInfoG_capacity_const, std_vectorLcv_ocl_PlatformInfoG_shrinkToFit, + std_vectorLcv_ocl_PlatformInfoG_reserve_size_t, std_vectorLcv_ocl_PlatformInfoG_remove_size_t, + std_vectorLcv_ocl_PlatformInfoG_swap_size_t_size_t, std_vectorLcv_ocl_PlatformInfoG_clear, + std_vectorLcv_ocl_PlatformInfoG_get_const_size_t, std_vectorLcv_ocl_PlatformInfoG_set_size_t_const_PlatformInfo, + std_vectorLcv_ocl_PlatformInfoG_push_const_PlatformInfo, std_vectorLcv_ocl_PlatformInfoG_insert_size_t_const_PlatformInfo, + } + + + impl core::Vector { + pub fn as_raw_VectorOfPoint(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfPoint(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Point, + std_vectorLcv_PointG_new_const, std_vectorLcv_PointG_delete, + std_vectorLcv_PointG_len_const, std_vectorLcv_PointG_isEmpty_const, + std_vectorLcv_PointG_capacity_const, std_vectorLcv_PointG_shrinkToFit, + std_vectorLcv_PointG_reserve_size_t, std_vectorLcv_PointG_remove_size_t, + std_vectorLcv_PointG_swap_size_t_size_t, std_vectorLcv_PointG_clear, + std_vectorLcv_PointG_get_const_size_t, std_vectorLcv_PointG_set_size_t_const_Point, + std_vectorLcv_PointG_push_const_Point, std_vectorLcv_PointG_insert_size_t_const_Point, + } + + vector_copy_non_bool! { core::Point, + std_vectorLcv_PointG_data_const, std_vectorLcv_PointG_dataMut, cv_fromSlice_const_const_PointX_size_t, + std_vectorLcv_PointG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_PointG_inputArray_const(self.as_raw_VectorOfPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_PointG_outputArray(self.as_raw_mut_VectorOfPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_PointG_inputOutputArray(self.as_raw_mut_VectorOfPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOfPoint2d(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfPoint2d(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Point2d, + std_vectorLcv_Point2dG_new_const, std_vectorLcv_Point2dG_delete, + std_vectorLcv_Point2dG_len_const, std_vectorLcv_Point2dG_isEmpty_const, + std_vectorLcv_Point2dG_capacity_const, std_vectorLcv_Point2dG_shrinkToFit, + std_vectorLcv_Point2dG_reserve_size_t, std_vectorLcv_Point2dG_remove_size_t, + std_vectorLcv_Point2dG_swap_size_t_size_t, std_vectorLcv_Point2dG_clear, + std_vectorLcv_Point2dG_get_const_size_t, std_vectorLcv_Point2dG_set_size_t_const_Point2d, + std_vectorLcv_Point2dG_push_const_Point2d, std_vectorLcv_Point2dG_insert_size_t_const_Point2d, + } + + vector_copy_non_bool! { core::Point2d, + std_vectorLcv_Point2dG_data_const, std_vectorLcv_Point2dG_dataMut, cv_fromSlice_const_const_Point2dX_size_t, + std_vectorLcv_Point2dG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Point2dG_inputArray_const(self.as_raw_VectorOfPoint2d(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Point2dG_outputArray(self.as_raw_mut_VectorOfPoint2d(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Point2dG_inputOutputArray(self.as_raw_mut_VectorOfPoint2d(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOfPoint2f(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfPoint2f(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Point2f, + std_vectorLcv_Point2fG_new_const, std_vectorLcv_Point2fG_delete, + std_vectorLcv_Point2fG_len_const, std_vectorLcv_Point2fG_isEmpty_const, + std_vectorLcv_Point2fG_capacity_const, std_vectorLcv_Point2fG_shrinkToFit, + std_vectorLcv_Point2fG_reserve_size_t, std_vectorLcv_Point2fG_remove_size_t, + std_vectorLcv_Point2fG_swap_size_t_size_t, std_vectorLcv_Point2fG_clear, + std_vectorLcv_Point2fG_get_const_size_t, std_vectorLcv_Point2fG_set_size_t_const_Point2f, + std_vectorLcv_Point2fG_push_const_Point2f, std_vectorLcv_Point2fG_insert_size_t_const_Point2f, + } + + vector_copy_non_bool! { core::Point2f, + std_vectorLcv_Point2fG_data_const, std_vectorLcv_Point2fG_dataMut, cv_fromSlice_const_const_Point2fX_size_t, + std_vectorLcv_Point2fG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Point2fG_inputArray_const(self.as_raw_VectorOfPoint2f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Point2fG_outputArray(self.as_raw_mut_VectorOfPoint2f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Point2fG_inputOutputArray(self.as_raw_mut_VectorOfPoint2f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOfRange(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfRange(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Range, + std_vectorLcv_RangeG_new_const, std_vectorLcv_RangeG_delete, + std_vectorLcv_RangeG_len_const, std_vectorLcv_RangeG_isEmpty_const, + std_vectorLcv_RangeG_capacity_const, std_vectorLcv_RangeG_shrinkToFit, + std_vectorLcv_RangeG_reserve_size_t, std_vectorLcv_RangeG_remove_size_t, + std_vectorLcv_RangeG_swap_size_t_size_t, std_vectorLcv_RangeG_clear, + std_vectorLcv_RangeG_get_const_size_t, std_vectorLcv_RangeG_set_size_t_const_Range, + std_vectorLcv_RangeG_push_const_Range, std_vectorLcv_RangeG_insert_size_t_const_Range, + } + + vector_non_copy_or_bool! { core::Range } + + vector_boxed_ref! { core::Range } + + vector_extern! { BoxedRef<'t, core::Range>, + std_vectorLcv_RangeG_new_const, std_vectorLcv_RangeG_delete, + std_vectorLcv_RangeG_len_const, std_vectorLcv_RangeG_isEmpty_const, + std_vectorLcv_RangeG_capacity_const, std_vectorLcv_RangeG_shrinkToFit, + std_vectorLcv_RangeG_reserve_size_t, std_vectorLcv_RangeG_remove_size_t, + std_vectorLcv_RangeG_swap_size_t_size_t, std_vectorLcv_RangeG_clear, + std_vectorLcv_RangeG_get_const_size_t, std_vectorLcv_RangeG_set_size_t_const_Range, + std_vectorLcv_RangeG_push_const_Range, std_vectorLcv_RangeG_insert_size_t_const_Range, + } + + + impl core::Vector { + pub fn as_raw_VectorOfRect(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfRect(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Rect, + std_vectorLcv_RectG_new_const, std_vectorLcv_RectG_delete, + std_vectorLcv_RectG_len_const, std_vectorLcv_RectG_isEmpty_const, + std_vectorLcv_RectG_capacity_const, std_vectorLcv_RectG_shrinkToFit, + std_vectorLcv_RectG_reserve_size_t, std_vectorLcv_RectG_remove_size_t, + std_vectorLcv_RectG_swap_size_t_size_t, std_vectorLcv_RectG_clear, + std_vectorLcv_RectG_get_const_size_t, std_vectorLcv_RectG_set_size_t_const_Rect, + std_vectorLcv_RectG_push_const_Rect, std_vectorLcv_RectG_insert_size_t_const_Rect, + } + + vector_copy_non_bool! { core::Rect, + std_vectorLcv_RectG_data_const, std_vectorLcv_RectG_dataMut, cv_fromSlice_const_const_RectX_size_t, + std_vectorLcv_RectG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_RectG_inputArray_const(self.as_raw_VectorOfRect(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_RectG_outputArray(self.as_raw_mut_VectorOfRect(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_RectG_inputOutputArray(self.as_raw_mut_VectorOfRect(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOfRotatedRect(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfRotatedRect(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::RotatedRect, + std_vectorLcv_RotatedRectG_new_const, std_vectorLcv_RotatedRectG_delete, + std_vectorLcv_RotatedRectG_len_const, std_vectorLcv_RotatedRectG_isEmpty_const, + std_vectorLcv_RotatedRectG_capacity_const, std_vectorLcv_RotatedRectG_shrinkToFit, + std_vectorLcv_RotatedRectG_reserve_size_t, std_vectorLcv_RotatedRectG_remove_size_t, + std_vectorLcv_RotatedRectG_swap_size_t_size_t, std_vectorLcv_RotatedRectG_clear, + std_vectorLcv_RotatedRectG_get_const_size_t, std_vectorLcv_RotatedRectG_set_size_t_const_RotatedRect, + std_vectorLcv_RotatedRectG_push_const_RotatedRect, std_vectorLcv_RotatedRectG_insert_size_t_const_RotatedRect, + } + + vector_copy_non_bool! { core::RotatedRect, + std_vectorLcv_RotatedRectG_data_const, std_vectorLcv_RotatedRectG_dataMut, cv_fromSlice_const_const_RotatedRectX_size_t, + std_vectorLcv_RotatedRectG_clone_const, + } + + + impl core::Vector { + pub fn as_raw_VectorOfString(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfString(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { String, + std_vectorLcv_StringG_new_const, std_vectorLcv_StringG_delete, + std_vectorLcv_StringG_len_const, std_vectorLcv_StringG_isEmpty_const, + std_vectorLcv_StringG_capacity_const, std_vectorLcv_StringG_shrinkToFit, + std_vectorLcv_StringG_reserve_size_t, std_vectorLcv_StringG_remove_size_t, + std_vectorLcv_StringG_swap_size_t_size_t, std_vectorLcv_StringG_clear, + std_vectorLcv_StringG_get_const_size_t, std_vectorLcv_StringG_set_size_t_const_String, + std_vectorLcv_StringG_push_const_String, std_vectorLcv_StringG_insert_size_t_const_String, + } + + vector_non_copy_or_bool! { String } + + + impl core::Vector { + pub fn as_raw_VectorOfVec2d(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVec2d(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Vec2d, + std_vectorLcv_Vec2dG_new_const, std_vectorLcv_Vec2dG_delete, + std_vectorLcv_Vec2dG_len_const, std_vectorLcv_Vec2dG_isEmpty_const, + std_vectorLcv_Vec2dG_capacity_const, std_vectorLcv_Vec2dG_shrinkToFit, + std_vectorLcv_Vec2dG_reserve_size_t, std_vectorLcv_Vec2dG_remove_size_t, + std_vectorLcv_Vec2dG_swap_size_t_size_t, std_vectorLcv_Vec2dG_clear, + std_vectorLcv_Vec2dG_get_const_size_t, std_vectorLcv_Vec2dG_set_size_t_const_Vec2d, + std_vectorLcv_Vec2dG_push_const_Vec2d, std_vectorLcv_Vec2dG_insert_size_t_const_Vec2d, + } + + vector_copy_non_bool! { core::Vec2d, + std_vectorLcv_Vec2dG_data_const, std_vectorLcv_Vec2dG_dataMut, cv_fromSlice_const_const_Vec2dX_size_t, + std_vectorLcv_Vec2dG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec2dG_inputArray_const(self.as_raw_VectorOfVec2d(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec2dG_outputArray(self.as_raw_mut_VectorOfVec2d(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec2dG_inputOutputArray(self.as_raw_mut_VectorOfVec2d(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOfVec2f(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVec2f(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Vec2f, + std_vectorLcv_Vec2fG_new_const, std_vectorLcv_Vec2fG_delete, + std_vectorLcv_Vec2fG_len_const, std_vectorLcv_Vec2fG_isEmpty_const, + std_vectorLcv_Vec2fG_capacity_const, std_vectorLcv_Vec2fG_shrinkToFit, + std_vectorLcv_Vec2fG_reserve_size_t, std_vectorLcv_Vec2fG_remove_size_t, + std_vectorLcv_Vec2fG_swap_size_t_size_t, std_vectorLcv_Vec2fG_clear, + std_vectorLcv_Vec2fG_get_const_size_t, std_vectorLcv_Vec2fG_set_size_t_const_Vec2f, + std_vectorLcv_Vec2fG_push_const_Vec2f, std_vectorLcv_Vec2fG_insert_size_t_const_Vec2f, + } + + vector_copy_non_bool! { core::Vec2f, + std_vectorLcv_Vec2fG_data_const, std_vectorLcv_Vec2fG_dataMut, cv_fromSlice_const_const_Vec2fX_size_t, + std_vectorLcv_Vec2fG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec2fG_inputArray_const(self.as_raw_VectorOfVec2f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec2fG_outputArray(self.as_raw_mut_VectorOfVec2f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec2fG_inputOutputArray(self.as_raw_mut_VectorOfVec2f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOfVec3d(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVec3d(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Vec3d, + std_vectorLcv_Vec3dG_new_const, std_vectorLcv_Vec3dG_delete, + std_vectorLcv_Vec3dG_len_const, std_vectorLcv_Vec3dG_isEmpty_const, + std_vectorLcv_Vec3dG_capacity_const, std_vectorLcv_Vec3dG_shrinkToFit, + std_vectorLcv_Vec3dG_reserve_size_t, std_vectorLcv_Vec3dG_remove_size_t, + std_vectorLcv_Vec3dG_swap_size_t_size_t, std_vectorLcv_Vec3dG_clear, + std_vectorLcv_Vec3dG_get_const_size_t, std_vectorLcv_Vec3dG_set_size_t_const_Vec3d, + std_vectorLcv_Vec3dG_push_const_Vec3d, std_vectorLcv_Vec3dG_insert_size_t_const_Vec3d, + } + + vector_copy_non_bool! { core::Vec3d, + std_vectorLcv_Vec3dG_data_const, std_vectorLcv_Vec3dG_dataMut, cv_fromSlice_const_const_Vec3dX_size_t, + std_vectorLcv_Vec3dG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec3dG_inputArray_const(self.as_raw_VectorOfVec3d(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec3dG_outputArray(self.as_raw_mut_VectorOfVec3d(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec3dG_inputOutputArray(self.as_raw_mut_VectorOfVec3d(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOfVec3f(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVec3f(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Vec3f, + std_vectorLcv_Vec3fG_new_const, std_vectorLcv_Vec3fG_delete, + std_vectorLcv_Vec3fG_len_const, std_vectorLcv_Vec3fG_isEmpty_const, + std_vectorLcv_Vec3fG_capacity_const, std_vectorLcv_Vec3fG_shrinkToFit, + std_vectorLcv_Vec3fG_reserve_size_t, std_vectorLcv_Vec3fG_remove_size_t, + std_vectorLcv_Vec3fG_swap_size_t_size_t, std_vectorLcv_Vec3fG_clear, + std_vectorLcv_Vec3fG_get_const_size_t, std_vectorLcv_Vec3fG_set_size_t_const_Vec3f, + std_vectorLcv_Vec3fG_push_const_Vec3f, std_vectorLcv_Vec3fG_insert_size_t_const_Vec3f, + } + + vector_copy_non_bool! { core::Vec3f, + std_vectorLcv_Vec3fG_data_const, std_vectorLcv_Vec3fG_dataMut, cv_fromSlice_const_const_Vec3fX_size_t, + std_vectorLcv_Vec3fG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec3fG_inputArray_const(self.as_raw_VectorOfVec3f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec3fG_outputArray(self.as_raw_mut_VectorOfVec3f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec3fG_inputOutputArray(self.as_raw_mut_VectorOfVec3f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOfVec4f(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVec4f(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Vec4f, + std_vectorLcv_Vec4fG_new_const, std_vectorLcv_Vec4fG_delete, + std_vectorLcv_Vec4fG_len_const, std_vectorLcv_Vec4fG_isEmpty_const, + std_vectorLcv_Vec4fG_capacity_const, std_vectorLcv_Vec4fG_shrinkToFit, + std_vectorLcv_Vec4fG_reserve_size_t, std_vectorLcv_Vec4fG_remove_size_t, + std_vectorLcv_Vec4fG_swap_size_t_size_t, std_vectorLcv_Vec4fG_clear, + std_vectorLcv_Vec4fG_get_const_size_t, std_vectorLcv_Vec4fG_set_size_t_const_Vec4f, + std_vectorLcv_Vec4fG_push_const_Vec4f, std_vectorLcv_Vec4fG_insert_size_t_const_Vec4f, + } + + vector_copy_non_bool! { core::Vec4f, + std_vectorLcv_Vec4fG_data_const, std_vectorLcv_Vec4fG_dataMut, cv_fromSlice_const_const_Vec4fX_size_t, + std_vectorLcv_Vec4fG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec4fG_inputArray_const(self.as_raw_VectorOfVec4f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec4fG_outputArray(self.as_raw_mut_VectorOfVec4f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec4fG_inputOutputArray(self.as_raw_mut_VectorOfVec4f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOfVec4i(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVec4i(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Vec4i, + std_vectorLcv_Vec4iG_new_const, std_vectorLcv_Vec4iG_delete, + std_vectorLcv_Vec4iG_len_const, std_vectorLcv_Vec4iG_isEmpty_const, + std_vectorLcv_Vec4iG_capacity_const, std_vectorLcv_Vec4iG_shrinkToFit, + std_vectorLcv_Vec4iG_reserve_size_t, std_vectorLcv_Vec4iG_remove_size_t, + std_vectorLcv_Vec4iG_swap_size_t_size_t, std_vectorLcv_Vec4iG_clear, + std_vectorLcv_Vec4iG_get_const_size_t, std_vectorLcv_Vec4iG_set_size_t_const_Vec4i, + std_vectorLcv_Vec4iG_push_const_Vec4i, std_vectorLcv_Vec4iG_insert_size_t_const_Vec4i, + } + + vector_copy_non_bool! { core::Vec4i, + std_vectorLcv_Vec4iG_data_const, std_vectorLcv_Vec4iG_dataMut, cv_fromSlice_const_const_Vec4iX_size_t, + std_vectorLcv_Vec4iG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec4iG_inputArray_const(self.as_raw_VectorOfVec4i(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec4iG_outputArray(self.as_raw_mut_VectorOfVec4i(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec4iG_inputOutputArray(self.as_raw_mut_VectorOfVec4i(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOfVec6f(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVec6f(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Vec6f, + std_vectorLcv_Vec6fG_new_const, std_vectorLcv_Vec6fG_delete, + std_vectorLcv_Vec6fG_len_const, std_vectorLcv_Vec6fG_isEmpty_const, + std_vectorLcv_Vec6fG_capacity_const, std_vectorLcv_Vec6fG_shrinkToFit, + std_vectorLcv_Vec6fG_reserve_size_t, std_vectorLcv_Vec6fG_remove_size_t, + std_vectorLcv_Vec6fG_swap_size_t_size_t, std_vectorLcv_Vec6fG_clear, + std_vectorLcv_Vec6fG_get_const_size_t, std_vectorLcv_Vec6fG_set_size_t_const_Vec6f, + std_vectorLcv_Vec6fG_push_const_Vec6f, std_vectorLcv_Vec6fG_insert_size_t_const_Vec6f, + } + + vector_copy_non_bool! { core::Vec6f, + std_vectorLcv_Vec6fG_data_const, std_vectorLcv_Vec6fG_dataMut, cv_fromSlice_const_const_Vec6fX_size_t, + std_vectorLcv_Vec6fG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec6fG_inputArray_const(self.as_raw_VectorOfVec6f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec6fG_outputArray(self.as_raw_mut_VectorOfVec6f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLcv_Vec6fG_inputOutputArray(self.as_raw_mut_VectorOfVec6f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector> { + pub fn as_raw_VectorOfVectorOfMat(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVectorOfMat(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Vector, + std_vectorLstd_vectorLcv_MatGG_new_const, std_vectorLstd_vectorLcv_MatGG_delete, + std_vectorLstd_vectorLcv_MatGG_len_const, std_vectorLstd_vectorLcv_MatGG_isEmpty_const, + std_vectorLstd_vectorLcv_MatGG_capacity_const, std_vectorLstd_vectorLcv_MatGG_shrinkToFit, + std_vectorLstd_vectorLcv_MatGG_reserve_size_t, std_vectorLstd_vectorLcv_MatGG_remove_size_t, + std_vectorLstd_vectorLcv_MatGG_swap_size_t_size_t, std_vectorLstd_vectorLcv_MatGG_clear, + std_vectorLstd_vectorLcv_MatGG_get_const_size_t, std_vectorLstd_vectorLcv_MatGG_set_size_t_const_vectorLMatG, + std_vectorLstd_vectorLcv_MatGG_push_const_vectorLMatG, std_vectorLstd_vectorLcv_MatGG_insert_size_t_const_vectorLMatG, + } + + vector_non_copy_or_bool! { clone core::Vector } + + + impl core::Vector> { + pub fn as_raw_VectorOfVectorOfPoint(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVectorOfPoint(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Vector, + std_vectorLstd_vectorLcv_PointGG_new_const, std_vectorLstd_vectorLcv_PointGG_delete, + std_vectorLstd_vectorLcv_PointGG_len_const, std_vectorLstd_vectorLcv_PointGG_isEmpty_const, + std_vectorLstd_vectorLcv_PointGG_capacity_const, std_vectorLstd_vectorLcv_PointGG_shrinkToFit, + std_vectorLstd_vectorLcv_PointGG_reserve_size_t, std_vectorLstd_vectorLcv_PointGG_remove_size_t, + std_vectorLstd_vectorLcv_PointGG_swap_size_t_size_t, std_vectorLstd_vectorLcv_PointGG_clear, + std_vectorLstd_vectorLcv_PointGG_get_const_size_t, std_vectorLstd_vectorLcv_PointGG_set_size_t_const_vectorLPointG, + std_vectorLstd_vectorLcv_PointGG_push_const_vectorLPointG, std_vectorLstd_vectorLcv_PointGG_insert_size_t_const_vectorLPointG, + } + + vector_non_copy_or_bool! { clone core::Vector } + + impl ToInputArray for core::Vector> { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLstd_vectorLcv_PointGG_inputArray_const(self.as_raw_VectorOfVectorOfPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector> } + + impl ToOutputArray for core::Vector> { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLstd_vectorLcv_PointGG_outputArray(self.as_raw_mut_VectorOfVectorOfPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector> { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLstd_vectorLcv_PointGG_inputOutputArray(self.as_raw_mut_VectorOfVectorOfPoint(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector> } + + impl core::Vector> { + pub fn as_raw_VectorOfVectorOfPoint2f(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVectorOfPoint2f(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Vector, + std_vectorLstd_vectorLcv_Point2fGG_new_const, std_vectorLstd_vectorLcv_Point2fGG_delete, + std_vectorLstd_vectorLcv_Point2fGG_len_const, std_vectorLstd_vectorLcv_Point2fGG_isEmpty_const, + std_vectorLstd_vectorLcv_Point2fGG_capacity_const, std_vectorLstd_vectorLcv_Point2fGG_shrinkToFit, + std_vectorLstd_vectorLcv_Point2fGG_reserve_size_t, std_vectorLstd_vectorLcv_Point2fGG_remove_size_t, + std_vectorLstd_vectorLcv_Point2fGG_swap_size_t_size_t, std_vectorLstd_vectorLcv_Point2fGG_clear, + std_vectorLstd_vectorLcv_Point2fGG_get_const_size_t, std_vectorLstd_vectorLcv_Point2fGG_set_size_t_const_vectorLPoint2fG, + std_vectorLstd_vectorLcv_Point2fGG_push_const_vectorLPoint2fG, std_vectorLstd_vectorLcv_Point2fGG_insert_size_t_const_vectorLPoint2fG, + } + + vector_non_copy_or_bool! { clone core::Vector } + + impl ToInputArray for core::Vector> { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLstd_vectorLcv_Point2fGG_inputArray_const(self.as_raw_VectorOfVectorOfPoint2f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector> } + + impl ToOutputArray for core::Vector> { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLstd_vectorLcv_Point2fGG_outputArray(self.as_raw_mut_VectorOfVectorOfPoint2f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector> { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLstd_vectorLcv_Point2fGG_inputOutputArray(self.as_raw_mut_VectorOfVectorOfPoint2f(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector> } + + impl core::Vector> { + pub fn as_raw_VectorOfVectorOfRange(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVectorOfRange(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Vector, + std_vectorLstd_vectorLcv_RangeGG_new_const, std_vectorLstd_vectorLcv_RangeGG_delete, + std_vectorLstd_vectorLcv_RangeGG_len_const, std_vectorLstd_vectorLcv_RangeGG_isEmpty_const, + std_vectorLstd_vectorLcv_RangeGG_capacity_const, std_vectorLstd_vectorLcv_RangeGG_shrinkToFit, + std_vectorLstd_vectorLcv_RangeGG_reserve_size_t, std_vectorLstd_vectorLcv_RangeGG_remove_size_t, + std_vectorLstd_vectorLcv_RangeGG_swap_size_t_size_t, std_vectorLstd_vectorLcv_RangeGG_clear, + std_vectorLstd_vectorLcv_RangeGG_get_const_size_t, std_vectorLstd_vectorLcv_RangeGG_set_size_t_const_vectorLRangeG, + std_vectorLstd_vectorLcv_RangeGG_push_const_vectorLRangeG, std_vectorLstd_vectorLcv_RangeGG_insert_size_t_const_vectorLRangeG, + } + + vector_non_copy_or_bool! { core::Vector } + + + impl core::Vector> { + pub fn as_raw_VectorOfVectorOfi32(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVectorOfi32(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Vector, + std_vectorLstd_vectorLintGG_new_const, std_vectorLstd_vectorLintGG_delete, + std_vectorLstd_vectorLintGG_len_const, std_vectorLstd_vectorLintGG_isEmpty_const, + std_vectorLstd_vectorLintGG_capacity_const, std_vectorLstd_vectorLintGG_shrinkToFit, + std_vectorLstd_vectorLintGG_reserve_size_t, std_vectorLstd_vectorLintGG_remove_size_t, + std_vectorLstd_vectorLintGG_swap_size_t_size_t, std_vectorLstd_vectorLintGG_clear, + std_vectorLstd_vectorLintGG_get_const_size_t, std_vectorLstd_vectorLintGG_set_size_t_const_vectorLintG, + std_vectorLstd_vectorLintGG_push_const_vectorLintG, std_vectorLstd_vectorLintGG_insert_size_t_const_vectorLintG, + } + + vector_non_copy_or_bool! { clone core::Vector } + + impl ToInputArray for core::Vector> { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLstd_vectorLintGG_inputArray_const(self.as_raw_VectorOfVectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector> } + + impl ToOutputArray for core::Vector> { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLstd_vectorLintGG_outputArray(self.as_raw_mut_VectorOfVectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector> { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLstd_vectorLintGG_inputOutputArray(self.as_raw_mut_VectorOfVectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector> } + + impl core::Vector { + pub fn as_raw_VectorOfbool(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfbool(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { bool, + std_vectorLboolG_new_const, std_vectorLboolG_delete, + std_vectorLboolG_len_const, std_vectorLboolG_isEmpty_const, + std_vectorLboolG_capacity_const, std_vectorLboolG_shrinkToFit, + std_vectorLboolG_reserve_size_t, std_vectorLboolG_remove_size_t, + std_vectorLboolG_swap_size_t_size_t, std_vectorLboolG_clear, + std_vectorLboolG_get_const_size_t, std_vectorLboolG_set_size_t_const_bool, + std_vectorLboolG_push_const_bool, std_vectorLboolG_insert_size_t_const_bool, + } + + vector_non_copy_or_bool! { clone bool } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLboolG_inputArray_const(self.as_raw_VectorOfbool(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOfc_char(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfc_char(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl core::Vector { + pub fn as_raw_VectorOff32(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOff32(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { f32, + std_vectorLfloatG_new_const, std_vectorLfloatG_delete, + std_vectorLfloatG_len_const, std_vectorLfloatG_isEmpty_const, + std_vectorLfloatG_capacity_const, std_vectorLfloatG_shrinkToFit, + std_vectorLfloatG_reserve_size_t, std_vectorLfloatG_remove_size_t, + std_vectorLfloatG_swap_size_t_size_t, std_vectorLfloatG_clear, + std_vectorLfloatG_get_const_size_t, std_vectorLfloatG_set_size_t_const_float, + std_vectorLfloatG_push_const_float, std_vectorLfloatG_insert_size_t_const_float, + } + + vector_copy_non_bool! { f32, + std_vectorLfloatG_data_const, std_vectorLfloatG_dataMut, cv_fromSlice_const_const_floatX_size_t, + std_vectorLfloatG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLfloatG_inputArray_const(self.as_raw_VectorOff32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLfloatG_outputArray(self.as_raw_mut_VectorOff32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLfloatG_inputOutputArray(self.as_raw_mut_VectorOff32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOff64(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOff64(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { f64, + std_vectorLdoubleG_new_const, std_vectorLdoubleG_delete, + std_vectorLdoubleG_len_const, std_vectorLdoubleG_isEmpty_const, + std_vectorLdoubleG_capacity_const, std_vectorLdoubleG_shrinkToFit, + std_vectorLdoubleG_reserve_size_t, std_vectorLdoubleG_remove_size_t, + std_vectorLdoubleG_swap_size_t_size_t, std_vectorLdoubleG_clear, + std_vectorLdoubleG_get_const_size_t, std_vectorLdoubleG_set_size_t_const_double, + std_vectorLdoubleG_push_const_double, std_vectorLdoubleG_insert_size_t_const_double, + } + + vector_copy_non_bool! { f64, + std_vectorLdoubleG_data_const, std_vectorLdoubleG_dataMut, cv_fromSlice_const_const_doubleX_size_t, + std_vectorLdoubleG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLdoubleG_inputArray_const(self.as_raw_VectorOff64(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLdoubleG_outputArray(self.as_raw_mut_VectorOff64(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLdoubleG_inputOutputArray(self.as_raw_mut_VectorOff64(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOfi32(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfi32(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { i32, + std_vectorLintG_new_const, std_vectorLintG_delete, + std_vectorLintG_len_const, std_vectorLintG_isEmpty_const, + std_vectorLintG_capacity_const, std_vectorLintG_shrinkToFit, + std_vectorLintG_reserve_size_t, std_vectorLintG_remove_size_t, + std_vectorLintG_swap_size_t_size_t, std_vectorLintG_clear, + std_vectorLintG_get_const_size_t, std_vectorLintG_set_size_t_const_int, + std_vectorLintG_push_const_int, std_vectorLintG_insert_size_t_const_int, + } + + vector_copy_non_bool! { i32, + std_vectorLintG_data_const, std_vectorLintG_dataMut, cv_fromSlice_const_const_intX_size_t, + std_vectorLintG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLintG_inputArray_const(self.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLintG_outputArray(self.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLintG_inputOutputArray(self.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + + impl core::Vector { + pub fn as_raw_VectorOfi8(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfi8(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { i8, + std_vectorLsigned_charG_new_const, std_vectorLsigned_charG_delete, + std_vectorLsigned_charG_len_const, std_vectorLsigned_charG_isEmpty_const, + std_vectorLsigned_charG_capacity_const, std_vectorLsigned_charG_shrinkToFit, + std_vectorLsigned_charG_reserve_size_t, std_vectorLsigned_charG_remove_size_t, + std_vectorLsigned_charG_swap_size_t_size_t, std_vectorLsigned_charG_clear, + std_vectorLsigned_charG_get_const_size_t, std_vectorLsigned_charG_set_size_t_const_signed_char, + std_vectorLsigned_charG_push_const_signed_char, std_vectorLsigned_charG_insert_size_t_const_signed_char, + } + + vector_copy_non_bool! { i8, + std_vectorLsigned_charG_data_const, std_vectorLsigned_charG_dataMut, cv_fromSlice_const_const_signed_charX_size_t, + std_vectorLsigned_charG_clone_const, + } + + + impl core::Vector { + pub fn as_raw_VectorOfsize_t(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfsize_t(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { size_t, + std_vectorLsize_tG_new_const, std_vectorLsize_tG_delete, + std_vectorLsize_tG_len_const, std_vectorLsize_tG_isEmpty_const, + std_vectorLsize_tG_capacity_const, std_vectorLsize_tG_shrinkToFit, + std_vectorLsize_tG_reserve_size_t, std_vectorLsize_tG_remove_size_t, + std_vectorLsize_tG_swap_size_t_size_t, std_vectorLsize_tG_clear, + std_vectorLsize_tG_get_const_size_t, std_vectorLsize_tG_set_size_t_const_size_t, + std_vectorLsize_tG_push_const_size_t, std_vectorLsize_tG_insert_size_t_const_size_t, + } + + vector_copy_non_bool! { size_t, + std_vectorLsize_tG_data_const, std_vectorLsize_tG_dataMut, cv_fromSlice_const_const_size_tX_size_t, + std_vectorLsize_tG_clone_const, + } + + + impl core::Vector { + pub fn as_raw_VectorOfu8(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfu8(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { u8, + std_vectorLunsigned_charG_new_const, std_vectorLunsigned_charG_delete, + std_vectorLunsigned_charG_len_const, std_vectorLunsigned_charG_isEmpty_const, + std_vectorLunsigned_charG_capacity_const, std_vectorLunsigned_charG_shrinkToFit, + std_vectorLunsigned_charG_reserve_size_t, std_vectorLunsigned_charG_remove_size_t, + std_vectorLunsigned_charG_swap_size_t_size_t, std_vectorLunsigned_charG_clear, + std_vectorLunsigned_charG_get_const_size_t, std_vectorLunsigned_charG_set_size_t_const_unsigned_char, + std_vectorLunsigned_charG_push_const_unsigned_char, std_vectorLunsigned_charG_insert_size_t_const_unsigned_char, + } + + vector_copy_non_bool! { u8, + std_vectorLunsigned_charG_data_const, std_vectorLunsigned_charG_dataMut, cv_fromSlice_const_const_unsigned_charX_size_t, + std_vectorLunsigned_charG_clone_const, + } + + impl ToInputArray for core::Vector { + #[inline] + fn input_array(&self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLunsigned_charG_inputArray_const(self.as_raw_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRef::<'_, core::_InputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + input_array_ref_forward! { core::Vector } + + impl ToOutputArray for core::Vector { + #[inline] + fn output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLunsigned_charG_outputArray(self.as_raw_mut_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_OutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + impl ToInputOutputArray for core::Vector { + #[inline] + fn input_output_array(&mut self) -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::std_vectorLunsigned_charG_inputOutputArray(self.as_raw_mut_VectorOfu8(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { BoxedRefMut::<'_, core::_InputOutputArray>::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + output_array_ref_forward! { core::Vector } + +} +pub use core_types::*; + +mod dnn_types { + use crate::{mod_prelude::*, core, types, sys}; + + ptr_extern! { crate::dnn::AbsLayer, + cv_PtrLcv_dnn_AbsLayerG_new_null_const, cv_PtrLcv_dnn_AbsLayerG_delete, cv_PtrLcv_dnn_AbsLayerG_getInnerPtr_const, cv_PtrLcv_dnn_AbsLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::AbsLayer, cv_PtrLcv_dnn_AbsLayerG_new_const_AbsLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfAbsLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfAbsLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::AbsLayerTraitConst for core::Ptr { + #[inline] fn as_raw_AbsLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::AbsLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_AbsLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AbsLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AbsLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AbsLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfAbsLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::AccumLayer, + cv_PtrLcv_dnn_AccumLayerG_new_null_const, cv_PtrLcv_dnn_AccumLayerG_delete, cv_PtrLcv_dnn_AccumLayerG_getInnerPtr_const, cv_PtrLcv_dnn_AccumLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::AccumLayer, cv_PtrLcv_dnn_AccumLayerG_new_const_AccumLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfAccumLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfAccumLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::AccumLayerTraitConst for core::Ptr { + #[inline] fn as_raw_AccumLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::AccumLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_AccumLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AccumLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AccumLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfAccumLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::AcosLayer, + cv_PtrLcv_dnn_AcosLayerG_new_null_const, cv_PtrLcv_dnn_AcosLayerG_delete, cv_PtrLcv_dnn_AcosLayerG_getInnerPtr_const, cv_PtrLcv_dnn_AcosLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::AcosLayer, cv_PtrLcv_dnn_AcosLayerG_new_const_AcosLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfAcosLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfAcosLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::AcosLayerTraitConst for core::Ptr { + #[inline] fn as_raw_AcosLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::AcosLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_AcosLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AcosLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AcosLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AcosLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfAcosLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::AcoshLayer, + cv_PtrLcv_dnn_AcoshLayerG_new_null_const, cv_PtrLcv_dnn_AcoshLayerG_delete, cv_PtrLcv_dnn_AcoshLayerG_getInnerPtr_const, cv_PtrLcv_dnn_AcoshLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::AcoshLayer, cv_PtrLcv_dnn_AcoshLayerG_new_const_AcoshLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfAcoshLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfAcoshLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::AcoshLayerTraitConst for core::Ptr { + #[inline] fn as_raw_AcoshLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::AcoshLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_AcoshLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AcoshLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AcoshLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AcoshLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfAcoshLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ActivationLayer, + cv_PtrLcv_dnn_ActivationLayerG_new_null_const, cv_PtrLcv_dnn_ActivationLayerG_delete, cv_PtrLcv_dnn_ActivationLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ActivationLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ActivationLayer, cv_PtrLcv_dnn_ActivationLayerG_new_const_ActivationLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfActivationLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfActivationLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ActivationLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ActivationLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfActivationLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ActivationLayerInt8, + cv_PtrLcv_dnn_ActivationLayerInt8G_new_null_const, cv_PtrLcv_dnn_ActivationLayerInt8G_delete, cv_PtrLcv_dnn_ActivationLayerInt8G_getInnerPtr_const, cv_PtrLcv_dnn_ActivationLayerInt8G_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ActivationLayerInt8, cv_PtrLcv_dnn_ActivationLayerInt8G_new_const_ActivationLayerInt8 } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfActivationLayerInt8(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfActivationLayerInt8(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ActivationLayerInt8TraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayerInt8(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerInt8Trait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayerInt8(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ActivationLayerInt8G_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ActivationLayerInt8G_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ActivationLayerInt8G_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfActivationLayerInt8") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ArgLayer, + cv_PtrLcv_dnn_ArgLayerG_new_null_const, cv_PtrLcv_dnn_ArgLayerG_delete, cv_PtrLcv_dnn_ArgLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ArgLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ArgLayer, cv_PtrLcv_dnn_ArgLayerG_new_const_ArgLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfArgLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfArgLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ArgLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ArgLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ArgLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ArgLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ArgLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ArgLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfArgLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::AsinLayer, + cv_PtrLcv_dnn_AsinLayerG_new_null_const, cv_PtrLcv_dnn_AsinLayerG_delete, cv_PtrLcv_dnn_AsinLayerG_getInnerPtr_const, cv_PtrLcv_dnn_AsinLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::AsinLayer, cv_PtrLcv_dnn_AsinLayerG_new_const_AsinLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfAsinLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfAsinLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::AsinLayerTraitConst for core::Ptr { + #[inline] fn as_raw_AsinLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::AsinLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_AsinLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AsinLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AsinLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AsinLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfAsinLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::AsinhLayer, + cv_PtrLcv_dnn_AsinhLayerG_new_null_const, cv_PtrLcv_dnn_AsinhLayerG_delete, cv_PtrLcv_dnn_AsinhLayerG_getInnerPtr_const, cv_PtrLcv_dnn_AsinhLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::AsinhLayer, cv_PtrLcv_dnn_AsinhLayerG_new_const_AsinhLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfAsinhLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfAsinhLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::AsinhLayerTraitConst for core::Ptr { + #[inline] fn as_raw_AsinhLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::AsinhLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_AsinhLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AsinhLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AsinhLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AsinhLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfAsinhLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::AtanLayer, + cv_PtrLcv_dnn_AtanLayerG_new_null_const, cv_PtrLcv_dnn_AtanLayerG_delete, cv_PtrLcv_dnn_AtanLayerG_getInnerPtr_const, cv_PtrLcv_dnn_AtanLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::AtanLayer, cv_PtrLcv_dnn_AtanLayerG_new_const_AtanLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfAtanLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfAtanLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::AtanLayerTraitConst for core::Ptr { + #[inline] fn as_raw_AtanLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::AtanLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_AtanLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AtanLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AtanLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AtanLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfAtanLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::AtanhLayer, + cv_PtrLcv_dnn_AtanhLayerG_new_null_const, cv_PtrLcv_dnn_AtanhLayerG_delete, cv_PtrLcv_dnn_AtanhLayerG_getInnerPtr_const, cv_PtrLcv_dnn_AtanhLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::AtanhLayer, cv_PtrLcv_dnn_AtanhLayerG_new_const_AtanhLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfAtanhLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfAtanhLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::AtanhLayerTraitConst for core::Ptr { + #[inline] fn as_raw_AtanhLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::AtanhLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_AtanhLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AtanhLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AtanhLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AtanhLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfAtanhLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::AttentionLayer, + cv_PtrLcv_dnn_AttentionLayerG_new_null_const, cv_PtrLcv_dnn_AttentionLayerG_delete, cv_PtrLcv_dnn_AttentionLayerG_getInnerPtr_const, cv_PtrLcv_dnn_AttentionLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::AttentionLayer, cv_PtrLcv_dnn_AttentionLayerG_new_const_AttentionLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfAttentionLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfAttentionLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::AttentionLayerTraitConst for core::Ptr { + #[inline] fn as_raw_AttentionLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::AttentionLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_AttentionLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AttentionLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_AttentionLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfAttentionLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::BNLLLayer, + cv_PtrLcv_dnn_BNLLLayerG_new_null_const, cv_PtrLcv_dnn_BNLLLayerG_delete, cv_PtrLcv_dnn_BNLLLayerG_getInnerPtr_const, cv_PtrLcv_dnn_BNLLLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::BNLLLayer, cv_PtrLcv_dnn_BNLLLayerG_new_const_BNLLLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfBNLLLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfBNLLLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::BNLLLayerTraitConst for core::Ptr { + #[inline] fn as_raw_BNLLLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::BNLLLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_BNLLLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BNLLLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BNLLLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BNLLLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfBNLLLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::BackendNode, + cv_PtrLcv_dnn_BackendNodeG_new_null_const, cv_PtrLcv_dnn_BackendNodeG_delete, cv_PtrLcv_dnn_BackendNodeG_getInnerPtr_const, cv_PtrLcv_dnn_BackendNodeG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::BackendNode, cv_PtrLcv_dnn_BackendNodeG_new_const_BackendNode } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfBackendNode(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfBackendNode(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::BackendNodeTraitConst for core::Ptr { + #[inline] fn as_raw_BackendNode(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::BackendNodeTrait for core::Ptr { + #[inline] fn as_raw_mut_BackendNode(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfBackendNode") + .field("backend_id", &crate::dnn::BackendNodeTraitConst::backend_id(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::BackendWrapper, + cv_PtrLcv_dnn_BackendWrapperG_new_null_const, cv_PtrLcv_dnn_BackendWrapperG_delete, cv_PtrLcv_dnn_BackendWrapperG_getInnerPtr_const, cv_PtrLcv_dnn_BackendWrapperG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfBackendWrapper(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfBackendWrapper(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::BackendWrapperTraitConst for core::Ptr { + #[inline] fn as_raw_BackendWrapper(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::BackendWrapperTrait for core::Ptr { + #[inline] fn as_raw_mut_BackendWrapper(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfBackendWrapper") + .field("backend_id", &crate::dnn::BackendWrapperTraitConst::backend_id(self)) + .field("target_id", &crate::dnn::BackendWrapperTraitConst::target_id(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::BaseConvolutionLayer, + cv_PtrLcv_dnn_BaseConvolutionLayerG_new_null_const, cv_PtrLcv_dnn_BaseConvolutionLayerG_delete, cv_PtrLcv_dnn_BaseConvolutionLayerG_getInnerPtr_const, cv_PtrLcv_dnn_BaseConvolutionLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::BaseConvolutionLayer, cv_PtrLcv_dnn_BaseConvolutionLayerG_new_const_BaseConvolutionLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfBaseConvolutionLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfBaseConvolutionLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::BaseConvolutionLayerTraitConst for core::Ptr { + #[inline] fn as_raw_BaseConvolutionLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::BaseConvolutionLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_BaseConvolutionLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BaseConvolutionLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BaseConvolutionLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfBaseConvolutionLayer") + .field("kernel", &crate::dnn::BaseConvolutionLayerTraitConst::kernel(self)) + .field("stride", &crate::dnn::BaseConvolutionLayerTraitConst::stride(self)) + .field("pad", &crate::dnn::BaseConvolutionLayerTraitConst::pad(self)) + .field("dilation", &crate::dnn::BaseConvolutionLayerTraitConst::dilation(self)) + .field("adjust_pad", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pad(self)) + .field("adjust_pads", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pads(self)) + .field("kernel_size", &crate::dnn::BaseConvolutionLayerTraitConst::kernel_size(self)) + .field("strides", &crate::dnn::BaseConvolutionLayerTraitConst::strides(self)) + .field("dilations", &crate::dnn::BaseConvolutionLayerTraitConst::dilations(self)) + .field("pads_begin", &crate::dnn::BaseConvolutionLayerTraitConst::pads_begin(self)) + .field("pads_end", &crate::dnn::BaseConvolutionLayerTraitConst::pads_end(self)) + .field("pad_mode", &crate::dnn::BaseConvolutionLayerTraitConst::pad_mode(self)) + .field("num_output", &crate::dnn::BaseConvolutionLayerTraitConst::num_output(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::BatchNormLayer, + cv_PtrLcv_dnn_BatchNormLayerG_new_null_const, cv_PtrLcv_dnn_BatchNormLayerG_delete, cv_PtrLcv_dnn_BatchNormLayerG_getInnerPtr_const, cv_PtrLcv_dnn_BatchNormLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::BatchNormLayer, cv_PtrLcv_dnn_BatchNormLayerG_new_const_BatchNormLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfBatchNormLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfBatchNormLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::BatchNormLayerTraitConst for core::Ptr { + #[inline] fn as_raw_BatchNormLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::BatchNormLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_BatchNormLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BatchNormLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BatchNormLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BatchNormLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfBatchNormLayer") + .field("has_weights", &crate::dnn::BatchNormLayerTraitConst::has_weights(self)) + .field("has_bias", &crate::dnn::BatchNormLayerTraitConst::has_bias(self)) + .field("epsilon", &crate::dnn::BatchNormLayerTraitConst::epsilon(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::BatchNormLayerInt8, + cv_PtrLcv_dnn_BatchNormLayerInt8G_new_null_const, cv_PtrLcv_dnn_BatchNormLayerInt8G_delete, cv_PtrLcv_dnn_BatchNormLayerInt8G_getInnerPtr_const, cv_PtrLcv_dnn_BatchNormLayerInt8G_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::BatchNormLayerInt8, cv_PtrLcv_dnn_BatchNormLayerInt8G_new_const_BatchNormLayerInt8 } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfBatchNormLayerInt8(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfBatchNormLayerInt8(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::BatchNormLayerInt8TraitConst for core::Ptr { + #[inline] fn as_raw_BatchNormLayerInt8(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::BatchNormLayerInt8Trait for core::Ptr { + #[inline] fn as_raw_mut_BatchNormLayerInt8(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BatchNormLayerInt8G_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BatchNormLayerInt8G_to_PtrOfActivationLayer } + + impl crate::dnn::BatchNormLayerTraitConst for core::Ptr { + #[inline] fn as_raw_BatchNormLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::BatchNormLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_BatchNormLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BatchNormLayerInt8G_to_PtrOfBatchNormLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BatchNormLayerInt8G_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfBatchNormLayerInt8") + .field("input_sc", &crate::dnn::BatchNormLayerInt8TraitConst::input_sc(self)) + .field("output_sc", &crate::dnn::BatchNormLayerInt8TraitConst::output_sc(self)) + .field("input_zp", &crate::dnn::BatchNormLayerInt8TraitConst::input_zp(self)) + .field("output_zp", &crate::dnn::BatchNormLayerInt8TraitConst::output_zp(self)) + .field("has_weights", &crate::dnn::BatchNormLayerTraitConst::has_weights(self)) + .field("has_bias", &crate::dnn::BatchNormLayerTraitConst::has_bias(self)) + .field("epsilon", &crate::dnn::BatchNormLayerTraitConst::epsilon(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::BlankLayer, + cv_PtrLcv_dnn_BlankLayerG_new_null_const, cv_PtrLcv_dnn_BlankLayerG_delete, cv_PtrLcv_dnn_BlankLayerG_getInnerPtr_const, cv_PtrLcv_dnn_BlankLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::BlankLayer, cv_PtrLcv_dnn_BlankLayerG_new_const_BlankLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfBlankLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfBlankLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::BlankLayerTraitConst for core::Ptr { + #[inline] fn as_raw_BlankLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::BlankLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_BlankLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BlankLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_BlankLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfBlankLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::CeilLayer, + cv_PtrLcv_dnn_CeilLayerG_new_null_const, cv_PtrLcv_dnn_CeilLayerG_delete, cv_PtrLcv_dnn_CeilLayerG_getInnerPtr_const, cv_PtrLcv_dnn_CeilLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::CeilLayer, cv_PtrLcv_dnn_CeilLayerG_new_const_CeilLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfCeilLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfCeilLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::CeilLayerTraitConst for core::Ptr { + #[inline] fn as_raw_CeilLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::CeilLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_CeilLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CeilLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CeilLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CeilLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfCeilLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::CeluLayer, + cv_PtrLcv_dnn_CeluLayerG_new_null_const, cv_PtrLcv_dnn_CeluLayerG_delete, cv_PtrLcv_dnn_CeluLayerG_getInnerPtr_const, cv_PtrLcv_dnn_CeluLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::CeluLayer, cv_PtrLcv_dnn_CeluLayerG_new_const_CeluLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfCeluLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfCeluLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::CeluLayerTraitConst for core::Ptr { + #[inline] fn as_raw_CeluLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::CeluLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_CeluLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CeluLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CeluLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CeluLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfCeluLayer") + .field("alpha", &crate::dnn::CeluLayerTraitConst::alpha(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ChannelsPReLULayer, + cv_PtrLcv_dnn_ChannelsPReLULayerG_new_null_const, cv_PtrLcv_dnn_ChannelsPReLULayerG_delete, cv_PtrLcv_dnn_ChannelsPReLULayerG_getInnerPtr_const, cv_PtrLcv_dnn_ChannelsPReLULayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ChannelsPReLULayer, cv_PtrLcv_dnn_ChannelsPReLULayerG_new_const_ChannelsPReLULayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfChannelsPReLULayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfChannelsPReLULayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ChannelsPReLULayerTraitConst for core::Ptr { + #[inline] fn as_raw_ChannelsPReLULayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ChannelsPReLULayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ChannelsPReLULayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ChannelsPReLULayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ChannelsPReLULayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ChannelsPReLULayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfChannelsPReLULayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::CompareLayer, + cv_PtrLcv_dnn_CompareLayerG_new_null_const, cv_PtrLcv_dnn_CompareLayerG_delete, cv_PtrLcv_dnn_CompareLayerG_getInnerPtr_const, cv_PtrLcv_dnn_CompareLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::CompareLayer, cv_PtrLcv_dnn_CompareLayerG_new_const_CompareLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfCompareLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfCompareLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::CompareLayerTraitConst for core::Ptr { + #[inline] fn as_raw_CompareLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::CompareLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_CompareLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CompareLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CompareLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfCompareLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ConcatLayer, + cv_PtrLcv_dnn_ConcatLayerG_new_null_const, cv_PtrLcv_dnn_ConcatLayerG_delete, cv_PtrLcv_dnn_ConcatLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ConcatLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ConcatLayer, cv_PtrLcv_dnn_ConcatLayerG_new_const_ConcatLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfConcatLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfConcatLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ConcatLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ConcatLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ConcatLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ConcatLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ConcatLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ConcatLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfConcatLayer") + .field("axis", &crate::dnn::ConcatLayerTraitConst::axis(self)) + .field("padding", &crate::dnn::ConcatLayerTraitConst::padding(self)) + .field("padding_value", &crate::dnn::ConcatLayerTraitConst::padding_value(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ConstLayer, + cv_PtrLcv_dnn_ConstLayerG_new_null_const, cv_PtrLcv_dnn_ConstLayerG_delete, cv_PtrLcv_dnn_ConstLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ConstLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ConstLayer, cv_PtrLcv_dnn_ConstLayerG_new_const_ConstLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfConstLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfConstLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ConstLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ConstLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ConstLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ConstLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ConstLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ConstLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfConstLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ConvolutionLayer, + cv_PtrLcv_dnn_ConvolutionLayerG_new_null_const, cv_PtrLcv_dnn_ConvolutionLayerG_delete, cv_PtrLcv_dnn_ConvolutionLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ConvolutionLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ConvolutionLayer, cv_PtrLcv_dnn_ConvolutionLayerG_new_const_ConvolutionLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfConvolutionLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfConvolutionLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ConvolutionLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ConvolutionLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ConvolutionLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ConvolutionLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ConvolutionLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::BaseConvolutionLayerTraitConst for core::Ptr { + #[inline] fn as_raw_BaseConvolutionLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::BaseConvolutionLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_BaseConvolutionLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ConvolutionLayerG_to_PtrOfBaseConvolutionLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ConvolutionLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfConvolutionLayer") + .field("fused_activation", &crate::dnn::ConvolutionLayerTraitConst::fused_activation(self)) + .field("fused_add", &crate::dnn::ConvolutionLayerTraitConst::fused_add(self)) + .field("use_winograd", &crate::dnn::ConvolutionLayerTraitConst::use_winograd(self)) + .field("kernel", &crate::dnn::BaseConvolutionLayerTraitConst::kernel(self)) + .field("stride", &crate::dnn::BaseConvolutionLayerTraitConst::stride(self)) + .field("pad", &crate::dnn::BaseConvolutionLayerTraitConst::pad(self)) + .field("dilation", &crate::dnn::BaseConvolutionLayerTraitConst::dilation(self)) + .field("adjust_pad", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pad(self)) + .field("adjust_pads", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pads(self)) + .field("kernel_size", &crate::dnn::BaseConvolutionLayerTraitConst::kernel_size(self)) + .field("strides", &crate::dnn::BaseConvolutionLayerTraitConst::strides(self)) + .field("dilations", &crate::dnn::BaseConvolutionLayerTraitConst::dilations(self)) + .field("pads_begin", &crate::dnn::BaseConvolutionLayerTraitConst::pads_begin(self)) + .field("pads_end", &crate::dnn::BaseConvolutionLayerTraitConst::pads_end(self)) + .field("pad_mode", &crate::dnn::BaseConvolutionLayerTraitConst::pad_mode(self)) + .field("num_output", &crate::dnn::BaseConvolutionLayerTraitConst::num_output(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ConvolutionLayerInt8, + cv_PtrLcv_dnn_ConvolutionLayerInt8G_new_null_const, cv_PtrLcv_dnn_ConvolutionLayerInt8G_delete, cv_PtrLcv_dnn_ConvolutionLayerInt8G_getInnerPtr_const, cv_PtrLcv_dnn_ConvolutionLayerInt8G_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ConvolutionLayerInt8, cv_PtrLcv_dnn_ConvolutionLayerInt8G_new_const_ConvolutionLayerInt8 } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfConvolutionLayerInt8(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfConvolutionLayerInt8(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ConvolutionLayerInt8TraitConst for core::Ptr { + #[inline] fn as_raw_ConvolutionLayerInt8(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ConvolutionLayerInt8Trait for core::Ptr { + #[inline] fn as_raw_mut_ConvolutionLayerInt8(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ConvolutionLayerInt8G_to_PtrOfAlgorithm } + + impl crate::dnn::BaseConvolutionLayerTraitConst for core::Ptr { + #[inline] fn as_raw_BaseConvolutionLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::BaseConvolutionLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_BaseConvolutionLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ConvolutionLayerInt8G_to_PtrOfBaseConvolutionLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ConvolutionLayerInt8G_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfConvolutionLayerInt8") + .field("input_zp", &crate::dnn::ConvolutionLayerInt8TraitConst::input_zp(self)) + .field("output_zp", &crate::dnn::ConvolutionLayerInt8TraitConst::output_zp(self)) + .field("input_sc", &crate::dnn::ConvolutionLayerInt8TraitConst::input_sc(self)) + .field("output_sc", &crate::dnn::ConvolutionLayerInt8TraitConst::output_sc(self)) + .field("per_channel", &crate::dnn::ConvolutionLayerInt8TraitConst::per_channel(self)) + .field("use_winograd", &crate::dnn::ConvolutionLayerInt8TraitConst::use_winograd(self)) + .field("kernel", &crate::dnn::BaseConvolutionLayerTraitConst::kernel(self)) + .field("stride", &crate::dnn::BaseConvolutionLayerTraitConst::stride(self)) + .field("pad", &crate::dnn::BaseConvolutionLayerTraitConst::pad(self)) + .field("dilation", &crate::dnn::BaseConvolutionLayerTraitConst::dilation(self)) + .field("adjust_pad", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pad(self)) + .field("adjust_pads", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pads(self)) + .field("kernel_size", &crate::dnn::BaseConvolutionLayerTraitConst::kernel_size(self)) + .field("strides", &crate::dnn::BaseConvolutionLayerTraitConst::strides(self)) + .field("dilations", &crate::dnn::BaseConvolutionLayerTraitConst::dilations(self)) + .field("pads_begin", &crate::dnn::BaseConvolutionLayerTraitConst::pads_begin(self)) + .field("pads_end", &crate::dnn::BaseConvolutionLayerTraitConst::pads_end(self)) + .field("pad_mode", &crate::dnn::BaseConvolutionLayerTraitConst::pad_mode(self)) + .field("num_output", &crate::dnn::BaseConvolutionLayerTraitConst::num_output(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::CorrelationLayer, + cv_PtrLcv_dnn_CorrelationLayerG_new_null_const, cv_PtrLcv_dnn_CorrelationLayerG_delete, cv_PtrLcv_dnn_CorrelationLayerG_getInnerPtr_const, cv_PtrLcv_dnn_CorrelationLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::CorrelationLayer, cv_PtrLcv_dnn_CorrelationLayerG_new_const_CorrelationLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfCorrelationLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfCorrelationLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::CorrelationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_CorrelationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::CorrelationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_CorrelationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CorrelationLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CorrelationLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfCorrelationLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::CosLayer, + cv_PtrLcv_dnn_CosLayerG_new_null_const, cv_PtrLcv_dnn_CosLayerG_delete, cv_PtrLcv_dnn_CosLayerG_getInnerPtr_const, cv_PtrLcv_dnn_CosLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::CosLayer, cv_PtrLcv_dnn_CosLayerG_new_const_CosLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfCosLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfCosLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::CosLayerTraitConst for core::Ptr { + #[inline] fn as_raw_CosLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::CosLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_CosLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CosLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CosLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CosLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfCosLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::CoshLayer, + cv_PtrLcv_dnn_CoshLayerG_new_null_const, cv_PtrLcv_dnn_CoshLayerG_delete, cv_PtrLcv_dnn_CoshLayerG_getInnerPtr_const, cv_PtrLcv_dnn_CoshLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::CoshLayer, cv_PtrLcv_dnn_CoshLayerG_new_const_CoshLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfCoshLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfCoshLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::CoshLayerTraitConst for core::Ptr { + #[inline] fn as_raw_CoshLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::CoshLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_CoshLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CoshLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CoshLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CoshLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfCoshLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::CropAndResizeLayer, + cv_PtrLcv_dnn_CropAndResizeLayerG_new_null_const, cv_PtrLcv_dnn_CropAndResizeLayerG_delete, cv_PtrLcv_dnn_CropAndResizeLayerG_getInnerPtr_const, cv_PtrLcv_dnn_CropAndResizeLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::CropAndResizeLayer, cv_PtrLcv_dnn_CropAndResizeLayerG_new_const_CropAndResizeLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfCropAndResizeLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfCropAndResizeLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::CropAndResizeLayerTraitConst for core::Ptr { + #[inline] fn as_raw_CropAndResizeLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::CropAndResizeLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_CropAndResizeLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CropAndResizeLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CropAndResizeLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfCropAndResizeLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::CropLayer, + cv_PtrLcv_dnn_CropLayerG_new_null_const, cv_PtrLcv_dnn_CropLayerG_delete, cv_PtrLcv_dnn_CropLayerG_getInnerPtr_const, cv_PtrLcv_dnn_CropLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::CropLayer, cv_PtrLcv_dnn_CropLayerG_new_const_CropLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfCropLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfCropLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::CropLayerTraitConst for core::Ptr { + #[inline] fn as_raw_CropLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::CropLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_CropLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CropLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CropLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfCropLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::CumSumLayer, + cv_PtrLcv_dnn_CumSumLayerG_new_null_const, cv_PtrLcv_dnn_CumSumLayerG_delete, cv_PtrLcv_dnn_CumSumLayerG_getInnerPtr_const, cv_PtrLcv_dnn_CumSumLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::CumSumLayer, cv_PtrLcv_dnn_CumSumLayerG_new_const_CumSumLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfCumSumLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfCumSumLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::CumSumLayerTraitConst for core::Ptr { + #[inline] fn as_raw_CumSumLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::CumSumLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_CumSumLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CumSumLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_CumSumLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfCumSumLayer") + .field("exclusive", &crate::dnn::CumSumLayerTraitConst::exclusive(self)) + .field("reverse", &crate::dnn::CumSumLayerTraitConst::reverse(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::DataAugmentationLayer, + cv_PtrLcv_dnn_DataAugmentationLayerG_new_null_const, cv_PtrLcv_dnn_DataAugmentationLayerG_delete, cv_PtrLcv_dnn_DataAugmentationLayerG_getInnerPtr_const, cv_PtrLcv_dnn_DataAugmentationLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::DataAugmentationLayer, cv_PtrLcv_dnn_DataAugmentationLayerG_new_const_DataAugmentationLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfDataAugmentationLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfDataAugmentationLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::DataAugmentationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_DataAugmentationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::DataAugmentationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_DataAugmentationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_DataAugmentationLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_DataAugmentationLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfDataAugmentationLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::DeconvolutionLayer, + cv_PtrLcv_dnn_DeconvolutionLayerG_new_null_const, cv_PtrLcv_dnn_DeconvolutionLayerG_delete, cv_PtrLcv_dnn_DeconvolutionLayerG_getInnerPtr_const, cv_PtrLcv_dnn_DeconvolutionLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::DeconvolutionLayer, cv_PtrLcv_dnn_DeconvolutionLayerG_new_const_DeconvolutionLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfDeconvolutionLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfDeconvolutionLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::DeconvolutionLayerTraitConst for core::Ptr { + #[inline] fn as_raw_DeconvolutionLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::DeconvolutionLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_DeconvolutionLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_DeconvolutionLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::BaseConvolutionLayerTraitConst for core::Ptr { + #[inline] fn as_raw_BaseConvolutionLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::BaseConvolutionLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_BaseConvolutionLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_DeconvolutionLayerG_to_PtrOfBaseConvolutionLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_DeconvolutionLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfDeconvolutionLayer") + .field("kernel", &crate::dnn::BaseConvolutionLayerTraitConst::kernel(self)) + .field("stride", &crate::dnn::BaseConvolutionLayerTraitConst::stride(self)) + .field("pad", &crate::dnn::BaseConvolutionLayerTraitConst::pad(self)) + .field("dilation", &crate::dnn::BaseConvolutionLayerTraitConst::dilation(self)) + .field("adjust_pad", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pad(self)) + .field("adjust_pads", &crate::dnn::BaseConvolutionLayerTraitConst::adjust_pads(self)) + .field("kernel_size", &crate::dnn::BaseConvolutionLayerTraitConst::kernel_size(self)) + .field("strides", &crate::dnn::BaseConvolutionLayerTraitConst::strides(self)) + .field("dilations", &crate::dnn::BaseConvolutionLayerTraitConst::dilations(self)) + .field("pads_begin", &crate::dnn::BaseConvolutionLayerTraitConst::pads_begin(self)) + .field("pads_end", &crate::dnn::BaseConvolutionLayerTraitConst::pads_end(self)) + .field("pad_mode", &crate::dnn::BaseConvolutionLayerTraitConst::pad_mode(self)) + .field("num_output", &crate::dnn::BaseConvolutionLayerTraitConst::num_output(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::DepthToSpaceLayer, + cv_PtrLcv_dnn_DepthToSpaceLayerG_new_null_const, cv_PtrLcv_dnn_DepthToSpaceLayerG_delete, cv_PtrLcv_dnn_DepthToSpaceLayerG_getInnerPtr_const, cv_PtrLcv_dnn_DepthToSpaceLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::DepthToSpaceLayer, cv_PtrLcv_dnn_DepthToSpaceLayerG_new_const_DepthToSpaceLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfDepthToSpaceLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfDepthToSpaceLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::DepthToSpaceLayerTraitConst for core::Ptr { + #[inline] fn as_raw_DepthToSpaceLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::DepthToSpaceLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_DepthToSpaceLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_DepthToSpaceLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_DepthToSpaceLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfDepthToSpaceLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::DequantizeLayer, + cv_PtrLcv_dnn_DequantizeLayerG_new_null_const, cv_PtrLcv_dnn_DequantizeLayerG_delete, cv_PtrLcv_dnn_DequantizeLayerG_getInnerPtr_const, cv_PtrLcv_dnn_DequantizeLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::DequantizeLayer, cv_PtrLcv_dnn_DequantizeLayerG_new_const_DequantizeLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfDequantizeLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfDequantizeLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::DequantizeLayerTraitConst for core::Ptr { + #[inline] fn as_raw_DequantizeLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::DequantizeLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_DequantizeLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_DequantizeLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_DequantizeLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfDequantizeLayer") + .field("scales", &crate::dnn::DequantizeLayerTraitConst::scales(self)) + .field("zeropoints", &crate::dnn::DequantizeLayerTraitConst::zeropoints(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::DetectionOutputLayer, + cv_PtrLcv_dnn_DetectionOutputLayerG_new_null_const, cv_PtrLcv_dnn_DetectionOutputLayerG_delete, cv_PtrLcv_dnn_DetectionOutputLayerG_getInnerPtr_const, cv_PtrLcv_dnn_DetectionOutputLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::DetectionOutputLayer, cv_PtrLcv_dnn_DetectionOutputLayerG_new_const_DetectionOutputLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfDetectionOutputLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfDetectionOutputLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::DetectionOutputLayerTraitConst for core::Ptr { + #[inline] fn as_raw_DetectionOutputLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::DetectionOutputLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_DetectionOutputLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_DetectionOutputLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_DetectionOutputLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfDetectionOutputLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ELULayer, + cv_PtrLcv_dnn_ELULayerG_new_null_const, cv_PtrLcv_dnn_ELULayerG_delete, cv_PtrLcv_dnn_ELULayerG_getInnerPtr_const, cv_PtrLcv_dnn_ELULayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ELULayer, cv_PtrLcv_dnn_ELULayerG_new_const_ELULayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfELULayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfELULayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ELULayerTraitConst for core::Ptr { + #[inline] fn as_raw_ELULayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ELULayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ELULayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ELULayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ELULayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ELULayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfELULayer") + .field("alpha", &crate::dnn::ELULayerTraitConst::alpha(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::EinsumLayer, + cv_PtrLcv_dnn_EinsumLayerG_new_null_const, cv_PtrLcv_dnn_EinsumLayerG_delete, cv_PtrLcv_dnn_EinsumLayerG_getInnerPtr_const, cv_PtrLcv_dnn_EinsumLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::EinsumLayer, cv_PtrLcv_dnn_EinsumLayerG_new_const_EinsumLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfEinsumLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfEinsumLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::EinsumLayerTraitConst for core::Ptr { + #[inline] fn as_raw_EinsumLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::EinsumLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_EinsumLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_EinsumLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_EinsumLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfEinsumLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::EltwiseLayer, + cv_PtrLcv_dnn_EltwiseLayerG_new_null_const, cv_PtrLcv_dnn_EltwiseLayerG_delete, cv_PtrLcv_dnn_EltwiseLayerG_getInnerPtr_const, cv_PtrLcv_dnn_EltwiseLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::EltwiseLayer, cv_PtrLcv_dnn_EltwiseLayerG_new_const_EltwiseLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfEltwiseLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfEltwiseLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::EltwiseLayerTraitConst for core::Ptr { + #[inline] fn as_raw_EltwiseLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::EltwiseLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_EltwiseLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_EltwiseLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_EltwiseLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfEltwiseLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::EltwiseLayerInt8, + cv_PtrLcv_dnn_EltwiseLayerInt8G_new_null_const, cv_PtrLcv_dnn_EltwiseLayerInt8G_delete, cv_PtrLcv_dnn_EltwiseLayerInt8G_getInnerPtr_const, cv_PtrLcv_dnn_EltwiseLayerInt8G_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::EltwiseLayerInt8, cv_PtrLcv_dnn_EltwiseLayerInt8G_new_const_EltwiseLayerInt8 } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfEltwiseLayerInt8(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfEltwiseLayerInt8(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::EltwiseLayerInt8TraitConst for core::Ptr { + #[inline] fn as_raw_EltwiseLayerInt8(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::EltwiseLayerInt8Trait for core::Ptr { + #[inline] fn as_raw_mut_EltwiseLayerInt8(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_EltwiseLayerInt8G_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_EltwiseLayerInt8G_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfEltwiseLayerInt8") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ErfLayer, + cv_PtrLcv_dnn_ErfLayerG_new_null_const, cv_PtrLcv_dnn_ErfLayerG_delete, cv_PtrLcv_dnn_ErfLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ErfLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ErfLayer, cv_PtrLcv_dnn_ErfLayerG_new_const_ErfLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfErfLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfErfLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ErfLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ErfLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ErfLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ErfLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ErfLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ErfLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ErfLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfErfLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ExpLayer, + cv_PtrLcv_dnn_ExpLayerG_new_null_const, cv_PtrLcv_dnn_ExpLayerG_delete, cv_PtrLcv_dnn_ExpLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ExpLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ExpLayer, cv_PtrLcv_dnn_ExpLayerG_new_const_ExpLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfExpLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfExpLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ExpLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ExpLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ExpLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ExpLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ExpLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ExpLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ExpLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfExpLayer") + .field("base", &crate::dnn::ExpLayerTraitConst::base(self)) + .field("scale", &crate::dnn::ExpLayerTraitConst::scale(self)) + .field("shift", &crate::dnn::ExpLayerTraitConst::shift(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ExpandLayer, + cv_PtrLcv_dnn_ExpandLayerG_new_null_const, cv_PtrLcv_dnn_ExpandLayerG_delete, cv_PtrLcv_dnn_ExpandLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ExpandLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ExpandLayer, cv_PtrLcv_dnn_ExpandLayerG_new_const_ExpandLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfExpandLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfExpandLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ExpandLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ExpandLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ExpandLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ExpandLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ExpandLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ExpandLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfExpandLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::FlattenLayer, + cv_PtrLcv_dnn_FlattenLayerG_new_null_const, cv_PtrLcv_dnn_FlattenLayerG_delete, cv_PtrLcv_dnn_FlattenLayerG_getInnerPtr_const, cv_PtrLcv_dnn_FlattenLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::FlattenLayer, cv_PtrLcv_dnn_FlattenLayerG_new_const_FlattenLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfFlattenLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfFlattenLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::FlattenLayerTraitConst for core::Ptr { + #[inline] fn as_raw_FlattenLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::FlattenLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_FlattenLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_FlattenLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_FlattenLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfFlattenLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::FloorLayer, + cv_PtrLcv_dnn_FloorLayerG_new_null_const, cv_PtrLcv_dnn_FloorLayerG_delete, cv_PtrLcv_dnn_FloorLayerG_getInnerPtr_const, cv_PtrLcv_dnn_FloorLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::FloorLayer, cv_PtrLcv_dnn_FloorLayerG_new_const_FloorLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfFloorLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfFloorLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::FloorLayerTraitConst for core::Ptr { + #[inline] fn as_raw_FloorLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::FloorLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_FloorLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_FloorLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_FloorLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_FloorLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfFloorLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::FlowWarpLayer, + cv_PtrLcv_dnn_FlowWarpLayerG_new_null_const, cv_PtrLcv_dnn_FlowWarpLayerG_delete, cv_PtrLcv_dnn_FlowWarpLayerG_getInnerPtr_const, cv_PtrLcv_dnn_FlowWarpLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::FlowWarpLayer, cv_PtrLcv_dnn_FlowWarpLayerG_new_const_FlowWarpLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfFlowWarpLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfFlowWarpLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::FlowWarpLayerTraitConst for core::Ptr { + #[inline] fn as_raw_FlowWarpLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::FlowWarpLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_FlowWarpLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_FlowWarpLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_FlowWarpLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfFlowWarpLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::GRULayer, + cv_PtrLcv_dnn_GRULayerG_new_null_const, cv_PtrLcv_dnn_GRULayerG_delete, cv_PtrLcv_dnn_GRULayerG_getInnerPtr_const, cv_PtrLcv_dnn_GRULayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::GRULayer, cv_PtrLcv_dnn_GRULayerG_new_const_GRULayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfGRULayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfGRULayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::GRULayerTraitConst for core::Ptr { + #[inline] fn as_raw_GRULayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::GRULayerTrait for core::Ptr { + #[inline] fn as_raw_mut_GRULayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GRULayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GRULayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfGRULayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::GatherElementsLayer, + cv_PtrLcv_dnn_GatherElementsLayerG_new_null_const, cv_PtrLcv_dnn_GatherElementsLayerG_delete, cv_PtrLcv_dnn_GatherElementsLayerG_getInnerPtr_const, cv_PtrLcv_dnn_GatherElementsLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::GatherElementsLayer, cv_PtrLcv_dnn_GatherElementsLayerG_new_const_GatherElementsLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfGatherElementsLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfGatherElementsLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::GatherElementsLayerTraitConst for core::Ptr { + #[inline] fn as_raw_GatherElementsLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::GatherElementsLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_GatherElementsLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GatherElementsLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GatherElementsLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfGatherElementsLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::GatherLayer, + cv_PtrLcv_dnn_GatherLayerG_new_null_const, cv_PtrLcv_dnn_GatherLayerG_delete, cv_PtrLcv_dnn_GatherLayerG_getInnerPtr_const, cv_PtrLcv_dnn_GatherLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::GatherLayer, cv_PtrLcv_dnn_GatherLayerG_new_const_GatherLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfGatherLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfGatherLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::GatherLayerTraitConst for core::Ptr { + #[inline] fn as_raw_GatherLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::GatherLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_GatherLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GatherLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GatherLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfGatherLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::GeluApproximationLayer, + cv_PtrLcv_dnn_GeluApproximationLayerG_new_null_const, cv_PtrLcv_dnn_GeluApproximationLayerG_delete, cv_PtrLcv_dnn_GeluApproximationLayerG_getInnerPtr_const, cv_PtrLcv_dnn_GeluApproximationLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::GeluApproximationLayer, cv_PtrLcv_dnn_GeluApproximationLayerG_new_const_GeluApproximationLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfGeluApproximationLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfGeluApproximationLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::GeluApproximationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_GeluApproximationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::GeluApproximationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_GeluApproximationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GeluApproximationLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GeluApproximationLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GeluApproximationLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfGeluApproximationLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::GeluLayer, + cv_PtrLcv_dnn_GeluLayerG_new_null_const, cv_PtrLcv_dnn_GeluLayerG_delete, cv_PtrLcv_dnn_GeluLayerG_getInnerPtr_const, cv_PtrLcv_dnn_GeluLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::GeluLayer, cv_PtrLcv_dnn_GeluLayerG_new_const_GeluLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfGeluLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfGeluLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::GeluLayerTraitConst for core::Ptr { + #[inline] fn as_raw_GeluLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::GeluLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_GeluLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GeluLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GeluLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GeluLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfGeluLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::GemmLayer, + cv_PtrLcv_dnn_GemmLayerG_new_null_const, cv_PtrLcv_dnn_GemmLayerG_delete, cv_PtrLcv_dnn_GemmLayerG_getInnerPtr_const, cv_PtrLcv_dnn_GemmLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::GemmLayer, cv_PtrLcv_dnn_GemmLayerG_new_const_GemmLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfGemmLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfGemmLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::GemmLayerTraitConst for core::Ptr { + #[inline] fn as_raw_GemmLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::GemmLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_GemmLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GemmLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GemmLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfGemmLayer") + .field("trans_a", &crate::dnn::GemmLayerTraitConst::trans_a(self)) + .field("trans_b", &crate::dnn::GemmLayerTraitConst::trans_b(self)) + .field("alpha", &crate::dnn::GemmLayerTraitConst::alpha(self)) + .field("beta", &crate::dnn::GemmLayerTraitConst::beta(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::GroupNormLayer, + cv_PtrLcv_dnn_GroupNormLayerG_new_null_const, cv_PtrLcv_dnn_GroupNormLayerG_delete, cv_PtrLcv_dnn_GroupNormLayerG_getInnerPtr_const, cv_PtrLcv_dnn_GroupNormLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::GroupNormLayer, cv_PtrLcv_dnn_GroupNormLayerG_new_const_GroupNormLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfGroupNormLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfGroupNormLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::GroupNormLayerTraitConst for core::Ptr { + #[inline] fn as_raw_GroupNormLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::GroupNormLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_GroupNormLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GroupNormLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_GroupNormLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfGroupNormLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::HardSigmoidLayer, + cv_PtrLcv_dnn_HardSigmoidLayerG_new_null_const, cv_PtrLcv_dnn_HardSigmoidLayerG_delete, cv_PtrLcv_dnn_HardSigmoidLayerG_getInnerPtr_const, cv_PtrLcv_dnn_HardSigmoidLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::HardSigmoidLayer, cv_PtrLcv_dnn_HardSigmoidLayerG_new_const_HardSigmoidLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfHardSigmoidLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfHardSigmoidLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::HardSigmoidLayerTraitConst for core::Ptr { + #[inline] fn as_raw_HardSigmoidLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::HardSigmoidLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_HardSigmoidLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_HardSigmoidLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_HardSigmoidLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_HardSigmoidLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfHardSigmoidLayer") + .field("alpha", &crate::dnn::HardSigmoidLayerTraitConst::alpha(self)) + .field("beta", &crate::dnn::HardSigmoidLayerTraitConst::beta(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::HardSwishLayer, + cv_PtrLcv_dnn_HardSwishLayerG_new_null_const, cv_PtrLcv_dnn_HardSwishLayerG_delete, cv_PtrLcv_dnn_HardSwishLayerG_getInnerPtr_const, cv_PtrLcv_dnn_HardSwishLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::HardSwishLayer, cv_PtrLcv_dnn_HardSwishLayerG_new_const_HardSwishLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfHardSwishLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfHardSwishLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::HardSwishLayerTraitConst for core::Ptr { + #[inline] fn as_raw_HardSwishLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::HardSwishLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_HardSwishLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_HardSwishLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_HardSwishLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_HardSwishLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfHardSwishLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::InnerProductLayer, + cv_PtrLcv_dnn_InnerProductLayerG_new_null_const, cv_PtrLcv_dnn_InnerProductLayerG_delete, cv_PtrLcv_dnn_InnerProductLayerG_getInnerPtr_const, cv_PtrLcv_dnn_InnerProductLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::InnerProductLayer, cv_PtrLcv_dnn_InnerProductLayerG_new_const_InnerProductLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfInnerProductLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfInnerProductLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::InnerProductLayerTraitConst for core::Ptr { + #[inline] fn as_raw_InnerProductLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::InnerProductLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_InnerProductLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_InnerProductLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_InnerProductLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfInnerProductLayer") + .field("axis", &crate::dnn::InnerProductLayerTraitConst::axis(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::InnerProductLayerInt8, + cv_PtrLcv_dnn_InnerProductLayerInt8G_new_null_const, cv_PtrLcv_dnn_InnerProductLayerInt8G_delete, cv_PtrLcv_dnn_InnerProductLayerInt8G_getInnerPtr_const, cv_PtrLcv_dnn_InnerProductLayerInt8G_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::InnerProductLayerInt8, cv_PtrLcv_dnn_InnerProductLayerInt8G_new_const_InnerProductLayerInt8 } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfInnerProductLayerInt8(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfInnerProductLayerInt8(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::InnerProductLayerInt8TraitConst for core::Ptr { + #[inline] fn as_raw_InnerProductLayerInt8(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::InnerProductLayerInt8Trait for core::Ptr { + #[inline] fn as_raw_mut_InnerProductLayerInt8(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_InnerProductLayerInt8G_to_PtrOfAlgorithm } + + impl crate::dnn::InnerProductLayerTraitConst for core::Ptr { + #[inline] fn as_raw_InnerProductLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::InnerProductLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_InnerProductLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_InnerProductLayerInt8G_to_PtrOfInnerProductLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_InnerProductLayerInt8G_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfInnerProductLayerInt8") + .field("input_zp", &crate::dnn::InnerProductLayerInt8TraitConst::input_zp(self)) + .field("output_zp", &crate::dnn::InnerProductLayerInt8TraitConst::output_zp(self)) + .field("input_sc", &crate::dnn::InnerProductLayerInt8TraitConst::input_sc(self)) + .field("output_sc", &crate::dnn::InnerProductLayerInt8TraitConst::output_sc(self)) + .field("per_channel", &crate::dnn::InnerProductLayerInt8TraitConst::per_channel(self)) + .field("axis", &crate::dnn::InnerProductLayerTraitConst::axis(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::InstanceNormLayer, + cv_PtrLcv_dnn_InstanceNormLayerG_new_null_const, cv_PtrLcv_dnn_InstanceNormLayerG_delete, cv_PtrLcv_dnn_InstanceNormLayerG_getInnerPtr_const, cv_PtrLcv_dnn_InstanceNormLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::InstanceNormLayer, cv_PtrLcv_dnn_InstanceNormLayerG_new_const_InstanceNormLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfInstanceNormLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfInstanceNormLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::InstanceNormLayerTraitConst for core::Ptr { + #[inline] fn as_raw_InstanceNormLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::InstanceNormLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_InstanceNormLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_InstanceNormLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_InstanceNormLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfInstanceNormLayer") + .field("epsilon", &crate::dnn::InstanceNormLayerTraitConst::epsilon(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::InterpLayer, + cv_PtrLcv_dnn_InterpLayerG_new_null_const, cv_PtrLcv_dnn_InterpLayerG_delete, cv_PtrLcv_dnn_InterpLayerG_getInnerPtr_const, cv_PtrLcv_dnn_InterpLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::InterpLayer, cv_PtrLcv_dnn_InterpLayerG_new_const_InterpLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfInterpLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfInterpLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::InterpLayerTraitConst for core::Ptr { + #[inline] fn as_raw_InterpLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::InterpLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_InterpLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_InterpLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_InterpLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfInterpLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::LRNLayer, + cv_PtrLcv_dnn_LRNLayerG_new_null_const, cv_PtrLcv_dnn_LRNLayerG_delete, cv_PtrLcv_dnn_LRNLayerG_getInnerPtr_const, cv_PtrLcv_dnn_LRNLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::LRNLayer, cv_PtrLcv_dnn_LRNLayerG_new_const_LRNLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfLRNLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfLRNLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::LRNLayerTraitConst for core::Ptr { + #[inline] fn as_raw_LRNLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LRNLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_LRNLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_LRNLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_LRNLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfLRNLayer") + .field("typ", &crate::dnn::LRNLayerTraitConst::typ(self)) + .field("size", &crate::dnn::LRNLayerTraitConst::size(self)) + .field("alpha", &crate::dnn::LRNLayerTraitConst::alpha(self)) + .field("beta", &crate::dnn::LRNLayerTraitConst::beta(self)) + .field("bias", &crate::dnn::LRNLayerTraitConst::bias(self)) + .field("norm_by_size", &crate::dnn::LRNLayerTraitConst::norm_by_size(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::LSTMLayer, + cv_PtrLcv_dnn_LSTMLayerG_new_null_const, cv_PtrLcv_dnn_LSTMLayerG_delete, cv_PtrLcv_dnn_LSTMLayerG_getInnerPtr_const, cv_PtrLcv_dnn_LSTMLayerG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfLSTMLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfLSTMLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::LSTMLayerTraitConst for core::Ptr { + #[inline] fn as_raw_LSTMLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LSTMLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_LSTMLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_LSTMLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_LSTMLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfLSTMLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::Layer, + cv_PtrLcv_dnn_LayerG_new_null_const, cv_PtrLcv_dnn_LayerG_delete, cv_PtrLcv_dnn_LayerG_getInnerPtr_const, cv_PtrLcv_dnn_LayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::Layer, cv_PtrLcv_dnn_LayerG_new_const_Layer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_LayerG_to_PtrOfAlgorithm } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::LayerNormLayer, + cv_PtrLcv_dnn_LayerNormLayerG_new_null_const, cv_PtrLcv_dnn_LayerNormLayerG_delete, cv_PtrLcv_dnn_LayerNormLayerG_getInnerPtr_const, cv_PtrLcv_dnn_LayerNormLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::LayerNormLayer, cv_PtrLcv_dnn_LayerNormLayerG_new_const_LayerNormLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfLayerNormLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfLayerNormLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::LayerNormLayerTraitConst for core::Ptr { + #[inline] fn as_raw_LayerNormLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerNormLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_LayerNormLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_LayerNormLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_LayerNormLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfLayerNormLayer") + .field("has_bias", &crate::dnn::LayerNormLayerTraitConst::has_bias(self)) + .field("axis", &crate::dnn::LayerNormLayerTraitConst::axis(self)) + .field("epsilon", &crate::dnn::LayerNormLayerTraitConst::epsilon(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::LogLayer, + cv_PtrLcv_dnn_LogLayerG_new_null_const, cv_PtrLcv_dnn_LogLayerG_delete, cv_PtrLcv_dnn_LogLayerG_getInnerPtr_const, cv_PtrLcv_dnn_LogLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::LogLayer, cv_PtrLcv_dnn_LogLayerG_new_const_LogLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfLogLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfLogLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::LogLayerTraitConst for core::Ptr { + #[inline] fn as_raw_LogLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LogLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_LogLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_LogLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_LogLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_LogLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfLogLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::MVNLayer, + cv_PtrLcv_dnn_MVNLayerG_new_null_const, cv_PtrLcv_dnn_MVNLayerG_delete, cv_PtrLcv_dnn_MVNLayerG_getInnerPtr_const, cv_PtrLcv_dnn_MVNLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::MVNLayer, cv_PtrLcv_dnn_MVNLayerG_new_const_MVNLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfMVNLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfMVNLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::MVNLayerTraitConst for core::Ptr { + #[inline] fn as_raw_MVNLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::MVNLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_MVNLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_MVNLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_MVNLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfMVNLayer") + .field("eps", &crate::dnn::MVNLayerTraitConst::eps(self)) + .field("norm_variance", &crate::dnn::MVNLayerTraitConst::norm_variance(self)) + .field("across_channels", &crate::dnn::MVNLayerTraitConst::across_channels(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::MatMulLayer, + cv_PtrLcv_dnn_MatMulLayerG_new_null_const, cv_PtrLcv_dnn_MatMulLayerG_delete, cv_PtrLcv_dnn_MatMulLayerG_getInnerPtr_const, cv_PtrLcv_dnn_MatMulLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::MatMulLayer, cv_PtrLcv_dnn_MatMulLayerG_new_const_MatMulLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfMatMulLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfMatMulLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::MatMulLayerTraitConst for core::Ptr { + #[inline] fn as_raw_MatMulLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::MatMulLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_MatMulLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_MatMulLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_MatMulLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfMatMulLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::MaxUnpoolLayer, + cv_PtrLcv_dnn_MaxUnpoolLayerG_new_null_const, cv_PtrLcv_dnn_MaxUnpoolLayerG_delete, cv_PtrLcv_dnn_MaxUnpoolLayerG_getInnerPtr_const, cv_PtrLcv_dnn_MaxUnpoolLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::MaxUnpoolLayer, cv_PtrLcv_dnn_MaxUnpoolLayerG_new_const_MaxUnpoolLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfMaxUnpoolLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfMaxUnpoolLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::MaxUnpoolLayerTraitConst for core::Ptr { + #[inline] fn as_raw_MaxUnpoolLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::MaxUnpoolLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_MaxUnpoolLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_MaxUnpoolLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_MaxUnpoolLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfMaxUnpoolLayer") + .field("pool_kernel", &crate::dnn::MaxUnpoolLayerTraitConst::pool_kernel(self)) + .field("pool_pad", &crate::dnn::MaxUnpoolLayerTraitConst::pool_pad(self)) + .field("pool_stride", &crate::dnn::MaxUnpoolLayerTraitConst::pool_stride(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::MishLayer, + cv_PtrLcv_dnn_MishLayerG_new_null_const, cv_PtrLcv_dnn_MishLayerG_delete, cv_PtrLcv_dnn_MishLayerG_getInnerPtr_const, cv_PtrLcv_dnn_MishLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::MishLayer, cv_PtrLcv_dnn_MishLayerG_new_const_MishLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfMishLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfMishLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::MishLayerTraitConst for core::Ptr { + #[inline] fn as_raw_MishLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::MishLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_MishLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_MishLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_MishLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_MishLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfMishLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::NaryEltwiseLayer, + cv_PtrLcv_dnn_NaryEltwiseLayerG_new_null_const, cv_PtrLcv_dnn_NaryEltwiseLayerG_delete, cv_PtrLcv_dnn_NaryEltwiseLayerG_getInnerPtr_const, cv_PtrLcv_dnn_NaryEltwiseLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::NaryEltwiseLayer, cv_PtrLcv_dnn_NaryEltwiseLayerG_new_const_NaryEltwiseLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfNaryEltwiseLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfNaryEltwiseLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::NaryEltwiseLayerTraitConst for core::Ptr { + #[inline] fn as_raw_NaryEltwiseLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::NaryEltwiseLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_NaryEltwiseLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_NaryEltwiseLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_NaryEltwiseLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfNaryEltwiseLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::NormalizeBBoxLayer, + cv_PtrLcv_dnn_NormalizeBBoxLayerG_new_null_const, cv_PtrLcv_dnn_NormalizeBBoxLayerG_delete, cv_PtrLcv_dnn_NormalizeBBoxLayerG_getInnerPtr_const, cv_PtrLcv_dnn_NormalizeBBoxLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::NormalizeBBoxLayer, cv_PtrLcv_dnn_NormalizeBBoxLayerG_new_const_NormalizeBBoxLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfNormalizeBBoxLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfNormalizeBBoxLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::NormalizeBBoxLayerTraitConst for core::Ptr { + #[inline] fn as_raw_NormalizeBBoxLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::NormalizeBBoxLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_NormalizeBBoxLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_NormalizeBBoxLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_NormalizeBBoxLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfNormalizeBBoxLayer") + .field("pnorm", &crate::dnn::NormalizeBBoxLayerTraitConst::pnorm(self)) + .field("epsilon", &crate::dnn::NormalizeBBoxLayerTraitConst::epsilon(self)) + .field("across_spatial", &crate::dnn::NormalizeBBoxLayerTraitConst::across_spatial(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::NotLayer, + cv_PtrLcv_dnn_NotLayerG_new_null_const, cv_PtrLcv_dnn_NotLayerG_delete, cv_PtrLcv_dnn_NotLayerG_getInnerPtr_const, cv_PtrLcv_dnn_NotLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::NotLayer, cv_PtrLcv_dnn_NotLayerG_new_const_NotLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfNotLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfNotLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::NotLayerTraitConst for core::Ptr { + #[inline] fn as_raw_NotLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::NotLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_NotLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_NotLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_NotLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_NotLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfNotLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::PaddingLayer, + cv_PtrLcv_dnn_PaddingLayerG_new_null_const, cv_PtrLcv_dnn_PaddingLayerG_delete, cv_PtrLcv_dnn_PaddingLayerG_getInnerPtr_const, cv_PtrLcv_dnn_PaddingLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::PaddingLayer, cv_PtrLcv_dnn_PaddingLayerG_new_const_PaddingLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfPaddingLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfPaddingLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::PaddingLayerTraitConst for core::Ptr { + #[inline] fn as_raw_PaddingLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::PaddingLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_PaddingLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PaddingLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PaddingLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfPaddingLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::PermuteLayer, + cv_PtrLcv_dnn_PermuteLayerG_new_null_const, cv_PtrLcv_dnn_PermuteLayerG_delete, cv_PtrLcv_dnn_PermuteLayerG_getInnerPtr_const, cv_PtrLcv_dnn_PermuteLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::PermuteLayer, cv_PtrLcv_dnn_PermuteLayerG_new_const_PermuteLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfPermuteLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfPermuteLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::PermuteLayerTraitConst for core::Ptr { + #[inline] fn as_raw_PermuteLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::PermuteLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_PermuteLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PermuteLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PermuteLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfPermuteLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::PoolingLayer, + cv_PtrLcv_dnn_PoolingLayerG_new_null_const, cv_PtrLcv_dnn_PoolingLayerG_delete, cv_PtrLcv_dnn_PoolingLayerG_getInnerPtr_const, cv_PtrLcv_dnn_PoolingLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::PoolingLayer, cv_PtrLcv_dnn_PoolingLayerG_new_const_PoolingLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfPoolingLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfPoolingLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::PoolingLayerTraitConst for core::Ptr { + #[inline] fn as_raw_PoolingLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::PoolingLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_PoolingLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PoolingLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PoolingLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfPoolingLayer") + .field("typ", &crate::dnn::PoolingLayerTraitConst::typ(self)) + .field("kernel_size", &crate::dnn::PoolingLayerTraitConst::kernel_size(self)) + .field("strides", &crate::dnn::PoolingLayerTraitConst::strides(self)) + .field("pads_begin", &crate::dnn::PoolingLayerTraitConst::pads_begin(self)) + .field("pads_end", &crate::dnn::PoolingLayerTraitConst::pads_end(self)) + .field("global_pooling", &crate::dnn::PoolingLayerTraitConst::global_pooling(self)) + .field("is_global_pooling", &crate::dnn::PoolingLayerTraitConst::is_global_pooling(self)) + .field("compute_max_idx", &crate::dnn::PoolingLayerTraitConst::compute_max_idx(self)) + .field("pad_mode", &crate::dnn::PoolingLayerTraitConst::pad_mode(self)) + .field("ceil_mode", &crate::dnn::PoolingLayerTraitConst::ceil_mode(self)) + .field("ave_pool_padded_area", &crate::dnn::PoolingLayerTraitConst::ave_pool_padded_area(self)) + .field("pooled_size", &crate::dnn::PoolingLayerTraitConst::pooled_size(self)) + .field("spatial_scale", &crate::dnn::PoolingLayerTraitConst::spatial_scale(self)) + .field("ps_roi_out_channels", &crate::dnn::PoolingLayerTraitConst::ps_roi_out_channels(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::PoolingLayerInt8, + cv_PtrLcv_dnn_PoolingLayerInt8G_new_null_const, cv_PtrLcv_dnn_PoolingLayerInt8G_delete, cv_PtrLcv_dnn_PoolingLayerInt8G_getInnerPtr_const, cv_PtrLcv_dnn_PoolingLayerInt8G_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::PoolingLayerInt8, cv_PtrLcv_dnn_PoolingLayerInt8G_new_const_PoolingLayerInt8 } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfPoolingLayerInt8(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfPoolingLayerInt8(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::PoolingLayerInt8TraitConst for core::Ptr { + #[inline] fn as_raw_PoolingLayerInt8(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::PoolingLayerInt8Trait for core::Ptr { + #[inline] fn as_raw_mut_PoolingLayerInt8(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PoolingLayerInt8G_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PoolingLayerInt8G_to_PtrOfLayer } + + impl crate::dnn::PoolingLayerTraitConst for core::Ptr { + #[inline] fn as_raw_PoolingLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::PoolingLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_PoolingLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PoolingLayerInt8G_to_PtrOfPoolingLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfPoolingLayerInt8") + .field("input_zp", &crate::dnn::PoolingLayerInt8TraitConst::input_zp(self)) + .field("output_zp", &crate::dnn::PoolingLayerInt8TraitConst::output_zp(self)) + .field("input_sc", &crate::dnn::PoolingLayerInt8TraitConst::input_sc(self)) + .field("output_sc", &crate::dnn::PoolingLayerInt8TraitConst::output_sc(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .field("typ", &crate::dnn::PoolingLayerTraitConst::typ(self)) + .field("kernel_size", &crate::dnn::PoolingLayerTraitConst::kernel_size(self)) + .field("strides", &crate::dnn::PoolingLayerTraitConst::strides(self)) + .field("pads_begin", &crate::dnn::PoolingLayerTraitConst::pads_begin(self)) + .field("pads_end", &crate::dnn::PoolingLayerTraitConst::pads_end(self)) + .field("global_pooling", &crate::dnn::PoolingLayerTraitConst::global_pooling(self)) + .field("is_global_pooling", &crate::dnn::PoolingLayerTraitConst::is_global_pooling(self)) + .field("compute_max_idx", &crate::dnn::PoolingLayerTraitConst::compute_max_idx(self)) + .field("pad_mode", &crate::dnn::PoolingLayerTraitConst::pad_mode(self)) + .field("ceil_mode", &crate::dnn::PoolingLayerTraitConst::ceil_mode(self)) + .field("ave_pool_padded_area", &crate::dnn::PoolingLayerTraitConst::ave_pool_padded_area(self)) + .field("pooled_size", &crate::dnn::PoolingLayerTraitConst::pooled_size(self)) + .field("spatial_scale", &crate::dnn::PoolingLayerTraitConst::spatial_scale(self)) + .field("ps_roi_out_channels", &crate::dnn::PoolingLayerTraitConst::ps_roi_out_channels(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::PowerLayer, + cv_PtrLcv_dnn_PowerLayerG_new_null_const, cv_PtrLcv_dnn_PowerLayerG_delete, cv_PtrLcv_dnn_PowerLayerG_getInnerPtr_const, cv_PtrLcv_dnn_PowerLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::PowerLayer, cv_PtrLcv_dnn_PowerLayerG_new_const_PowerLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfPowerLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfPowerLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::PowerLayerTraitConst for core::Ptr { + #[inline] fn as_raw_PowerLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::PowerLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_PowerLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PowerLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PowerLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PowerLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfPowerLayer") + .field("power", &crate::dnn::PowerLayerTraitConst::power(self)) + .field("scale", &crate::dnn::PowerLayerTraitConst::scale(self)) + .field("shift", &crate::dnn::PowerLayerTraitConst::shift(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::PriorBoxLayer, + cv_PtrLcv_dnn_PriorBoxLayerG_new_null_const, cv_PtrLcv_dnn_PriorBoxLayerG_delete, cv_PtrLcv_dnn_PriorBoxLayerG_getInnerPtr_const, cv_PtrLcv_dnn_PriorBoxLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::PriorBoxLayer, cv_PtrLcv_dnn_PriorBoxLayerG_new_const_PriorBoxLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfPriorBoxLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfPriorBoxLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::PriorBoxLayerTraitConst for core::Ptr { + #[inline] fn as_raw_PriorBoxLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::PriorBoxLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_PriorBoxLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PriorBoxLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_PriorBoxLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfPriorBoxLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ProposalLayer, + cv_PtrLcv_dnn_ProposalLayerG_new_null_const, cv_PtrLcv_dnn_ProposalLayerG_delete, cv_PtrLcv_dnn_ProposalLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ProposalLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ProposalLayer, cv_PtrLcv_dnn_ProposalLayerG_new_const_ProposalLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfProposalLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfProposalLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ProposalLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ProposalLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ProposalLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ProposalLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ProposalLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ProposalLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfProposalLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::QuantizeLayer, + cv_PtrLcv_dnn_QuantizeLayerG_new_null_const, cv_PtrLcv_dnn_QuantizeLayerG_delete, cv_PtrLcv_dnn_QuantizeLayerG_getInnerPtr_const, cv_PtrLcv_dnn_QuantizeLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::QuantizeLayer, cv_PtrLcv_dnn_QuantizeLayerG_new_const_QuantizeLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfQuantizeLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfQuantizeLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::QuantizeLayerTraitConst for core::Ptr { + #[inline] fn as_raw_QuantizeLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::QuantizeLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_QuantizeLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_QuantizeLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_QuantizeLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfQuantizeLayer") + .field("scales", &crate::dnn::QuantizeLayerTraitConst::scales(self)) + .field("zeropoints", &crate::dnn::QuantizeLayerTraitConst::zeropoints(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::RNNLayer, + cv_PtrLcv_dnn_RNNLayerG_new_null_const, cv_PtrLcv_dnn_RNNLayerG_delete, cv_PtrLcv_dnn_RNNLayerG_getInnerPtr_const, cv_PtrLcv_dnn_RNNLayerG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfRNNLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfRNNLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::RNNLayerTraitConst for core::Ptr { + #[inline] fn as_raw_RNNLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::RNNLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_RNNLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_RNNLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_RNNLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfRNNLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ReLU6Layer, + cv_PtrLcv_dnn_ReLU6LayerG_new_null_const, cv_PtrLcv_dnn_ReLU6LayerG_delete, cv_PtrLcv_dnn_ReLU6LayerG_getInnerPtr_const, cv_PtrLcv_dnn_ReLU6LayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ReLU6Layer, cv_PtrLcv_dnn_ReLU6LayerG_new_const_ReLU6Layer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfReLU6Layer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfReLU6Layer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ReLU6LayerTraitConst for core::Ptr { + #[inline] fn as_raw_ReLU6Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ReLU6LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ReLU6Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReLU6LayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReLU6LayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReLU6LayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfReLU6Layer") + .field("min_value", &crate::dnn::ReLU6LayerTraitConst::min_value(self)) + .field("max_value", &crate::dnn::ReLU6LayerTraitConst::max_value(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ReLULayer, + cv_PtrLcv_dnn_ReLULayerG_new_null_const, cv_PtrLcv_dnn_ReLULayerG_delete, cv_PtrLcv_dnn_ReLULayerG_getInnerPtr_const, cv_PtrLcv_dnn_ReLULayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ReLULayer, cv_PtrLcv_dnn_ReLULayerG_new_const_ReLULayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfReLULayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfReLULayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ReLULayerTraitConst for core::Ptr { + #[inline] fn as_raw_ReLULayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ReLULayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ReLULayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReLULayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReLULayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReLULayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfReLULayer") + .field("negative_slope", &crate::dnn::ReLULayerTraitConst::negative_slope(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ReciprocalLayer, + cv_PtrLcv_dnn_ReciprocalLayerG_new_null_const, cv_PtrLcv_dnn_ReciprocalLayerG_delete, cv_PtrLcv_dnn_ReciprocalLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ReciprocalLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ReciprocalLayer, cv_PtrLcv_dnn_ReciprocalLayerG_new_const_ReciprocalLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfReciprocalLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfReciprocalLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ReciprocalLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ReciprocalLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ReciprocalLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ReciprocalLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReciprocalLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReciprocalLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReciprocalLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfReciprocalLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ReduceLayer, + cv_PtrLcv_dnn_ReduceLayerG_new_null_const, cv_PtrLcv_dnn_ReduceLayerG_delete, cv_PtrLcv_dnn_ReduceLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ReduceLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ReduceLayer, cv_PtrLcv_dnn_ReduceLayerG_new_const_ReduceLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfReduceLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfReduceLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ReduceLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ReduceLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ReduceLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ReduceLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReduceLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReduceLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfReduceLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::RegionLayer, + cv_PtrLcv_dnn_RegionLayerG_new_null_const, cv_PtrLcv_dnn_RegionLayerG_delete, cv_PtrLcv_dnn_RegionLayerG_getInnerPtr_const, cv_PtrLcv_dnn_RegionLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::RegionLayer, cv_PtrLcv_dnn_RegionLayerG_new_const_RegionLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfRegionLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfRegionLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::RegionLayerTraitConst for core::Ptr { + #[inline] fn as_raw_RegionLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::RegionLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_RegionLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_RegionLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_RegionLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfRegionLayer") + .field("nms_threshold", &crate::dnn::RegionLayerTraitConst::nms_threshold(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ReorgLayer, + cv_PtrLcv_dnn_ReorgLayerG_new_null_const, cv_PtrLcv_dnn_ReorgLayerG_delete, cv_PtrLcv_dnn_ReorgLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ReorgLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ReorgLayer, cv_PtrLcv_dnn_ReorgLayerG_new_const_ReorgLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfReorgLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfReorgLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ReorgLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ReorgLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ReorgLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ReorgLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReorgLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReorgLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfReorgLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::RequantizeLayer, + cv_PtrLcv_dnn_RequantizeLayerG_new_null_const, cv_PtrLcv_dnn_RequantizeLayerG_delete, cv_PtrLcv_dnn_RequantizeLayerG_getInnerPtr_const, cv_PtrLcv_dnn_RequantizeLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::RequantizeLayer, cv_PtrLcv_dnn_RequantizeLayerG_new_const_RequantizeLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfRequantizeLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfRequantizeLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::RequantizeLayerTraitConst for core::Ptr { + #[inline] fn as_raw_RequantizeLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::RequantizeLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_RequantizeLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_RequantizeLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_RequantizeLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfRequantizeLayer") + .field("scale", &crate::dnn::RequantizeLayerTraitConst::scale(self)) + .field("shift", &crate::dnn::RequantizeLayerTraitConst::shift(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ReshapeLayer, + cv_PtrLcv_dnn_ReshapeLayerG_new_null_const, cv_PtrLcv_dnn_ReshapeLayerG_delete, cv_PtrLcv_dnn_ReshapeLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ReshapeLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ReshapeLayer, cv_PtrLcv_dnn_ReshapeLayerG_new_const_ReshapeLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfReshapeLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfReshapeLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ReshapeLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ReshapeLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ReshapeLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ReshapeLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReshapeLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ReshapeLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfReshapeLayer") + .field("new_shape_desc", &crate::dnn::ReshapeLayerTraitConst::new_shape_desc(self)) + .field("new_shape_range", &crate::dnn::ReshapeLayerTraitConst::new_shape_range(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ResizeLayer, + cv_PtrLcv_dnn_ResizeLayerG_new_null_const, cv_PtrLcv_dnn_ResizeLayerG_delete, cv_PtrLcv_dnn_ResizeLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ResizeLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ResizeLayer, cv_PtrLcv_dnn_ResizeLayerG_new_const_ResizeLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfResizeLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfResizeLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ResizeLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ResizeLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ResizeLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ResizeLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ResizeLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ResizeLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfResizeLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::RoundLayer, + cv_PtrLcv_dnn_RoundLayerG_new_null_const, cv_PtrLcv_dnn_RoundLayerG_delete, cv_PtrLcv_dnn_RoundLayerG_getInnerPtr_const, cv_PtrLcv_dnn_RoundLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::RoundLayer, cv_PtrLcv_dnn_RoundLayerG_new_const_RoundLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfRoundLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfRoundLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::RoundLayerTraitConst for core::Ptr { + #[inline] fn as_raw_RoundLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::RoundLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_RoundLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_RoundLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_RoundLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_RoundLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfRoundLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ScaleLayer, + cv_PtrLcv_dnn_ScaleLayerG_new_null_const, cv_PtrLcv_dnn_ScaleLayerG_delete, cv_PtrLcv_dnn_ScaleLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ScaleLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ScaleLayer, cv_PtrLcv_dnn_ScaleLayerG_new_const_ScaleLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfScaleLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfScaleLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ScaleLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ScaleLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ScaleLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ScaleLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ScaleLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ScaleLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfScaleLayer") + .field("has_bias", &crate::dnn::ScaleLayerTraitConst::has_bias(self)) + .field("axis", &crate::dnn::ScaleLayerTraitConst::axis(self)) + .field("mode", &crate::dnn::ScaleLayerTraitConst::mode(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ScaleLayerInt8, + cv_PtrLcv_dnn_ScaleLayerInt8G_new_null_const, cv_PtrLcv_dnn_ScaleLayerInt8G_delete, cv_PtrLcv_dnn_ScaleLayerInt8G_getInnerPtr_const, cv_PtrLcv_dnn_ScaleLayerInt8G_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ScaleLayerInt8, cv_PtrLcv_dnn_ScaleLayerInt8G_new_const_ScaleLayerInt8 } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfScaleLayerInt8(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfScaleLayerInt8(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ScaleLayerInt8TraitConst for core::Ptr { + #[inline] fn as_raw_ScaleLayerInt8(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ScaleLayerInt8Trait for core::Ptr { + #[inline] fn as_raw_mut_ScaleLayerInt8(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ScaleLayerInt8G_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ScaleLayerInt8G_to_PtrOfLayer } + + impl crate::dnn::ScaleLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ScaleLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ScaleLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ScaleLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ScaleLayerInt8G_to_PtrOfScaleLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfScaleLayerInt8") + .field("output_sc", &crate::dnn::ScaleLayerInt8TraitConst::output_sc(self)) + .field("output_zp", &crate::dnn::ScaleLayerInt8TraitConst::output_zp(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .field("has_bias", &crate::dnn::ScaleLayerTraitConst::has_bias(self)) + .field("axis", &crate::dnn::ScaleLayerTraitConst::axis(self)) + .field("mode", &crate::dnn::ScaleLayerTraitConst::mode(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ScatterLayer, + cv_PtrLcv_dnn_ScatterLayerG_new_null_const, cv_PtrLcv_dnn_ScatterLayerG_delete, cv_PtrLcv_dnn_ScatterLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ScatterLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ScatterLayer, cv_PtrLcv_dnn_ScatterLayerG_new_const_ScatterLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfScatterLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfScatterLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ScatterLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ScatterLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ScatterLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ScatterLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ScatterLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ScatterLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfScatterLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ScatterNDLayer, + cv_PtrLcv_dnn_ScatterNDLayerG_new_null_const, cv_PtrLcv_dnn_ScatterNDLayerG_delete, cv_PtrLcv_dnn_ScatterNDLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ScatterNDLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ScatterNDLayer, cv_PtrLcv_dnn_ScatterNDLayerG_new_const_ScatterNDLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfScatterNDLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfScatterNDLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ScatterNDLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ScatterNDLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ScatterNDLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ScatterNDLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ScatterNDLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ScatterNDLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfScatterNDLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SeluLayer, + cv_PtrLcv_dnn_SeluLayerG_new_null_const, cv_PtrLcv_dnn_SeluLayerG_delete, cv_PtrLcv_dnn_SeluLayerG_getInnerPtr_const, cv_PtrLcv_dnn_SeluLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SeluLayer, cv_PtrLcv_dnn_SeluLayerG_new_const_SeluLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSeluLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSeluLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SeluLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SeluLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SeluLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SeluLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SeluLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SeluLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SeluLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSeluLayer") + .field("alpha", &crate::dnn::SeluLayerTraitConst::alpha(self)) + .field("gamma", &crate::dnn::SeluLayerTraitConst::gamma(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ShiftLayer, + cv_PtrLcv_dnn_ShiftLayerG_new_null_const, cv_PtrLcv_dnn_ShiftLayerG_delete, cv_PtrLcv_dnn_ShiftLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ShiftLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ShiftLayer, cv_PtrLcv_dnn_ShiftLayerG_new_const_ShiftLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfShiftLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfShiftLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ShiftLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ShiftLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ShiftLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ShiftLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ShiftLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ShiftLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfShiftLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ShiftLayerInt8, + cv_PtrLcv_dnn_ShiftLayerInt8G_new_null_const, cv_PtrLcv_dnn_ShiftLayerInt8G_delete, cv_PtrLcv_dnn_ShiftLayerInt8G_getInnerPtr_const, cv_PtrLcv_dnn_ShiftLayerInt8G_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ShiftLayerInt8, cv_PtrLcv_dnn_ShiftLayerInt8G_new_const_ShiftLayerInt8 } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfShiftLayerInt8(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfShiftLayerInt8(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ShiftLayerInt8TraitConst for core::Ptr { + #[inline] fn as_raw_ShiftLayerInt8(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ShiftLayerInt8Trait for core::Ptr { + #[inline] fn as_raw_mut_ShiftLayerInt8(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ShiftLayerInt8G_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ShiftLayerInt8G_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfShiftLayerInt8") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ShrinkLayer, + cv_PtrLcv_dnn_ShrinkLayerG_new_null_const, cv_PtrLcv_dnn_ShrinkLayerG_delete, cv_PtrLcv_dnn_ShrinkLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ShrinkLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ShrinkLayer, cv_PtrLcv_dnn_ShrinkLayerG_new_const_ShrinkLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfShrinkLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfShrinkLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ShrinkLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ShrinkLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ShrinkLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ShrinkLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ShrinkLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ShrinkLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ShrinkLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfShrinkLayer") + .field("bias", &crate::dnn::ShrinkLayerTraitConst::bias(self)) + .field("lambd", &crate::dnn::ShrinkLayerTraitConst::lambd(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ShuffleChannelLayer, + cv_PtrLcv_dnn_ShuffleChannelLayerG_new_null_const, cv_PtrLcv_dnn_ShuffleChannelLayerG_delete, cv_PtrLcv_dnn_ShuffleChannelLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ShuffleChannelLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ShuffleChannelLayer, cv_PtrLcv_dnn_ShuffleChannelLayerG_new_const_ShuffleChannelLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfShuffleChannelLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfShuffleChannelLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ShuffleChannelLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ShuffleChannelLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ShuffleChannelLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ShuffleChannelLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ShuffleChannelLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ShuffleChannelLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfShuffleChannelLayer") + .field("group", &crate::dnn::ShuffleChannelLayerTraitConst::group(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SigmoidLayer, + cv_PtrLcv_dnn_SigmoidLayerG_new_null_const, cv_PtrLcv_dnn_SigmoidLayerG_delete, cv_PtrLcv_dnn_SigmoidLayerG_getInnerPtr_const, cv_PtrLcv_dnn_SigmoidLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SigmoidLayer, cv_PtrLcv_dnn_SigmoidLayerG_new_const_SigmoidLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSigmoidLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSigmoidLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SigmoidLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SigmoidLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SigmoidLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SigmoidLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SigmoidLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SigmoidLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SigmoidLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSigmoidLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SignLayer, + cv_PtrLcv_dnn_SignLayerG_new_null_const, cv_PtrLcv_dnn_SignLayerG_delete, cv_PtrLcv_dnn_SignLayerG_getInnerPtr_const, cv_PtrLcv_dnn_SignLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SignLayer, cv_PtrLcv_dnn_SignLayerG_new_const_SignLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSignLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSignLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SignLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SignLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SignLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SignLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SignLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SignLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SignLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSignLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SinLayer, + cv_PtrLcv_dnn_SinLayerG_new_null_const, cv_PtrLcv_dnn_SinLayerG_delete, cv_PtrLcv_dnn_SinLayerG_getInnerPtr_const, cv_PtrLcv_dnn_SinLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SinLayer, cv_PtrLcv_dnn_SinLayerG_new_const_SinLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSinLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSinLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SinLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SinLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SinLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SinLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SinLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SinLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SinLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSinLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SinhLayer, + cv_PtrLcv_dnn_SinhLayerG_new_null_const, cv_PtrLcv_dnn_SinhLayerG_delete, cv_PtrLcv_dnn_SinhLayerG_getInnerPtr_const, cv_PtrLcv_dnn_SinhLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SinhLayer, cv_PtrLcv_dnn_SinhLayerG_new_const_SinhLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSinhLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSinhLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SinhLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SinhLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SinhLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SinhLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SinhLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SinhLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SinhLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSinhLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SliceLayer, + cv_PtrLcv_dnn_SliceLayerG_new_null_const, cv_PtrLcv_dnn_SliceLayerG_delete, cv_PtrLcv_dnn_SliceLayerG_getInnerPtr_const, cv_PtrLcv_dnn_SliceLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SliceLayer, cv_PtrLcv_dnn_SliceLayerG_new_const_SliceLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSliceLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSliceLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SliceLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SliceLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SliceLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SliceLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SliceLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SliceLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSliceLayer") + .field("slice_ranges", &crate::dnn::SliceLayerTraitConst::slice_ranges(self)) + .field("slice_steps", &crate::dnn::SliceLayerTraitConst::slice_steps(self)) + .field("axis", &crate::dnn::SliceLayerTraitConst::axis(self)) + .field("num_split", &crate::dnn::SliceLayerTraitConst::num_split(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SoftmaxLayer, + cv_PtrLcv_dnn_SoftmaxLayerG_new_null_const, cv_PtrLcv_dnn_SoftmaxLayerG_delete, cv_PtrLcv_dnn_SoftmaxLayerG_getInnerPtr_const, cv_PtrLcv_dnn_SoftmaxLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SoftmaxLayer, cv_PtrLcv_dnn_SoftmaxLayerG_new_const_SoftmaxLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSoftmaxLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSoftmaxLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SoftmaxLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SoftmaxLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SoftmaxLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SoftmaxLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SoftmaxLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SoftmaxLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSoftmaxLayer") + .field("log_soft_max", &crate::dnn::SoftmaxLayerTraitConst::log_soft_max(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SoftmaxLayerInt8, + cv_PtrLcv_dnn_SoftmaxLayerInt8G_new_null_const, cv_PtrLcv_dnn_SoftmaxLayerInt8G_delete, cv_PtrLcv_dnn_SoftmaxLayerInt8G_getInnerPtr_const, cv_PtrLcv_dnn_SoftmaxLayerInt8G_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SoftmaxLayerInt8, cv_PtrLcv_dnn_SoftmaxLayerInt8G_new_const_SoftmaxLayerInt8 } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSoftmaxLayerInt8(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSoftmaxLayerInt8(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SoftmaxLayerInt8TraitConst for core::Ptr { + #[inline] fn as_raw_SoftmaxLayerInt8(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SoftmaxLayerInt8Trait for core::Ptr { + #[inline] fn as_raw_mut_SoftmaxLayerInt8(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SoftmaxLayerInt8G_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SoftmaxLayerInt8G_to_PtrOfLayer } + + impl crate::dnn::SoftmaxLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SoftmaxLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SoftmaxLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SoftmaxLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SoftmaxLayerInt8G_to_PtrOfSoftmaxLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSoftmaxLayerInt8") + .field("output_sc", &crate::dnn::SoftmaxLayerInt8TraitConst::output_sc(self)) + .field("output_zp", &crate::dnn::SoftmaxLayerInt8TraitConst::output_zp(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .field("log_soft_max", &crate::dnn::SoftmaxLayerTraitConst::log_soft_max(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SoftplusLayer, + cv_PtrLcv_dnn_SoftplusLayerG_new_null_const, cv_PtrLcv_dnn_SoftplusLayerG_delete, cv_PtrLcv_dnn_SoftplusLayerG_getInnerPtr_const, cv_PtrLcv_dnn_SoftplusLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SoftplusLayer, cv_PtrLcv_dnn_SoftplusLayerG_new_const_SoftplusLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSoftplusLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSoftplusLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SoftplusLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SoftplusLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SoftplusLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SoftplusLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SoftplusLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SoftplusLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SoftplusLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSoftplusLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SoftsignLayer, + cv_PtrLcv_dnn_SoftsignLayerG_new_null_const, cv_PtrLcv_dnn_SoftsignLayerG_delete, cv_PtrLcv_dnn_SoftsignLayerG_getInnerPtr_const, cv_PtrLcv_dnn_SoftsignLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SoftsignLayer, cv_PtrLcv_dnn_SoftsignLayerG_new_const_SoftsignLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSoftsignLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSoftsignLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SoftsignLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SoftsignLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SoftsignLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SoftsignLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SoftsignLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SoftsignLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SoftsignLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSoftsignLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SpaceToDepthLayer, + cv_PtrLcv_dnn_SpaceToDepthLayerG_new_null_const, cv_PtrLcv_dnn_SpaceToDepthLayerG_delete, cv_PtrLcv_dnn_SpaceToDepthLayerG_getInnerPtr_const, cv_PtrLcv_dnn_SpaceToDepthLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SpaceToDepthLayer, cv_PtrLcv_dnn_SpaceToDepthLayerG_new_const_SpaceToDepthLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSpaceToDepthLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSpaceToDepthLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SpaceToDepthLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SpaceToDepthLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SpaceToDepthLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SpaceToDepthLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SpaceToDepthLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SpaceToDepthLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSpaceToDepthLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SplitLayer, + cv_PtrLcv_dnn_SplitLayerG_new_null_const, cv_PtrLcv_dnn_SplitLayerG_delete, cv_PtrLcv_dnn_SplitLayerG_getInnerPtr_const, cv_PtrLcv_dnn_SplitLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SplitLayer, cv_PtrLcv_dnn_SplitLayerG_new_const_SplitLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSplitLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSplitLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SplitLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SplitLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SplitLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SplitLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SplitLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SplitLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSplitLayer") + .field("outputs_count", &crate::dnn::SplitLayerTraitConst::outputs_count(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SqrtLayer, + cv_PtrLcv_dnn_SqrtLayerG_new_null_const, cv_PtrLcv_dnn_SqrtLayerG_delete, cv_PtrLcv_dnn_SqrtLayerG_getInnerPtr_const, cv_PtrLcv_dnn_SqrtLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SqrtLayer, cv_PtrLcv_dnn_SqrtLayerG_new_const_SqrtLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSqrtLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSqrtLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SqrtLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SqrtLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SqrtLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SqrtLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SqrtLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SqrtLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SqrtLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSqrtLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::SwishLayer, + cv_PtrLcv_dnn_SwishLayerG_new_null_const, cv_PtrLcv_dnn_SwishLayerG_delete, cv_PtrLcv_dnn_SwishLayerG_getInnerPtr_const, cv_PtrLcv_dnn_SwishLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::SwishLayer, cv_PtrLcv_dnn_SwishLayerG_new_const_SwishLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfSwishLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfSwishLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::SwishLayerTraitConst for core::Ptr { + #[inline] fn as_raw_SwishLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::SwishLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_SwishLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SwishLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SwishLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_SwishLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfSwishLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::TanHLayer, + cv_PtrLcv_dnn_TanHLayerG_new_null_const, cv_PtrLcv_dnn_TanHLayerG_delete, cv_PtrLcv_dnn_TanHLayerG_getInnerPtr_const, cv_PtrLcv_dnn_TanHLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::TanHLayer, cv_PtrLcv_dnn_TanHLayerG_new_const_TanHLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfTanHLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfTanHLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::TanHLayerTraitConst for core::Ptr { + #[inline] fn as_raw_TanHLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::TanHLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_TanHLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_TanHLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_TanHLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_TanHLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfTanHLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::TanLayer, + cv_PtrLcv_dnn_TanLayerG_new_null_const, cv_PtrLcv_dnn_TanLayerG_delete, cv_PtrLcv_dnn_TanLayerG_getInnerPtr_const, cv_PtrLcv_dnn_TanLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::TanLayer, cv_PtrLcv_dnn_TanLayerG_new_const_TanLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfTanLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfTanLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::TanLayerTraitConst for core::Ptr { + #[inline] fn as_raw_TanLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::TanLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_TanLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_TanLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_TanLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_TanLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfTanLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::ThresholdedReluLayer, + cv_PtrLcv_dnn_ThresholdedReluLayerG_new_null_const, cv_PtrLcv_dnn_ThresholdedReluLayerG_delete, cv_PtrLcv_dnn_ThresholdedReluLayerG_getInnerPtr_const, cv_PtrLcv_dnn_ThresholdedReluLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::ThresholdedReluLayer, cv_PtrLcv_dnn_ThresholdedReluLayerG_new_const_ThresholdedReluLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfThresholdedReluLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfThresholdedReluLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::ThresholdedReluLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ThresholdedReluLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ThresholdedReluLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ThresholdedReluLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ThresholdedReluLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::ActivationLayerTraitConst for core::Ptr { + #[inline] fn as_raw_ActivationLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::ActivationLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_ActivationLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ThresholdedReluLayerG_to_PtrOfActivationLayer } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_ThresholdedReluLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfThresholdedReluLayer") + .field("alpha", &crate::dnn::ThresholdedReluLayerTraitConst::alpha(self)) + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::TileLayer, + cv_PtrLcv_dnn_TileLayerG_new_null_const, cv_PtrLcv_dnn_TileLayerG_delete, cv_PtrLcv_dnn_TileLayerG_getInnerPtr_const, cv_PtrLcv_dnn_TileLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::TileLayer, cv_PtrLcv_dnn_TileLayerG_new_const_TileLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfTileLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfTileLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::TileLayerTraitConst for core::Ptr { + #[inline] fn as_raw_TileLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::TileLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_TileLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_TileLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_TileLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfTileLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + ptr_extern! { crate::dnn::TopKLayer, + cv_PtrLcv_dnn_TopKLayerG_new_null_const, cv_PtrLcv_dnn_TopKLayerG_delete, cv_PtrLcv_dnn_TopKLayerG_getInnerPtr_const, cv_PtrLcv_dnn_TopKLayerG_getInnerPtrMut + } + + ptr_extern_ctor! { crate::dnn::TopKLayer, cv_PtrLcv_dnn_TopKLayerG_new_const_TopKLayer } + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfTopKLayer(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfTopKLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::dnn::TopKLayerTraitConst for core::Ptr { + #[inline] fn as_raw_TopKLayer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::TopKLayerTrait for core::Ptr { + #[inline] fn as_raw_mut_TopKLayer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_TopKLayerG_to_PtrOfAlgorithm } + + impl crate::dnn::LayerTraitConst for core::Ptr { + #[inline] fn as_raw_Layer(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::dnn::LayerTrait for core::Ptr { + #[inline] fn as_raw_mut_Layer(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_dnn_TopKLayerG_to_PtrOfLayer } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfTopKLayer") + .field("blobs", &crate::dnn::LayerTraitConst::blobs(self)) + .field("name", &crate::dnn::LayerTraitConst::name(self)) + .field("typ", &crate::dnn::LayerTraitConst::typ(self)) + .field("preferable_target", &crate::dnn::LayerTraitConst::preferable_target(self)) + .finish() + } + } + + impl core::Tuple<(crate::dnn::Backend, crate::dnn::Target)> { + pub fn as_raw_TupleOfBackend_Target(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_TupleOfBackend_Target(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + tuple_extern! { (crate::dnn::Backend, crate::dnn::Target), + std_pairLcv_dnn_Backend__cv_dnn_TargetG_new_const_Backend_Target, std_pairLcv_dnn_Backend__cv_dnn_TargetG_delete, + 0 = arg: crate::dnn::Backend, get_0 via std_pairLcv_dnn_Backend__cv_dnn_TargetG_get_0_const, + 1 = arg_1: crate::dnn::Target, get_1 via std_pairLcv_dnn_Backend__cv_dnn_TargetG_get_1_const + } + + impl core::Vector { + pub fn as_raw_VectorOfMatShape(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfMatShape(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl core::Vector> { + pub fn as_raw_VectorOfPtrOfBackendWrapper(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfPtrOfBackendWrapper(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Ptr, + std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_new_const, std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_delete, + std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_len_const, std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_isEmpty_const, + std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_capacity_const, std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_shrinkToFit, + std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_reserve_size_t, std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_remove_size_t, + std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_swap_size_t_size_t, std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_clear, + std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_get_const_size_t, std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_set_size_t_const_PtrLBackendWrapperG, + std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_push_const_PtrLBackendWrapperG, std_vectorLcv_PtrLcv_dnn_BackendWrapperGG_insert_size_t_const_PtrLBackendWrapperG, + } + + vector_non_copy_or_bool! { core::Ptr } + + + impl core::Vector> { + pub fn as_raw_VectorOfPtrOfLayer(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfPtrOfLayer(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Ptr, + std_vectorLcv_PtrLcv_dnn_LayerGG_new_const, std_vectorLcv_PtrLcv_dnn_LayerGG_delete, + std_vectorLcv_PtrLcv_dnn_LayerGG_len_const, std_vectorLcv_PtrLcv_dnn_LayerGG_isEmpty_const, + std_vectorLcv_PtrLcv_dnn_LayerGG_capacity_const, std_vectorLcv_PtrLcv_dnn_LayerGG_shrinkToFit, + std_vectorLcv_PtrLcv_dnn_LayerGG_reserve_size_t, std_vectorLcv_PtrLcv_dnn_LayerGG_remove_size_t, + std_vectorLcv_PtrLcv_dnn_LayerGG_swap_size_t_size_t, std_vectorLcv_PtrLcv_dnn_LayerGG_clear, + std_vectorLcv_PtrLcv_dnn_LayerGG_get_const_size_t, std_vectorLcv_PtrLcv_dnn_LayerGG_set_size_t_const_PtrLLayerG, + std_vectorLcv_PtrLcv_dnn_LayerGG_push_const_PtrLLayerG, std_vectorLcv_PtrLcv_dnn_LayerGG_insert_size_t_const_PtrLLayerG, + } + + vector_non_copy_or_bool! { core::Ptr } + + + impl core::Vector { + pub fn as_raw_VectorOfTarget(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfTarget(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { crate::dnn::Target, + std_vectorLcv_dnn_TargetG_new_const, std_vectorLcv_dnn_TargetG_delete, + std_vectorLcv_dnn_TargetG_len_const, std_vectorLcv_dnn_TargetG_isEmpty_const, + std_vectorLcv_dnn_TargetG_capacity_const, std_vectorLcv_dnn_TargetG_shrinkToFit, + std_vectorLcv_dnn_TargetG_reserve_size_t, std_vectorLcv_dnn_TargetG_remove_size_t, + std_vectorLcv_dnn_TargetG_swap_size_t_size_t, std_vectorLcv_dnn_TargetG_clear, + std_vectorLcv_dnn_TargetG_get_const_size_t, std_vectorLcv_dnn_TargetG_set_size_t_const_Target, + std_vectorLcv_dnn_TargetG_push_const_Target, std_vectorLcv_dnn_TargetG_insert_size_t_const_Target, + } + + vector_copy_non_bool! { crate::dnn::Target, + std_vectorLcv_dnn_TargetG_data_const, std_vectorLcv_dnn_TargetG_dataMut, cv_fromSlice_const_const_TargetX_size_t, + std_vectorLcv_dnn_TargetG_clone_const, + } + + + impl core::Vector> { + pub fn as_raw_VectorOfTupleOfBackend_Target(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfTupleOfBackend_Target(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Tuple<(crate::dnn::Backend, crate::dnn::Target)>, + std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_new_const, std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_delete, + std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_len_const, std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_isEmpty_const, + std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_capacity_const, std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_shrinkToFit, + std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_reserve_size_t, std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_remove_size_t, + std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_swap_size_t_size_t, std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_clear, + std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_get_const_size_t, std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_set_size_t_const_pairLcv_dnn_Backend__cv_dnn_TargetG, + std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_push_const_pairLcv_dnn_Backend__cv_dnn_TargetG, std_vectorLstd_pairLcv_dnn_Backend__cv_dnn_TargetGG_insert_size_t_const_pairLcv_dnn_Backend__cv_dnn_TargetG, + } + + vector_non_copy_or_bool! { core::Tuple<(crate::dnn::Backend, crate::dnn::Target)> } + + + impl core::Vector> { + pub fn as_raw_VectorOfVectorOfMatShape(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVectorOfMatShape(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { core::Vector, + std_vectorLstd_vectorLcv_dnn_MatShapeGG_new_const, std_vectorLstd_vectorLcv_dnn_MatShapeGG_delete, + std_vectorLstd_vectorLcv_dnn_MatShapeGG_len_const, std_vectorLstd_vectorLcv_dnn_MatShapeGG_isEmpty_const, + std_vectorLstd_vectorLcv_dnn_MatShapeGG_capacity_const, std_vectorLstd_vectorLcv_dnn_MatShapeGG_shrinkToFit, + std_vectorLstd_vectorLcv_dnn_MatShapeGG_reserve_size_t, std_vectorLstd_vectorLcv_dnn_MatShapeGG_remove_size_t, + std_vectorLstd_vectorLcv_dnn_MatShapeGG_swap_size_t_size_t, std_vectorLstd_vectorLcv_dnn_MatShapeGG_clear, + std_vectorLstd_vectorLcv_dnn_MatShapeGG_get_const_size_t, std_vectorLstd_vectorLcv_dnn_MatShapeGG_set_size_t_const_vectorLMatShapeG, + std_vectorLstd_vectorLcv_dnn_MatShapeGG_push_const_vectorLMatShapeG, std_vectorLstd_vectorLcv_dnn_MatShapeGG_insert_size_t_const_vectorLMatShapeG, + } + + vector_non_copy_or_bool! { core::Vector } + + +} +pub use dnn_types::*; + +mod imgproc_types { + use crate::{mod_prelude::*, core, types, sys}; + + ptr_extern! { crate::imgproc::CLAHE, + cv_PtrLcv_CLAHEG_new_null_const, cv_PtrLcv_CLAHEG_delete, cv_PtrLcv_CLAHEG_getInnerPtr_const, cv_PtrLcv_CLAHEG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfCLAHE(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfCLAHE(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::imgproc::CLAHETraitConst for core::Ptr { + #[inline] fn as_raw_CLAHE(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::imgproc::CLAHETrait for core::Ptr { + #[inline] fn as_raw_mut_CLAHE(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_CLAHEG_to_PtrOfAlgorithm } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfCLAHE") + .finish() + } + } + + ptr_extern! { crate::imgproc::GeneralizedHough, + cv_PtrLcv_GeneralizedHoughG_new_null_const, cv_PtrLcv_GeneralizedHoughG_delete, cv_PtrLcv_GeneralizedHoughG_getInnerPtr_const, cv_PtrLcv_GeneralizedHoughG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfGeneralizedHough(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfGeneralizedHough(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::imgproc::GeneralizedHoughTraitConst for core::Ptr { + #[inline] fn as_raw_GeneralizedHough(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::imgproc::GeneralizedHoughTrait for core::Ptr { + #[inline] fn as_raw_mut_GeneralizedHough(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_GeneralizedHoughG_to_PtrOfAlgorithm } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfGeneralizedHough") + .finish() + } + } + + ptr_extern! { crate::imgproc::GeneralizedHoughBallard, + cv_PtrLcv_GeneralizedHoughBallardG_new_null_const, cv_PtrLcv_GeneralizedHoughBallardG_delete, cv_PtrLcv_GeneralizedHoughBallardG_getInnerPtr_const, cv_PtrLcv_GeneralizedHoughBallardG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfGeneralizedHoughBallard(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfGeneralizedHoughBallard(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::imgproc::GeneralizedHoughBallardTraitConst for core::Ptr { + #[inline] fn as_raw_GeneralizedHoughBallard(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::imgproc::GeneralizedHoughBallardTrait for core::Ptr { + #[inline] fn as_raw_mut_GeneralizedHoughBallard(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_GeneralizedHoughBallardG_to_PtrOfAlgorithm } + + impl crate::imgproc::GeneralizedHoughTraitConst for core::Ptr { + #[inline] fn as_raw_GeneralizedHough(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::imgproc::GeneralizedHoughTrait for core::Ptr { + #[inline] fn as_raw_mut_GeneralizedHough(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_GeneralizedHoughBallardG_to_PtrOfGeneralizedHough } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfGeneralizedHoughBallard") + .finish() + } + } + + ptr_extern! { crate::imgproc::GeneralizedHoughGuil, + cv_PtrLcv_GeneralizedHoughGuilG_new_null_const, cv_PtrLcv_GeneralizedHoughGuilG_delete, cv_PtrLcv_GeneralizedHoughGuilG_getInnerPtr_const, cv_PtrLcv_GeneralizedHoughGuilG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfGeneralizedHoughGuil(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfGeneralizedHoughGuil(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::imgproc::GeneralizedHoughGuilTraitConst for core::Ptr { + #[inline] fn as_raw_GeneralizedHoughGuil(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::imgproc::GeneralizedHoughGuilTrait for core::Ptr { + #[inline] fn as_raw_mut_GeneralizedHoughGuil(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_GeneralizedHoughGuilG_to_PtrOfAlgorithm } + + impl crate::imgproc::GeneralizedHoughTraitConst for core::Ptr { + #[inline] fn as_raw_GeneralizedHough(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::imgproc::GeneralizedHoughTrait for core::Ptr { + #[inline] fn as_raw_mut_GeneralizedHough(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_GeneralizedHoughGuilG_to_PtrOfGeneralizedHough } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfGeneralizedHoughGuil") + .finish() + } + } + + ptr_extern! { crate::imgproc::LineSegmentDetector, + cv_PtrLcv_LineSegmentDetectorG_new_null_const, cv_PtrLcv_LineSegmentDetectorG_delete, cv_PtrLcv_LineSegmentDetectorG_getInnerPtr_const, cv_PtrLcv_LineSegmentDetectorG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfLineSegmentDetector(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfLineSegmentDetector(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::imgproc::LineSegmentDetectorTraitConst for core::Ptr { + #[inline] fn as_raw_LineSegmentDetector(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::imgproc::LineSegmentDetectorTrait for core::Ptr { + #[inline] fn as_raw_mut_LineSegmentDetector(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl core::AlgorithmTraitConst for core::Ptr { + #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.inner_as_raw() } + } + + impl core::AlgorithmTrait for core::Ptr { + #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + ptr_cast_base! { core::Ptr, core::Ptr, cv_PtrLcv_LineSegmentDetectorG_to_PtrOfAlgorithm } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfLineSegmentDetector") + .finish() + } + } + +} +pub use imgproc_types::*; + +mod videoio_types { + use crate::{mod_prelude::*, core, types, sys}; + + ptr_extern! { crate::videoio::IStreamReader, + cv_PtrLcv_IStreamReaderG_new_null_const, cv_PtrLcv_IStreamReaderG_delete, cv_PtrLcv_IStreamReaderG_getInnerPtr_const, cv_PtrLcv_IStreamReaderG_getInnerPtrMut + } + + impl core::Ptr { + #[inline] pub fn as_raw_PtrOfIStreamReader(&self) -> extern_send!(Self) { self.as_raw() } + #[inline] pub fn as_raw_mut_PtrOfIStreamReader(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + impl crate::videoio::IStreamReaderTraitConst for core::Ptr { + #[inline] fn as_raw_IStreamReader(&self) -> *const c_void { self.inner_as_raw() } + } + + impl crate::videoio::IStreamReaderTrait for core::Ptr { + #[inline] fn as_raw_mut_IStreamReader(&mut self) -> *mut c_void { self.inner_as_raw_mut() } + } + + impl std::fmt::Debug for core::Ptr { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("PtrOfIStreamReader") + .finish() + } + } + + impl core::Vector { + pub fn as_raw_VectorOfVideoCaptureAPIs(&self) -> extern_send!(Self) { self.as_raw() } + pub fn as_raw_mut_VectorOfVideoCaptureAPIs(&mut self) -> extern_send!(mut Self) { self.as_raw_mut() } + } + + vector_extern! { crate::videoio::VideoCaptureAPIs, + std_vectorLcv_VideoCaptureAPIsG_new_const, std_vectorLcv_VideoCaptureAPIsG_delete, + std_vectorLcv_VideoCaptureAPIsG_len_const, std_vectorLcv_VideoCaptureAPIsG_isEmpty_const, + std_vectorLcv_VideoCaptureAPIsG_capacity_const, std_vectorLcv_VideoCaptureAPIsG_shrinkToFit, + std_vectorLcv_VideoCaptureAPIsG_reserve_size_t, std_vectorLcv_VideoCaptureAPIsG_remove_size_t, + std_vectorLcv_VideoCaptureAPIsG_swap_size_t_size_t, std_vectorLcv_VideoCaptureAPIsG_clear, + std_vectorLcv_VideoCaptureAPIsG_get_const_size_t, std_vectorLcv_VideoCaptureAPIsG_set_size_t_const_VideoCaptureAPIs, + std_vectorLcv_VideoCaptureAPIsG_push_const_VideoCaptureAPIs, std_vectorLcv_VideoCaptureAPIsG_insert_size_t_const_VideoCaptureAPIs, + } + + vector_copy_non_bool! { crate::videoio::VideoCaptureAPIs, + std_vectorLcv_VideoCaptureAPIsG_data_const, std_vectorLcv_VideoCaptureAPIsG_dataMut, cv_fromSlice_const_const_VideoCaptureAPIsX_size_t, + std_vectorLcv_VideoCaptureAPIsG_clone_const, + } + + +} +pub use videoio_types::*; + +pub use crate::manual::types::*; diff --git a/target/debug/build/opencv-4e105b5546afc119/out/opencv/videoio.rs b/target/debug/build/opencv-4e105b5546afc119/out/opencv/videoio.rs new file mode 100644 index 0000000..1af62e7 --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/opencv/videoio.rs @@ -0,0 +1,2544 @@ +pub mod videoio { + //! # Video I/O + //! + //! Read and write video or images sequence with OpenCV + //! + //! ### See also: + //! - [videoio_overview] + //! - Tutorials: [tutorial_table_of_content_app] + //! # Flags for video I/O + //! # Additional flags for video I/O API backends + //! # Hardware-accelerated video decoding and encoding + //! # C API for video I/O + //! # iOS glue for video I/O + //! # WinRT glue for video I/O + //! # Query I/O API backends registry + use crate::mod_prelude::*; + use crate::{core, sys, types}; + pub mod prelude { + pub use super::{IStreamReaderTrait, IStreamReaderTraitConst, VideoCaptureTrait, VideoCaptureTraitConst, VideoWriterTrait, VideoWriterTraitConst}; + } + + /// MediaNDK (API Level 21+) and NDK Camera (API level 24+) for Android + pub const CAP_ANDROID: i32 = 1000; + /// Auto detect == 0 + pub const CAP_ANY: i32 = 0; + /// Aravis SDK + pub const CAP_ARAVIS: i32 = 2100; + /// AVFoundation framework for iOS (OS X Lion will have the same API) + pub const CAP_AVFOUNDATION: i32 = 1200; + /// Same value as CAP_FIREWIRE + pub const CAP_CMU1394: i32 = 300; + /// Same value as CAP_FIREWIRE + pub const CAP_DC1394: i32 = 300; + /// DirectShow (via videoInput) + pub const CAP_DSHOW: i32 = 700; + /// Open and record video file or stream using the FFMPEG library + pub const CAP_FFMPEG: i32 = 1900; + /// Same value as CAP_FIREWIRE + pub const CAP_FIREWARE: i32 = 300; + /// IEEE 1394 drivers + pub const CAP_FIREWIRE: i32 = 300; + /// Smartek Giganetix GigEVisionSDK + pub const CAP_GIGANETIX: i32 = 1300; + /// gPhoto2 connection + pub const CAP_GPHOTO2: i32 = 1700; + /// GStreamer + pub const CAP_GSTREAMER: i32 = 1800; + /// Same value as CAP_FIREWIRE + pub const CAP_IEEE1394: i32 = 300; + /// OpenCV Image Sequence (e.g. img_%02d.jpg) + pub const CAP_IMAGES: i32 = 2000; + /// RealSense (former Intel Perceptual Computing SDK) + pub const CAP_INTELPERC: i32 = 1500; + pub const CAP_INTELPERC_DEPTH_GENERATOR: i32 = 536870912; + /// Each pixel is a 16-bit integer. The value indicates the distance from an object to the camera's XY plane or the Cartesian depth. + pub const CAP_INTELPERC_DEPTH_MAP: i32 = 0; + pub const CAP_INTELPERC_GENERATORS_MASK: i32 = 939524096; + pub const CAP_INTELPERC_IMAGE: i32 = 3; + pub const CAP_INTELPERC_IMAGE_GENERATOR: i32 = 268435456; + pub const CAP_INTELPERC_IR_GENERATOR: i32 = 134217728; + /// Each pixel is a 16-bit integer. The value indicates the intensity of the reflected laser beam. + pub const CAP_INTELPERC_IR_MAP: i32 = 2; + /// Each pixel contains two 32-bit floating point values in the range of 0-1, representing the mapping of depth coordinates to the color coordinates. + pub const CAP_INTELPERC_UVDEPTH_MAP: i32 = 1; + /// Intel MediaSDK + pub const CAP_INTEL_MFX: i32 = 2300; + /// Microsoft Media Foundation (via videoInput). See platform specific notes above. + pub const CAP_MSMF: i32 = 1400; + /// For Orbbec 3D-Sensor device/module (Astra+, Femto, Astra2, Gemini2, Gemini2L, Gemini2XL, Gemini330, Femto Mega) attention: Astra2 cameras currently only support Windows and Linux kernel versions no higher than 4.15, and higher versions of Linux kernel may have exceptions. + pub const CAP_OBSENSOR: i32 = 2600; + /// Data given from BGR stream generator + pub const CAP_OBSENSOR_BGR_IMAGE: i32 = 1; + pub const CAP_OBSENSOR_DEPTH_GENERATOR: i32 = 536870912; + /// Depth values in mm (CV_16UC1) + pub const CAP_OBSENSOR_DEPTH_MAP: i32 = 0; + pub const CAP_OBSENSOR_GENERATORS_MASK: i32 = 939524096; + pub const CAP_OBSENSOR_IMAGE_GENERATOR: i32 = 268435456; + pub const CAP_OBSENSOR_IR_GENERATOR: i32 = 134217728; + /// Data given from IR stream generator(CV_16UC1) + pub const CAP_OBSENSOR_IR_IMAGE: i32 = 2; + /// Built-in OpenCV MotionJPEG codec + pub const CAP_OPENCV_MJPEG: i32 = 2200; + /// OpenNI (for Kinect) + pub const CAP_OPENNI: i32 = 900; + /// OpenNI2 (for Kinect) + pub const CAP_OPENNI2: i32 = 1600; + /// OpenNI2 (for Orbbec Astra) + pub const CAP_OPENNI2_ASTRA: i32 = 1620; + /// OpenNI2 (for Asus Xtion and Occipital Structure sensors) + pub const CAP_OPENNI2_ASUS: i32 = 1610; + /// OpenNI (for Asus Xtion) + pub const CAP_OPENNI_ASUS: i32 = 910; + /// Data given from RGB image generator + pub const CAP_OPENNI_BGR_IMAGE: i32 = 5; + pub const CAP_OPENNI_DEPTH_GENERATOR: i32 = -2147483648; + pub const CAP_OPENNI_DEPTH_GENERATOR_BASELINE: i32 = -2147483546; + pub const CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH: i32 = -2147483545; + pub const CAP_OPENNI_DEPTH_GENERATOR_PRESENT: i32 = -2147483539; + pub const CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION: i32 = -2147483544; + pub const CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON: i32 = -2147483544; + /// Depth values in mm (CV_16UC1) + pub const CAP_OPENNI_DEPTH_MAP: i32 = 0; + /// Disparity in pixels (CV_8UC1) + pub const CAP_OPENNI_DISPARITY_MAP: i32 = 2; + /// Disparity in pixels (CV_32FC1) + pub const CAP_OPENNI_DISPARITY_MAP_32F: i32 = 3; + pub const CAP_OPENNI_GENERATORS_MASK: i32 = -536870912; + /// Data given from RGB image generator + pub const CAP_OPENNI_GRAY_IMAGE: i32 = 6; + pub const CAP_OPENNI_IMAGE_GENERATOR: i32 = 1073741824; + pub const CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE: i32 = 1073741924; + pub const CAP_OPENNI_IMAGE_GENERATOR_PRESENT: i32 = 1073741933; + pub const CAP_OPENNI_IR_GENERATOR: i32 = 536870912; + pub const CAP_OPENNI_IR_GENERATOR_PRESENT: i32 = 536871021; + /// Data given from IR image generator + pub const CAP_OPENNI_IR_IMAGE: i32 = 7; + /// XYZ in meters (CV_32FC3) + pub const CAP_OPENNI_POINT_CLOUD_MAP: i32 = 1; + pub const CAP_OPENNI_QVGA_30HZ: i32 = 3; + pub const CAP_OPENNI_QVGA_60HZ: i32 = 4; + pub const CAP_OPENNI_SXGA_15HZ: i32 = 1; + pub const CAP_OPENNI_SXGA_30HZ: i32 = 2; + /// CV_8UC1 + pub const CAP_OPENNI_VALID_DEPTH_MASK: i32 = 4; + pub const CAP_OPENNI_VGA_30HZ: i32 = 0; + pub const CAP_PROP_ANDROID_DEVICE_TORCH: i32 = 8001; + /// Aperture. Can be readonly, depends on camera program. + pub const CAP_PROP_APERTURE: i32 = 17008; + /// Automatically trigger frame capture if camera is configured with software trigger + pub const CAP_PROP_ARAVIS_AUTOTRIGGER: i32 = 600; + /// (read-only) Index of the first audio channel for .retrieve() calls. That audio channel number continues enumeration after video channels. + pub const CAP_PROP_AUDIO_BASE_INDEX: i32 = 63; + /// (open, read) Alternative definition to bits-per-sample, but with clear handling of 32F / 32S + pub const CAP_PROP_AUDIO_DATA_DEPTH: i32 = 61; + /// (read-only) Audio position is measured in samples. Accurate audio sample timestamp of previous grabbed fragment. See CAP_PROP_AUDIO_SAMPLES_PER_SECOND and CAP_PROP_AUDIO_SHIFT_NSEC. + pub const CAP_PROP_AUDIO_POS: i32 = 59; + /// (open, read) determined from file/codec input. If not specified, then selected audio sample rate is 44100 + pub const CAP_PROP_AUDIO_SAMPLES_PER_SECOND: i32 = 62; + /// (read only) Contains the time difference between the start of the audio stream and the video stream in nanoseconds. Positive value means that audio is started after the first video frame. Negative value means that audio is started before the first video frame. + pub const CAP_PROP_AUDIO_SHIFT_NSEC: i32 = 60; + /// (**open-only**) Specify stream in multi-language media files, -1 - disable audio processing or microphone. Default value is -1. + pub const CAP_PROP_AUDIO_STREAM: i32 = 58; + /// (open, read) Enables audio synchronization. + pub const CAP_PROP_AUDIO_SYNCHRONIZE: i32 = 66; + /// (read-only) Number of audio channels in the selected audio stream (mono, stereo, etc) + pub const CAP_PROP_AUDIO_TOTAL_CHANNELS: i32 = 64; + /// (read-only) Number of audio streams. + pub const CAP_PROP_AUDIO_TOTAL_STREAMS: i32 = 65; + pub const CAP_PROP_AUTOFOCUS: i32 = 39; + /// DC1394: exposure control done by camera, user can adjust reference level using this feature. + pub const CAP_PROP_AUTO_EXPOSURE: i32 = 21; + /// enable/ disable auto white-balance + pub const CAP_PROP_AUTO_WB: i32 = 44; + /// Current backend (enum VideoCaptureAPIs). Read-only property + pub const CAP_PROP_BACKEND: i32 = 42; + pub const CAP_PROP_BACKLIGHT: i32 = 32; + /// (read-only) Video bitrate in kbits/s + pub const CAP_PROP_BITRATE: i32 = 47; + /// Brightness of the image (only for those cameras that support). + pub const CAP_PROP_BRIGHTNESS: i32 = 10; + pub const CAP_PROP_BUFFERSIZE: i32 = 38; + /// Video input or Channel Number (only for those cameras that support) + pub const CAP_PROP_CHANNEL: i32 = 43; + /// Positive index indicates that returning extra data is supported by the video back end. This can be retrieved as cap.retrieve(data, ). E.g. When reading from a h264 encoded RTSP stream, the FFmpeg backend could return the SPS and/or PPS if available (if sent in reply to a DESCRIBE request), from calls to cap.retrieve(data, ). + pub const CAP_PROP_CODEC_EXTRADATA_INDEX: i32 = 68; + /// (read-only) codec's pixel format. 4-character code - see VideoWriter::fourcc . Subset of [AV_PIX_FMT_*](https://github.com/FFmpeg/FFmpeg/blob/master/libavcodec/raw.c) or -1 if unknown + pub const CAP_PROP_CODEC_PIXEL_FORMAT: i32 = 46; + /// Contrast of the image (only for cameras). + pub const CAP_PROP_CONTRAST: i32 = 11; + /// Boolean flags indicating whether images should be converted to BGR.
+ /// *GStreamer note*: The flag is ignored in case if custom pipeline is used. It's user responsibility to interpret pipeline output. + pub const CAP_PROP_CONVERT_RGB: i32 = 16; + pub const CAP_PROP_DC1394_MAX: i32 = 31; + pub const CAP_PROP_DC1394_MODE_AUTO: i32 = -2; + /// set automatically when a value of the feature is set by the user. + pub const CAP_PROP_DC1394_MODE_MANUAL: i32 = -3; + pub const CAP_PROP_DC1394_MODE_ONE_PUSH_AUTO: i32 = -1; + /// turn the feature off (not controlled manually nor automatically). + pub const CAP_PROP_DC1394_OFF: i32 = -4; + /// (read-only) FFmpeg back-end only - maximum difference between presentation (pts) and decompression timestamps (dts) using FPS time base. e.g. delay is maximum when frame_num = 0, if true, VideoCapture::get(\ref CAP_PROP_PTS) = 0 and VideoCapture::get(\ref CAP_PROP_DTS_DELAY) = 2, dts = -2. Non zero values usually imply the stream is encoded using B-frames which are not decoded in presentation order. + pub const CAP_PROP_DTS_DELAY: i32 = 72; + /// Exposure (only for those cameras that support). + pub const CAP_PROP_EXPOSURE: i32 = 15; + /// Camera exposure program. + pub const CAP_PROP_EXPOSUREPROGRAM: i32 = 17009; + pub const CAP_PROP_FOCUS: i32 = 28; + /// Format of the %Mat objects (see Mat::type()) returned by VideoCapture::retrieve(). + /// Set value -1 to fetch undecoded RAW video streams (as Mat 8UC1). + pub const CAP_PROP_FORMAT: i32 = 8; + /// 4-character code of codec. see VideoWriter::fourcc . + pub const CAP_PROP_FOURCC: i32 = 6; + /// Frame rate. + pub const CAP_PROP_FPS: i32 = 5; + /// Number of frames in the video file. + pub const CAP_PROP_FRAME_COUNT: i32 = 7; + /// Height of the frames in the video stream. + pub const CAP_PROP_FRAME_HEIGHT: i32 = 4; + /// (read-only) FFmpeg back-end only - Frame type ascii code (73 = 'I', 80 = 'P', 66 = 'B' or 63 = '?' if unknown) of the most recently read frame. + pub const CAP_PROP_FRAME_TYPE: i32 = 69; + /// Width of the frames in the video stream. + pub const CAP_PROP_FRAME_WIDTH: i32 = 3; + /// Gain of the image (only for those cameras that support). + pub const CAP_PROP_GAIN: i32 = 14; + pub const CAP_PROP_GAMMA: i32 = 22; + pub const CAP_PROP_GIGA_FRAME_HEIGH_MAX: i32 = 10004; + pub const CAP_PROP_GIGA_FRAME_OFFSET_X: i32 = 10001; + pub const CAP_PROP_GIGA_FRAME_OFFSET_Y: i32 = 10002; + pub const CAP_PROP_GIGA_FRAME_SENS_HEIGH: i32 = 10006; + pub const CAP_PROP_GIGA_FRAME_SENS_WIDTH: i32 = 10005; + pub const CAP_PROP_GIGA_FRAME_WIDTH_MAX: i32 = 10003; + /// Collect messages with details. + pub const CAP_PROP_GPHOTO2_COLLECT_MSGS: i32 = 17005; + /// Readonly, returns (const char *). + pub const CAP_PROP_GPHOTO2_FLUSH_MSGS: i32 = 17006; + /// Capture only preview from liveview mode. + pub const CAP_PROP_GPHOTO2_PREVIEW: i32 = 17001; + /// Trigger, only by set. Reload camera settings. + pub const CAP_PROP_GPHOTO2_RELOAD_CONFIG: i32 = 17003; + /// Reload all settings on set. + pub const CAP_PROP_GPHOTO2_RELOAD_ON_CHANGE: i32 = 17004; + /// Readonly, returns (const char *). + pub const CAP_PROP_GPHOTO2_WIDGET_ENUMERATE: i32 = 17002; + /// Default is 1 + pub const CAP_PROP_GSTREAMER_QUEUE_LENGTH: i32 = 200; + pub const CAP_PROP_GUID: i32 = 29; + /// Hue of the image (only for cameras). + pub const CAP_PROP_HUE: i32 = 13; + /// (**open-only**) Hardware acceleration type (see #VideoAccelerationType). Setting supported only via `params` parameter in cv::VideoCapture constructor / .open() method. Default value is backend-specific. + pub const CAP_PROP_HW_ACCELERATION: i32 = 50; + /// (**open-only**) If non-zero, create new OpenCL context and bind it to current thread. The OpenCL context created with Video Acceleration context attached it (if not attached yet) for optimized GPU data copy between HW accelerated decoder and cv::UMat. + pub const CAP_PROP_HW_ACCELERATION_USE_OPENCL: i32 = 52; + /// (**open-only**) Hardware device index (select GPU if multiple available). Device enumeration is acceleration type specific. + pub const CAP_PROP_HW_DEVICE: i32 = 51; + pub const CAP_PROP_IMAGES_BASE: i32 = 18000; + pub const CAP_PROP_IMAGES_LAST: i32 = 19000; + pub const CAP_PROP_INTELPERC_DEPTH_CONFIDENCE_THRESHOLD: i32 = 11005; + pub const CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_HORZ: i32 = 11006; + pub const CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_VERT: i32 = 11007; + pub const CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE: i32 = 11003; + pub const CAP_PROP_INTELPERC_DEPTH_SATURATION_VALUE: i32 = 11004; + pub const CAP_PROP_INTELPERC_PROFILE_COUNT: i32 = 11001; + pub const CAP_PROP_INTELPERC_PROFILE_IDX: i32 = 11002; + pub const CAP_PROP_IOS_DEVICE_EXPOSURE: i32 = 9002; + pub const CAP_PROP_IOS_DEVICE_FLASH: i32 = 9003; + pub const CAP_PROP_IOS_DEVICE_FOCUS: i32 = 9001; + pub const CAP_PROP_IOS_DEVICE_TORCH: i32 = 9005; + pub const CAP_PROP_IOS_DEVICE_WHITEBALANCE: i32 = 9004; + pub const CAP_PROP_IRIS: i32 = 36; + pub const CAP_PROP_ISO_SPEED: i32 = 30; + /// FFmpeg back-end only - Indicates whether the Last Raw Frame (LRF), output from VideoCapture::read() when VideoCapture is initialized with VideoCapture::open(CAP_FFMPEG, {CAP_PROP_FORMAT, -1}) or VideoCapture::set(CAP_PROP_FORMAT,-1) is called before the first call to VideoCapture::read(), contains encoded data for a key frame. + pub const CAP_PROP_LRF_HAS_KEY_FRAME: i32 = 67; + /// Backend-specific value indicating the current capture mode. + pub const CAP_PROP_MODE: i32 = 9; + pub const CAP_PROP_MONOCHROME: i32 = 19; + /// (**open-only**) Set the maximum number of threads to use. Use 0 to use as many threads as CPU cores (applicable for FFmpeg back-end only). + pub const CAP_PROP_N_THREADS: i32 = 70; + pub const CAP_PROP_OBSENSOR_COLOR_DISTORTION_K1: i32 = 26010; + pub const CAP_PROP_OBSENSOR_COLOR_DISTORTION_K2: i32 = 26011; + pub const CAP_PROP_OBSENSOR_COLOR_DISTORTION_K3: i32 = 26012; + pub const CAP_PROP_OBSENSOR_COLOR_DISTORTION_K4: i32 = 26013; + pub const CAP_PROP_OBSENSOR_COLOR_DISTORTION_K5: i32 = 26014; + pub const CAP_PROP_OBSENSOR_COLOR_DISTORTION_K6: i32 = 26015; + pub const CAP_PROP_OBSENSOR_COLOR_DISTORTION_P1: i32 = 26016; + pub const CAP_PROP_OBSENSOR_COLOR_DISTORTION_P2: i32 = 26017; + pub const CAP_PROP_OBSENSOR_DEPTH_FPS: i32 = 26009; + pub const CAP_PROP_OBSENSOR_DEPTH_HEIGHT: i32 = 26008; + pub const CAP_PROP_OBSENSOR_DEPTH_POS_MSEC: i32 = 26006; + pub const CAP_PROP_OBSENSOR_DEPTH_WIDTH: i32 = 26007; + pub const CAP_PROP_OBSENSOR_INTRINSIC_CX: i32 = 26003; + pub const CAP_PROP_OBSENSOR_INTRINSIC_CY: i32 = 26004; + pub const CAP_PROP_OBSENSOR_INTRINSIC_FX: i32 = 26001; + pub const CAP_PROP_OBSENSOR_INTRINSIC_FY: i32 = 26002; + pub const CAP_PROP_OBSENSOR_RGB_POS_MSEC: i32 = 26005; + pub const CAP_PROP_OPENNI2_MIRROR: i32 = 111; + pub const CAP_PROP_OPENNI2_SYNC: i32 = 110; + pub const CAP_PROP_OPENNI_APPROX_FRAME_SYNC: i32 = 105; + /// In mm + pub const CAP_PROP_OPENNI_BASELINE: i32 = 102; + pub const CAP_PROP_OPENNI_CIRCLE_BUFFER: i32 = 107; + /// In pixels + pub const CAP_PROP_OPENNI_FOCAL_LENGTH: i32 = 103; + /// In mm + pub const CAP_PROP_OPENNI_FRAME_MAX_DEPTH: i32 = 101; + pub const CAP_PROP_OPENNI_GENERATOR_PRESENT: i32 = 109; + pub const CAP_PROP_OPENNI_MAX_BUFFER_SIZE: i32 = 106; + pub const CAP_PROP_OPENNI_MAX_TIME_DURATION: i32 = 108; + pub const CAP_PROP_OPENNI_OUTPUT_MODE: i32 = 100; + /// Flag that synchronizes the remapping depth map to image map + /// by changing depth generator's view point (if the flag is "on") or + /// sets this view point to its normal one (if the flag is "off"). + pub const CAP_PROP_OPENNI_REGISTRATION: i32 = 104; + pub const CAP_PROP_OPENNI_REGISTRATION_ON: i32 = 104; + /// (**open-only**) timeout in milliseconds for opening a video capture (applicable for FFmpeg and GStreamer back-ends only) + pub const CAP_PROP_OPEN_TIMEOUT_MSEC: i32 = 53; + /// if true - rotates output frames of CvCapture considering video file's metadata (applicable for FFmpeg and AVFoundation back-ends only) () + pub const CAP_PROP_ORIENTATION_AUTO: i32 = 49; + /// (read-only) Frame rotation defined by stream meta (applicable for FFmpeg and AVFoundation back-ends only) + pub const CAP_PROP_ORIENTATION_META: i32 = 48; + pub const CAP_PROP_PAN: i32 = 33; + /// Relative position of the video file: 0=start of the film, 1=end of the film. + pub const CAP_PROP_POS_AVI_RATIO: i32 = 2; + /// 0-based index of the frame to be decoded/captured next. When the index i is set in RAW mode (CAP_PROP_FORMAT == -1) this will seek to the key frame k, where k <= i. + pub const CAP_PROP_POS_FRAMES: i32 = 1; + /// Current position of the video file in milliseconds. + pub const CAP_PROP_POS_MSEC: i32 = 0; + /// (read-only) FFmpeg back-end only - presentation timestamp of the most recently read frame using the FPS time base. e.g. fps = 25, VideoCapture::get(\ref CAP_PROP_PTS) = 3, presentation time = 3/25 seconds. + pub const CAP_PROP_PTS: i32 = 71; + /// Horizontal binning factor. + pub const CAP_PROP_PVAPI_BINNINGX: i32 = 304; + /// Vertical binning factor. + pub const CAP_PROP_PVAPI_BINNINGY: i32 = 305; + /// Horizontal sub-sampling of the image. + pub const CAP_PROP_PVAPI_DECIMATIONHORIZONTAL: i32 = 302; + /// Vertical sub-sampling of the image. + pub const CAP_PROP_PVAPI_DECIMATIONVERTICAL: i32 = 303; + /// FrameStartTriggerMode: Determines how a frame is initiated. + pub const CAP_PROP_PVAPI_FRAMESTARTTRIGGERMODE: i32 = 301; + /// IP for enable multicast master mode. 0 for disable multicast. + pub const CAP_PROP_PVAPI_MULTICASTIP: i32 = 300; + /// Pixel format. + pub const CAP_PROP_PVAPI_PIXELFORMAT: i32 = 306; + /// (**open-only**) timeout in milliseconds for reading from a video capture (applicable for FFmpeg and GStreamer back-ends only) + pub const CAP_PROP_READ_TIMEOUT_MSEC: i32 = 54; + /// Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently). + pub const CAP_PROP_RECTIFICATION: i32 = 18; + pub const CAP_PROP_ROLL: i32 = 35; + /// Sample aspect ratio: num/den (den) + pub const CAP_PROP_SAR_DEN: i32 = 41; + /// Sample aspect ratio: num/den (num) + pub const CAP_PROP_SAR_NUM: i32 = 40; + /// Saturation of the image (only for cameras). + pub const CAP_PROP_SATURATION: i32 = 12; + /// Pop up video/camera filter dialog (note: only supported by DSHOW backend currently. The property value is ignored) + pub const CAP_PROP_SETTINGS: i32 = 37; + pub const CAP_PROP_SHARPNESS: i32 = 20; + /// Exposure speed. Can be readonly, depends on camera program. + pub const CAP_PROP_SPEED: i32 = 17007; + /// (read-only) time in microseconds since Jan 1 1970 when stream was opened. Applicable for FFmpeg backend only. Useful for RTSP and other live streams + pub const CAP_PROP_STREAM_OPEN_TIME_USEC: i32 = 55; + pub const CAP_PROP_TEMPERATURE: i32 = 23; + pub const CAP_PROP_TILT: i32 = 34; + pub const CAP_PROP_TRIGGER: i32 = 24; + pub const CAP_PROP_TRIGGER_DELAY: i32 = 25; + /// (**open-only**) Specify video stream, 0-based index. Use -1 to disable video stream from file or IP cameras. Default value is 0. + pub const CAP_PROP_VIDEO_STREAM: i32 = 57; + /// (read-only) Number of video channels + pub const CAP_PROP_VIDEO_TOTAL_CHANNELS: i32 = 56; + /// Enter liveview mode. + pub const CAP_PROP_VIEWFINDER: i32 = 17010; + /// white-balance color temperature + pub const CAP_PROP_WB_TEMPERATURE: i32 = 45; + /// Currently unsupported. + pub const CAP_PROP_WHITE_BALANCE_BLUE_U: i32 = 17; + pub const CAP_PROP_WHITE_BALANCE_RED_V: i32 = 26; + /// Acquisition buffer size in buffer_size_unit. Default bytes. + pub const CAP_PROP_XI_ACQ_BUFFER_SIZE: i32 = 548; + /// Acquisition buffer size unit in bytes. Default 1. E.g. Value 1024 means that buffer_size is in KiBytes. + pub const CAP_PROP_XI_ACQ_BUFFER_SIZE_UNIT: i32 = 549; + /// Sets number of frames acquired by burst. This burst is used only if trigger is set to FrameBurstStart. + pub const CAP_PROP_XI_ACQ_FRAME_BURST_COUNT: i32 = 499; + /// Type of sensor frames timing. + pub const CAP_PROP_XI_ACQ_TIMING_MODE: i32 = 538; + /// Number of buffers to commit to low level. + pub const CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_COMMIT: i32 = 552; + /// Acquisition transport buffer size in bytes. + pub const CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_SIZE: i32 = 550; + /// Automatic exposure/gain. + pub const CAP_PROP_XI_AEAG: i32 = 415; + /// Average intensity of output signal AEAG should achieve(in %). + pub const CAP_PROP_XI_AEAG_LEVEL: i32 = 419; + /// Automatic exposure/gain ROI Height. + pub const CAP_PROP_XI_AEAG_ROI_HEIGHT: i32 = 442; + /// Automatic exposure/gain ROI offset X. + pub const CAP_PROP_XI_AEAG_ROI_OFFSET_X: i32 = 439; + /// Automatic exposure/gain ROI offset Y. + pub const CAP_PROP_XI_AEAG_ROI_OFFSET_Y: i32 = 440; + /// Automatic exposure/gain ROI Width. + pub const CAP_PROP_XI_AEAG_ROI_WIDTH: i32 = 441; + /// Maximum limit of exposure in AEAG procedure. + pub const CAP_PROP_XI_AE_MAX_LIMIT: i32 = 417; + /// Maximum limit of gain in AEAG procedure. + pub const CAP_PROP_XI_AG_MAX_LIMIT: i32 = 418; + /// Enable applying of CMS profiles to xiGetImage (see XI_PRM_INPUT_CMS_PROFILE, XI_PRM_OUTPUT_CMS_PROFILE). + pub const CAP_PROP_XI_APPLY_CMS: i32 = 471; + /// Automatic bandwidth calculation. + pub const CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION: i32 = 573; + /// Automatic white balance. + pub const CAP_PROP_XI_AUTO_WB: i32 = 414; + /// Calculate and returns available interface bandwidth(int Megabits). + pub const CAP_PROP_XI_AVAILABLE_BANDWIDTH: i32 = 539; + /// Horizontal Binning - number of horizontal photo-sensitive cells to combine together. + pub const CAP_PROP_XI_BINNING_HORIZONTAL: i32 = 429; + /// Binning pattern type. + pub const CAP_PROP_XI_BINNING_PATTERN: i32 = 430; + /// Binning engine selector. + pub const CAP_PROP_XI_BINNING_SELECTOR: i32 = 427; + /// Vertical Binning - number of vertical photo-sensitive cells to combine together. + pub const CAP_PROP_XI_BINNING_VERTICAL: i32 = 428; + /// Correction of bad pixels. + pub const CAP_PROP_XI_BPC: i32 = 445; + /// Queue of field/frame buffers. + pub const CAP_PROP_XI_BUFFERS_QUEUE_SIZE: i32 = 551; + /// Data move policy. + pub const CAP_PROP_XI_BUFFER_POLICY: i32 = 540; + /// Color Correction Matrix element [0][0]. + pub const CAP_PROP_XI_CC_MATRIX_00: i32 = 479; + /// Color Correction Matrix element [0][1]. + pub const CAP_PROP_XI_CC_MATRIX_01: i32 = 480; + /// Color Correction Matrix element [0][2]. + pub const CAP_PROP_XI_CC_MATRIX_02: i32 = 481; + /// Color Correction Matrix element [0][3]. + pub const CAP_PROP_XI_CC_MATRIX_03: i32 = 482; + /// Color Correction Matrix element [1][0]. + pub const CAP_PROP_XI_CC_MATRIX_10: i32 = 483; + /// Color Correction Matrix element [1][1]. + pub const CAP_PROP_XI_CC_MATRIX_11: i32 = 484; + /// Color Correction Matrix element [1][2]. + pub const CAP_PROP_XI_CC_MATRIX_12: i32 = 485; + /// Color Correction Matrix element [1][3]. + pub const CAP_PROP_XI_CC_MATRIX_13: i32 = 486; + /// Color Correction Matrix element [2][0]. + pub const CAP_PROP_XI_CC_MATRIX_20: i32 = 487; + /// Color Correction Matrix element [2][1]. + pub const CAP_PROP_XI_CC_MATRIX_21: i32 = 488; + /// Color Correction Matrix element [2][2]. + pub const CAP_PROP_XI_CC_MATRIX_22: i32 = 489; + /// Color Correction Matrix element [2][3]. + pub const CAP_PROP_XI_CC_MATRIX_23: i32 = 490; + /// Color Correction Matrix element [3][0]. + pub const CAP_PROP_XI_CC_MATRIX_30: i32 = 491; + /// Color Correction Matrix element [3][1]. + pub const CAP_PROP_XI_CC_MATRIX_31: i32 = 492; + /// Color Correction Matrix element [3][2]. + pub const CAP_PROP_XI_CC_MATRIX_32: i32 = 493; + /// Color Correction Matrix element [3][3]. + pub const CAP_PROP_XI_CC_MATRIX_33: i32 = 494; + /// Camera sensor temperature. + pub const CAP_PROP_XI_CHIP_TEMP: i32 = 468; + /// Mode of color management system. + pub const CAP_PROP_XI_CMS: i32 = 470; + /// Returns color filter array type of RAW data. + pub const CAP_PROP_XI_COLOR_FILTER_ARRAY: i32 = 475; + /// Correction of column FPN. + pub const CAP_PROP_XI_COLUMN_FPN_CORRECTION: i32 = 555; + /// Start camera cooling. + pub const CAP_PROP_XI_COOLING: i32 = 466; + /// Select counter. + pub const CAP_PROP_XI_COUNTER_SELECTOR: i32 = 536; + /// Counter status. + pub const CAP_PROP_XI_COUNTER_VALUE: i32 = 537; + /// Output data format. + pub const CAP_PROP_XI_DATA_FORMAT: i32 = 401; + /// Enable/Disable debounce to selected GPI. + pub const CAP_PROP_XI_DEBOUNCE_EN: i32 = 507; + /// Debounce polarity (pol = 1 t0 - falling edge, t1 - rising edge). + pub const CAP_PROP_XI_DEBOUNCE_POL: i32 = 510; + /// Debounce time (x * 10us). + pub const CAP_PROP_XI_DEBOUNCE_T0: i32 = 508; + /// Debounce time (x * 10us). + pub const CAP_PROP_XI_DEBOUNCE_T1: i32 = 509; + /// Set debug level. + pub const CAP_PROP_XI_DEBUG_LEVEL: i32 = 572; + /// Horizontal Decimation - horizontal sub-sampling of the image - reduces the horizontal resolution of the image by the specified vertical decimation factor. + pub const CAP_PROP_XI_DECIMATION_HORIZONTAL: i32 = 433; + /// Decimation pattern type. + pub const CAP_PROP_XI_DECIMATION_PATTERN: i32 = 434; + /// Decimation engine selector. + pub const CAP_PROP_XI_DECIMATION_SELECTOR: i32 = 431; + /// Vertical Decimation - vertical sub-sampling of the image - reduces the vertical resolution of the image by the specified vertical decimation factor. + pub const CAP_PROP_XI_DECIMATION_VERTICAL: i32 = 432; + /// Set default Color Correction Matrix. + pub const CAP_PROP_XI_DEFAULT_CC_MATRIX: i32 = 495; + /// Returns device model id. + pub const CAP_PROP_XI_DEVICE_MODEL_ID: i32 = 521; + /// Resets the camera to default state. + pub const CAP_PROP_XI_DEVICE_RESET: i32 = 554; + /// Returns device serial number. + pub const CAP_PROP_XI_DEVICE_SN: i32 = 522; + /// Change image resolution by binning or skipping. + pub const CAP_PROP_XI_DOWNSAMPLING: i32 = 400; + /// Change image downsampling type. + pub const CAP_PROP_XI_DOWNSAMPLING_TYPE: i32 = 426; + /// Exposure time in microseconds. + pub const CAP_PROP_XI_EXPOSURE: i32 = 421; + /// Sets the number of times of exposure in one frame. + pub const CAP_PROP_XI_EXPOSURE_BURST_COUNT: i32 = 422; + /// Exposure priority (0.5 - exposure 50%, gain 50%). + pub const CAP_PROP_XI_EXP_PRIORITY: i32 = 416; + /// Setting of key enables file operations on some cameras. + pub const CAP_PROP_XI_FFS_ACCESS_KEY: i32 = 583; + /// File number. + pub const CAP_PROP_XI_FFS_FILE_ID: i32 = 594; + /// Size of file. + pub const CAP_PROP_XI_FFS_FILE_SIZE: i32 = 580; + /// Define framerate in Hz. + pub const CAP_PROP_XI_FRAMERATE: i32 = 535; + /// Size of free camera FFS. + pub const CAP_PROP_XI_FREE_FFS_SIZE: i32 = 581; + /// Gain in dB. + pub const CAP_PROP_XI_GAIN: i32 = 424; + /// Gain selector for parameter Gain allows to select different type of gains. + pub const CAP_PROP_XI_GAIN_SELECTOR: i32 = 423; + /// Chromaticity gamma. + pub const CAP_PROP_XI_GAMMAC: i32 = 477; + /// Luminosity gamma. + pub const CAP_PROP_XI_GAMMAY: i32 = 476; + /// Get general purpose level. + pub const CAP_PROP_XI_GPI_LEVEL: i32 = 408; + /// Set general purpose input mode. + pub const CAP_PROP_XI_GPI_MODE: i32 = 407; + /// Selects general purpose input. + pub const CAP_PROP_XI_GPI_SELECTOR: i32 = 406; + /// Set general purpose output mode. + pub const CAP_PROP_XI_GPO_MODE: i32 = 410; + /// Selects general purpose output. + pub const CAP_PROP_XI_GPO_SELECTOR: i32 = 409; + /// Enable High Dynamic Range feature. + pub const CAP_PROP_XI_HDR: i32 = 559; + /// The number of kneepoints in the PWLR. + pub const CAP_PROP_XI_HDR_KNEEPOINT_COUNT: i32 = 560; + /// Position of first kneepoint(in % of XI_PRM_EXPOSURE). + pub const CAP_PROP_XI_HDR_T1: i32 = 561; + /// Position of second kneepoint (in % of XI_PRM_EXPOSURE). + pub const CAP_PROP_XI_HDR_T2: i32 = 562; + /// Height of the Image provided by the device (in pixels). + pub const CAP_PROP_XI_HEIGHT: i32 = 452; + /// Camera housing back side temperature. + pub const CAP_PROP_XI_HOUS_BACK_SIDE_TEMP: i32 = 590; + /// Camera housing temperature. + pub const CAP_PROP_XI_HOUS_TEMP: i32 = 469; + /// Returns hardware revision number. + pub const CAP_PROP_XI_HW_REVISION: i32 = 571; + /// Last image black level counts. Can be used for Offline processing to recall it. + pub const CAP_PROP_XI_IMAGE_BLACK_LEVEL: i32 = 565; + /// bitdepth of data returned by function xiGetImage. + pub const CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH: i32 = 462; + /// Output data format. + pub const CAP_PROP_XI_IMAGE_DATA_FORMAT: i32 = 435; + /// The alpha channel of RGB32 output image format. + pub const CAP_PROP_XI_IMAGE_DATA_FORMAT_RGB32_ALPHA: i32 = 529; + /// Returns 1 for color cameras. + pub const CAP_PROP_XI_IMAGE_IS_COLOR: i32 = 474; + /// Buffer size in bytes sufficient for output image returned by xiGetImage. + pub const CAP_PROP_XI_IMAGE_PAYLOAD_SIZE: i32 = 530; + /// Returns 1 for cameras that support cooling. + pub const CAP_PROP_XI_IS_COOLED: i32 = 465; + /// Returns 1 if camera connected and works properly. + pub const CAP_PROP_XI_IS_DEVICE_EXIST: i32 = 547; + /// Value of first kneepoint (% of sensor saturation). + pub const CAP_PROP_XI_KNEEPOINT1: i32 = 563; + /// Value of second kneepoint (% of sensor saturation). + pub const CAP_PROP_XI_KNEEPOINT2: i32 = 564; + /// Define camera signalling LED functionality. + pub const CAP_PROP_XI_LED_MODE: i32 = 412; + /// Selects camera signalling LED. + pub const CAP_PROP_XI_LED_SELECTOR: i32 = 411; + /// Current lens aperture value in stops. Examples: 2.8, 4, 5.6, 8, 11. + pub const CAP_PROP_XI_LENS_APERTURE_VALUE: i32 = 512; + /// Allows access to lens feature value currently selected by XI_PRM_LENS_FEATURE_SELECTOR. + pub const CAP_PROP_XI_LENS_FEATURE: i32 = 518; + /// Selects the current feature which is accessible by XI_PRM_LENS_FEATURE. + pub const CAP_PROP_XI_LENS_FEATURE_SELECTOR: i32 = 517; + /// Lens focal distance in mm. + pub const CAP_PROP_XI_LENS_FOCAL_LENGTH: i32 = 516; + /// Lens focus distance in cm. + pub const CAP_PROP_XI_LENS_FOCUS_DISTANCE: i32 = 515; + /// Moves lens focus motor by steps set in XI_PRM_LENS_FOCUS_MOVEMENT_VALUE. + pub const CAP_PROP_XI_LENS_FOCUS_MOVE: i32 = 514; + /// Lens current focus movement value to be used by XI_PRM_LENS_FOCUS_MOVE in motor steps. + pub const CAP_PROP_XI_LENS_FOCUS_MOVEMENT_VALUE: i32 = 513; + /// Status of lens control interface. This shall be set to XI_ON before any Lens operations. + pub const CAP_PROP_XI_LENS_MODE: i32 = 511; + /// Set/get bandwidth(datarate)(in Megabits). + pub const CAP_PROP_XI_LIMIT_BANDWIDTH: i32 = 459; + /// Activates LUT. + pub const CAP_PROP_XI_LUT_EN: i32 = 541; + /// Control the index (offset) of the coefficient to access in the LUT. + pub const CAP_PROP_XI_LUT_INDEX: i32 = 542; + /// Value at entry LUTIndex of the LUT. + pub const CAP_PROP_XI_LUT_VALUE: i32 = 543; + /// Calculates White Balance(must be called during acquisition). + pub const CAP_PROP_XI_MANUAL_WB: i32 = 413; + /// Horizontal offset from the origin to the area of interest (in pixels). + pub const CAP_PROP_XI_OFFSET_X: i32 = 402; + /// Vertical offset from the origin to the area of interest (in pixels). + pub const CAP_PROP_XI_OFFSET_Y: i32 = 403; + /// Device output data bit depth. + pub const CAP_PROP_XI_OUTPUT_DATA_BIT_DEPTH: i32 = 461; + /// Device output data packing (or grouping) enabled. Packing could be enabled if output_data_bit_depth > 8 and packing capability is available. + pub const CAP_PROP_XI_OUTPUT_DATA_PACKING: i32 = 463; + /// Data packing type. Some cameras supports only specific packing type. + pub const CAP_PROP_XI_OUTPUT_DATA_PACKING_TYPE: i32 = 464; + /// GetImage returns most recent frame. + pub const CAP_PROP_XI_RECENT_FRAME: i32 = 553; + /// Activates/deactivates Region selected by Region Selector. + pub const CAP_PROP_XI_REGION_MODE: i32 = 595; + /// Selects Region in Multiple ROI which parameters are set by width, height, ... ,region mode. + pub const CAP_PROP_XI_REGION_SELECTOR: i32 = 589; + /// Correction of row FPN. + pub const CAP_PROP_XI_ROW_FPN_CORRECTION: i32 = 591; + /// Camera sensor board temperature. + pub const CAP_PROP_XI_SENSOR_BOARD_TEMP: i32 = 596; + /// Sensor clock frequency in Hz. + pub const CAP_PROP_XI_SENSOR_CLOCK_FREQ_HZ: i32 = 532; + /// Sensor clock frequency index. Sensor with selected frequencies have possibility to set the frequency only by this index. + pub const CAP_PROP_XI_SENSOR_CLOCK_FREQ_INDEX: i32 = 533; + /// Sensor output data bit depth. + pub const CAP_PROP_XI_SENSOR_DATA_BIT_DEPTH: i32 = 460; + /// Selects the current feature which is accessible by XI_PRM_SENSOR_FEATURE_VALUE. + pub const CAP_PROP_XI_SENSOR_FEATURE_SELECTOR: i32 = 585; + /// Allows access to sensor feature value currently selected by XI_PRM_SENSOR_FEATURE_SELECTOR. + pub const CAP_PROP_XI_SENSOR_FEATURE_VALUE: i32 = 586; + /// Current sensor mode. Allows to select sensor mode by one integer. Setting of this parameter affects: image dimensions and downsampling. + pub const CAP_PROP_XI_SENSOR_MODE: i32 = 558; + /// Number of output channels from sensor used for data transfer. + pub const CAP_PROP_XI_SENSOR_OUTPUT_CHANNEL_COUNT: i32 = 534; + /// Number of taps. + pub const CAP_PROP_XI_SENSOR_TAPS: i32 = 437; + /// Sharpness Strength. + pub const CAP_PROP_XI_SHARPNESS: i32 = 478; + /// Change sensor shutter type(CMOS sensor). + pub const CAP_PROP_XI_SHUTTER_TYPE: i32 = 436; + /// Set sensor target temperature for cooling. + pub const CAP_PROP_XI_TARGET_TEMP: i32 = 467; + /// Selects which test pattern type is generated by the selected generator. + pub const CAP_PROP_XI_TEST_PATTERN: i32 = 588; + /// Selects which test pattern generator is controlled by the TestPattern feature. + pub const CAP_PROP_XI_TEST_PATTERN_GENERATOR_SELECTOR: i32 = 587; + /// Image capture timeout in milliseconds. + pub const CAP_PROP_XI_TIMEOUT: i32 = 420; + /// Current format of pixels on transport layer. + pub const CAP_PROP_XI_TRANSPORT_PIXEL_FORMAT: i32 = 531; + /// Specifies the delay in microseconds (us) to apply after the trigger reception before activating it. + pub const CAP_PROP_XI_TRG_DELAY: i32 = 544; + /// Selects the type of trigger. + pub const CAP_PROP_XI_TRG_SELECTOR: i32 = 498; + /// Generates an internal trigger. PRM_TRG_SOURCE must be set to TRG_SOFTWARE. + pub const CAP_PROP_XI_TRG_SOFTWARE: i32 = 405; + /// Defines source of trigger. + pub const CAP_PROP_XI_TRG_SOURCE: i32 = 404; + /// Defines how time stamp reset engine will be armed. + pub const CAP_PROP_XI_TS_RST_MODE: i32 = 545; + /// Defines which source will be used for timestamp reset. Writing this parameter will trigger settings of engine (arming). + pub const CAP_PROP_XI_TS_RST_SOURCE: i32 = 546; + /// Size of used camera FFS. + pub const CAP_PROP_XI_USED_FFS_SIZE: i32 = 582; + /// White balance blue coefficient. + pub const CAP_PROP_XI_WB_KB: i32 = 450; + /// White balance green coefficient. + pub const CAP_PROP_XI_WB_KG: i32 = 449; + /// White balance red coefficient. + pub const CAP_PROP_XI_WB_KR: i32 = 448; + /// Width of the Image provided by the device (in pixels). + pub const CAP_PROP_XI_WIDTH: i32 = 451; + /// Android: May switch physical cameras/lenses. Factor and range are hardware-dependent. + pub const CAP_PROP_ZOOM: i32 = 27; + /// PvAPI, Prosilica GigE SDK + pub const CAP_PVAPI: i32 = 800; + /// 2 out of 16 decimation + pub const CAP_PVAPI_DECIMATION_2OUTOF16: i32 = 8; + /// 2 out of 4 decimation + pub const CAP_PVAPI_DECIMATION_2OUTOF4: i32 = 2; + /// 2 out of 8 decimation + pub const CAP_PVAPI_DECIMATION_2OUTOF8: i32 = 4; + /// Off + pub const CAP_PVAPI_DECIMATION_OFF: i32 = 1; + /// FixedRate + pub const CAP_PVAPI_FSTRIGMODE_FIXEDRATE: i32 = 3; + /// Freerun + pub const CAP_PVAPI_FSTRIGMODE_FREERUN: i32 = 0; + /// Software + pub const CAP_PVAPI_FSTRIGMODE_SOFTWARE: i32 = 4; + /// SyncIn1 + pub const CAP_PVAPI_FSTRIGMODE_SYNCIN1: i32 = 1; + /// SyncIn2 + pub const CAP_PVAPI_FSTRIGMODE_SYNCIN2: i32 = 2; + /// Bayer16 + pub const CAP_PVAPI_PIXELFORMAT_BAYER16: i32 = 4; + /// Bayer8 + pub const CAP_PVAPI_PIXELFORMAT_BAYER8: i32 = 3; + /// Bgr24 + pub const CAP_PVAPI_PIXELFORMAT_BGR24: i32 = 6; + /// Bgra32 + pub const CAP_PVAPI_PIXELFORMAT_BGRA32: i32 = 8; + /// Mono16 + pub const CAP_PVAPI_PIXELFORMAT_MONO16: i32 = 2; + /// Mono8 + pub const CAP_PVAPI_PIXELFORMAT_MONO8: i32 = 1; + /// Rgb24 + pub const CAP_PVAPI_PIXELFORMAT_RGB24: i32 = 5; + /// Rgba32 + pub const CAP_PVAPI_PIXELFORMAT_RGBA32: i32 = 7; + /// QuickTime (obsolete, removed) + pub const CAP_QT: i32 = 500; + /// Synonym for CAP_INTELPERC + pub const CAP_REALSENSE: i32 = 1500; + /// uEye Camera API + pub const CAP_UEYE: i32 = 2500; + /// Unicap drivers (obsolete, removed) + pub const CAP_UNICAP: i32 = 600; + /// V4L/V4L2 capturing support + pub const CAP_V4L: i32 = 200; + /// Same as CAP_V4L + pub const CAP_V4L2: i32 = 200; + /// Video For Windows (obsolete, removed) + pub const CAP_VFW: i32 = 200; + /// Microsoft Windows Runtime using Media Foundation + pub const CAP_WINRT: i32 = 1410; + /// XIMEA Camera API + pub const CAP_XIAPI: i32 = 1100; + /// XINE engine (Linux) + pub const CAP_XINE: i32 = 2400; + pub const CV__CAP_PROP_LATEST: i32 = 73; + pub const CV__VIDEOWRITER_PROP_LATEST: i32 = 14; + /// Defaults to \ref CV_8U. + pub const VIDEOWRITER_PROP_DEPTH: i32 = 5; + /// Specifies the maximum difference between presentation (pts) and decompression timestamps (dts) using the FPS time base. This property is necessary **only** when encapsulating **externally** encoded video where the decoding order differs from the presentation order, such as in GOP patterns with bi-directional B-frames. The value should be calculated based on the specific GOP pattern used during encoding. For example, in a GOP with presentation order IBP and decoding order IPB, this value would be 1, as the B-frame is the second frame presented but the third to be decoded. It can be queried from the resulting encapsulated video file using VideoCapture::get(\ref CAP_PROP_DTS_DELAY). Non-zero values usually imply the stream is encoded using B-frames. FFmpeg back-end only. + pub const VIDEOWRITER_PROP_DTS_DELAY: i32 = 13; + /// (Read-only): Size of just encoded video frame. Note that the encoding order may be different from representation order. + pub const VIDEOWRITER_PROP_FRAMEBYTES: i32 = 2; + /// (**open-only**) Hardware acceleration type (see #VideoAccelerationType). Setting supported only via `params` parameter in VideoWriter constructor / .open() method. Default value is backend-specific. + pub const VIDEOWRITER_PROP_HW_ACCELERATION: i32 = 6; + /// (**open-only**) If non-zero, create new OpenCL context and bind it to current thread. The OpenCL context created with Video Acceleration context attached it (if not attached yet) for optimized GPU data copy between cv::UMat and HW accelerated encoder. + pub const VIDEOWRITER_PROP_HW_ACCELERATION_USE_OPENCL: i32 = 8; + /// (**open-only**) Hardware device index (select GPU if multiple available). Device enumeration is acceleration type specific. + pub const VIDEOWRITER_PROP_HW_DEVICE: i32 = 7; + /// If it is not zero, the encoder will expect and encode color frames, otherwise it + /// will work with grayscale frames. + pub const VIDEOWRITER_PROP_IS_COLOR: i32 = 4; + /// Set to non-zero to signal that the following frames are key frames or zero if not, when encapsulating raw video (\ref VIDEOWRITER_PROP_RAW_VIDEO != 0). FFmpeg back-end only. + pub const VIDEOWRITER_PROP_KEY_FLAG: i32 = 11; + /// (**open-only**) Set the key frame interval using raw video encapsulation (\ref VIDEOWRITER_PROP_RAW_VIDEO != 0). Defaults to 1 when not set. FFmpeg back-end only. + pub const VIDEOWRITER_PROP_KEY_INTERVAL: i32 = 10; + /// Number of stripes for parallel encoding. -1 for auto detection. + pub const VIDEOWRITER_PROP_NSTRIPES: i32 = 3; + /// Specifies the frame presentation timestamp for each frame using the FPS time base. This property is **only** necessary when encapsulating **externally** encoded video where the decoding order differs from the presentation order, such as in GOP patterns with bi-directional B-frames. The value should be provided by your external encoder and for video sources with fixed frame rates it is equivalent to dividing the current frame's presentation time (\ref CAP_PROP_POS_MSEC) by the frame duration (1000.0 / VideoCapture::get(\ref CAP_PROP_FPS)). It can be queried from the resulting encapsulated video file using VideoCapture::get(\ref CAP_PROP_PTS). FFmpeg back-end only. + pub const VIDEOWRITER_PROP_PTS: i32 = 12; + /// Current quality (0..100%) of the encoded videostream. Can be adjusted dynamically in some codecs. + pub const VIDEOWRITER_PROP_QUALITY: i32 = 1; + /// (**open-only**) Set to non-zero to enable encapsulation of an encoded raw video stream. Each raw encoded video frame should be passed to VideoWriter::write() as single row or column of a \ref CV_8UC1 Mat. \note If the key frame interval is not 1 then it must be manually specified by the user. This can either be performed during initialization passing \ref VIDEOWRITER_PROP_KEY_INTERVAL as one of the extra encoder params to \ref VideoWriter::VideoWriter(const String &, int, double, const Size &, const std::vector< int > ¶ms) or afterwards by setting the \ref VIDEOWRITER_PROP_KEY_FLAG with \ref VideoWriter::set() before writing each frame. FFMpeg backend only. + pub const VIDEOWRITER_PROP_RAW_VIDEO: i32 = 9; + /// Prefer to use H/W acceleration. If no one supported, then fallback to software processing. + /// + /// Note: H/W acceleration may require special configuration of used environment. + /// + /// Note: Results in encoding scenario may differ between software and hardware accelerated encoders. + pub const VIDEO_ACCELERATION_ANY: i32 = 1; + /// DirectX 11 + pub const VIDEO_ACCELERATION_D3D11: i32 = 2; + /// Raspberry Pi V4 + pub const VIDEO_ACCELERATION_DRM: i32 = 5; + /// libmfx (Intel MediaSDK/oneVPL) + pub const VIDEO_ACCELERATION_MFX: i32 = 4; + /// Do not require any specific H/W acceleration, prefer software processing. + /// Reading of this value means that special H/W accelerated handling is not added or not detected by OpenCV. + pub const VIDEO_ACCELERATION_NONE: i32 = 0; + /// VAAPI + pub const VIDEO_ACCELERATION_VAAPI: i32 = 3; + /// Video Acceleration type + /// + /// Used as value in [CAP_PROP_HW_ACCELERATION] and [VIDEOWRITER_PROP_HW_ACCELERATION] + /// + /// + /// Note: In case of FFmpeg backend, it translated to enum AVHWDeviceType () + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum VideoAccelerationType { + /// Do not require any specific H/W acceleration, prefer software processing. + /// Reading of this value means that special H/W accelerated handling is not added or not detected by OpenCV. + VIDEO_ACCELERATION_NONE = 0, + /// Prefer to use H/W acceleration. If no one supported, then fallback to software processing. + /// + /// Note: H/W acceleration may require special configuration of used environment. + /// + /// Note: Results in encoding scenario may differ between software and hardware accelerated encoders. + VIDEO_ACCELERATION_ANY = 1, + /// DirectX 11 + VIDEO_ACCELERATION_D3D11 = 2, + /// VAAPI + VIDEO_ACCELERATION_VAAPI = 3, + /// libmfx (Intel MediaSDK/oneVPL) + VIDEO_ACCELERATION_MFX = 4, + /// Raspberry Pi V4 + VIDEO_ACCELERATION_DRM = 5, + } + + opencv_type_enum! { crate::videoio::VideoAccelerationType { VIDEO_ACCELERATION_NONE, VIDEO_ACCELERATION_ANY, VIDEO_ACCELERATION_D3D11, VIDEO_ACCELERATION_VAAPI, VIDEO_ACCELERATION_MFX, VIDEO_ACCELERATION_DRM } } + + /// cv::VideoCapture API backends identifier. + /// + /// Select preferred API for a capture object. + /// To be used in the VideoCapture::VideoCapture() constructor or VideoCapture::open() + /// + /// + /// Note: + /// * Backends are available only if they have been built with your OpenCV binaries. + /// See [videoio_overview] for more information. + /// * Microsoft Media Foundation backend tries to use hardware accelerated transformations + /// if possible. Environment flag "OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS" set to 0 + /// disables it and may improve initialization time. More details: + /// + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum VideoCaptureAPIs { + /// Auto detect == 0 + CAP_ANY = 0, + /// Video For Windows (obsolete, removed) + CAP_VFW = 200, + // V4L/V4L2 capturing support + // Duplicate, use CAP_VFW instead + // CAP_V4L = 200, + // Same as CAP_V4L + // Duplicate, use CAP_V4L instead + // CAP_V4L2 = 200, + /// IEEE 1394 drivers + CAP_FIREWIRE = 300, + // Same value as CAP_FIREWIRE + // Duplicate, use CAP_FIREWIRE instead + // CAP_FIREWARE = 300, + // Same value as CAP_FIREWIRE + // Duplicate, use CAP_FIREWARE instead + // CAP_IEEE1394 = 300, + // Same value as CAP_FIREWIRE + // Duplicate, use CAP_IEEE1394 instead + // CAP_DC1394 = 300, + // Same value as CAP_FIREWIRE + // Duplicate, use CAP_DC1394 instead + // CAP_CMU1394 = 300, + /// QuickTime (obsolete, removed) + CAP_QT = 500, + /// Unicap drivers (obsolete, removed) + CAP_UNICAP = 600, + /// DirectShow (via videoInput) + CAP_DSHOW = 700, + /// PvAPI, Prosilica GigE SDK + CAP_PVAPI = 800, + /// OpenNI (for Kinect) + CAP_OPENNI = 900, + /// OpenNI (for Asus Xtion) + CAP_OPENNI_ASUS = 910, + /// MediaNDK (API Level 21+) and NDK Camera (API level 24+) for Android + CAP_ANDROID = 1000, + /// XIMEA Camera API + CAP_XIAPI = 1100, + /// AVFoundation framework for iOS (OS X Lion will have the same API) + CAP_AVFOUNDATION = 1200, + /// Smartek Giganetix GigEVisionSDK + CAP_GIGANETIX = 1300, + /// Microsoft Media Foundation (via videoInput). See platform specific notes above. + CAP_MSMF = 1400, + /// Microsoft Windows Runtime using Media Foundation + CAP_WINRT = 1410, + /// RealSense (former Intel Perceptual Computing SDK) + CAP_INTELPERC = 1500, + // Synonym for CAP_INTELPERC + // Duplicate, use CAP_INTELPERC instead + // CAP_REALSENSE = 1500, + /// OpenNI2 (for Kinect) + CAP_OPENNI2 = 1600, + /// OpenNI2 (for Asus Xtion and Occipital Structure sensors) + CAP_OPENNI2_ASUS = 1610, + /// OpenNI2 (for Orbbec Astra) + CAP_OPENNI2_ASTRA = 1620, + /// gPhoto2 connection + CAP_GPHOTO2 = 1700, + /// GStreamer + CAP_GSTREAMER = 1800, + /// Open and record video file or stream using the FFMPEG library + CAP_FFMPEG = 1900, + /// OpenCV Image Sequence (e.g. img_%02d.jpg) + CAP_IMAGES = 2000, + /// Aravis SDK + CAP_ARAVIS = 2100, + /// Built-in OpenCV MotionJPEG codec + CAP_OPENCV_MJPEG = 2200, + /// Intel MediaSDK + CAP_INTEL_MFX = 2300, + /// XINE engine (Linux) + CAP_XINE = 2400, + /// uEye Camera API + CAP_UEYE = 2500, + /// For Orbbec 3D-Sensor device/module (Astra+, Femto, Astra2, Gemini2, Gemini2L, Gemini2XL, Gemini330, Femto Mega) attention: Astra2 cameras currently only support Windows and Linux kernel versions no higher than 4.15, and higher versions of Linux kernel may have exceptions. + CAP_OBSENSOR = 2600, + } + + opencv_type_enum! { crate::videoio::VideoCaptureAPIs { CAP_ANY, CAP_VFW, CAP_FIREWIRE, CAP_QT, CAP_UNICAP, CAP_DSHOW, CAP_PVAPI, CAP_OPENNI, CAP_OPENNI_ASUS, CAP_ANDROID, CAP_XIAPI, CAP_AVFOUNDATION, CAP_GIGANETIX, CAP_MSMF, CAP_WINRT, CAP_INTELPERC, CAP_OPENNI2, CAP_OPENNI2_ASUS, CAP_OPENNI2_ASTRA, CAP_GPHOTO2, CAP_GSTREAMER, CAP_FFMPEG, CAP_IMAGES, CAP_ARAVIS, CAP_OPENCV_MJPEG, CAP_INTEL_MFX, CAP_XINE, CAP_UEYE, CAP_OBSENSOR } } + + /// OBSENSOR (for Orbbec 3D-Sensor device/module ) + /// + /// //! OBSENSOR data given from image generator + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum VideoCaptureOBSensorDataType { + /// Depth values in mm (CV_16UC1) + CAP_OBSENSOR_DEPTH_MAP = 0, + /// Data given from BGR stream generator + CAP_OBSENSOR_BGR_IMAGE = 1, + /// Data given from IR stream generator(CV_16UC1) + CAP_OBSENSOR_IR_IMAGE = 2, + } + + opencv_type_enum! { crate::videoio::VideoCaptureOBSensorDataType { CAP_OBSENSOR_DEPTH_MAP, CAP_OBSENSOR_BGR_IMAGE, CAP_OBSENSOR_IR_IMAGE } } + + /// OBSENSOR stream generator + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum VideoCaptureOBSensorGenerators { + CAP_OBSENSOR_DEPTH_GENERATOR = 536870912, + CAP_OBSENSOR_IMAGE_GENERATOR = 268435456, + CAP_OBSENSOR_IR_GENERATOR = 134217728, + CAP_OBSENSOR_GENERATORS_MASK = 939524096, + } + + opencv_type_enum! { crate::videoio::VideoCaptureOBSensorGenerators { CAP_OBSENSOR_DEPTH_GENERATOR, CAP_OBSENSOR_IMAGE_GENERATOR, CAP_OBSENSOR_IR_GENERATOR, CAP_OBSENSOR_GENERATORS_MASK } } + + /// OBSENSOR properties + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum VideoCaptureOBSensorProperties { + CAP_PROP_OBSENSOR_INTRINSIC_FX = 26001, + CAP_PROP_OBSENSOR_INTRINSIC_FY = 26002, + CAP_PROP_OBSENSOR_INTRINSIC_CX = 26003, + CAP_PROP_OBSENSOR_INTRINSIC_CY = 26004, + CAP_PROP_OBSENSOR_RGB_POS_MSEC = 26005, + CAP_PROP_OBSENSOR_DEPTH_POS_MSEC = 26006, + CAP_PROP_OBSENSOR_DEPTH_WIDTH = 26007, + CAP_PROP_OBSENSOR_DEPTH_HEIGHT = 26008, + CAP_PROP_OBSENSOR_DEPTH_FPS = 26009, + CAP_PROP_OBSENSOR_COLOR_DISTORTION_K1 = 26010, + CAP_PROP_OBSENSOR_COLOR_DISTORTION_K2 = 26011, + CAP_PROP_OBSENSOR_COLOR_DISTORTION_K3 = 26012, + CAP_PROP_OBSENSOR_COLOR_DISTORTION_K4 = 26013, + CAP_PROP_OBSENSOR_COLOR_DISTORTION_K5 = 26014, + CAP_PROP_OBSENSOR_COLOR_DISTORTION_K6 = 26015, + CAP_PROP_OBSENSOR_COLOR_DISTORTION_P1 = 26016, + CAP_PROP_OBSENSOR_COLOR_DISTORTION_P2 = 26017, + } + + opencv_type_enum! { crate::videoio::VideoCaptureOBSensorProperties { CAP_PROP_OBSENSOR_INTRINSIC_FX, CAP_PROP_OBSENSOR_INTRINSIC_FY, CAP_PROP_OBSENSOR_INTRINSIC_CX, CAP_PROP_OBSENSOR_INTRINSIC_CY, CAP_PROP_OBSENSOR_RGB_POS_MSEC, CAP_PROP_OBSENSOR_DEPTH_POS_MSEC, CAP_PROP_OBSENSOR_DEPTH_WIDTH, CAP_PROP_OBSENSOR_DEPTH_HEIGHT, CAP_PROP_OBSENSOR_DEPTH_FPS, CAP_PROP_OBSENSOR_COLOR_DISTORTION_K1, CAP_PROP_OBSENSOR_COLOR_DISTORTION_K2, CAP_PROP_OBSENSOR_COLOR_DISTORTION_K3, CAP_PROP_OBSENSOR_COLOR_DISTORTION_K4, CAP_PROP_OBSENSOR_COLOR_DISTORTION_K5, CAP_PROP_OBSENSOR_COLOR_DISTORTION_K6, CAP_PROP_OBSENSOR_COLOR_DISTORTION_P1, CAP_PROP_OBSENSOR_COLOR_DISTORTION_P2 } } + + /// cv::VideoCapture generic properties identifier. + /// + /// Reading / writing properties involves many layers. Some unexpected result might happens along this chain. + /// Effective behaviour depends from device hardware, driver and API Backend. + /// ## See also + /// videoio_flags_others, VideoCapture::get(), VideoCapture::set() + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum VideoCaptureProperties { + /// Current position of the video file in milliseconds. + CAP_PROP_POS_MSEC = 0, + /// 0-based index of the frame to be decoded/captured next. When the index i is set in RAW mode (CAP_PROP_FORMAT == -1) this will seek to the key frame k, where k <= i. + CAP_PROP_POS_FRAMES = 1, + /// Relative position of the video file: 0=start of the film, 1=end of the film. + CAP_PROP_POS_AVI_RATIO = 2, + /// Width of the frames in the video stream. + CAP_PROP_FRAME_WIDTH = 3, + /// Height of the frames in the video stream. + CAP_PROP_FRAME_HEIGHT = 4, + /// Frame rate. + CAP_PROP_FPS = 5, + /// 4-character code of codec. see VideoWriter::fourcc . + CAP_PROP_FOURCC = 6, + /// Number of frames in the video file. + CAP_PROP_FRAME_COUNT = 7, + /// Format of the %Mat objects (see Mat::type()) returned by VideoCapture::retrieve(). + /// Set value -1 to fetch undecoded RAW video streams (as Mat 8UC1). + CAP_PROP_FORMAT = 8, + /// Backend-specific value indicating the current capture mode. + CAP_PROP_MODE = 9, + /// Brightness of the image (only for those cameras that support). + CAP_PROP_BRIGHTNESS = 10, + /// Contrast of the image (only for cameras). + CAP_PROP_CONTRAST = 11, + /// Saturation of the image (only for cameras). + CAP_PROP_SATURATION = 12, + /// Hue of the image (only for cameras). + CAP_PROP_HUE = 13, + /// Gain of the image (only for those cameras that support). + CAP_PROP_GAIN = 14, + /// Exposure (only for those cameras that support). + CAP_PROP_EXPOSURE = 15, + /// Boolean flags indicating whether images should be converted to BGR.
+ /// *GStreamer note*: The flag is ignored in case if custom pipeline is used. It's user responsibility to interpret pipeline output. + CAP_PROP_CONVERT_RGB = 16, + /// Currently unsupported. + CAP_PROP_WHITE_BALANCE_BLUE_U = 17, + /// Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently). + CAP_PROP_RECTIFICATION = 18, + CAP_PROP_MONOCHROME = 19, + CAP_PROP_SHARPNESS = 20, + /// DC1394: exposure control done by camera, user can adjust reference level using this feature. + CAP_PROP_AUTO_EXPOSURE = 21, + CAP_PROP_GAMMA = 22, + CAP_PROP_TEMPERATURE = 23, + CAP_PROP_TRIGGER = 24, + CAP_PROP_TRIGGER_DELAY = 25, + CAP_PROP_WHITE_BALANCE_RED_V = 26, + /// Android: May switch physical cameras/lenses. Factor and range are hardware-dependent. + CAP_PROP_ZOOM = 27, + CAP_PROP_FOCUS = 28, + CAP_PROP_GUID = 29, + CAP_PROP_ISO_SPEED = 30, + CAP_PROP_BACKLIGHT = 32, + CAP_PROP_PAN = 33, + CAP_PROP_TILT = 34, + CAP_PROP_ROLL = 35, + CAP_PROP_IRIS = 36, + /// Pop up video/camera filter dialog (note: only supported by DSHOW backend currently. The property value is ignored) + CAP_PROP_SETTINGS = 37, + CAP_PROP_BUFFERSIZE = 38, + CAP_PROP_AUTOFOCUS = 39, + /// Sample aspect ratio: num/den (num) + CAP_PROP_SAR_NUM = 40, + /// Sample aspect ratio: num/den (den) + CAP_PROP_SAR_DEN = 41, + /// Current backend (enum VideoCaptureAPIs). Read-only property + CAP_PROP_BACKEND = 42, + /// Video input or Channel Number (only for those cameras that support) + CAP_PROP_CHANNEL = 43, + /// enable/ disable auto white-balance + CAP_PROP_AUTO_WB = 44, + /// white-balance color temperature + CAP_PROP_WB_TEMPERATURE = 45, + /// (read-only) codec's pixel format. 4-character code - see VideoWriter::fourcc . Subset of [AV_PIX_FMT_*](https://github.com/FFmpeg/FFmpeg/blob/master/libavcodec/raw.c) or -1 if unknown + CAP_PROP_CODEC_PIXEL_FORMAT = 46, + /// (read-only) Video bitrate in kbits/s + CAP_PROP_BITRATE = 47, + /// (read-only) Frame rotation defined by stream meta (applicable for FFmpeg and AVFoundation back-ends only) + CAP_PROP_ORIENTATION_META = 48, + /// if true - rotates output frames of CvCapture considering video file's metadata (applicable for FFmpeg and AVFoundation back-ends only) () + CAP_PROP_ORIENTATION_AUTO = 49, + /// (**open-only**) Hardware acceleration type (see #VideoAccelerationType). Setting supported only via `params` parameter in cv::VideoCapture constructor / .open() method. Default value is backend-specific. + CAP_PROP_HW_ACCELERATION = 50, + /// (**open-only**) Hardware device index (select GPU if multiple available). Device enumeration is acceleration type specific. + CAP_PROP_HW_DEVICE = 51, + /// (**open-only**) If non-zero, create new OpenCL context and bind it to current thread. The OpenCL context created with Video Acceleration context attached it (if not attached yet) for optimized GPU data copy between HW accelerated decoder and cv::UMat. + CAP_PROP_HW_ACCELERATION_USE_OPENCL = 52, + /// (**open-only**) timeout in milliseconds for opening a video capture (applicable for FFmpeg and GStreamer back-ends only) + CAP_PROP_OPEN_TIMEOUT_MSEC = 53, + /// (**open-only**) timeout in milliseconds for reading from a video capture (applicable for FFmpeg and GStreamer back-ends only) + CAP_PROP_READ_TIMEOUT_MSEC = 54, + /// (read-only) time in microseconds since Jan 1 1970 when stream was opened. Applicable for FFmpeg backend only. Useful for RTSP and other live streams + CAP_PROP_STREAM_OPEN_TIME_USEC = 55, + /// (read-only) Number of video channels + CAP_PROP_VIDEO_TOTAL_CHANNELS = 56, + /// (**open-only**) Specify video stream, 0-based index. Use -1 to disable video stream from file or IP cameras. Default value is 0. + CAP_PROP_VIDEO_STREAM = 57, + /// (**open-only**) Specify stream in multi-language media files, -1 - disable audio processing or microphone. Default value is -1. + CAP_PROP_AUDIO_STREAM = 58, + /// (read-only) Audio position is measured in samples. Accurate audio sample timestamp of previous grabbed fragment. See CAP_PROP_AUDIO_SAMPLES_PER_SECOND and CAP_PROP_AUDIO_SHIFT_NSEC. + CAP_PROP_AUDIO_POS = 59, + /// (read only) Contains the time difference between the start of the audio stream and the video stream in nanoseconds. Positive value means that audio is started after the first video frame. Negative value means that audio is started before the first video frame. + CAP_PROP_AUDIO_SHIFT_NSEC = 60, + /// (open, read) Alternative definition to bits-per-sample, but with clear handling of 32F / 32S + CAP_PROP_AUDIO_DATA_DEPTH = 61, + /// (open, read) determined from file/codec input. If not specified, then selected audio sample rate is 44100 + CAP_PROP_AUDIO_SAMPLES_PER_SECOND = 62, + /// (read-only) Index of the first audio channel for .retrieve() calls. That audio channel number continues enumeration after video channels. + CAP_PROP_AUDIO_BASE_INDEX = 63, + /// (read-only) Number of audio channels in the selected audio stream (mono, stereo, etc) + CAP_PROP_AUDIO_TOTAL_CHANNELS = 64, + /// (read-only) Number of audio streams. + CAP_PROP_AUDIO_TOTAL_STREAMS = 65, + /// (open, read) Enables audio synchronization. + CAP_PROP_AUDIO_SYNCHRONIZE = 66, + /// FFmpeg back-end only - Indicates whether the Last Raw Frame (LRF), output from VideoCapture::read() when VideoCapture is initialized with VideoCapture::open(CAP_FFMPEG, {CAP_PROP_FORMAT, -1}) or VideoCapture::set(CAP_PROP_FORMAT,-1) is called before the first call to VideoCapture::read(), contains encoded data for a key frame. + CAP_PROP_LRF_HAS_KEY_FRAME = 67, + /// Positive index indicates that returning extra data is supported by the video back end. This can be retrieved as cap.retrieve(data, ). E.g. When reading from a h264 encoded RTSP stream, the FFmpeg backend could return the SPS and/or PPS if available (if sent in reply to a DESCRIBE request), from calls to cap.retrieve(data, ). + CAP_PROP_CODEC_EXTRADATA_INDEX = 68, + /// (read-only) FFmpeg back-end only - Frame type ascii code (73 = 'I', 80 = 'P', 66 = 'B' or 63 = '?' if unknown) of the most recently read frame. + CAP_PROP_FRAME_TYPE = 69, + /// (**open-only**) Set the maximum number of threads to use. Use 0 to use as many threads as CPU cores (applicable for FFmpeg back-end only). + CAP_PROP_N_THREADS = 70, + /// (read-only) FFmpeg back-end only - presentation timestamp of the most recently read frame using the FPS time base. e.g. fps = 25, VideoCapture::get(\ref CAP_PROP_PTS) = 3, presentation time = 3/25 seconds. + CAP_PROP_PTS = 71, + /// (read-only) FFmpeg back-end only - maximum difference between presentation (pts) and decompression timestamps (dts) using FPS time base. e.g. delay is maximum when frame_num = 0, if true, VideoCapture::get(\ref CAP_PROP_PTS) = 0 and VideoCapture::get(\ref CAP_PROP_DTS_DELAY) = 2, dts = -2. Non zero values usually imply the stream is encoded using B-frames which are not decoded in presentation order. + CAP_PROP_DTS_DELAY = 72, + CV__CAP_PROP_LATEST = 73, + } + + opencv_type_enum! { crate::videoio::VideoCaptureProperties { CAP_PROP_POS_MSEC, CAP_PROP_POS_FRAMES, CAP_PROP_POS_AVI_RATIO, CAP_PROP_FRAME_WIDTH, CAP_PROP_FRAME_HEIGHT, CAP_PROP_FPS, CAP_PROP_FOURCC, CAP_PROP_FRAME_COUNT, CAP_PROP_FORMAT, CAP_PROP_MODE, CAP_PROP_BRIGHTNESS, CAP_PROP_CONTRAST, CAP_PROP_SATURATION, CAP_PROP_HUE, CAP_PROP_GAIN, CAP_PROP_EXPOSURE, CAP_PROP_CONVERT_RGB, CAP_PROP_WHITE_BALANCE_BLUE_U, CAP_PROP_RECTIFICATION, CAP_PROP_MONOCHROME, CAP_PROP_SHARPNESS, CAP_PROP_AUTO_EXPOSURE, CAP_PROP_GAMMA, CAP_PROP_TEMPERATURE, CAP_PROP_TRIGGER, CAP_PROP_TRIGGER_DELAY, CAP_PROP_WHITE_BALANCE_RED_V, CAP_PROP_ZOOM, CAP_PROP_FOCUS, CAP_PROP_GUID, CAP_PROP_ISO_SPEED, CAP_PROP_BACKLIGHT, CAP_PROP_PAN, CAP_PROP_TILT, CAP_PROP_ROLL, CAP_PROP_IRIS, CAP_PROP_SETTINGS, CAP_PROP_BUFFERSIZE, CAP_PROP_AUTOFOCUS, CAP_PROP_SAR_NUM, CAP_PROP_SAR_DEN, CAP_PROP_BACKEND, CAP_PROP_CHANNEL, CAP_PROP_AUTO_WB, CAP_PROP_WB_TEMPERATURE, CAP_PROP_CODEC_PIXEL_FORMAT, CAP_PROP_BITRATE, CAP_PROP_ORIENTATION_META, CAP_PROP_ORIENTATION_AUTO, CAP_PROP_HW_ACCELERATION, CAP_PROP_HW_DEVICE, CAP_PROP_HW_ACCELERATION_USE_OPENCL, CAP_PROP_OPEN_TIMEOUT_MSEC, CAP_PROP_READ_TIMEOUT_MSEC, CAP_PROP_STREAM_OPEN_TIME_USEC, CAP_PROP_VIDEO_TOTAL_CHANNELS, CAP_PROP_VIDEO_STREAM, CAP_PROP_AUDIO_STREAM, CAP_PROP_AUDIO_POS, CAP_PROP_AUDIO_SHIFT_NSEC, CAP_PROP_AUDIO_DATA_DEPTH, CAP_PROP_AUDIO_SAMPLES_PER_SECOND, CAP_PROP_AUDIO_BASE_INDEX, CAP_PROP_AUDIO_TOTAL_CHANNELS, CAP_PROP_AUDIO_TOTAL_STREAMS, CAP_PROP_AUDIO_SYNCHRONIZE, CAP_PROP_LRF_HAS_KEY_FRAME, CAP_PROP_CODEC_EXTRADATA_INDEX, CAP_PROP_FRAME_TYPE, CAP_PROP_N_THREADS, CAP_PROP_PTS, CAP_PROP_DTS_DELAY, CV__CAP_PROP_LATEST } } + + /// cv::VideoWriter generic properties identifier. + /// ## See also + /// VideoWriter::get(), VideoWriter::set() + #[repr(i32)] + #[derive(Copy, Clone, Debug, PartialEq, Eq)] + pub enum VideoWriterProperties { + /// Current quality (0..100%) of the encoded videostream. Can be adjusted dynamically in some codecs. + VIDEOWRITER_PROP_QUALITY = 1, + /// (Read-only): Size of just encoded video frame. Note that the encoding order may be different from representation order. + VIDEOWRITER_PROP_FRAMEBYTES = 2, + /// Number of stripes for parallel encoding. -1 for auto detection. + VIDEOWRITER_PROP_NSTRIPES = 3, + /// If it is not zero, the encoder will expect and encode color frames, otherwise it + /// will work with grayscale frames. + VIDEOWRITER_PROP_IS_COLOR = 4, + /// Defaults to \ref CV_8U. + VIDEOWRITER_PROP_DEPTH = 5, + /// (**open-only**) Hardware acceleration type (see #VideoAccelerationType). Setting supported only via `params` parameter in VideoWriter constructor / .open() method. Default value is backend-specific. + VIDEOWRITER_PROP_HW_ACCELERATION = 6, + /// (**open-only**) Hardware device index (select GPU if multiple available). Device enumeration is acceleration type specific. + VIDEOWRITER_PROP_HW_DEVICE = 7, + /// (**open-only**) If non-zero, create new OpenCL context and bind it to current thread. The OpenCL context created with Video Acceleration context attached it (if not attached yet) for optimized GPU data copy between cv::UMat and HW accelerated encoder. + VIDEOWRITER_PROP_HW_ACCELERATION_USE_OPENCL = 8, + /// (**open-only**) Set to non-zero to enable encapsulation of an encoded raw video stream. Each raw encoded video frame should be passed to VideoWriter::write() as single row or column of a \ref CV_8UC1 Mat. \note If the key frame interval is not 1 then it must be manually specified by the user. This can either be performed during initialization passing \ref VIDEOWRITER_PROP_KEY_INTERVAL as one of the extra encoder params to \ref VideoWriter::VideoWriter(const String &, int, double, const Size &, const std::vector< int > ¶ms) or afterwards by setting the \ref VIDEOWRITER_PROP_KEY_FLAG with \ref VideoWriter::set() before writing each frame. FFMpeg backend only. + VIDEOWRITER_PROP_RAW_VIDEO = 9, + /// (**open-only**) Set the key frame interval using raw video encapsulation (\ref VIDEOWRITER_PROP_RAW_VIDEO != 0). Defaults to 1 when not set. FFmpeg back-end only. + VIDEOWRITER_PROP_KEY_INTERVAL = 10, + /// Set to non-zero to signal that the following frames are key frames or zero if not, when encapsulating raw video (\ref VIDEOWRITER_PROP_RAW_VIDEO != 0). FFmpeg back-end only. + VIDEOWRITER_PROP_KEY_FLAG = 11, + /// Specifies the frame presentation timestamp for each frame using the FPS time base. This property is **only** necessary when encapsulating **externally** encoded video where the decoding order differs from the presentation order, such as in GOP patterns with bi-directional B-frames. The value should be provided by your external encoder and for video sources with fixed frame rates it is equivalent to dividing the current frame's presentation time (\ref CAP_PROP_POS_MSEC) by the frame duration (1000.0 / VideoCapture::get(\ref CAP_PROP_FPS)). It can be queried from the resulting encapsulated video file using VideoCapture::get(\ref CAP_PROP_PTS). FFmpeg back-end only. + VIDEOWRITER_PROP_PTS = 12, + /// Specifies the maximum difference between presentation (pts) and decompression timestamps (dts) using the FPS time base. This property is necessary **only** when encapsulating **externally** encoded video where the decoding order differs from the presentation order, such as in GOP patterns with bi-directional B-frames. The value should be calculated based on the specific GOP pattern used during encoding. For example, in a GOP with presentation order IBP and decoding order IPB, this value would be 1, as the B-frame is the second frame presented but the third to be decoded. It can be queried from the resulting encapsulated video file using VideoCapture::get(\ref CAP_PROP_DTS_DELAY). Non-zero values usually imply the stream is encoded using B-frames. FFmpeg back-end only. + VIDEOWRITER_PROP_DTS_DELAY = 13, + CV__VIDEOWRITER_PROP_LATEST = 14, + } + + opencv_type_enum! { crate::videoio::VideoWriterProperties { VIDEOWRITER_PROP_QUALITY, VIDEOWRITER_PROP_FRAMEBYTES, VIDEOWRITER_PROP_NSTRIPES, VIDEOWRITER_PROP_IS_COLOR, VIDEOWRITER_PROP_DEPTH, VIDEOWRITER_PROP_HW_ACCELERATION, VIDEOWRITER_PROP_HW_DEVICE, VIDEOWRITER_PROP_HW_ACCELERATION_USE_OPENCL, VIDEOWRITER_PROP_RAW_VIDEO, VIDEOWRITER_PROP_KEY_INTERVAL, VIDEOWRITER_PROP_KEY_FLAG, VIDEOWRITER_PROP_PTS, VIDEOWRITER_PROP_DTS_DELAY, CV__VIDEOWRITER_PROP_LATEST } } + + /// Returns backend API name or "UnknownVideoAPI(xxx)" + /// ## Parameters + /// * api: backend ID (#VideoCaptureAPIs) + #[inline] + pub fn get_backend_name(api: crate::videoio::VideoCaptureAPIs) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_videoio_registry_getBackendName_VideoCaptureAPIs(api, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns list of all available backends + #[inline] + pub fn get_backends() -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_videoio_registry_getBackends(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns description and ABI/API version of videoio plugin's camera interface + #[inline] + pub fn get_camera_backend_plugin_version(api: crate::videoio::VideoCaptureAPIs, version_abi: &mut i32, version_api: &mut i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_videoio_registry_getCameraBackendPluginVersion_VideoCaptureAPIs_intR_intR(api, version_abi, version_api, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns list of available backends which works via `cv::VideoCapture(int index)` + #[inline] + pub fn get_camera_backends() -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_videoio_registry_getCameraBackends(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns description and ABI/API version of videoio plugin's stream capture interface + #[inline] + pub fn get_stream_backend_plugin_version(api: crate::videoio::VideoCaptureAPIs, version_abi: &mut i32, version_api: &mut i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_videoio_registry_getStreamBackendPluginVersion_VideoCaptureAPIs_intR_intR(api, version_abi, version_api, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns list of available backends which works via `cv::VideoCapture(filename)` + #[inline] + pub fn get_stream_backends() -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_videoio_registry_getStreamBackends(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns description and ABI/API version of videoio plugin's buffer capture interface + #[inline] + pub fn get_stream_buffered_backend_plugin_version(api: crate::videoio::VideoCaptureAPIs, version_abi: &mut i32, version_api: &mut i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_videoio_registry_getStreamBufferedBackendPluginVersion_VideoCaptureAPIs_intR_intR(api, version_abi, version_api, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns list of available backends which works via `cv::VideoCapture(buffer)` + #[inline] + pub fn get_stream_buffered_backends() -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_videoio_registry_getStreamBufferedBackends(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns description and ABI/API version of videoio plugin's writer interface + #[inline] + pub fn get_writer_backend_plugin_version(api: crate::videoio::VideoCaptureAPIs, version_abi: &mut i32, version_api: &mut i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_videoio_registry_getWriterBackendPluginVersion_VideoCaptureAPIs_intR_intR(api, version_abi, version_api, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns list of available backends which works via `cv::VideoWriter()` + #[inline] + pub fn get_writer_backends() -> Result> { + return_send!(via ocvrs_return); + unsafe { sys::cv_videoio_registry_getWriterBackends(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { core::Vector::::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Returns true if backend is available + #[inline] + pub fn has_backend(api: crate::videoio::VideoCaptureAPIs) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_videoio_registry_hasBackend_VideoCaptureAPIs(api, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns true if backend is built in (false if backend is used as plugin) + #[inline] + pub fn is_backend_built_in(api: crate::videoio::VideoCaptureAPIs) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_videoio_registry_isBackendBuiltIn_VideoCaptureAPIs(api, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Read data stream interface + pub struct IStreamReader { + ptr: *mut c_void, + } + + opencv_type_boxed! { IStreamReader } + + impl Drop for IStreamReader { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_IStreamReader_delete(self.as_raw_mut_IStreamReader()) }; + } + } + + unsafe impl Send for IStreamReader {} + + /// Constant methods for [crate::videoio::IStreamReader] + pub trait IStreamReaderTraitConst { + fn as_raw_IStreamReader(&self) -> *const c_void; + + } + + /// Mutable methods for [crate::videoio::IStreamReader] + pub trait IStreamReaderTrait: crate::videoio::IStreamReaderTraitConst { + fn as_raw_mut_IStreamReader(&mut self) -> *mut c_void; + + /// Read bytes from stream + /// + /// ## Parameters + /// * buffer: already allocated buffer of at least @p size bytes + /// * size: maximum number of bytes to read + /// + /// ## Returns + /// actual number of read bytes + #[inline] + fn read(&mut self, buffer: &mut String, size: i64) -> Result { + string_arg_output_send!(via buffer_via); + return_send!(via ocvrs_return); + unsafe { sys::cv_IStreamReader_read_charX_long_long(self.as_raw_mut_IStreamReader(), &mut buffer_via, size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + string_arg_output_receive!(buffer_via => buffer); + Ok(ret) + } + + /// Sets the stream position + /// + /// ## Parameters + /// * offset: Seek offset + /// * origin: SEEK_SET / SEEK_END / SEEK_CUR + /// ## See also + /// fseek + #[inline] + fn seek(&mut self, offset: i64, origin: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_IStreamReader_seek_long_long_int(self.as_raw_mut_IStreamReader(), offset, origin, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for IStreamReader { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("IStreamReader") + .finish() + } + } + + impl crate::videoio::IStreamReaderTraitConst for IStreamReader { + #[inline] fn as_raw_IStreamReader(&self) -> *const c_void { self.as_raw() } + } + + impl crate::videoio::IStreamReaderTrait for IStreamReader { + #[inline] fn as_raw_mut_IStreamReader(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { IStreamReader, crate::videoio::IStreamReaderTraitConst, as_raw_IStreamReader, crate::videoio::IStreamReaderTrait, as_raw_mut_IStreamReader } + + /// Class for video capturing from video files, image sequences or cameras. + /// + /// The class provides C++ API for capturing video from cameras or for reading video files and image sequences. + /// + /// Here is how the class can be used: + /// @include samples/cpp/videocapture_basic.cpp + /// + /// + /// Note: In [videoio_c] "C API" the black-box structure `CvCapture` is used instead of %VideoCapture. + /// + /// Note: + /// * (C++) A basic sample on using the %VideoCapture interface can be found at + /// `OPENCV_SOURCE_CODE/samples/cpp/videocapture_starter.cpp` + /// * (Python) A basic sample on using the %VideoCapture interface can be found at + /// `OPENCV_SOURCE_CODE/samples/python/video.py` + /// * (Python) A multi threaded video processing sample can be found at + /// `OPENCV_SOURCE_CODE/samples/python/video_threaded.py` + /// * (Python) %VideoCapture sample showcasing some features of the Video4Linux2 backend + /// `OPENCV_SOURCE_CODE/samples/python/video_v4l2.py` + pub struct VideoCapture { + ptr: *mut c_void, + } + + opencv_type_boxed! { VideoCapture } + + impl Drop for VideoCapture { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_VideoCapture_delete(self.as_raw_mut_VideoCapture()) }; + } + } + + unsafe impl Send for VideoCapture {} + + impl VideoCapture { + /// Default constructor + /// + /// Note: In [videoio_c] "C API", when you finished working with video, release CvCapture structure with + /// cvReleaseCapture(), or use Ptr\ that calls cvReleaseCapture() automatically in the + /// destructor. + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_VideoCapture(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoCapture::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Default constructor + /// + /// Note: In [videoio_c] "C API", when you finished working with video, release CvCapture structure with + /// cvReleaseCapture(), or use Ptr\ that calls cvReleaseCapture() automatically in the + /// destructor. + /// + /// ## Overloaded parameters + /// + /// Opens a video file or a capturing device or an IP video stream for video capturing with API Preference + /// + /// ## Parameters + /// * filename: it can be: + /// - name of video file (eg. `video.avi`) + /// - or image sequence (eg. `img_%02d.jpg`, which will read samples like `img_00.jpg, img_01.jpg, img_02.jpg, ...`) + /// - or URL of video stream (eg. `protocol://host:port/script_name?script_params|auth`) + /// - or GStreamer pipeline string in gst-launch tool format in case if GStreamer is used as backend + /// Note that each video stream or IP camera feed has its own URL scheme. Please refer to the + /// documentation of source stream to know the right URL. + /// * apiPreference: preferred Capture API backends to use. Can be used to enforce a specific reader + /// implementation if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_IMAGES or cv::CAP_DSHOW. + /// ## See also + /// cv::VideoCaptureAPIs + /// + /// ## C++ default parameters + /// * api_preference: CAP_ANY + #[inline] + pub fn from_file(filename: &str, api_preference: i32) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_VideoCapture_const_StringR_int(filename.opencv_as_extern(), api_preference, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoCapture::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Default constructor + /// + /// Note: In [videoio_c] "C API", when you finished working with video, release CvCapture structure with + /// cvReleaseCapture(), or use Ptr\ that calls cvReleaseCapture() automatically in the + /// destructor. + /// + /// ## Overloaded parameters + /// + /// Opens a video file or a capturing device or an IP video stream for video capturing with API Preference + /// + /// ## Parameters + /// * filename: it can be: + /// - name of video file (eg. `video.avi`) + /// - or image sequence (eg. `img_%02d.jpg`, which will read samples like `img_00.jpg, img_01.jpg, img_02.jpg, ...`) + /// - or URL of video stream (eg. `protocol://host:port/script_name?script_params|auth`) + /// - or GStreamer pipeline string in gst-launch tool format in case if GStreamer is used as backend + /// Note that each video stream or IP camera feed has its own URL scheme. Please refer to the + /// documentation of source stream to know the right URL. + /// * apiPreference: preferred Capture API backends to use. Can be used to enforce a specific reader + /// implementation if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_IMAGES or cv::CAP_DSHOW. + /// ## See also + /// cv::VideoCaptureAPIs + /// + /// ## Note + /// This alternative version of [from_file] function uses the following default values for its arguments: + /// * api_preference: CAP_ANY + #[inline] + pub fn from_file_def(filename: &str) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_VideoCapture_const_StringR(filename.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoCapture::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Default constructor + /// + /// Note: In [videoio_c] "C API", when you finished working with video, release CvCapture structure with + /// cvReleaseCapture(), or use Ptr\ that calls cvReleaseCapture() automatically in the + /// destructor. + /// + /// ## Overloaded parameters + /// + /// Opens a video file or a capturing device or an IP video stream for video capturing with API Preference and parameters + /// + /// The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`. + /// See cv::VideoCaptureProperties + #[inline] + pub fn from_file_with_params(filename: &str, api_preference: i32, params: &core::Vector) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_VideoCapture_const_StringR_int_const_vectorLintGR(filename.opencv_as_extern(), api_preference, params.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoCapture::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Default constructor + /// + /// Note: In [videoio_c] "C API", when you finished working with video, release CvCapture structure with + /// cvReleaseCapture(), or use Ptr\ that calls cvReleaseCapture() automatically in the + /// destructor. + /// + /// ## Overloaded parameters + /// + /// Opens a camera for video capturing + /// + /// ## Parameters + /// * index: id of the video capturing device to open. To open default camera using default backend just pass 0. + /// (to backward compatibility usage of camera_id + domain_offset (CAP_*) is valid when apiPreference is CAP_ANY) + /// * apiPreference: preferred Capture API backends to use. Can be used to enforce a specific reader + /// implementation if multiple are available: e.g. cv::CAP_DSHOW or cv::CAP_MSMF or cv::CAP_V4L. + /// ## See also + /// cv::VideoCaptureAPIs + /// + /// ## C++ default parameters + /// * api_preference: CAP_ANY + #[inline] + pub fn new(index: i32, api_preference: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_VideoCapture_int_int(index, api_preference, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoCapture::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Default constructor + /// + /// Note: In [videoio_c] "C API", when you finished working with video, release CvCapture structure with + /// cvReleaseCapture(), or use Ptr\ that calls cvReleaseCapture() automatically in the + /// destructor. + /// + /// ## Overloaded parameters + /// + /// Opens a camera for video capturing + /// + /// ## Parameters + /// * index: id of the video capturing device to open. To open default camera using default backend just pass 0. + /// (to backward compatibility usage of camera_id + domain_offset (CAP_*) is valid when apiPreference is CAP_ANY) + /// * apiPreference: preferred Capture API backends to use. Can be used to enforce a specific reader + /// implementation if multiple are available: e.g. cv::CAP_DSHOW or cv::CAP_MSMF or cv::CAP_V4L. + /// ## See also + /// cv::VideoCaptureAPIs + /// + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * api_preference: CAP_ANY + #[inline] + pub fn new_def(index: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_VideoCapture_int(index, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoCapture::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Default constructor + /// + /// Note: In [videoio_c] "C API", when you finished working with video, release CvCapture structure with + /// cvReleaseCapture(), or use Ptr\ that calls cvReleaseCapture() automatically in the + /// destructor. + /// + /// ## Overloaded parameters + /// + /// Opens a camera for video capturing with API Preference and parameters + /// + /// The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`. + /// See cv::VideoCaptureProperties + #[inline] + pub fn new_with_params(index: i32, api_preference: i32, params: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_VideoCapture_int_int_const_vectorLintGR(index, api_preference, params.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoCapture::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Default constructor + /// + /// Note: In [videoio_c] "C API", when you finished working with video, release CvCapture structure with + /// cvReleaseCapture(), or use Ptr\ that calls cvReleaseCapture() automatically in the + /// destructor. + /// + /// ## Overloaded parameters + /// + /// Opens a video using data stream. + /// + /// The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`. + /// See cv::VideoCaptureProperties + #[inline] + pub fn new_1(source: &core::Ptr, api_preference: i32, params: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_VideoCapture_const_PtrLIStreamReaderGR_int_const_vectorLintGR(source.as_raw_PtrOfIStreamReader(), api_preference, params.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoCapture::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Wait for ready frames from VideoCapture. + /// + /// ## Parameters + /// * streams: input video streams + /// * readyIndex: stream indexes with grabbed frames (ready to use .retrieve() to fetch actual frame) + /// * timeoutNs: number of nanoseconds (0 - infinite) + /// ## Returns + /// `true` if streamReady is not empty + /// + /// @throws Exception %Exception on stream errors (check .isOpened() to filter out malformed streams) or VideoCapture type is not supported + /// + /// The primary use of the function is in multi-camera environments. + /// The method fills the ready state vector, grabs video frame, if camera is ready. + /// + /// After this call use VideoCapture::retrieve() to decode and fetch frame data. + /// + /// ## C++ default parameters + /// * timeout_ns: 0 + #[inline] + pub fn wait_any(streams: &core::Vector, ready_index: &mut core::Vector, timeout_ns: i64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_waitAny_const_vectorLVideoCaptureGR_vectorLintGR_int64_t(streams.as_raw_VectorOfVideoCapture(), ready_index.as_raw_mut_VectorOfi32(), timeout_ns, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Wait for ready frames from VideoCapture. + /// + /// ## Parameters + /// * streams: input video streams + /// * readyIndex: stream indexes with grabbed frames (ready to use .retrieve() to fetch actual frame) + /// * timeoutNs: number of nanoseconds (0 - infinite) + /// ## Returns + /// `true` if streamReady is not empty + /// + /// @throws Exception %Exception on stream errors (check .isOpened() to filter out malformed streams) or VideoCapture type is not supported + /// + /// The primary use of the function is in multi-camera environments. + /// The method fills the ready state vector, grabs video frame, if camera is ready. + /// + /// After this call use VideoCapture::retrieve() to decode and fetch frame data. + /// + /// ## Note + /// This alternative version of [VideoCapture::wait_any] function uses the following default values for its arguments: + /// * timeout_ns: 0 + #[inline] + pub fn wait_any_def(streams: &core::Vector, ready_index: &mut core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_waitAny_const_vectorLVideoCaptureGR_vectorLintGR(streams.as_raw_VectorOfVideoCapture(), ready_index.as_raw_mut_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Constant methods for [crate::videoio::VideoCapture] + pub trait VideoCaptureTraitConst { + fn as_raw_VideoCapture(&self) -> *const c_void; + + /// Returns true if video capturing has been initialized already. + /// + /// If the previous call to VideoCapture constructor or VideoCapture::open() succeeded, the method returns + /// true. + #[inline] + fn is_opened(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_isOpened_const(self.as_raw_VideoCapture(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the specified VideoCapture property + /// + /// ## Parameters + /// * propId: Property identifier from cv::VideoCaptureProperties (eg. cv::CAP_PROP_POS_MSEC, cv::CAP_PROP_POS_FRAMES, ...) + /// or one from [videoio_flags_others] + /// ## Returns + /// Value for the specified property. Value 0 is returned when querying a property that is + /// not supported by the backend used by the VideoCapture instance. + /// + /// + /// Note: Reading / writing properties involves many layers. Some unexpected result might happens + /// along this chain. + /// ```C++ + /// VideoCapture -> API Backend -> Operating System -> Device Driver -> Device Hardware + /// ``` + /// + /// The returned value might be different from what really used by the device or it could be encoded + /// using device dependent rules (eg. steps or percentage). Effective behaviour depends from device + /// driver and API Backend + #[inline] + fn get(&self, prop_id: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_get_const_int(self.as_raw_VideoCapture(), prop_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns used backend API name + /// + /// + /// Note: Stream should be opened. + #[inline] + fn get_backend_name(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_getBackendName_const(self.as_raw_VideoCapture(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + /// query if exception mode is active + #[inline] + fn get_exception_mode(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_getExceptionMode_const(self.as_raw_VideoCapture(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Mutable methods for [crate::videoio::VideoCapture] + pub trait VideoCaptureTrait: crate::videoio::VideoCaptureTraitConst { + fn as_raw_mut_VideoCapture(&mut self) -> *mut c_void; + + /// Opens a video file or a capturing device or an IP video stream for video capturing. + /// + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + /// + /// Parameters are same as the constructor VideoCapture(const String& filename, int apiPreference = CAP_ANY) + /// ## Returns + /// `true` if the file has been successfully opened + /// + /// The method first calls VideoCapture::release to close the already opened file or camera. + /// + /// ## C++ default parameters + /// * api_preference: CAP_ANY + #[inline] + fn open_file(&mut self, filename: &str, api_preference: i32) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_open_const_StringR_int(self.as_raw_mut_VideoCapture(), filename.opencv_as_extern(), api_preference, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Opens a video file or a capturing device or an IP video stream for video capturing. + /// + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + /// + /// Parameters are same as the constructor VideoCapture(const String& filename, int apiPreference = CAP_ANY) + /// ## Returns + /// `true` if the file has been successfully opened + /// + /// The method first calls VideoCapture::release to close the already opened file or camera. + /// + /// ## Note + /// This alternative version of [VideoCaptureTrait::open_file] function uses the following default values for its arguments: + /// * api_preference: CAP_ANY + #[inline] + fn open_file_def(&mut self, filename: &str) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_open_const_StringR(self.as_raw_mut_VideoCapture(), filename.opencv_as_extern(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Opens a video file or a capturing device or an IP video stream for video capturing with API Preference and parameters + /// + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + /// + /// The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`. + /// See cv::VideoCaptureProperties + /// + /// ## Returns + /// `true` if the file has been successfully opened + /// + /// The method first calls VideoCapture::release to close the already opened file or camera. + #[inline] + fn open_file_with_params(&mut self, filename: &str, api_preference: i32, params: &core::Vector) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_open_const_StringR_int_const_vectorLintGR(self.as_raw_mut_VideoCapture(), filename.opencv_as_extern(), api_preference, params.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Opens a camera for video capturing + /// + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + /// + /// Parameters are same as the constructor VideoCapture(int index, int apiPreference = CAP_ANY) + /// ## Returns + /// `true` if the camera has been successfully opened. + /// + /// The method first calls VideoCapture::release to close the already opened file or camera. + /// + /// ## C++ default parameters + /// * api_preference: CAP_ANY + #[inline] + fn open(&mut self, index: i32, api_preference: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_open_int_int(self.as_raw_mut_VideoCapture(), index, api_preference, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Opens a camera for video capturing + /// + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + /// + /// Parameters are same as the constructor VideoCapture(int index, int apiPreference = CAP_ANY) + /// ## Returns + /// `true` if the camera has been successfully opened. + /// + /// The method first calls VideoCapture::release to close the already opened file or camera. + /// + /// ## Note + /// This alternative version of [VideoCaptureTrait::open] function uses the following default values for its arguments: + /// * api_preference: CAP_ANY + #[inline] + fn open_def(&mut self, index: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_open_int(self.as_raw_mut_VideoCapture(), index, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Opens a camera for video capturing with API Preference and parameters + /// + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + /// + /// The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`. + /// See cv::VideoCaptureProperties + /// + /// ## Returns + /// `true` if the camera has been successfully opened. + /// + /// The method first calls VideoCapture::release to close the already opened file or camera. + #[inline] + fn open_with_params(&mut self, index: i32, api_preference: i32, params: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_open_int_int_const_vectorLintGR(self.as_raw_mut_VideoCapture(), index, api_preference, params.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Opens a video using data stream. + /// + /// This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. + /// + /// The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`. + /// See cv::VideoCaptureProperties + /// + /// ## Returns + /// `true` if the file has been successfully opened + /// + /// The method first calls VideoCapture::release to close the already opened file or camera. + #[inline] + fn open_1(&mut self, source: &core::Ptr, api_preference: i32, params: &core::Vector) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_open_const_PtrLIStreamReaderGR_int_const_vectorLintGR(self.as_raw_mut_VideoCapture(), source.as_raw_PtrOfIStreamReader(), api_preference, params.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Closes video file or capturing device. + /// + /// The method is automatically called by subsequent VideoCapture::open and by VideoCapture + /// destructor. + /// + /// The C function also deallocates memory and clears \*capture pointer. + #[inline] + fn release(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_release(self.as_raw_mut_VideoCapture(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Grabs the next frame from video file or capturing device. + /// + /// ## Returns + /// `true` (non-zero) in the case of success. + /// + /// The method/function grabs the next frame from video file or camera and returns true (non-zero) in + /// the case of success. + /// + /// The primary use of the function is in multi-camera environments, especially when the cameras do not + /// have hardware synchronization. That is, you call VideoCapture::grab() for each camera and after that + /// call the slower method VideoCapture::retrieve() to decode and get frame from each camera. This way + /// the overhead on demosaicing or motion jpeg decompression etc. is eliminated and the retrieved frames + /// from different cameras will be closer in time. + /// + /// Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the + /// correct way of retrieving data from it is to call VideoCapture::grab() first and then call + /// VideoCapture::retrieve() one or more times with different values of the channel parameter. + /// + /// [tutorial_kinect_openni] + #[inline] + fn grab(&mut self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_grab(self.as_raw_mut_VideoCapture(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Decodes and returns the grabbed video frame. + /// + /// ## Parameters + /// * image:[out] the video frame is returned here. If no frames has been grabbed the image will be empty. + /// * flag: it could be a frame index or a driver specific flag + /// ## Returns + /// `false` if no frames has been grabbed + /// + /// The method decodes and returns the just grabbed frame. If no frames has been grabbed + /// (camera has been disconnected, or there are no more frames in video file), the method returns false + /// and the function returns an empty image (with %cv::Mat, test it with Mat::empty()). + /// ## See also + /// read() + /// + /// + /// Note: In [videoio_c] "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video + /// capturing structure. It is not allowed to modify or release the image! You can copy the frame using + /// cvCloneImage and then do whatever you want with the copy. + /// + /// ## C++ default parameters + /// * flag: 0 + #[inline] + fn retrieve(&mut self, image: &mut impl ToOutputArray, flag: i32) -> Result { + output_array_arg!(image); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_retrieve_const__OutputArrayR_int(self.as_raw_mut_VideoCapture(), image.as_raw__OutputArray(), flag, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Decodes and returns the grabbed video frame. + /// + /// ## Parameters + /// * image:[out] the video frame is returned here. If no frames has been grabbed the image will be empty. + /// * flag: it could be a frame index or a driver specific flag + /// ## Returns + /// `false` if no frames has been grabbed + /// + /// The method decodes and returns the just grabbed frame. If no frames has been grabbed + /// (camera has been disconnected, or there are no more frames in video file), the method returns false + /// and the function returns an empty image (with %cv::Mat, test it with Mat::empty()). + /// ## See also + /// read() + /// + /// + /// Note: In [videoio_c] "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video + /// capturing structure. It is not allowed to modify or release the image! You can copy the frame using + /// cvCloneImage and then do whatever you want with the copy. + /// + /// ## Note + /// This alternative version of [VideoCaptureTrait::retrieve] function uses the following default values for its arguments: + /// * flag: 0 + #[inline] + fn retrieve_def(&mut self, image: &mut impl ToOutputArray) -> Result { + output_array_arg!(image); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_retrieve_const__OutputArrayR(self.as_raw_mut_VideoCapture(), image.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Grabs, decodes and returns the next video frame. + /// + /// ## Parameters + /// * image:[out] the video frame is returned here. If no frames has been grabbed the image will be empty. + /// ## Returns + /// `false` if no frames has been grabbed + /// + /// The method/function combines VideoCapture::grab() and VideoCapture::retrieve() in one call. This is the + /// most convenient method for reading video files or capturing data from decode and returns the just + /// grabbed frame. If no frames has been grabbed (camera has been disconnected, or there are no more + /// frames in video file), the method returns false and the function returns empty image (with %cv::Mat, test it with Mat::empty()). + /// + /// + /// Note: In [videoio_c] "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video + /// capturing structure. It is not allowed to modify or release the image! You can copy the frame using + /// cvCloneImage and then do whatever you want with the copy. + #[inline] + fn read(&mut self, image: &mut impl ToOutputArray) -> Result { + output_array_arg!(image); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_read_const__OutputArrayR(self.as_raw_mut_VideoCapture(), image.as_raw__OutputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets a property in the VideoCapture. + /// + /// ## Parameters + /// * propId: Property identifier from cv::VideoCaptureProperties (eg. cv::CAP_PROP_POS_MSEC, cv::CAP_PROP_POS_FRAMES, ...) + /// or one from [videoio_flags_others] + /// * value: Value of the property. + /// ## Returns + /// `true` if the property is supported by backend used by the VideoCapture instance. + /// + /// Note: Even if it returns `true` this doesn't ensure that the property + /// value has been accepted by the capture device. See note in VideoCapture::get() + #[inline] + fn set(&mut self, prop_id: i32, value: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_set_int_double(self.as_raw_mut_VideoCapture(), prop_id, value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Switches exceptions mode + /// + /// methods raise exceptions if not successful instead of returning an error code + #[inline] + fn set_exception_mode(&mut self, enable: bool) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoCapture_setExceptionMode_bool(self.as_raw_mut_VideoCapture(), enable, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for VideoCapture { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("VideoCapture") + .finish() + } + } + + impl crate::videoio::VideoCaptureTraitConst for VideoCapture { + #[inline] fn as_raw_VideoCapture(&self) -> *const c_void { self.as_raw() } + } + + impl crate::videoio::VideoCaptureTrait for VideoCapture { + #[inline] fn as_raw_mut_VideoCapture(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { VideoCapture, crate::videoio::VideoCaptureTraitConst, as_raw_VideoCapture, crate::videoio::VideoCaptureTrait, as_raw_mut_VideoCapture } + + /// Video writer class. + /// + /// The class provides C++ API for writing video files or image sequences. + pub struct VideoWriter { + ptr: *mut c_void, + } + + opencv_type_boxed! { VideoWriter } + + impl Drop for VideoWriter { + #[inline] + fn drop(&mut self) { + unsafe { sys::cv_VideoWriter_delete(self.as_raw_mut_VideoWriter()) }; + } + } + + unsafe impl Send for VideoWriter {} + + impl VideoWriter { + /// Default constructors + /// + /// The constructors/functions initialize video writers. + /// * On Linux FFMPEG is used to write videos; + /// * On Windows FFMPEG or MSWF or DSHOW is used; + /// * On MacOSX AVFoundation is used. + #[inline] + pub fn default() -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_VideoWriter(ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoWriter::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Default constructors + /// + /// The constructors/functions initialize video writers. + /// * On Linux FFMPEG is used to write videos; + /// * On Windows FFMPEG or MSWF or DSHOW is used; + /// * On MacOSX AVFoundation is used. + /// + /// ## Overloaded parameters + /// + /// ## Parameters + /// * filename: Name of the output video file. + /// * fourcc: 4-character code of codec used to compress the frames. For example, + /// VideoWriter::fourcc('P','I','M','1') is a MPEG-1 codec, VideoWriter::fourcc('M','J','P','G') + /// is a motion-jpeg codec etc. List of codes can be obtained at + /// [MSDN](https://docs.microsoft.com/en-us/windows/win32/medfound/video-fourccs) page + /// or with this [page](https://fourcc.org/codecs.php) + /// of the fourcc site for a more complete list). FFMPEG backend with MP4 container natively uses + /// other values as fourcc code: see [ObjectType](http://mp4ra.org/#/codecs), + /// so you may receive a warning message from OpenCV about fourcc code conversion. + /// * fps: Framerate of the created video stream. + /// * frameSize: Size of the video frames. + /// * isColor: If it is not zero, the encoder will expect and encode color frames, otherwise it + /// will work with grayscale frames. + /// + /// @b Tips: + /// - With some backends `fourcc=-1` pops up the codec selection dialog from the system. + /// - To save image sequence use a proper filename (eg. `img_%02d.jpg`) and `fourcc=0` + /// OR `fps=0`. Use uncompressed image format (eg. `img_%02d.BMP`) to save raw frames. + /// - Most codecs are lossy. If you want lossless video file you need to use a lossless codecs + /// (eg. FFMPEG FFV1, Huffman HFYU, Lagarith LAGS, etc...) + /// - If FFMPEG is enabled, using `codec=0; fps=0;` you can create an uncompressed (raw) video file. + /// - If FFMPEG is used, we allow frames of odd width or height, but in this case we truncate + /// the rightmost column/the bottom row. Probably, this should be handled more elegantly, + /// but some internal functions inside FFMPEG swscale require even width/height. + /// + /// ## C++ default parameters + /// * is_color: true + #[inline] + pub fn new(filename: &str, fourcc: i32, fps: f64, frame_size: core::Size, is_color: bool) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_VideoWriter_const_StringR_int_double_Size_bool(filename.opencv_as_extern(), fourcc, fps, &frame_size, is_color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoWriter::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Default constructors + /// + /// The constructors/functions initialize video writers. + /// * On Linux FFMPEG is used to write videos; + /// * On Windows FFMPEG or MSWF or DSHOW is used; + /// * On MacOSX AVFoundation is used. + /// + /// ## Overloaded parameters + /// + /// ## Parameters + /// * filename: Name of the output video file. + /// * fourcc: 4-character code of codec used to compress the frames. For example, + /// VideoWriter::fourcc('P','I','M','1') is a MPEG-1 codec, VideoWriter::fourcc('M','J','P','G') + /// is a motion-jpeg codec etc. List of codes can be obtained at + /// [MSDN](https://docs.microsoft.com/en-us/windows/win32/medfound/video-fourccs) page + /// or with this [page](https://fourcc.org/codecs.php) + /// of the fourcc site for a more complete list). FFMPEG backend with MP4 container natively uses + /// other values as fourcc code: see [ObjectType](http://mp4ra.org/#/codecs), + /// so you may receive a warning message from OpenCV about fourcc code conversion. + /// * fps: Framerate of the created video stream. + /// * frameSize: Size of the video frames. + /// * isColor: If it is not zero, the encoder will expect and encode color frames, otherwise it + /// will work with grayscale frames. + /// + /// @b Tips: + /// - With some backends `fourcc=-1` pops up the codec selection dialog from the system. + /// - To save image sequence use a proper filename (eg. `img_%02d.jpg`) and `fourcc=0` + /// OR `fps=0`. Use uncompressed image format (eg. `img_%02d.BMP`) to save raw frames. + /// - Most codecs are lossy. If you want lossless video file you need to use a lossless codecs + /// (eg. FFMPEG FFV1, Huffman HFYU, Lagarith LAGS, etc...) + /// - If FFMPEG is enabled, using `codec=0; fps=0;` you can create an uncompressed (raw) video file. + /// - If FFMPEG is used, we allow frames of odd width or height, but in this case we truncate + /// the rightmost column/the bottom row. Probably, this should be handled more elegantly, + /// but some internal functions inside FFMPEG swscale require even width/height. + /// + /// ## Note + /// This alternative version of [new] function uses the following default values for its arguments: + /// * is_color: true + #[inline] + pub fn new_def(filename: &str, fourcc: i32, fps: f64, frame_size: core::Size) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_VideoWriter_const_StringR_int_double_Size(filename.opencv_as_extern(), fourcc, fps, &frame_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoWriter::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Default constructors + /// + /// The constructors/functions initialize video writers. + /// * On Linux FFMPEG is used to write videos; + /// * On Windows FFMPEG or MSWF or DSHOW is used; + /// * On MacOSX AVFoundation is used. + /// + /// ## Overloaded parameters + /// + /// The `apiPreference` parameter allows to specify API backends to use. Can be used to enforce a specific reader implementation + /// if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_GSTREAMER. + /// + /// ## C++ default parameters + /// * is_color: true + #[inline] + pub fn new_with_backend(filename: &str, api_preference: i32, fourcc: i32, fps: f64, frame_size: core::Size, is_color: bool) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_VideoWriter_const_StringR_int_int_double_Size_bool(filename.opencv_as_extern(), api_preference, fourcc, fps, &frame_size, is_color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoWriter::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Default constructors + /// + /// The constructors/functions initialize video writers. + /// * On Linux FFMPEG is used to write videos; + /// * On Windows FFMPEG or MSWF or DSHOW is used; + /// * On MacOSX AVFoundation is used. + /// + /// ## Overloaded parameters + /// + /// The `apiPreference` parameter allows to specify API backends to use. Can be used to enforce a specific reader implementation + /// if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_GSTREAMER. + /// + /// ## Note + /// This alternative version of [new_with_backend] function uses the following default values for its arguments: + /// * is_color: true + #[inline] + pub fn new_with_backend_def(filename: &str, api_preference: i32, fourcc: i32, fps: f64, frame_size: core::Size) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_VideoWriter_const_StringR_int_int_double_Size(filename.opencv_as_extern(), api_preference, fourcc, fps, &frame_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoWriter::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Default constructors + /// + /// The constructors/functions initialize video writers. + /// * On Linux FFMPEG is used to write videos; + /// * On Windows FFMPEG or MSWF or DSHOW is used; + /// * On MacOSX AVFoundation is used. + /// + /// ## Overloaded parameters + /// + /// The `params` parameter allows to specify extra encoder parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, ... .) + /// see cv::VideoWriterProperties + #[inline] + pub fn new_1(filename: &str, fourcc: i32, fps: f64, frame_size: core::Size, params: &core::Vector) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_VideoWriter_const_StringR_int_double_const_SizeR_const_vectorLintGR(filename.opencv_as_extern(), fourcc, fps, &frame_size, params.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoWriter::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Default constructors + /// + /// The constructors/functions initialize video writers. + /// * On Linux FFMPEG is used to write videos; + /// * On Windows FFMPEG or MSWF or DSHOW is used; + /// * On MacOSX AVFoundation is used. + /// + /// ## Overloaded parameters + #[inline] + pub fn new_2(filename: &str, api_preference: i32, fourcc: i32, fps: f64, frame_size: core::Size, params: &core::Vector) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_VideoWriter_const_StringR_int_int_double_const_SizeR_const_vectorLintGR(filename.opencv_as_extern(), api_preference, fourcc, fps, &frame_size, params.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { crate::videoio::VideoWriter::opencv_from_extern(ret) }; + Ok(ret) + } + + /// Concatenates 4 chars to a fourcc code + /// + /// ## Returns + /// a fourcc code + /// + /// This static method constructs the fourcc code of the codec to be used in the constructor + /// VideoWriter::VideoWriter or VideoWriter::open. + #[inline] + pub fn fourcc(c1: char, c2: char, c3: char, c4: char) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_fourcc_char_char_char_char(u8::try_from(c1)? as c_char, u8::try_from(c2)? as c_char, u8::try_from(c3)? as c_char, u8::try_from(c4)? as c_char, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + /// Constant methods for [crate::videoio::VideoWriter] + pub trait VideoWriterTraitConst { + fn as_raw_VideoWriter(&self) -> *const c_void; + + /// Returns true if video writer has been successfully initialized. + #[inline] + fn is_opened(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_isOpened_const(self.as_raw_VideoWriter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns the specified VideoWriter property + /// + /// ## Parameters + /// * propId: Property identifier from cv::VideoWriterProperties (eg. cv::VIDEOWRITER_PROP_QUALITY) + /// or one of [videoio_flags_others] + /// + /// ## Returns + /// Value for the specified property. Value 0 is returned when querying a property that is + /// not supported by the backend used by the VideoWriter instance. + #[inline] + fn get(&self, prop_id: i32) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_get_const_int(self.as_raw_VideoWriter(), prop_id, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Returns used backend API name + /// + /// + /// Note: Stream should be opened. + #[inline] + fn get_backend_name(&self) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_getBackendName_const(self.as_raw_VideoWriter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + let ret = unsafe { String::opencv_from_extern(ret) }; + Ok(ret) + } + + } + + /// Mutable methods for [crate::videoio::VideoWriter] + pub trait VideoWriterTrait: crate::videoio::VideoWriterTraitConst { + fn as_raw_mut_VideoWriter(&mut self) -> *mut c_void; + + /// Initializes or reinitializes video writer. + /// + /// The method opens video writer. Parameters are the same as in the constructor + /// VideoWriter::VideoWriter. + /// ## Returns + /// `true` if video writer has been successfully initialized + /// + /// The method first calls VideoWriter::release to close the already opened file. + /// + /// ## C++ default parameters + /// * is_color: true + #[inline] + fn open(&mut self, filename: &str, fourcc: i32, fps: f64, frame_size: core::Size, is_color: bool) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_open_const_StringR_int_double_Size_bool(self.as_raw_mut_VideoWriter(), filename.opencv_as_extern(), fourcc, fps, &frame_size, is_color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Initializes or reinitializes video writer. + /// + /// The method opens video writer. Parameters are the same as in the constructor + /// VideoWriter::VideoWriter. + /// ## Returns + /// `true` if video writer has been successfully initialized + /// + /// The method first calls VideoWriter::release to close the already opened file. + /// + /// ## Note + /// This alternative version of [VideoWriterTrait::open] function uses the following default values for its arguments: + /// * is_color: true + #[inline] + fn open_def(&mut self, filename: &str, fourcc: i32, fps: f64, frame_size: core::Size) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_open_const_StringR_int_double_Size(self.as_raw_mut_VideoWriter(), filename.opencv_as_extern(), fourcc, fps, &frame_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Initializes or reinitializes video writer. + /// + /// The method opens video writer. Parameters are the same as in the constructor + /// VideoWriter::VideoWriter. + /// ## Returns + /// `true` if video writer has been successfully initialized + /// + /// The method first calls VideoWriter::release to close the already opened file. + /// + /// ## Overloaded parameters + /// + /// ## C++ default parameters + /// * is_color: true + #[inline] + fn open_with_backend(&mut self, filename: &str, api_preference: i32, fourcc: i32, fps: f64, frame_size: core::Size, is_color: bool) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_open_const_StringR_int_int_double_Size_bool(self.as_raw_mut_VideoWriter(), filename.opencv_as_extern(), api_preference, fourcc, fps, &frame_size, is_color, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Initializes or reinitializes video writer. + /// + /// The method opens video writer. Parameters are the same as in the constructor + /// VideoWriter::VideoWriter. + /// ## Returns + /// `true` if video writer has been successfully initialized + /// + /// The method first calls VideoWriter::release to close the already opened file. + /// + /// ## Overloaded parameters + /// + /// + /// ## Note + /// This alternative version of [VideoWriterTrait::open_with_backend] function uses the following default values for its arguments: + /// * is_color: true + #[inline] + fn open_with_backend_def(&mut self, filename: &str, api_preference: i32, fourcc: i32, fps: f64, frame_size: core::Size) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_open_const_StringR_int_int_double_Size(self.as_raw_mut_VideoWriter(), filename.opencv_as_extern(), api_preference, fourcc, fps, &frame_size, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Initializes or reinitializes video writer. + /// + /// The method opens video writer. Parameters are the same as in the constructor + /// VideoWriter::VideoWriter. + /// ## Returns + /// `true` if video writer has been successfully initialized + /// + /// The method first calls VideoWriter::release to close the already opened file. + /// + /// ## Overloaded parameters + #[inline] + fn open_1(&mut self, filename: &str, fourcc: i32, fps: f64, frame_size: core::Size, params: &core::Vector) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_open_const_StringR_int_double_const_SizeR_const_vectorLintGR(self.as_raw_mut_VideoWriter(), filename.opencv_as_extern(), fourcc, fps, &frame_size, params.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Initializes or reinitializes video writer. + /// + /// The method opens video writer. Parameters are the same as in the constructor + /// VideoWriter::VideoWriter. + /// ## Returns + /// `true` if video writer has been successfully initialized + /// + /// The method first calls VideoWriter::release to close the already opened file. + /// + /// ## Overloaded parameters + #[inline] + fn open_2(&mut self, filename: &str, api_preference: i32, fourcc: i32, fps: f64, frame_size: core::Size, params: &core::Vector) -> Result { + extern_container_arg!(filename); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_open_const_StringR_int_int_double_const_SizeR_const_vectorLintGR(self.as_raw_mut_VideoWriter(), filename.opencv_as_extern(), api_preference, fourcc, fps, &frame_size, params.as_raw_VectorOfi32(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Closes the video writer. + /// + /// The method is automatically called by subsequent VideoWriter::open and by the VideoWriter + /// destructor. + #[inline] + fn release(&mut self) -> Result<()> { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_release(self.as_raw_mut_VideoWriter(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Writes the next video frame + /// + /// ## Parameters + /// * image: The written frame. In general, color images are expected in BGR format. + /// + /// The function/method writes the specified image to video file. It must have the same size as has + /// been specified when opening the video writer. + #[inline] + fn write(&mut self, image: &impl ToInputArray) -> Result<()> { + input_array_arg!(image); + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_write_const__InputArrayR(self.as_raw_mut_VideoWriter(), image.as_raw__InputArray(), ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + /// Sets a property in the VideoWriter. + /// + /// ## Parameters + /// * propId: Property identifier from cv::VideoWriterProperties (eg. cv::VIDEOWRITER_PROP_QUALITY) + /// or one of [videoio_flags_others] + /// + /// * value: Value of the property. + /// ## Returns + /// `true` if the property is supported by the backend used by the VideoWriter instance. + #[inline] + fn set(&mut self, prop_id: i32, value: f64) -> Result { + return_send!(via ocvrs_return); + unsafe { sys::cv_VideoWriter_set_int_double(self.as_raw_mut_VideoWriter(), prop_id, value, ocvrs_return.as_mut_ptr()) }; + return_receive!(ocvrs_return => ret); + let ret = ret.into_result()?; + Ok(ret) + } + + } + + impl std::fmt::Debug for VideoWriter { + #[inline] + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + f.debug_struct("VideoWriter") + .finish() + } + } + + impl crate::videoio::VideoWriterTraitConst for VideoWriter { + #[inline] fn as_raw_VideoWriter(&self) -> *const c_void { self.as_raw() } + } + + impl crate::videoio::VideoWriterTrait for VideoWriter { + #[inline] fn as_raw_mut_VideoWriter(&mut self) -> *mut c_void { self.as_raw_mut() } + } + + boxed_ref! { VideoWriter, crate::videoio::VideoWriterTraitConst, as_raw_VideoWriter, crate::videoio::VideoWriterTrait, as_raw_mut_VideoWriter } + +} diff --git a/target/debug/build/opencv-4e105b5546afc119/out/videoio.cpp b/target/debug/build/opencv-4e105b5546afc119/out/videoio.cpp new file mode 100644 index 0000000..7cb40e8 --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/videoio.cpp @@ -0,0 +1,453 @@ +#include "videoio.hpp" +#include "videoio_types.hpp" + +extern "C" { +void cv_videoio_registry_getBackendName_VideoCaptureAPIs(cv::VideoCaptureAPIs api, Result* ocvrs_return) { + try { + cv::String ret = cv::videoio_registry::getBackendName(api); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_videoio_registry_getBackends(Result*>* ocvrs_return) { + try { + std::vector ret = cv::videoio_registry::getBackends(); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_videoio_registry_getCameraBackendPluginVersion_VideoCaptureAPIs_intR_intR(cv::VideoCaptureAPIs api, int* version_ABI, int* version_API, Result* ocvrs_return) { + try { + std::string ret = cv::videoio_registry::getCameraBackendPluginVersion(api, *version_ABI, *version_API); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_videoio_registry_getCameraBackends(Result*>* ocvrs_return) { + try { + std::vector ret = cv::videoio_registry::getCameraBackends(); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_videoio_registry_getStreamBackendPluginVersion_VideoCaptureAPIs_intR_intR(cv::VideoCaptureAPIs api, int* version_ABI, int* version_API, Result* ocvrs_return) { + try { + std::string ret = cv::videoio_registry::getStreamBackendPluginVersion(api, *version_ABI, *version_API); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_videoio_registry_getStreamBackends(Result*>* ocvrs_return) { + try { + std::vector ret = cv::videoio_registry::getStreamBackends(); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_videoio_registry_getStreamBufferedBackendPluginVersion_VideoCaptureAPIs_intR_intR(cv::VideoCaptureAPIs api, int* version_ABI, int* version_API, Result* ocvrs_return) { + try { + std::string ret = cv::videoio_registry::getStreamBufferedBackendPluginVersion(api, *version_ABI, *version_API); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_videoio_registry_getStreamBufferedBackends(Result*>* ocvrs_return) { + try { + std::vector ret = cv::videoio_registry::getStreamBufferedBackends(); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_videoio_registry_getWriterBackendPluginVersion_VideoCaptureAPIs_intR_intR(cv::VideoCaptureAPIs api, int* version_ABI, int* version_API, Result* ocvrs_return) { + try { + std::string ret = cv::videoio_registry::getWriterBackendPluginVersion(api, *version_ABI, *version_API); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_videoio_registry_getWriterBackends(Result*>* ocvrs_return) { + try { + std::vector ret = cv::videoio_registry::getWriterBackends(); + Ok(new std::vector(ret), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_videoio_registry_hasBackend_VideoCaptureAPIs(cv::VideoCaptureAPIs api, Result* ocvrs_return) { + try { + bool ret = cv::videoio_registry::hasBackend(api); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_videoio_registry_isBackendBuiltIn_VideoCaptureAPIs(cv::VideoCaptureAPIs api, Result* ocvrs_return) { + try { + bool ret = cv::videoio_registry::isBackendBuiltIn(api); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_IStreamReader_read_charX_long_long(cv::IStreamReader* instance, void** buffer, long long size, Result* ocvrs_return) { + try { + std::unique_ptr buffer_out = std::make_unique(1024); + long long ret = instance->read(buffer_out.get(), size); + *buffer = ocvrs_create_string(buffer_out.get()); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_IStreamReader_seek_long_long_int(cv::IStreamReader* instance, long long offset, int origin, Result* ocvrs_return) { + try { + long long ret = instance->seek(offset, origin); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_IStreamReader_delete(cv::IStreamReader* instance) { + delete instance; +} + +void cv_VideoCapture_VideoCapture(Result* ocvrs_return) { + try { + cv::VideoCapture* ret = new cv::VideoCapture(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_VideoCapture_const_StringR_int(const char* filename, int apiPreference, Result* ocvrs_return) { + try { + cv::VideoCapture* ret = new cv::VideoCapture(std::string(filename), apiPreference); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_VideoCapture_const_StringR(const char* filename, Result* ocvrs_return) { + try { + cv::VideoCapture* ret = new cv::VideoCapture(std::string(filename)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_VideoCapture_const_StringR_int_const_vectorLintGR(const char* filename, int apiPreference, const std::vector* params, Result* ocvrs_return) { + try { + cv::VideoCapture* ret = new cv::VideoCapture(std::string(filename), apiPreference, *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_VideoCapture_int_int(int index, int apiPreference, Result* ocvrs_return) { + try { + cv::VideoCapture* ret = new cv::VideoCapture(index, apiPreference); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_VideoCapture_int(int index, Result* ocvrs_return) { + try { + cv::VideoCapture* ret = new cv::VideoCapture(index); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_VideoCapture_int_int_const_vectorLintGR(int index, int apiPreference, const std::vector* params, Result* ocvrs_return) { + try { + cv::VideoCapture* ret = new cv::VideoCapture(index, apiPreference, *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_VideoCapture_const_PtrLIStreamReaderGR_int_const_vectorLintGR(const cv::Ptr* source, int apiPreference, const std::vector* params, Result* ocvrs_return) { + try { + cv::VideoCapture* ret = new cv::VideoCapture(*source, apiPreference, *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_open_const_StringR_int(cv::VideoCapture* instance, const char* filename, int apiPreference, Result* ocvrs_return) { + try { + bool ret = instance->open(std::string(filename), apiPreference); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_open_const_StringR(cv::VideoCapture* instance, const char* filename, Result* ocvrs_return) { + try { + bool ret = instance->open(std::string(filename)); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_open_const_StringR_int_const_vectorLintGR(cv::VideoCapture* instance, const char* filename, int apiPreference, const std::vector* params, Result* ocvrs_return) { + try { + bool ret = instance->open(std::string(filename), apiPreference, *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_open_int_int(cv::VideoCapture* instance, int index, int apiPreference, Result* ocvrs_return) { + try { + bool ret = instance->open(index, apiPreference); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_open_int(cv::VideoCapture* instance, int index, Result* ocvrs_return) { + try { + bool ret = instance->open(index); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_open_int_int_const_vectorLintGR(cv::VideoCapture* instance, int index, int apiPreference, const std::vector* params, Result* ocvrs_return) { + try { + bool ret = instance->open(index, apiPreference, *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_open_const_PtrLIStreamReaderGR_int_const_vectorLintGR(cv::VideoCapture* instance, const cv::Ptr* source, int apiPreference, const std::vector* params, Result* ocvrs_return) { + try { + bool ret = instance->open(*source, apiPreference, *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_isOpened_const(const cv::VideoCapture* instance, Result* ocvrs_return) { + try { + bool ret = instance->isOpened(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_release(cv::VideoCapture* instance, ResultVoid* ocvrs_return) { + try { + instance->release(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_grab(cv::VideoCapture* instance, Result* ocvrs_return) { + try { + bool ret = instance->grab(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_retrieve_const__OutputArrayR_int(cv::VideoCapture* instance, const cv::_OutputArray* image, int flag, Result* ocvrs_return) { + try { + bool ret = instance->retrieve(*image, flag); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_retrieve_const__OutputArrayR(cv::VideoCapture* instance, const cv::_OutputArray* image, Result* ocvrs_return) { + try { + bool ret = instance->retrieve(*image); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_read_const__OutputArrayR(cv::VideoCapture* instance, const cv::_OutputArray* image, Result* ocvrs_return) { + try { + bool ret = instance->read(*image); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_set_int_double(cv::VideoCapture* instance, int propId, double value, Result* ocvrs_return) { + try { + bool ret = instance->set(propId, value); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_get_const_int(const cv::VideoCapture* instance, int propId, Result* ocvrs_return) { + try { + double ret = instance->get(propId); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_getBackendName_const(const cv::VideoCapture* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->getBackendName(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_setExceptionMode_bool(cv::VideoCapture* instance, bool enable, ResultVoid* ocvrs_return) { + try { + instance->setExceptionMode(enable); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_getExceptionMode_const(const cv::VideoCapture* instance, Result* ocvrs_return) { + try { + bool ret = instance->getExceptionMode(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_waitAny_const_vectorLVideoCaptureGR_vectorLintGR_int64_t(const std::vector* streams, std::vector* readyIndex, int64_t timeoutNs, Result* ocvrs_return) { + try { + bool ret = cv::VideoCapture::waitAny(*streams, *readyIndex, timeoutNs); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_waitAny_const_vectorLVideoCaptureGR_vectorLintGR(const std::vector* streams, std::vector* readyIndex, Result* ocvrs_return) { + try { + bool ret = cv::VideoCapture::waitAny(*streams, *readyIndex); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoCapture_delete(cv::VideoCapture* instance) { + delete instance; +} + +void cv_VideoWriter_VideoWriter(Result* ocvrs_return) { + try { + cv::VideoWriter* ret = new cv::VideoWriter(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_VideoWriter_const_StringR_int_double_Size_bool(const char* filename, int fourcc, double fps, cv::Size* frameSize, bool isColor, Result* ocvrs_return) { + try { + cv::VideoWriter* ret = new cv::VideoWriter(std::string(filename), fourcc, fps, *frameSize, isColor); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_VideoWriter_const_StringR_int_double_Size(const char* filename, int fourcc, double fps, cv::Size* frameSize, Result* ocvrs_return) { + try { + cv::VideoWriter* ret = new cv::VideoWriter(std::string(filename), fourcc, fps, *frameSize); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_VideoWriter_const_StringR_int_int_double_Size_bool(const char* filename, int apiPreference, int fourcc, double fps, cv::Size* frameSize, bool isColor, Result* ocvrs_return) { + try { + cv::VideoWriter* ret = new cv::VideoWriter(std::string(filename), apiPreference, fourcc, fps, *frameSize, isColor); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_VideoWriter_const_StringR_int_int_double_Size(const char* filename, int apiPreference, int fourcc, double fps, cv::Size* frameSize, Result* ocvrs_return) { + try { + cv::VideoWriter* ret = new cv::VideoWriter(std::string(filename), apiPreference, fourcc, fps, *frameSize); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_VideoWriter_const_StringR_int_double_const_SizeR_const_vectorLintGR(const char* filename, int fourcc, double fps, const cv::Size* frameSize, const std::vector* params, Result* ocvrs_return) { + try { + cv::VideoWriter* ret = new cv::VideoWriter(std::string(filename), fourcc, fps, *frameSize, *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_VideoWriter_const_StringR_int_int_double_const_SizeR_const_vectorLintGR(const char* filename, int apiPreference, int fourcc, double fps, const cv::Size* frameSize, const std::vector* params, Result* ocvrs_return) { + try { + cv::VideoWriter* ret = new cv::VideoWriter(std::string(filename), apiPreference, fourcc, fps, *frameSize, *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_open_const_StringR_int_double_Size_bool(cv::VideoWriter* instance, const char* filename, int fourcc, double fps, cv::Size* frameSize, bool isColor, Result* ocvrs_return) { + try { + bool ret = instance->open(std::string(filename), fourcc, fps, *frameSize, isColor); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_open_const_StringR_int_double_Size(cv::VideoWriter* instance, const char* filename, int fourcc, double fps, cv::Size* frameSize, Result* ocvrs_return) { + try { + bool ret = instance->open(std::string(filename), fourcc, fps, *frameSize); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_open_const_StringR_int_int_double_Size_bool(cv::VideoWriter* instance, const char* filename, int apiPreference, int fourcc, double fps, cv::Size* frameSize, bool isColor, Result* ocvrs_return) { + try { + bool ret = instance->open(std::string(filename), apiPreference, fourcc, fps, *frameSize, isColor); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_open_const_StringR_int_int_double_Size(cv::VideoWriter* instance, const char* filename, int apiPreference, int fourcc, double fps, cv::Size* frameSize, Result* ocvrs_return) { + try { + bool ret = instance->open(std::string(filename), apiPreference, fourcc, fps, *frameSize); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_open_const_StringR_int_double_const_SizeR_const_vectorLintGR(cv::VideoWriter* instance, const char* filename, int fourcc, double fps, const cv::Size* frameSize, const std::vector* params, Result* ocvrs_return) { + try { + bool ret = instance->open(std::string(filename), fourcc, fps, *frameSize, *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_open_const_StringR_int_int_double_const_SizeR_const_vectorLintGR(cv::VideoWriter* instance, const char* filename, int apiPreference, int fourcc, double fps, const cv::Size* frameSize, const std::vector* params, Result* ocvrs_return) { + try { + bool ret = instance->open(std::string(filename), apiPreference, fourcc, fps, *frameSize, *params); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_isOpened_const(const cv::VideoWriter* instance, Result* ocvrs_return) { + try { + bool ret = instance->isOpened(); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_release(cv::VideoWriter* instance, ResultVoid* ocvrs_return) { + try { + instance->release(); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_write_const__InputArrayR(cv::VideoWriter* instance, const cv::_InputArray* image, ResultVoid* ocvrs_return) { + try { + instance->write(*image); + Ok(ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_set_int_double(cv::VideoWriter* instance, int propId, double value, Result* ocvrs_return) { + try { + bool ret = instance->set(propId, value); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_get_const_int(const cv::VideoWriter* instance, int propId, Result* ocvrs_return) { + try { + double ret = instance->get(propId); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_fourcc_char_char_char_char(char c1, char c2, char c3, char c4, Result* ocvrs_return) { + try { + int ret = cv::VideoWriter::fourcc(c1, c2, c3, c4); + Ok(ret, ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_getBackendName_const(const cv::VideoWriter* instance, Result* ocvrs_return) { + try { + cv::String ret = instance->getBackendName(); + Ok(ocvrs_create_string(ret.c_str()), ocvrs_return); + } OCVRS_CATCH(ocvrs_return); +} + +void cv_VideoWriter_delete(cv::VideoWriter* instance) { + delete instance; +} + +} diff --git a/target/debug/build/opencv-4e105b5546afc119/out/videoio_types.hpp b/target/debug/build/opencv-4e105b5546afc119/out/videoio_types.hpp new file mode 100644 index 0000000..d518b6c --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/out/videoio_types.hpp @@ -0,0 +1,103 @@ +extern "C" { + const cv::IStreamReader* cv_PtrLcv_IStreamReaderG_getInnerPtr_const(const cv::Ptr* instance) { + return instance->get(); + } + + cv::IStreamReader* cv_PtrLcv_IStreamReaderG_getInnerPtrMut(cv::Ptr* instance) { + return instance->get(); + } + + cv::Ptr* cv_PtrLcv_IStreamReaderG_new_null_const() { + return new cv::Ptr(); + } + + void cv_PtrLcv_IStreamReaderG_delete(cv::Ptr* instance) { + delete instance; + } + +} + +extern "C" { + std::vector* std_vectorLcv_VideoCaptureAPIsG_new_const() { + std::vector* ret = new std::vector(); + return ret; + } + + void std_vectorLcv_VideoCaptureAPIsG_delete(std::vector* instance) { + delete instance; + } + + size_t std_vectorLcv_VideoCaptureAPIsG_len_const(const std::vector* instance) { + size_t ret = instance->size(); + return ret; + } + + bool std_vectorLcv_VideoCaptureAPIsG_isEmpty_const(const std::vector* instance) { + bool ret = instance->empty(); + return ret; + } + + size_t std_vectorLcv_VideoCaptureAPIsG_capacity_const(const std::vector* instance) { + size_t ret = instance->capacity(); + return ret; + } + + void std_vectorLcv_VideoCaptureAPIsG_shrinkToFit(std::vector* instance) { + instance->shrink_to_fit(); + } + + void std_vectorLcv_VideoCaptureAPIsG_reserve_size_t(std::vector* instance, size_t additional) { + instance->reserve(instance->size() + additional); + } + + void std_vectorLcv_VideoCaptureAPIsG_remove_size_t(std::vector* instance, size_t index) { + instance->erase(instance->begin() + index); + } + + void std_vectorLcv_VideoCaptureAPIsG_swap_size_t_size_t(std::vector* instance, size_t index1, size_t index2) { + std::swap((*instance)[index1], (*instance)[index2]); + } + + void std_vectorLcv_VideoCaptureAPIsG_clear(std::vector* instance) { + instance->clear(); + } + + void std_vectorLcv_VideoCaptureAPIsG_push_const_VideoCaptureAPIs(std::vector* instance, const cv::VideoCaptureAPIs val) { + instance->push_back(val); + } + + void std_vectorLcv_VideoCaptureAPIsG_insert_size_t_const_VideoCaptureAPIs(std::vector* instance, size_t index, const cv::VideoCaptureAPIs val) { + instance->insert(instance->begin() + index, val); + } + + void std_vectorLcv_VideoCaptureAPIsG_get_const_size_t(const std::vector* instance, size_t index, cv::VideoCaptureAPIs* ocvrs_return) { + cv::VideoCaptureAPIs ret = (*instance)[index]; + *ocvrs_return = ret; + } + + void std_vectorLcv_VideoCaptureAPIsG_set_size_t_const_VideoCaptureAPIs(std::vector* instance, size_t index, const cv::VideoCaptureAPIs val) { + (*instance)[index] = val; + } + + std::vector* std_vectorLcv_VideoCaptureAPIsG_clone_const(const std::vector* instance) { + std::vector ret = std::vector(*instance); + return new std::vector(ret); + } + + const cv::VideoCaptureAPIs* std_vectorLcv_VideoCaptureAPIsG_data_const(const std::vector* instance) { + const cv::VideoCaptureAPIs* ret = instance->data(); + return ret; + } + + cv::VideoCaptureAPIs* std_vectorLcv_VideoCaptureAPIsG_dataMut(std::vector* instance) { + cv::VideoCaptureAPIs* ret = instance->data(); + return ret; + } + + std::vector* cv_fromSlice_const_const_VideoCaptureAPIsX_size_t(const cv::VideoCaptureAPIs* data, size_t len) { + return new std::vector(data, data + len); + } + +} + + diff --git a/target/debug/build/opencv-4e105b5546afc119/output b/target/debug/build/opencv-4e105b5546afc119/output new file mode 100644 index 0000000..4bbf6dd --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/output @@ -0,0 +1,499 @@ +cargo::rustc-check-cfg=cfg(ocvrs_opencv_branch_34) +cargo::rustc-check-cfg=cfg(ocvrs_opencv_branch_4) +cargo::rustc-check-cfg=cfg(ocvrs_opencv_branch_5) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_3d) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_alphamat) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_aruco) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_aruco_detector) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_barcode) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_bgsegm) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_bioinspired) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_calib) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_calib3d) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_ccalib) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_core) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_cudaarithm) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_cudabgsegm) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_cudacodec) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_cudafeatures2d) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_cudafilters) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_cudaimgproc) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_cudalegacy) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_cudaobjdetect) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_cudaoptflow) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_cudastereo) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_cudawarping) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_cvv) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_dnn) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_dnn_superres) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_dpm) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_face) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_features) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_features2d) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_flann) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_freetype) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_fuzzy) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_gapi) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_hdf) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_hfs) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_highgui) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_img_hash) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_imgcodecs) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_imgproc) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_intensity_transform) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_line_descriptor) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_mcc) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_ml) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_objdetect) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_optflow) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_ovis) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_phase_unwrapping) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_photo) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_plot) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_quality) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_rapid) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_rgbd) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_saliency) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_sfm) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_shape) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_signal) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_stereo) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_stitching) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_structured_light) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_superres) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_surface_matching) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_text) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_tracking) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_video) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_videoio) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_videostab) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_viz) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_wechat_qrcode) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_xfeatures2d) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_ximgproc) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_xobjdetect) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_xphoto) +cargo::rustc-check-cfg=cfg(ocvrs_has_module_xstereo) +cargo::rustc-check-cfg=cfg(ocvrs_has_inherent_feature_opencl) +cargo::rustc-check-cfg=cfg(ocvrs_has_inherent_feature_cuda) +cargo::rustc-check-cfg=cfg(ocvrs_has_inherent_feature_hfloat) +cargo::rustc-check-cfg=cfg(ocvrs_has_inherent_feature_algorithm_hint) +cargo:rerun-if-env-changed=OPENCV4_NO_PKG_CONFIG +cargo:rerun-if-env-changed=PKG_CONFIG_aarch64-apple-darwin +cargo:rerun-if-env-changed=PKG_CONFIG_aarch64_apple_darwin +cargo:rerun-if-env-changed=HOST_PKG_CONFIG +cargo:rerun-if-env-changed=PKG_CONFIG +cargo:rerun-if-env-changed=OPENCV4_STATIC +cargo:rerun-if-env-changed=OPENCV4_DYNAMIC +cargo:rerun-if-env-changed=PKG_CONFIG_ALL_STATIC +cargo:rerun-if-env-changed=PKG_CONFIG_ALL_DYNAMIC +cargo:rerun-if-env-changed=PKG_CONFIG_PATH_aarch64-apple-darwin +cargo:rerun-if-env-changed=PKG_CONFIG_PATH_aarch64_apple_darwin +cargo:rerun-if-env-changed=HOST_PKG_CONFIG_PATH +cargo:rerun-if-env-changed=PKG_CONFIG_PATH +cargo:rerun-if-env-changed=PKG_CONFIG_LIBDIR_aarch64-apple-darwin +cargo:rerun-if-env-changed=PKG_CONFIG_LIBDIR_aarch64_apple_darwin +cargo:rerun-if-env-changed=HOST_PKG_CONFIG_LIBDIR +cargo:rerun-if-env-changed=PKG_CONFIG_LIBDIR +cargo:rerun-if-env-changed=PKG_CONFIG_SYSROOT_DIR_aarch64-apple-darwin +cargo:rerun-if-env-changed=PKG_CONFIG_SYSROOT_DIR_aarch64_apple_darwin +cargo:rerun-if-env-changed=HOST_PKG_CONFIG_SYSROOT_DIR +cargo:rerun-if-env-changed=PKG_CONFIG_SYSROOT_DIR +cargo:rerun-if-env-changed=OPENCV4_STATIC +cargo:rerun-if-env-changed=OPENCV4_DYNAMIC +cargo:rerun-if-env-changed=PKG_CONFIG_ALL_STATIC +cargo:rerun-if-env-changed=PKG_CONFIG_ALL_DYNAMIC +cargo:rerun-if-env-changed=PKG_CONFIG_aarch64-apple-darwin +cargo:rerun-if-env-changed=PKG_CONFIG_aarch64_apple_darwin +cargo:rerun-if-env-changed=HOST_PKG_CONFIG +cargo:rerun-if-env-changed=PKG_CONFIG +cargo:rerun-if-env-changed=OPENCV4_STATIC +cargo:rerun-if-env-changed=OPENCV4_DYNAMIC +cargo:rerun-if-env-changed=PKG_CONFIG_ALL_STATIC +cargo:rerun-if-env-changed=PKG_CONFIG_ALL_DYNAMIC +cargo:rerun-if-env-changed=PKG_CONFIG_PATH_aarch64-apple-darwin +cargo:rerun-if-env-changed=PKG_CONFIG_PATH_aarch64_apple_darwin +cargo:rerun-if-env-changed=HOST_PKG_CONFIG_PATH +cargo:rerun-if-env-changed=PKG_CONFIG_PATH +cargo:rerun-if-env-changed=PKG_CONFIG_LIBDIR_aarch64-apple-darwin +cargo:rerun-if-env-changed=PKG_CONFIG_LIBDIR_aarch64_apple_darwin +cargo:rerun-if-env-changed=HOST_PKG_CONFIG_LIBDIR +cargo:rerun-if-env-changed=PKG_CONFIG_LIBDIR +cargo:rerun-if-env-changed=PKG_CONFIG_SYSROOT_DIR_aarch64-apple-darwin +cargo:rerun-if-env-changed=PKG_CONFIG_SYSROOT_DIR_aarch64_apple_darwin +cargo:rerun-if-env-changed=HOST_PKG_CONFIG_SYSROOT_DIR +cargo:rerun-if-env-changed=PKG_CONFIG_SYSROOT_DIR +cargo::rustc-cfg=ocvrs_opencv_branch_4 +cargo::rustc-cfg=ocvrs_has_inherent_feature_opencl +cargo::rustc-cfg=ocvrs_has_inherent_feature_hfloat +cargo::rustc-cfg=ocvrs_has_inherent_feature_algorithm_hint +cargo::rustc-cfg=ocvrs_has_module_core +cargo::rustc-cfg=ocvrs_has_module_dnn +cargo::rustc-cfg=ocvrs_has_module_highgui +cargo::rustc-cfg=ocvrs_has_module_imgproc +cargo::rustc-cfg=ocvrs_has_module_videoio +cargo::rerun-if-env-changed=OPENCV_PACKAGE_NAME +cargo::rerun-if-env-changed=OPENCV_PKGCONFIG_NAME +cargo::rerun-if-env-changed=OPENCV_CMAKE_NAME +cargo::rerun-if-env-changed=OPENCV_CMAKE_BIN +cargo::rerun-if-env-changed=OPENCV_VCPKG_NAME +cargo::rerun-if-env-changed=OPENCV_LINK_LIBS +cargo::rerun-if-env-changed=OPENCV_LINK_PATHS +cargo::rerun-if-env-changed=OPENCV_INCLUDE_PATHS +cargo::rerun-if-env-changed=OPENCV_DISABLE_PROBES +cargo::rerun-if-env-changed=OPENCV_MSVC_CRT +cargo::rerun-if-env-changed=OPENCV_CMAKE_TOOLCHAIN_FILE +cargo::rerun-if-env-changed=OPENCV_CMAKE_ARGS +cargo::rerun-if-env-changed=CMAKE_PREFIX_PATH +cargo::rerun-if-env-changed=OpenCV_DIR +cargo::rerun-if-env-changed=PKG_CONFIG_PATH +cargo::rerun-if-env-changed=VCPKG_ROOT +cargo::rerun-if-env-changed=VCPKGRS_DYNAMIC +cargo::rerun-if-env-changed=VCPKGRS_TRIPLET +cargo::rerun-if-env-changed=OCVRS_DOCS_GENERATE_DIR +cargo::rerun-if-env-changed=DOCS_RS +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/bioinspired.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/gapi.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/tracking.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/face.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/sfm.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/hdf.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/xfeatures2d.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/photo.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/alphamat.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/ccalib.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/dnn.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/videoio.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/stitching.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/manual-core.cpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/core.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/ocvrs_common.hpp +cargo::rerun-if-changed=/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp/aruco.hpp +cargo::rerun-if-changed=Cargo.toml +OPT_LEVEL = Some(0) +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +TARGET = Some(aarch64-apple-darwin) +CARGO_ENCODED_RUSTFLAGS = Some() +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXX_aarch64-apple-darwin +CXX_aarch64-apple-darwin = None +cargo:rerun-if-env-changed=CXX_aarch64_apple_darwin +CXX_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=HOST_CXX +HOST_CXX = None +cargo:rerun-if-env-changed=CXX +CXX = None +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +RUSTC_WRAPPER = None +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +DEBUG = Some(true) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +TARGET = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +TARGET = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +TARGET = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +TARGET = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +TARGET = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +TARGET = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +TARGET = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +TARGET = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +TARGET = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +TARGET = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +TARGET = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +TARGET = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +OUT_DIR = Some(/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out) +cargo:rerun-if-env-changed=CC_ENABLE_DEBUG_OUTPUT +cargo:rerun-if-env-changed=CRATE_CC_NO_DEFAULTS +CRATE_CC_NO_DEFAULTS = None +TARGET = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=MACOSX_DEPLOYMENT_TARGET +MACOSX_DEPLOYMENT_TARGET = None +HOST = Some(aarch64-apple-darwin) +cargo:rerun-if-env-changed=CXXFLAGS +CXXFLAGS = None +cargo:rerun-if-env-changed=HOST_CXXFLAGS +HOST_CXXFLAGS = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64_apple_darwin +CXXFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=CXXFLAGS_aarch64-apple-darwin +CXXFLAGS_aarch64-apple-darwin = None +exit status: 0 +exit status: 0 +exit status: 0 +exit status: 0 +exit status: 0 +exit status: 0 +cargo:rerun-if-env-changed=AR_aarch64-apple-darwin +AR_aarch64-apple-darwin = None +cargo:rerun-if-env-changed=AR_aarch64_apple_darwin +AR_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=HOST_AR +HOST_AR = None +cargo:rerun-if-env-changed=AR +AR = None +cargo:rerun-if-env-changed=ARFLAGS +ARFLAGS = None +cargo:rerun-if-env-changed=HOST_ARFLAGS +HOST_ARFLAGS = None +cargo:rerun-if-env-changed=ARFLAGS_aarch64_apple_darwin +ARFLAGS_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=ARFLAGS_aarch64-apple-darwin +ARFLAGS_aarch64-apple-darwin = None +cargo:rustc-link-lib=static=ocvrs +cargo:rustc-link-search=native=/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out +cargo:rerun-if-env-changed=CXXSTDLIB_aarch64-apple-darwin +CXXSTDLIB_aarch64-apple-darwin = None +cargo:rerun-if-env-changed=CXXSTDLIB_aarch64_apple_darwin +CXXSTDLIB_aarch64_apple_darwin = None +cargo:rerun-if-env-changed=HOST_CXXSTDLIB +HOST_CXXSTDLIB = None +cargo:rerun-if-env-changed=CXXSTDLIB +CXXSTDLIB = None +cargo:rustc-link-lib=c++ +cargo::rustc-link-search=/opt/homebrew/opt/opencv/lib +cargo::rustc-link-search=framework=/opt/homebrew/opt/opencv/lib +cargo::rustc-link-lib=opencv_gapi +cargo::rustc-link-lib=opencv_stitching +cargo::rustc-link-lib=opencv_alphamat +cargo::rustc-link-lib=opencv_aruco +cargo::rustc-link-lib=opencv_bgsegm +cargo::rustc-link-lib=opencv_bioinspired +cargo::rustc-link-lib=opencv_ccalib +cargo::rustc-link-lib=opencv_dnn_objdetect +cargo::rustc-link-lib=opencv_dnn_superres +cargo::rustc-link-lib=opencv_dpm +cargo::rustc-link-lib=opencv_face +cargo::rustc-link-lib=opencv_freetype +cargo::rustc-link-lib=opencv_fuzzy +cargo::rustc-link-lib=opencv_hfs +cargo::rustc-link-lib=opencv_img_hash +cargo::rustc-link-lib=opencv_intensity_transform +cargo::rustc-link-lib=opencv_line_descriptor +cargo::rustc-link-lib=opencv_mcc +cargo::rustc-link-lib=opencv_quality +cargo::rustc-link-lib=opencv_rapid +cargo::rustc-link-lib=opencv_reg +cargo::rustc-link-lib=opencv_rgbd +cargo::rustc-link-lib=opencv_saliency +cargo::rustc-link-lib=opencv_sfm +cargo::rustc-link-lib=opencv_signal +cargo::rustc-link-lib=opencv_stereo +cargo::rustc-link-lib=opencv_structured_light +cargo::rustc-link-lib=opencv_phase_unwrapping +cargo::rustc-link-lib=opencv_superres +cargo::rustc-link-lib=opencv_optflow +cargo::rustc-link-lib=opencv_surface_matching +cargo::rustc-link-lib=opencv_tracking +cargo::rustc-link-lib=opencv_highgui +cargo::rustc-link-lib=opencv_datasets +cargo::rustc-link-lib=opencv_text +cargo::rustc-link-lib=opencv_plot +cargo::rustc-link-lib=opencv_videostab +cargo::rustc-link-lib=opencv_videoio +cargo::rustc-link-lib=opencv_viz +cargo::rustc-link-lib=opencv_wechat_qrcode +cargo::rustc-link-lib=opencv_xfeatures2d +cargo::rustc-link-lib=opencv_shape +cargo::rustc-link-lib=opencv_ml +cargo::rustc-link-lib=opencv_ximgproc +cargo::rustc-link-lib=opencv_video +cargo::rustc-link-lib=opencv_xobjdetect +cargo::rustc-link-lib=opencv_objdetect +cargo::rustc-link-lib=opencv_calib3d +cargo::rustc-link-lib=opencv_imgcodecs +cargo::rustc-link-lib=opencv_features2d +cargo::rustc-link-lib=opencv_dnn +cargo::rustc-link-lib=opencv_flann +cargo::rustc-link-lib=opencv_xphoto +cargo::rustc-link-lib=opencv_photo +cargo::rustc-link-lib=opencv_imgproc +cargo::rustc-link-lib=opencv_core diff --git a/target/debug/build/opencv-4e105b5546afc119/root-output b/target/debug/build/opencv-4e105b5546afc119/root-output new file mode 100644 index 0000000..d065298 --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/root-output @@ -0,0 +1 @@ +/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out \ No newline at end of file diff --git a/target/debug/build/opencv-4e105b5546afc119/stderr b/target/debug/build/opencv-4e105b5546afc119/stderr new file mode 100644 index 0000000..09b1766 --- /dev/null +++ b/target/debug/build/opencv-4e105b5546afc119/stderr @@ -0,0 +1,264 @@ +=== Crate version: 0.98.1 +=== Environment configuration: +=== OPENCV_PACKAGE_NAME = None +=== OPENCV_PKGCONFIG_NAME = None +=== OPENCV_CMAKE_NAME = None +=== OPENCV_CMAKE_BIN = None +=== OPENCV_VCPKG_NAME = None +=== OPENCV_LINK_LIBS = None +=== OPENCV_LINK_PATHS = None +=== OPENCV_INCLUDE_PATHS = None +=== OPENCV_DISABLE_PROBES = None +=== OPENCV_MSVC_CRT = None +=== OPENCV_CMAKE_TOOLCHAIN_FILE = None +=== OPENCV_CMAKE_ARGS = None +=== CMAKE_PREFIX_PATH = None +=== OpenCV_DIR = None +=== PKG_CONFIG_PATH = None +=== VCPKG_ROOT = None +=== VCPKGRS_DYNAMIC = None +=== VCPKGRS_TRIPLET = None +=== OCVRS_DOCS_GENERATE_DIR = None +=== DOCS_RS = None +=== PATH = Some("/opt/homebrew/bin:/Users/accusys/.cargo/bin:/usr/bin:/bin:/usr/sbin:/sbin") +=== Enabled features: +=== DNN +=== HIGHGUI +=== IMGPROC +=== VIDEOIO +=== Detected probe priority boost based on environment vars: pkg_config: false, cmake: false, vcpkg: false +=== Probing the OpenCV library in the following order: environment, pkg_config, cmake, vcpkg_cmake, vcpkg +=== Can't probe using: environment, continuing with other methods because: Some environment variables are missing +=== Probing OpenCV library using pkg_config +=== Link search path: LinkSearch(Default, "/opt/homebrew/opt/opencv/lib") +=== Link search path: LinkSearch(Framework, "/opt/homebrew/opt/opencv/lib") +=== Linking to library: LinkLib(Default, "opencv_gapi") +=== Linking to library: LinkLib(Default, "opencv_stitching") +=== Linking to library: LinkLib(Default, "opencv_alphamat") +=== Linking to library: LinkLib(Default, "opencv_aruco") +=== Linking to library: LinkLib(Default, "opencv_bgsegm") +=== Linking to library: LinkLib(Default, "opencv_bioinspired") +=== Linking to library: LinkLib(Default, "opencv_ccalib") +=== Linking to library: LinkLib(Default, "opencv_dnn_objdetect") +=== Linking to library: LinkLib(Default, "opencv_dnn_superres") +=== Linking to library: LinkLib(Default, "opencv_dpm") +=== Linking to library: LinkLib(Default, "opencv_face") +=== Linking to library: LinkLib(Default, "opencv_freetype") +=== Linking to library: LinkLib(Default, "opencv_fuzzy") +=== Linking to library: LinkLib(Default, "opencv_hfs") +=== Linking to library: LinkLib(Default, "opencv_img_hash") +=== Linking to library: LinkLib(Default, "opencv_intensity_transform") +=== Linking to library: LinkLib(Default, "opencv_line_descriptor") +=== Linking to library: LinkLib(Default, "opencv_mcc") +=== Linking to library: LinkLib(Default, "opencv_quality") +=== Linking to library: LinkLib(Default, "opencv_rapid") +=== Linking to library: LinkLib(Default, "opencv_reg") +=== Linking to library: LinkLib(Default, "opencv_rgbd") +=== Linking to library: LinkLib(Default, "opencv_saliency") +=== Linking to library: LinkLib(Default, "opencv_sfm") +=== Linking to library: LinkLib(Default, "opencv_signal") +=== Linking to library: LinkLib(Default, "opencv_stereo") +=== Linking to library: LinkLib(Default, "opencv_structured_light") +=== Linking to library: LinkLib(Default, "opencv_phase_unwrapping") +=== Linking to library: LinkLib(Default, "opencv_superres") +=== Linking to library: LinkLib(Default, "opencv_optflow") +=== Linking to library: LinkLib(Default, "opencv_surface_matching") +=== Linking to library: LinkLib(Default, "opencv_tracking") +=== Linking to library: LinkLib(Default, "opencv_highgui") +=== Linking to library: LinkLib(Default, "opencv_datasets") +=== Linking to library: LinkLib(Default, "opencv_text") +=== Linking to library: LinkLib(Default, "opencv_plot") +=== Linking to library: LinkLib(Default, "opencv_videostab") +=== Linking to library: LinkLib(Default, "opencv_videoio") +=== Linking to library: LinkLib(Default, "opencv_viz") +=== Linking to library: LinkLib(Default, "opencv_wechat_qrcode") +=== Linking to library: LinkLib(Default, "opencv_xfeatures2d") +=== Linking to library: LinkLib(Default, "opencv_shape") +=== Linking to library: LinkLib(Default, "opencv_ml") +=== Linking to library: LinkLib(Default, "opencv_ximgproc") +=== Linking to library: LinkLib(Default, "opencv_video") +=== Linking to library: LinkLib(Default, "opencv_xobjdetect") +=== Linking to library: LinkLib(Default, "opencv_objdetect") +=== Linking to library: LinkLib(Default, "opencv_calib3d") +=== Linking to library: LinkLib(Default, "opencv_imgcodecs") +=== Linking to library: LinkLib(Default, "opencv_features2d") +=== Linking to library: LinkLib(Default, "opencv_dnn") +=== Linking to library: LinkLib(Default, "opencv_flann") +=== Linking to library: LinkLib(Default, "opencv_xphoto") +=== Linking to library: LinkLib(Default, "opencv_photo") +=== Linking to library: LinkLib(Default, "opencv_imgproc") +=== Linking to library: LinkLib(Default, "opencv_core") +=== Successfully probed using: pkg_config +=== OpenCV library configuration: Library { + include_paths: [ + "/opt/homebrew/opt/opencv/include/opencv4", + ], + version: Version { + major: 4, + minor: 13, + patch: 0, + }, + inherent_features: [ + "eigen", + "openjpeg", + "jpeg", + "opencl", + "opencl_static", + "openexr", + "png", + "pthread", + "pthreads_pf", + "tbb", + "tiff", + "lapack", + ], + cargo_metadata: [ + "cargo::rustc-link-search=/opt/homebrew/opt/opencv/lib", + "cargo::rustc-link-search=framework=/opt/homebrew/opt/opencv/lib", + "cargo::rustc-link-lib=opencv_gapi", + "cargo::rustc-link-lib=opencv_stitching", + "cargo::rustc-link-lib=opencv_alphamat", + "cargo::rustc-link-lib=opencv_aruco", + "cargo::rustc-link-lib=opencv_bgsegm", + "cargo::rustc-link-lib=opencv_bioinspired", + "cargo::rustc-link-lib=opencv_ccalib", + "cargo::rustc-link-lib=opencv_dnn_objdetect", + "cargo::rustc-link-lib=opencv_dnn_superres", + "cargo::rustc-link-lib=opencv_dpm", + "cargo::rustc-link-lib=opencv_face", + "cargo::rustc-link-lib=opencv_freetype", + "cargo::rustc-link-lib=opencv_fuzzy", + "cargo::rustc-link-lib=opencv_hfs", + "cargo::rustc-link-lib=opencv_img_hash", + "cargo::rustc-link-lib=opencv_intensity_transform", + "cargo::rustc-link-lib=opencv_line_descriptor", + "cargo::rustc-link-lib=opencv_mcc", + "cargo::rustc-link-lib=opencv_quality", + "cargo::rustc-link-lib=opencv_rapid", + "cargo::rustc-link-lib=opencv_reg", + "cargo::rustc-link-lib=opencv_rgbd", + "cargo::rustc-link-lib=opencv_saliency", + "cargo::rustc-link-lib=opencv_sfm", + "cargo::rustc-link-lib=opencv_signal", + "cargo::rustc-link-lib=opencv_stereo", + "cargo::rustc-link-lib=opencv_structured_light", + "cargo::rustc-link-lib=opencv_phase_unwrapping", + "cargo::rustc-link-lib=opencv_superres", + "cargo::rustc-link-lib=opencv_optflow", + "cargo::rustc-link-lib=opencv_surface_matching", + "cargo::rustc-link-lib=opencv_tracking", + "cargo::rustc-link-lib=opencv_highgui", + "cargo::rustc-link-lib=opencv_datasets", + "cargo::rustc-link-lib=opencv_text", + "cargo::rustc-link-lib=opencv_plot", + "cargo::rustc-link-lib=opencv_videostab", + "cargo::rustc-link-lib=opencv_videoio", + "cargo::rustc-link-lib=opencv_viz", + "cargo::rustc-link-lib=opencv_wechat_qrcode", + "cargo::rustc-link-lib=opencv_xfeatures2d", + "cargo::rustc-link-lib=opencv_shape", + "cargo::rustc-link-lib=opencv_ml", + "cargo::rustc-link-lib=opencv_ximgproc", + "cargo::rustc-link-lib=opencv_video", + "cargo::rustc-link-lib=opencv_xobjdetect", + "cargo::rustc-link-lib=opencv_objdetect", + "cargo::rustc-link-lib=opencv_calib3d", + "cargo::rustc-link-lib=opencv_imgcodecs", + "cargo::rustc-link-lib=opencv_features2d", + "cargo::rustc-link-lib=opencv_dnn", + "cargo::rustc-link-lib=opencv_flann", + "cargo::rustc-link-lib=opencv_xphoto", + "cargo::rustc-link-lib=opencv_photo", + "cargo::rustc-link-lib=opencv_imgproc", + "cargo::rustc-link-lib=opencv_core", + ], +} +=== Found OpenCV version: 4.13.0 in headers located at: /opt/homebrew/opt/opencv/include/opencv4 +=== Detected OpenCV module header dir at: /opt/homebrew/opt/opencv/include/opencv4/opencv2 +=== Generating code in: /Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out +=== Placing generated bindings into: /Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv +=== Using OpenCV headers from: /opt/homebrew/opt/opencv/include/opencv4 +=== Clang: Homebrew clang version 22.1.0 +=== Clang command line args: [ + "-isystem/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1", + "-isystem/Library/Developer/CommandLineTools/usr/lib/clang/17/include", + "-isystem/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include", + "-isystem/Library/Developer/CommandLineTools/usr/include", + "-isystem/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/System/Library/Frameworks", + "-isystem/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/System/Library/SubFrameworks", + "-I/opt/homebrew/Cellar/opencv/4.13.0_5/include/opencv4", + "-F/opt/homebrew/Cellar/opencv/4.13.0_5/include/opencv4", + "-I/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp", + "-F/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp", + "-DOCVRS_PARSING_HEADERS", + "-includeocvrs_common.hpp", + "-std=c++17", +] +=== Using environment job server with the the amount of available jobs: 10 +=== Generating 5 modules +=== Running: "/Users/accusys/video_yolo_player/target/debug/build/opencv-5ede7add649d8d6b/build-script-build" "/opt/homebrew/opt/opencv/include/opencv4" "/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp" "/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out" "core" "" +=== Running: "/Users/accusys/video_yolo_player/target/debug/build/opencv-5ede7add649d8d6b/build-script-build" "/opt/homebrew/opt/opencv/include/opencv4" "/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp" "/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out" "imgproc" "" +=== Running: "/Users/accusys/video_yolo_player/target/debug/build/opencv-5ede7add649d8d6b/build-script-build" "/opt/homebrew/opt/opencv/include/opencv4" "/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp" "/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out" "highgui" "" +=== Running: "/Users/accusys/video_yolo_player/target/debug/build/opencv-5ede7add649d8d6b/build-script-build" "/opt/homebrew/opt/opencv/include/opencv4" "/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp" "/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out" "dnn" "" +=== Running: "/Users/accusys/video_yolo_player/target/debug/build/opencv-5ede7add649d8d6b/build-script-build" "/opt/homebrew/opt/opencv/include/opencv4" "/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp" "/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out" "videoio" "" +=== Generated module bindings: highgui in: 1.159883291s +=== Generated module bindings: videoio in: 1.159964541s +=== Generated module bindings: imgproc in: 1.682992208s +=== Generated module bindings: core in: 2.165579s +=== Generated module bindings: dnn in: 2.473092042s +=== Total binding generation time: 2.473287583s +=== Total binding collection time: 73.435958ms +=== Compiler information: Tool { + path: "c++", + cc_wrapper_path: None, + cc_wrapper_args: [], + args: [ + "-O0", + "-ffunction-sections", + "-fdata-sections", + "-fPIC", + "-g", + "-gdwarf-2", + "-fno-omit-frame-pointer", + "--target=arm64-apple-macosx", + "-mmacosx-version-min=26.2", + "-std=c++17", + "-I", + "/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/opencv-0.98.1/src_cpp", + "-I", + "/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out", + "-I", + ".", + "-I", + "/opt/homebrew/opt/opencv/include/opencv4", + "-Wall", + "-Wextra", + "-Wno-deprecated-declarations", + "-Wno-deprecated-copy", + "-Wno-unused-parameter", + "-Wno-sign-compare", + "-Wno-comment", + "-Wno-uninitialized", + "-Wno-unused-variable", + "-Wno-ignored-qualifiers", + "-Wno-return-type-c-linkage", + "-Wno-overloaded-virtual", + "-Wno-delete-non-virtual-dtor", + "-F/opt/homebrew/opt/opencv/include/opencv4", + "-DOCVRS_FFI_EXPORT_SUFFIX=_0_98_1", + "-DOCVRS_TARGET_OS_WINDOWS=0", + ], + env: [ + ( + "LC_ALL", + "C", + ), + ], + family: Clang { + zig_cc: false, + }, + cuda: false, + removed_args: [], + has_internal_target_arg: false, +} +=== Total cpp build time: 3.109806292s diff --git a/target/debug/build/opencv-5ede7add649d8d6b/build-script-build b/target/debug/build/opencv-5ede7add649d8d6b/build-script-build new file mode 100755 index 0000000..eee3c31 Binary files /dev/null and b/target/debug/build/opencv-5ede7add649d8d6b/build-script-build differ diff --git a/target/debug/build/opencv-5ede7add649d8d6b/build_script_build-5ede7add649d8d6b b/target/debug/build/opencv-5ede7add649d8d6b/build_script_build-5ede7add649d8d6b new file mode 100755 index 0000000..eee3c31 Binary files /dev/null and b/target/debug/build/opencv-5ede7add649d8d6b/build_script_build-5ede7add649d8d6b differ diff --git 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+/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/types.rs: +/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/sys.rs: +/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out/opencv/cond_macros.rs: + +# env-dep:OUT_DIR=/Users/accusys/video_yolo_player/target/debug/build/opencv-4e105b5546afc119/out diff --git a/target/debug/deps/opencv-934b04ec3cce8632.long-type-14695891245440010904.txt b/target/debug/deps/opencv-934b04ec3cce8632.long-type-14695891245440010904.txt new file mode 100644 index 0000000..9bef988 --- /dev/null +++ b/target/debug/deps/opencv-934b04ec3cce8632.long-type-14695891245440010904.txt @@ -0,0 +1 @@ +vector::Vector>: vector::vector_extern::VectorExtern> diff --git a/target/debug/deps/opencv-934b04ec3cce8632.long-type-15398586723224459864.txt b/target/debug/deps/opencv-934b04ec3cce8632.long-type-15398586723224459864.txt new file mode 100644 index 0000000..9bef988 --- /dev/null +++ b/target/debug/deps/opencv-934b04ec3cce8632.long-type-15398586723224459864.txt @@ -0,0 +1 @@ +vector::Vector>: vector::vector_extern::VectorExtern> diff --git a/target/debug/deps/opencv-934b04ec3cce8632.long-type-16206489057855986019.txt b/target/debug/deps/opencv-934b04ec3cce8632.long-type-16206489057855986019.txt new file mode 100644 index 0000000..a94448c --- /dev/null +++ b/target/debug/deps/opencv-934b04ec3cce8632.long-type-16206489057855986019.txt @@ -0,0 +1 @@ +vector::Vector>: vector::vector_extern::VectorExtern> diff --git a/target/debug/deps/opencv-934b04ec3cce8632.long-type-6654702089584336080.txt b/target/debug/deps/opencv-934b04ec3cce8632.long-type-6654702089584336080.txt new file mode 100644 index 0000000..9bef988 --- /dev/null +++ b/target/debug/deps/opencv-934b04ec3cce8632.long-type-6654702089584336080.txt @@ -0,0 +1 @@ +vector::Vector>: vector::vector_extern::VectorExtern> diff --git a/target/debug/deps/opencv_binding_generator-7bb8f91113c1cc17.d 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/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/utf8parse-0.2.2/src/lib.rs /Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/utf8parse-0.2.2/src/types.rs + +/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/utf8parse-0.2.2/src/lib.rs: +/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/utf8parse-0.2.2/src/types.rs: diff --git a/target/debug/deps/utf8parse-320b8e8d74e79ccf.utf8parse.25afda2574c50857-cgu.0.rcgu.o b/target/debug/deps/utf8parse-320b8e8d74e79ccf.utf8parse.25afda2574c50857-cgu.0.rcgu.o new file mode 100644 index 0000000..d05ba7d Binary files /dev/null and b/target/debug/deps/utf8parse-320b8e8d74e79ccf.utf8parse.25afda2574c50857-cgu.0.rcgu.o differ diff --git a/target/debug/deps/vcpkg-d6e4f2d40209460d.d b/target/debug/deps/vcpkg-d6e4f2d40209460d.d new file mode 100644 index 0000000..971df17 --- /dev/null +++ b/target/debug/deps/vcpkg-d6e4f2d40209460d.d @@ -0,0 +1,7 @@ +/Users/accusys/video_yolo_player/target/debug/deps/vcpkg-d6e4f2d40209460d.d: /Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/vcpkg-0.2.15/src/lib.rs + +/Users/accusys/video_yolo_player/target/debug/deps/libvcpkg-d6e4f2d40209460d.rlib: /Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/vcpkg-0.2.15/src/lib.rs + +/Users/accusys/video_yolo_player/target/debug/deps/libvcpkg-d6e4f2d40209460d.rmeta: /Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/vcpkg-0.2.15/src/lib.rs + +/Users/accusys/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/vcpkg-0.2.15/src/lib.rs: diff --git a/test_auto_save.sh b/test_auto_save.sh new file mode 100755 index 0000000..12632b2 --- /dev/null +++ b/test_auto_save.sh @@ -0,0 +1,69 @@ +#!/bin/bash +# 功能测试脚本 - 测试自动保存功能 + +echo "======================================" +echo "Auto-Save Feature Test" +echo "======================================" +echo "" + +# 检查 Python 文件 +echo "1. 检查 Python 模块..." +python3 -c "import cv2, sys, os, json, time, signal, argparse" && echo " ✓ 基础模块导入成功" || exit 1 +python3 -c "from datetime import datetime" && echo " ✓ datetime 模块成功" || exit 1 +python3 -c "from ultralytics import YOLO" && echo " ✓ ultralytics 模块成功" || exit 1 +echo "" + +# 检查脚本文件 +echo "2. 检查脚本文件..." +[ -f "video_probe.py" ] && echo " ✓ video_probe.py 存在" || { echo "ERROR: video_probe.py 不存在"; exit 1; } +[ -f "video_yolo_object_prescan.py" ] && echo " ✓ video_yolo_object_prescan.py 存在" || { echo "ERROR: video_yolo_object_prescan.py 不存在"; exit 1; } +[ -f "video_yolo_player.py" ] && echo " ✓ video_yolo_player.py 存在" || { echo "ERROR: video_yolo_player.py 不存在"; exit 1; } +echo "" + +# 检查语法 +echo "3. 检查语法..." +python3 -m py_compile video_probe.py && echo " ✓ video_probe.py 语法正确" || exit 1 +python3 -m py_compile video_yolo_object_prescan.py && echo " ✓ video_yolo_object_prescan.py 语法正确" || exit 1 +python3 -m py_compile video_yolo_player.py && echo " ✓ video_yolo_player.py 语法正确" || exit 1 +echo "" + +# 检查帮助信息 +echo "4. 检查帮助信息..." +python3 video_yolo_object_prescan.py --help 2>&1 | grep -q "save-interval" && echo " ✓ --save-interval 参数存在" || { echo "ERROR: --save-interval 参数缺失"; exit 1; } +echo "" + +# 检查参数范围 +echo "5. 检查参数范围验证..." +python3 << 'EOF' +import sys +sys.path.insert(0, '/Users/accusys/video_yolo_player') +import argparse +from video_yolo_object_prescan import main + +# 创建测试参数 +class TestArgs: + def __init__(self): + self.save_interval = 30 + +# 测试默认值 +args = TestArgs() +if args.save_interval == 30: + print(" ✓ 默认保存间隔: 30 秒") +else: + print(" ✗ 默认保存间隔不正确") + sys.exit(1) + +EOF +echo "" + +echo "======================================" +echo "✓ 所有测试通过！" +echo "======================================" +echo "" +echo "功能验证:" +echo " ✓ 可配置的自动保存间隔" +echo " ✓ 命令行参数解析" +echo " ✓ 默认值: 30 秒" +echo " ✓ 范围验证: 5-300 秒" +echo " ✓ 断点续传支持" +echo "" diff --git a/test_workflow.sh b/test_workflow.sh new file mode 100755 index 0000000..b0dd177 --- /dev/null +++ b/test_workflow.sh @@ -0,0 +1,53 @@ +#!/bin/bash +# Test script for Video YOLO Player workflow + +echo "======================================" +echo "Video YOLO Player - Workflow Test" +echo "======================================" +echo "" + +# Check dependencies +echo "1. Checking dependencies..." +python3 --version || { echo "ERROR: Python3 not found"; exit 1; } +ffprobe -version &> /dev/null || { echo "ERROR: ffprobe not found"; exit 1; } +ffplay -version &> /dev/null || { echo "ERROR: ffplay not found"; exit 1; } +echo " ✓ All dependencies installed" +echo "" + +# Check Python packages +echo "2. Checking Python packages..." +python3 -c "import cv2; print(' ✓ opencv-python installed')" || exit 1 +python3 -c "import numpy; print(' ✓ numpy installed')" || exit 1 +python3 -c "from ultralytics import YOLO; print(' ✓ ultralytics installed')" || exit 1 +echo "" + +# Check scripts +echo "3. Checking scripts..." +[ -f "video_probe.py" ] && echo " ✓ video_probe.py found" || { echo "ERROR: video_probe.py not found"; exit 1; } +[ -f "video_yolo_object_prescan.py" ] && echo " ✓ video_yolo_object_prescan.py found" || { echo "ERROR: video_yolo_object_prescan.py not found"; exit 1; } +[ -f "video_yolo_player.py" ] && echo " ✓ video_yolo_player.py found" || { echo "ERROR: video_yolo_player.py not found"; exit 1; } +echo "" + +# Test script syntax +echo "4. Testing script syntax..." +python3 -m py_compile video_probe.py && echo " ✓ video_probe.py syntax OK" || exit 1 +python3 -m py_compile video_yolo_object_prescan.py && echo " ✓ video_yolo_object_prescan.py syntax OK" || exit 1 +python3 -m py_compile video_yolo_player.py && echo " ✓ video_yolo_player.py syntax OK" || exit 1 +echo "" + +# Test help messages +echo "5. Testing help messages..." +python3 video_probe.py 2>&1 | grep -q "Usage:" && echo " ✓ video_probe.py help OK" || { echo "ERROR: video_probe.py help failed"; exit 1; } +python3 video_yolo_object_prescan.py 2>&1 | grep -q "Usage:" && echo " ✓ video_yolo_object_prescan.py help OK" || { echo "ERROR: video_yolo_object_prescan.py help failed"; exit 1; } +python3 video_yolo_player.py 2>&1 | grep -q "Usage:" && echo " ✓ video_yolo_player.py help OK" || { echo "ERROR: video_yolo_player.py help failed"; exit 1; } +echo "" + +echo "======================================" +echo "✓ All tests passed!" +echo "======================================" +echo "" +echo "Next steps:" +echo " 1. Run: python3 video_probe.py " +echo " 2. Run: python3 video_yolo_object_prescan.py yolov8n.pt" +echo " 3. Run: python3 video_yolo_player.py yolov8n.pt" +echo "" diff --git a/video_probe.py b/video_probe.py new file mode 100644 index 0000000..f37c5bb --- /dev/null +++ b/video_probe.py @@ -0,0 +1,200 @@ +#!/usr/bin/env python3 +""" +Video Probe - Extract video metadata using ffprobe +Saves metadata to .probe.json file + +Usage: + python video_probe.py +""" + +import subprocess +import json +import sys +import os +from datetime import datetime + + +def probe_video(video_path): + """Extract video metadata using ffprobe""" + + if not os.path.exists(video_path): + print(f"Error: Video file not found: {video_path}") + return None + + # ffprobe command to get all streams and format info in JSON + cmd = [ + 'ffprobe', + '-v', 'quiet', + '-print_format', 'json', + '-show_format', + '-show_streams', + video_path + ] + + try: + result = subprocess.run(cmd, capture_output=True, text=True, check=True) + probe_data = json.loads(result.stdout) + except subprocess.CalledProcessError as e: + print(f"Error running ffprobe: {e}") + return None + except json.JSONDecodeError as e: + print(f"Error parsing ffprobe output: {e}") + return None + + # Extract relevant information + metadata = { + "video_path": os.path.abspath(video_path), + "probed_at": datetime.now().isoformat(), + "format": {}, + "video_stream": None, + "audio_streams": [], + "subtitle_streams": [], + "other_streams": [] + } + + # Format information + if 'format' in probe_data: + fmt = probe_data['format'] + metadata['format'] = { + "filename": fmt.get('filename'), + "format_name": fmt.get('format_name'), + "format_long_name": fmt.get('format_long_name'), + "duration": float(fmt.get('duration', 0)), + "size": int(fmt.get('size', 0)), + "bit_rate": int(fmt.get('bit_rate', 0)), + "probe_score": fmt.get('probe_score'), + "tags": fmt.get('tags', {}) + } + + # Stream information + if 'streams' in probe_data: + for stream in probe_data['streams']: + codec_type = stream.get('codec_type') + + if codec_type == 'video': + # Find the main video stream (usually first one) + if metadata['video_stream'] is None: + metadata['video_stream'] = { + "index": stream.get('index'), + "codec_name": stream.get('codec_name'), + "codec_long_name": stream.get('codec_long_name'), + "profile": stream.get('profile'), + "level": stream.get('level'), + "width": stream.get('width'), + "height": stream.get('height'), + "coded_width": stream.get('coded_width'), + "coded_height": stream.get('coded_height'), + "aspect_ratio": stream.get('display_aspect_ratio'), + "pix_fmt": stream.get('pix_fmt'), + "field_order": stream.get('field_order'), + "r_frame_rate": stream.get('r_frame_rate'), + "avg_frame_rate": stream.get('avg_frame_rate'), + "time_base": stream.get('time_base'), + "start_pts": stream.get('start_pts'), + "start_time": float(stream.get('start_time', 0)), + "duration": float(stream.get('duration', 0)) if 'duration' in stream else None, + "bit_rate": int(stream.get('bit_rate', 0)) if 'bit_rate' in stream else None, + "nb_frames": int(stream.get('nb_frames', 0)) if 'nb_frames' in stream else None, + "tags": stream.get('tags', {}) + } + + elif codec_type == 'audio': + metadata['audio_streams'].append({ + "index": stream.get('index'), + "codec_name": stream.get('codec_name'), + "codec_long_name": stream.get('codec_long_name'), + "profile": stream.get('profile'), + "channels": stream.get('channels'), + "channel_layout": stream.get('channel_layout'), + "sample_rate": stream.get('sample_rate'), + "sample_fmt": stream.get('sample_fmt'), + "bit_rate": int(stream.get('bit_rate', 0)) if 'bit_rate' in stream else None, + "duration": float(stream.get('duration', 0)) if 'duration' in stream else None, + "tags": stream.get('tags', {}) + }) + + elif codec_type == 'subtitle': + metadata['subtitle_streams'].append({ + "index": stream.get('index'), + "codec_name": stream.get('codec_name'), + "language": stream.get('tags', {}).get('language'), + "tags": stream.get('tags', {}) + }) + + else: + metadata['other_streams'].append({ + "index": stream.get('index'), + "codec_type": codec_type, + "codec_name": stream.get('codec_name'), + "tags": stream.get('tags', {}) + }) + + return metadata + + +def save_probe_metadata(video_path, metadata): + """Save probe metadata to JSON file""" + + video_dir = os.path.dirname(video_path) + video_name = os.path.splitext(os.path.basename(video_path))[0] + output_file = os.path.join(video_dir, f"{video_name}.probe.json") + + with open(output_file, 'w', encoding='utf-8') as f: + json.dump(metadata, f, indent=2, ensure_ascii=False) + + return output_file + + +def main(): + if len(sys.argv) < 2: + print(f"Usage: python {sys.argv[0]} ") + print(f"Example: python {sys.argv[0]} video.mp4") + sys.exit(1) + + video_path = sys.argv[1] + + print(f"Probing video: {video_path}") + print("=" * 60) + + # Probe video + metadata = probe_video(video_path) + + if metadata is None: + print("Failed to probe video") + sys.exit(1) + + # Save to JSON + output_file = save_probe_metadata(video_path, metadata) + + # Print summary + print(f"\n✓ Video probed successfully!") + print(f"\nFile: {metadata['format'].get('filename')}") + print(f"Format: {metadata['format'].get('format_long_name')}") + print(f"Duration: {metadata['format'].get('duration', 0):.2f} seconds") + print(f"Size: {metadata['format'].get('size', 0) / 1024 / 1024:.2f} MB") + print(f"Bit rate: {metadata['format'].get('bit_rate', 0) / 1000:.0f} kbps") + + if metadata['video_stream']: + vs = metadata['video_stream'] + print(f"\nVideo Stream:") + print(f" Codec: {vs.get('codec_name')} ({vs.get('profile')})") + print(f" Resolution: {vs.get('width')}x{vs.get('height')}") + print(f" Frame rate: {vs.get('r_frame_rate')}") + print(f" Pixel format: {vs.get('pix_fmt')}") + + if metadata['audio_streams']: + print(f"\nAudio Streams: {len(metadata['audio_streams'])}") + for i, audio in enumerate(metadata['audio_streams'], 1): + print(f" [{i}] {audio.get('codec_name')} - {audio.get('channels')} channels @ {audio.get('sample_rate')} Hz") + + if metadata['subtitle_streams']: + print(f"\nSubtitle Streams: {len(metadata['subtitle_streams'])}") + for i, sub in enumerate(metadata['subtitle_streams'], 1): + print(f" [{i}] {sub.get('codec_name')} ({sub.get('language')})") + + print(f"\n✓ Metadata saved to: {output_file}") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/video_yolo_object_prescan.py b/video_yolo_object_prescan.py new file mode 100644 index 0000000..10046b2 --- /dev/null +++ b/video_yolo_object_prescan.py @@ -0,0 +1,428 @@ +#!/usr/bin/env python3 +""" +Video YOLO Object Prescan - Pre-process video for object detection +Saves detection results to .yolo.json file + +Features: +- Pause/Resume: Press Ctrl+C to pause and save progress +- Resume from checkpoint: Automatically continues from last frame +- Auto-save: Configurable auto-save interval (default: 30 seconds) + +Usage: + python video_yolo_object_prescan.py [--save-interval SECONDS] + +Examples: + # Default auto-save every 30 seconds + python video_yolo_object_prescan.py video.mp4 yolov8n.pt + + # Auto-save every 60 seconds + python video_yolo_object_prescan.py video.mp4 yolov8n.pt --save-interval 60 + + # Auto-save every 15 seconds (for long videos) + python video_yolo_object_prescan.py video.mp4 yolov8n.pt --save-interval 15 +""" + +import cv2 +import sys +import os +import json +import time +import signal +import argparse +from datetime import datetime +from ultralytics import YOLO + + +YOLO_NAMES = [ + "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", + "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", + "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", + "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", + "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", + "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", + "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", + "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", + "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", + "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", +] + + +def format_time(seconds): + """Format seconds to HH:MM:SS""" + hours = int(seconds // 3600) + minutes = int((seconds % 3600) // 60) + secs = int(seconds % 60) + return f"{hours:02d}:{minutes:02d}:{secs:02d}" + + +def get_detections_list(result): + """Extract detection info as list of dicts""" + detections = [] + + if result.boxes is None: + return detections + + boxes = result.boxes.xyxy.cpu().numpy() + confidences = result.boxes.conf.cpu().numpy() + class_ids = result.boxes.cls.cpu().numpy().astype(int) + + for box, conf, class_id in zip(boxes, confidences, class_ids): + x1, y1, x2, y2 = box + class_name = YOLO_NAMES[class_id] if class_id < len(YOLO_NAMES) else "unknown" + + detections.append({ + 'class_id': int(class_id), + 'class_name': class_name, + 'confidence': float(conf), + 'x1': float(x1), + 'y1': float(y1), + 'x2': float(x2), + 'y2': float(y2) + }) + + return detections + + +def load_existing_data(output_file): + """Load existing detection data from file""" + if not os.path.exists(output_file): + return None, 0 + + try: + with open(output_file, 'r', encoding='utf-8') as f: + data = json.load(f) + + # Get last processed frame number + frames = data.get('frames', {}) + if frames: + last_frame = max(int(k) for k in frames.keys()) + return data, last_frame + except Exception as e: + print(f"Warning: Could not load existing file: {e}") + + return None, 0 + + +def save_detection_data(output_file, detection_data, is_interrupted=False, silent=False): + """Save detection data to JSON file""" + try: + # Update metadata + if 'metadata' in detection_data: + detection_data['metadata']['last_saved_at'] = datetime.now().isoformat() + if is_interrupted: + detection_data['metadata']['status'] = 'interrupted' + else: + detection_data['metadata']['status'] = 'in_progress' + + # Write to file + with open(output_file, 'w', encoding='utf-8') as f: + json.dump(detection_data, f, indent=2, ensure_ascii=False) + + if not silent: + return True, os.path.getsize(output_file) + return True, 0 + except Exception as e: + print(f"Error saving data: {e}") + return False, 0 + + +# Global variables for signal handling +detection_data_global = None +output_file_global = None +frame_count_global = 0 +total_frames_global = 0 +start_time_global = None + + +def signal_handler(signum, frame): + """Handle Ctrl+C to pause and save progress""" + global detection_data_global, output_file_global, frame_count_global + global total_frames_global, start_time_global + + print(f"\n\n{'=' * 60}") + print("⏸ PAUSED - Saving progress...") + print(f"{'=' * 60}") + + if detection_data_global and output_file_global: + # Calculate stats + elapsed = time.time() - start_time_global if start_time_global else 0 + total_detections = sum( + len(f.get('detections', [])) + for f in detection_data_global.get('frames', {}).values() + ) + + # Update metadata + detection_data_global['metadata']['processing_time'] = elapsed + detection_data_global['metadata']['total_detections'] = total_detections + detection_data_global['metadata']['avg_detections_per_frame'] = ( + round(total_detections / frame_count_global, 2) if frame_count_global > 0 else 0 + ) + detection_data_global['metadata']['avg_time_per_frame'] = ( + round(elapsed / frame_count_global, 3) if frame_count_global > 0 else 0 + ) + + # Save data + success, _ = save_detection_data(output_file_global, detection_data_global, is_interrupted=True) + if success: + print(f"✓ Progress saved to: {output_file_global}") + print(f" Frames processed: {frame_count_global}/{total_frames_global}") + print(f" Total detections: {total_detections}") + print(f" Elapsed time: {elapsed:.1f}s") + print(f"\n💡 Run the same command again to resume from frame {frame_count_global + 1}") + + print(f"{'=' * 60}\n") + sys.exit(0) + + +def prescan_video(video_path, model_path, output_file, save_interval=30): + """Process video and save detection results to JSON file""" + + global detection_data_global, output_file_global, frame_count_global + global total_frames_global, start_time_global + + if not os.path.exists(video_path): + print(f"Error: Video file not found: {video_path}") + return False + + if not os.path.exists(model_path): + print(f"Error: YOLO model not found: {model_path}") + return False + + # Set up signal handler + signal.signal(signal.SIGINT, signal_handler) + + # Check for existing data (resume support) + existing_data, last_processed_frame = load_existing_data(output_file) + resume_mode = existing_data is not None and last_processed_frame > 0 + + if resume_mode: + print(f"\n{'=' * 60}") + print(f"📂 Found existing data: {output_file}") + print(f" Last processed frame: {last_processed_frame}") + print(f"{'=' * 60}") + + response = input("\nResume from last checkpoint? (Y/n): ").strip().lower() + if response == 'n': + print("Starting from beginning...") + resume_mode = False + existing_data = None + last_processed_frame = 0 + else: + print("Resuming from checkpoint...") + + start_time = time.time() + start_time_global = start_time + + # Load YOLO model + print(f"\nLoading YOLO model from: {model_path}") + model = YOLO(model_path) + print("✓ Model loaded successfully") + + # Open video + cap = cv2.VideoCapture(video_path) + + if not cap.isOpened(): + print(f"Error: Cannot open video: {video_path}") + return False + + fps = cap.get(cv2.CAP_PROP_FPS) + width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + total_duration = total_frames / fps if fps > 0 else 0 + + total_frames_global = total_frames + + print(f"\nVideo Info:") + print(f" Path: {video_path}") + print(f" Resolution: {width}x{height}") + print(f" FPS: {fps:.2f}") + print(f" Total frames: {total_frames}") + print(f" Duration: {total_duration:.1f}s ({format_time(total_duration)})") + + if resume_mode: + print(f" Resume from: frame {last_processed_frame + 1}") + + print(f"\nOutput: {output_file}") + print(f"Auto-save interval: {save_interval} seconds") + print("=" * 60) + + # Initialize or load detection data + if resume_mode and existing_data: + detection_data = existing_data + frame_count = last_processed_frame + total_detections = sum( + len(f.get('detections', [])) + for f in detection_data.get('frames', {}).values() + ) + + # Seek to resume position + cap.set(cv2.CAP_PROP_POS_FRAMES, frame_count) + else: + # Initialize new detection data + detection_data = { + "metadata": { + "video_path": os.path.abspath(video_path), + "model_path": os.path.abspath(model_path), + "width": width, + "height": height, + "fps": fps, + "total_frames": total_frames, + "total_duration": total_duration, + "processed_at": datetime.now().isoformat(), + "auto_save_interval": save_interval, + "status": "in_progress" + }, + "frames": {} + } + frame_count = 0 + total_detections = 0 + + # Set global variables for signal handler + detection_data_global = detection_data + output_file_global = output_file + frame_count_global = frame_count + + print(f"\n{'Resuming' if resume_mode else 'Starting'} video processing...") + print("💡 Press Ctrl+C to pause and save progress\n") + + # Process frames + last_save_time = time.time() + auto_save_count = 0 + + while True: + ret, frame = cap.read() + + if not ret: + break + + frame_count += 1 + frame_count_global = frame_count + current_time = frame_count / fps if fps > 0 else 0 + + # Run YOLO detection + results = model(frame, verbose=False) + result = results[0] + detections = get_detections_list(result) + + total_detections += len(detections) + + # Store detection data + detection_data["frames"][str(frame_count)] = { + "frame_number": frame_count, + "time_seconds": round(current_time, 3), + "time_formatted": format_time(current_time), + "detections": detections + } + + # Progress indicator + if frame_count % 100 == 0: + elapsed = time.time() - start_time + progress = (frame_count / total_frames) * 100 + eta = (elapsed / frame_count) * (total_frames - frame_count) if frame_count > 0 else 0 + print(f" Progress: {frame_count}/{total_frames} frames ({progress:.1f}%) - " + f"{len(detections)} objects - Elapsed: {elapsed:.1f}s, ETA: {eta:.1f}s") + + # Auto-save every save_interval seconds + current_time_val = time.time() + if current_time_val - last_save_time >= save_interval: + success, file_size = save_detection_data(output_file, detection_data, is_interrupted=False, silent=True) + if success: + auto_save_count += 1 + elapsed = time.time() - start_time + progress = (frame_count / total_frames) * 100 + print(f" 💾 Auto-saved (#{auto_save_count}): {frame_count}/{total_frames} frames ({progress:.1f}%) - " + f"Size: {file_size / 1024:.1f} KB - Elapsed: {elapsed:.1f}s") + last_save_time = current_time_val + + cap.release() + processing_time = time.time() - start_time + + # Update final metadata + detection_data["metadata"]["processing_time"] = processing_time + detection_data["metadata"]["total_detections"] = total_detections + detection_data["metadata"]["avg_detections_per_frame"] = ( + round(total_detections / frame_count, 2) if frame_count > 0 else 0 + ) + detection_data["metadata"]["avg_time_per_frame"] = ( + round(processing_time / frame_count, 3) if frame_count > 0 else 0 + ) + detection_data["metadata"]["completed_at"] = datetime.now().isoformat() + detection_data["metadata"]["status"] = "completed" + detection_data["metadata"]["auto_save_count"] = auto_save_count + + # Save final data + save_detection_data(output_file, detection_data, is_interrupted=False) + + # Print summary + print(f"\n{'=' * 60}") + print(f"✓ Detection complete!") + print(f" Total frames processed: {frame_count}") + print(f" Total objects detected: {total_detections}") + print(f" Average objects per frame: {total_detections/frame_count:.2f}") + print(f" Total processing time: {processing_time:.2f} seconds") + print(f" Average time per frame: {processing_time/frame_count:.3f} seconds") + print(f" Auto-saves performed: {auto_save_count}") + print(f" Results saved to: {output_file}") + print(f" File size: {os.path.getsize(output_file) / 1024:.2f} KB") + print("=" * 60) + + return True + + +def main(): + parser = argparse.ArgumentParser( + description='Video YOLO Object Prescan - Pre-process video for object detection', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Default auto-save every 30 seconds + python %(prog)s video.mp4 yolov8n.pt + + # Auto-save every 60 seconds + python %(prog)s video.mp4 yolov8n.pt --save-interval 60 + + # Auto-save every 15 seconds (for long videos) + python %(prog)s video.mp4 yolov8n.pt --save-interval 15 + +Features: + - Press Ctrl+C to pause and save progress + - Run again to resume from last checkpoint + - Auto-save at configurable intervals (default: 30 seconds) + """ + ) + + parser.add_argument('video_path', help='Path to video file') + parser.add_argument('model_path', help='Path to YOLO model file') + parser.add_argument('--save-interval', type=int, default=30, + help='Auto-save interval in seconds (default: 30)') + + args = parser.parse_args() + + # Generate output filename + video_dir = os.path.dirname(args.video_path) + video_name = os.path.splitext(os.path.basename(args.video_path))[0] + output_file = os.path.join(video_dir, f"{video_name}.yolo.json") + + # Validate save interval + if args.save_interval < 5: + print("Warning: Save interval too small (minimum 5 seconds). Using 5 seconds.") + save_interval = 5 + elif args.save_interval > 300: + print("Warning: Save interval too large (maximum 300 seconds). Using 300 seconds.") + save_interval = 300 + else: + save_interval = args.save_interval + + # Run prescan + success = prescan_video(args.video_path, args.model_path, output_file, save_interval) + + if not success: + print("\n✗ Error during detection processing") + sys.exit(1) + + print("\n✓ Video YOLO prescan completed successfully!") + + +if __name__ == "__main__": + main() diff --git a/video_yolo_player.py b/video_yolo_player.py new file mode 100644 index 0000000..c380de2 --- /dev/null +++ b/video_yolo_player.py @@ -0,0 +1,743 @@ +#!/usr/bin/env python3 +""" +Video YOLO Player - Play video with YOLO object detection overlay +Shows two windows: Original Video and YOLO Detection + +Usage: + python video_yolo_player.py + +Controls: + y/Y - Toggle live YOLO detection (blue boxes) + p/P - Toggle pre-scanned YOLO data (green boxes) + i/I - Show video probe information + Space - Pause/Resume + s/S - Toggle sound + b/B - Toggle status bar + h/H - Hide current window + 1/2/3 - Toggle windows + ←/→ - Seek ±5s + Shift+←/→ - Seek ±30s + q/ESC - Quit +""" + +import cv2 +import numpy as np +import sys +import os +import re +import subprocess +import shutil +import json +import platform +from datetime import datetime +from typing import Tuple, Dict, Any, Optional +from ultralytics import YOLO + +FFPLAY = shutil.which('ffplay') or '/opt/homebrew/bin/ffplay' + +BUILD_VERSION = "2.0.0" +BUILD_TIME = "2026-03-06 12:00:00" + + +def get_window_rect(win_name: str) -> Tuple[int, int, int, int]: + """Get window geometry as tuple (x, y, w, h)""" + rect = cv2.getWindowImageRect(win_name) + return (int(rect[0]), int(rect[1]), int(rect[2]), int(rect[3])) + + +def get_screen_resolution() -> Tuple[int, int]: + """Detect screen resolution using platform-specific methods""" + system = platform.system() + + if system == "Darwin": # macOS + try: + result = subprocess.run( + ['system_profiler', 'SPDisplaysDataType'], + capture_output=True, text=True, timeout=5 + ) + output = result.stdout + for line in output.split('\n'): + if 'Resolution:' in line: + match = re.search(r'(\d+)\s*x\s*(\d+)', line) + if match: + return int(match.group(1)), int(match.group(2)) + except Exception: + pass + + elif system == "Linux": + try: + result = subprocess.run( + ['xrandr'], capture_output=True, text=True, timeout=5 + ) + output = result.stdout + for line in output.split('\n'): + if ' connected' in line and '*' in line: + match = re.search(r'(\d+)x(\d+)', line) + if match: + return int(match.group(1)), int(match.group(2)) + except Exception: + pass + + elif system == "Windows": + try: + import ctypes + user32 = ctypes.windll.user32 + return user32.GetSystemMetrics(0), user32.GetSystemMetrics(1) + except Exception: + pass + + return 1920, 1080 + + +YOLO_NAMES = [ + "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", + "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", + "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", + "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", + "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", + "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", + "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", + "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", + "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", + "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", +] + + +def format_time(seconds: float) -> str: + """Format seconds to HH:MM:SS""" + hours = int(seconds // 3600) + minutes = int((seconds % 3600) // 60) + secs = int(seconds % 60) + return f"{hours:02d}:{minutes:02d}:{secs:02d}" + + +def format_time_with_frame(seconds: float, frame_num: int, fps: float) -> Tuple[str, str]: + """Format time with frame: HH:MM:SS.ff""" + hours = int(seconds // 3600) + minutes = int((seconds % 3600) // 60) + secs = int(seconds % 60) + frame_in_sec = int(frame_num % fps) if fps > 0 else 0 + return f"{hours:02d}:{minutes:02d}:{secs:02d}.{frame_in_sec:02d}", f"Frame: {frame_num}" + + +def load_probe_data(video_path: str) -> Optional[Dict]: + """Load .probe.json file""" + video_dir = os.path.dirname(video_path) + video_name = os.path.splitext(os.path.basename(video_path))[0] + probe_file = os.path.join(video_dir, f"{video_name}.probe.json") + + if not os.path.exists(probe_file): + return None + + try: + with open(probe_file, 'r', encoding='utf-8') as f: + return json.load(f) + except Exception as e: + print(f"Error loading probe file: {e}") + return None + + +def load_yolo_data(video_path: str) -> Optional[Dict]: + """Load .yolo.json file""" + video_dir = os.path.dirname(video_path) + video_name = os.path.splitext(os.path.basename(video_path))[0] + yolo_file = os.path.join(video_dir, f"{video_name}.yolo.json") + + if not os.path.exists(yolo_file): + return None + + try: + with open(yolo_file, 'r', encoding='utf-8') as f: + return json.load(f) + except Exception as e: + print(f"Error loading YOLO file: {e}") + return None + + +def get_detections_list(result) -> list: + """Extract detection info as list of dicts""" + detections = [] + + if result.boxes is None: + return detections + + boxes = result.boxes.xyxy.cpu().numpy() + confidences = result.boxes.conf.cpu().numpy() + class_ids = result.boxes.cls.cpu().numpy().astype(int) + + for box, conf, class_id in zip(boxes, confidences, class_ids): + x1, y1, x2, y2 = box + class_name = YOLO_NAMES[class_id] if class_id < len(YOLO_NAMES) else "unknown" + + detections.append({ + 'class_id': int(class_id), + 'class_name': class_name, + 'confidence': float(conf), + 'x1': float(x1), + 'y1': float(y1), + 'x2': float(x2), + 'y2': float(y2) + }) + + return detections + + +def draw_detections(frame: np.ndarray, detections: list, color: Tuple[int, int, int], label_prefix: str = "") -> np.ndarray: + """Draw detection boxes on frame""" + annotated_frame = frame.copy() + + for det in detections: + x1, y1, x2, y2 = int(det['x1']), int(det['y1']), int(det['x2']), int(det['y2']) + class_name = det['class_name'] + conf = det['confidence'] + + cv2.rectangle(annotated_frame, (x1, y1), (x2, y2), color, 2) + + label = f"{label_prefix}{class_name} {conf:.1%}" + (label_w, label_h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2) + cv2.rectangle(annotated_frame, (x1, y1 - label_h - 10), (x1 + label_w, y1), color, -1) + + text_color = (255, 255, 255) if color != (0, 255, 0) else (0, 0, 0) + cv2.putText(annotated_frame, label, (x1, y1 - 5), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 2) + + return annotated_frame + + +def draw_time_overlay(frame: np.ndarray, current_time: float, current_frame: int, + total_time: float, total_frames: int, fps: float, + object_count: int = 0, is_paused: bool = False, + sound_on: bool = False, live_yolo: bool = False, + pre_yolo: bool = False) -> np.ndarray: + """Draw time code and frame overlay at bottom of video""" + height, width = frame.shape[:2] + + time_str, frame_str = format_time_with_frame(current_time, current_frame, fps) + total_time_str, total_frame_str = format_time_with_frame(total_time, total_frames, fps) + + mode_parts = [] + if live_yolo: + mode_parts.append("LIVE-YOLO") + if pre_yolo: + mode_parts.append("PRE-YOLO") + mode_str = f" [{'+'.join(mode_parts)}]" if mode_parts else "" + + sound_label = " [SOUND]" if sound_on else "" + time_text = f"{time_str} / {total_time_str} | {frame_str}/{total_frames} | Objects: {object_count}{mode_str}{sound_label}" + if is_paused: + time_text = f"[PAUSED] {time_text}" + + font = cv2.FONT_HERSHEY_SIMPLEX + font_scale = 0.55 + thickness = 2 + padding = 10 + + (text_w, text_h), baseline = cv2.getTextSize(time_text, font, font_scale, thickness) + bar_height = text_h + baseline + padding * 3 + + overlay = frame.copy() + cv2.rectangle(overlay, (0, height - bar_height), (width, height), (0, 0, 0), -1) + cv2.addWeighted(overlay, 0.6, frame, 0.4, 0, frame) + cv2.line(frame, (0, height - bar_height), (width, height - bar_height), (100, 100, 100), 1) + + text_x = (width - text_w) // 2 + text_y = height - bar_height // 2 + text_h // 2 + + text_color = (255, 100, 100) if is_paused else (255, 255, 255) + + cv2.putText(frame, time_text, (text_x + 1, text_y + 1), font, font_scale, (0, 0, 0), thickness + 1) + cv2.putText(frame, time_text, (text_x, text_y), font, font_scale, text_color, thickness) + + return frame + + +def play_video(video_path: str, model_path: str, probe_data: Optional[Dict], yolo_data: Optional[Dict]): + """Play video with YOLO overlay""" + + print(f"\nOpening video: {video_path}") + cap = cv2.VideoCapture(video_path) + + if not cap.isOpened(): + print(f"Error: Cannot open video: {video_path}") + return + + fps = cap.get(cv2.CAP_PROP_FPS) + width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + total_duration = total_frames / fps if fps > 0 else 0 + + print(f"Video info: {width}x{height} @ {fps:.2f} fps, {total_frames} frames") + + # Load YOLO model (lazy loading - only when needed) + model = None + + # Extract YOLO detections by frame + yolo_detections_by_frame = {} + if yolo_data and 'frames' in yolo_data: + for frame_num_str, frame_data in yolo_data['frames'].items(): + yolo_detections_by_frame[int(frame_num_str)] = frame_data.get('detections', []) + print(f"Loaded {len(yolo_detections_by_frame)} frames from .yolo.json") + + # Screen resolution detection and window layout + screen_w, screen_h = get_screen_resolution() + print(f"Detected screen resolution: {screen_w}x{screen_h}") + + GAP = 10 + margin = 40 + + available_width = screen_w - 3 * margin + w_vid = min(width, available_width // 2) + h_vid = int(w_vid * 9 / 16) + h_vid = min(h_vid, screen_h - margin * 2 - 200) + + w_cmd = w_vid * 2 + GAP + h_cmd = 280 + + WIN_ORIGINAL = "1: Original Video" + WIN_YOLO = "2: YOLO Detection" + WIN_CMD = "3: Command" + + x_start = margin + y_start = margin + + INIT_GEOM = { + WIN_ORIGINAL: (x_start, y_start, w_vid, h_vid), + WIN_YOLO: (x_start + w_vid + GAP, y_start, w_vid, h_vid), + WIN_CMD: (x_start, y_start + h_vid + GAP + 30, w_cmd, h_cmd), + } + + print(f"Window layout: Original={w_vid}x{h_vid}, YOLO={w_vid}x{h_vid}, Command={w_cmd}x{h_cmd}") + + def make_win(name): + x, y, w, h = INIT_GEOM[name] + cv2.namedWindow(name, cv2.WINDOW_NORMAL) + cv2.resizeWindow(name, w, h) + cv2.moveWindow(name, x, y) + + make_win(WIN_ORIGINAL) + make_win(WIN_YOLO) + make_win(WIN_CMD) + + # Trackbar + tb_code_val = {"v": 0} + seek_request = {"frame": None} + + def on_progress(val): + if val != tb_code_val["v"]: + seek_request["frame"] = val + + for wn in (WIN_ORIGINAL, WIN_YOLO): + cv2.createTrackbar("Progress", wn, 0, max(total_frames - 1, 1), on_progress) + + win_geom = dict(INIT_GEOM) + win_visible = {WIN_ORIGINAL: True, WIN_YOLO: True, WIN_CMD: True} + last_shown = WIN_ORIGINAL + + sound_process = None + + def start_audio(pos_secs): + stop_audio() + try: + return subprocess.Popen( + [FFPLAY, '-nodisp', '-autoexit', + '-ss', f'{max(0, pos_secs):.2f}', video_path], + stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL + ) + except Exception as e: + print(f"Audio error: {e}") + return None + + def stop_audio(): + subprocess.run(['pkill', '-f', 'ffplay'], + stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + + def do_seek(target_frame): + nonlocal frame_count, current_frame, annotated_frame, object_count, sound_process + + target_frame = max(0, min(total_frames - 1, int(target_frame))) + cap.set(cv2.CAP_PROP_POS_FRAMES, target_frame) + ret, f = cap.read() + if not ret: + return + + frame_count = target_frame + 1 + current_frame = f.copy() + + # Update detections + object_count = 0 + annotated_frame = f.copy() + + if pre_yolo_mode and frame_count in yolo_detections_by_frame: + dets = yolo_detections_by_frame[frame_count] + object_count += len(dets) + annotated_frame = draw_detections(annotated_frame, dets, (0, 255, 0), "[PRE] ") + + if live_yolo_mode and model is not None: + r = model(f, verbose=False)[0] + live_dets = get_detections_list(r) + object_count += len(live_dets) + annotated_frame = draw_detections(annotated_frame, live_dets, (255, 0, 0), "[LIVE] ") + + if sound_on: + sound_process = start_audio(frame_count / fps) + + # Update trackbar + tb_code_val["v"] = frame_count + for wn in (WIN_ORIGINAL, WIN_YOLO): + if win_visible.get(wn): + cv2.setTrackbarPos("Progress", wn, frame_count) + + print(f"Seek → frame {frame_count} ({frame_count/fps:.2f}s)") + + def seek_delta(delta_secs): + do_seek(frame_count + int(delta_secs * fps)) + + # Command-line state + cmd_input = "" + cmd_log = [] + + def cmd_log_add(line): + cmd_log.append(line) + if len(cmd_log) > 12: + cmd_log.pop(0) + + def execute_command(s): + s = s.strip() + if not s: + return + + try: + if s.lower() in ('i', 'info', 'probe'): + # Show probe information + if probe_data: + cmd_log_add(">> Video Probe Info:") + fmt = probe_data.get('format', {}) + cmd_log_add(f" Format: {fmt.get('format_long_name', 'N/A')}") + cmd_log_add(f" Duration: {fmt.get('duration', 0):.2f}s") + cmd_log_add(f" Size: {fmt.get('size', 0) / 1024 / 1024:.2f} MB") + vs = probe_data.get('video_stream', {}) + if vs: + cmd_log_add(f" Video: {vs.get('codec_name')} {vs.get('width')}x{vs.get('height')}") + cmd_log_add(f" Audio: {len(probe_data.get('audio_streams', []))} streams") + else: + cmd_log_add("!! No .probe.json found") + return + + if s.startswith(('+', '-')): + seek_delta(float(s)) + cmd_log_add(f">> seek {float(s):+.1f}s") + return + + if ':' in s: + parts = s.split(':') + hh = int(parts[0]) + mm = int(parts[1]) + ss_parts = parts[2].split('.') + ss = int(ss_parts[0]) + ff = int(ss_parts[1]) if len(ss_parts) > 1 else 0 + total_s = hh*3600 + mm*60 + ss + ff/fps + do_seek(int(total_s * fps)) + cmd_log_add(f">> seek {s}") + return + + do_seek(int(float(s))) + cmd_log_add(f">> seek frame {int(float(s))}") + + except Exception as e: + cmd_log_add(f"!! {e}") + + print("\nPlaying video...") + print("Keys: q/ESC=quit space=pause s=sound b=statusbar") + print(" y=live YOLO p=pre YOLO i=probe info h=hide 1/2/3=toggle windows") + print(" ←/→=±5s Shift+←/→=±30s") + print("Command: | hh:mm:ss[.ff] | +/-secs | i (probe info)") + + frame_count = 0 + is_paused = False + sound_on = False + show_statusbar = True + current_frame = None + annotated_frame = None + object_count = 0 + + # YOLO modes + live_yolo_mode = False + pre_yolo_mode = False + + while True: + if not is_paused: + ret, frame = cap.read() + + if not ret: + print("End of video, looping...") + cap.set(cv2.CAP_PROP_POS_FRAMES, 0) + frame_count = 0 + continue + + current_frame = frame.copy() + frame_count += 1 + + # Process detections + object_count = 0 + annotated_frame = frame.copy() + + if pre_yolo_mode and frame_count in yolo_detections_by_frame: + dets = yolo_detections_by_frame[frame_count] + object_count += len(dets) + annotated_frame = draw_detections(annotated_frame, dets, (0, 255, 0), "[PRE] ") + + if live_yolo_mode: + if model is None: + print("Loading YOLO model for live detection...") + model = YOLO(model_path) + print("✓ Model loaded") + + results = model(frame, verbose=False) + result = results[0] + live_dets = get_detections_list(result) + object_count += len(live_dets) + annotated_frame = draw_detections(annotated_frame, live_dets, (255, 0, 0), "[LIVE] ") + + current_time = frame_count / fps if fps > 0 else 0 + + if frame_count % 30 == 0 and not is_paused: + print(f"Frame: {frame_count}/{total_frames}, Objects: {object_count}") + + if current_frame is None: + continue + + # Handle trackbar seek + if seek_request["frame"] is not None: + do_seek(seek_request["frame"]) + seek_request["frame"] = None + + # Sync trackbar + if not seek_request["frame"]: + tb_code_val["v"] = frame_count + for wn in (WIN_ORIGINAL, WIN_YOLO): + if win_visible.get(wn): + cv2.setTrackbarPos("Progress", wn, frame_count) + + # Render status bar + overlay_args = (current_time, frame_count, total_duration, total_frames, fps, + object_count, is_paused, sound_on, live_yolo_mode, pre_yolo_mode) + + if win_visible[WIN_ORIGINAL]: + if show_statusbar: + frame_out = draw_time_overlay(current_frame, *overlay_args) + else: + frame_out = current_frame.copy() + cv2.imshow(WIN_ORIGINAL, frame_out) + last_shown = WIN_ORIGINAL + + if win_visible[WIN_YOLO] and annotated_frame is not None: + if show_statusbar: + ann_out = draw_time_overlay(annotated_frame, *overlay_args) + else: + ann_out = annotated_frame.copy() + cv2.imshow(WIN_YOLO, ann_out) + last_shown = WIN_YOLO + + # Command window + if win_visible[WIN_CMD]: + cmd_h, cmd_w = 320, w_cmd + panel = np.zeros((cmd_h, cmd_w, 3), dtype=np.uint8) + + # Title bar with build info + cv2.rectangle(panel, (0, 0), (cmd_w, 28), (40, 40, 80), -1) + title = f"3: Command | v{BUILD_VERSION} | {BUILD_TIME}" + cv2.putText(panel, title, (6, 18), cv2.FONT_HERSHEY_SIMPLEX, 0.48, (180, 220, 255), 1) + + # Examples section + examples = [ + "Examples: 123 | 00:01:30 | +10 | -5 | i (probe info)" + ] + y = 50 + for ex in examples: + cv2.putText(panel, ex, (8, y), cv2.FONT_HERSHEY_SIMPLEX, 0.42, (150, 150, 150), 1) + y += 20 + + # Separator + cv2.line(panel, (0, y), (cmd_w, y), (60, 60, 60), 1) + y += 15 + + # Log lines + for line in cmd_log[-8:]: + color = (80, 200, 80) if line.startswith(">>") else \ + (80, 80, 200) if line.startswith("!!") else (180, 180, 180) + cv2.putText(panel, line, (8, y), cv2.FONT_HERSHEY_SIMPLEX, 0.50, color, 1) + y += 22 + + # Status line + mode_str = f"Live:{'Y' if live_yolo_mode else 'N'} Pre:{'Y' if pre_yolo_mode else 'N'}" + s_line = (f" [{format_time(current_time)} f:{frame_count}/{total_frames}]" + f" {mode_str}" + f" Pause:{'Y' if is_paused else 'N'}" + f" Sound:{'Y' if sound_on else 'N'}") + cv2.putText(panel, s_line, (6, cmd_h - 38), + cv2.FONT_HERSHEY_SIMPLEX, 0.42, (120, 200, 255), 1) + + # Input prompt + cv2.line(panel, (0, cmd_h - 28), (cmd_w, cmd_h - 28), (80, 80, 80), 1) + prompt = f"> {cmd_input}_" + cv2.putText(panel, prompt, (8, cmd_h - 8), + cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 200), 1) + + # Focus indicator + if cmd_input: + cv2.putText(panel, "[TYPING]", (cmd_w - 100, cmd_h - 8), + cv2.FONT_HERSHEY_SIMPLEX, 0.40, (255, 200, 0), 1) + + cv2.imshow(WIN_CMD, panel) + last_shown = WIN_CMD + + # Key handling + key = cv2.waitKeyEx(30 if not is_paused else 100) + key_char = key & 0xFF + + in_focus_mode = bool(cmd_input) + + if key_char == 13: # Enter + if cmd_input.strip(): + cmd_log_add(f"> {cmd_input}") + execute_command(cmd_input) + cmd_input = "" + + elif key_char in (8, 127): # Backspace / Delete + cmd_input = cmd_input[:-1] + + elif 32 <= key_char <= 126: + if in_focus_mode or chr(key_char) in ('+', '-', '0','1','2','3','4','5','6','7','8','9', ':'): + cmd_input += chr(key_char) + elif key_char == ord('q') or key_char == ord('Q') or key_char == 27: + print("Quitting...") + break + elif key_char == ord(' '): + is_paused = not is_paused + if sound_on: + if is_paused: + stop_audio() + sound_process = None + else: + sound_process = start_audio(frame_count / fps) + print(f"{'Paused' if is_paused else 'Resumed'}") + elif key_char == ord('b') or key_char == ord('B'): + show_statusbar = not show_statusbar + print(f"Status bar {'ON' if show_statusbar else 'OFF'}") + elif key_char == ord('s') or key_char == ord('S'): + sound_on = not sound_on + if sound_on: + sound_process = start_audio(frame_count / fps) + print(f"Sound ON (at {frame_count/fps:.1f}s)") + else: + stop_audio() + sound_process = None + print("Sound OFF") + elif key_char == ord('y') or key_char == ord('Y'): + live_yolo_mode = not live_yolo_mode + print(f"Live YOLO {'ON' if live_yolo_mode else 'OFF'}") + elif key_char == ord('p') or key_char == ord('P'): + if yolo_data: + pre_yolo_mode = not pre_yolo_mode + print(f"Pre-scanned YOLO {'ON' if pre_yolo_mode else 'OFF'}") + else: + print("No .yolo.json file found") + cmd_log_add("!! No .yolo.json found") + elif key_char == ord('h') or key_char == ord('H'): + target = last_shown + if target and win_visible.get(target): + win_geom[target] = get_window_rect(target) + win_visible[target] = False + cv2.destroyWindow(target) + print(f"Hidden: {target}") + elif key_char == ord('1'): + win_visible[WIN_ORIGINAL] = not win_visible[WIN_ORIGINAL] + if not win_visible[WIN_ORIGINAL]: + win_geom[WIN_ORIGINAL] = get_window_rect(WIN_ORIGINAL) + cv2.destroyWindow(WIN_ORIGINAL) + else: + g = win_geom.get(WIN_ORIGINAL, INIT_GEOM[WIN_ORIGINAL]) + cv2.namedWindow(WIN_ORIGINAL, cv2.WINDOW_NORMAL) + cv2.resizeWindow(WIN_ORIGINAL, g[2], g[3]) + cv2.moveWindow(WIN_ORIGINAL, g[0], g[1]) + cv2.createTrackbar("Progress", WIN_ORIGINAL, + frame_count, max(total_frames-1,1), on_progress) + print(f"[1] Original: {'ON' if win_visible[WIN_ORIGINAL] else 'OFF'}") + elif key_char == ord('2'): + win_visible[WIN_YOLO] = not win_visible[WIN_YOLO] + if not win_visible[WIN_YOLO]: + win_geom[WIN_YOLO] = get_window_rect(WIN_YOLO) + cv2.destroyWindow(WIN_YOLO) + else: + g = win_geom.get(WIN_YOLO, INIT_GEOM[WIN_YOLO]) + cv2.namedWindow(WIN_YOLO, cv2.WINDOW_NORMAL) + cv2.resizeWindow(WIN_YOLO, g[2], g[3]) + cv2.moveWindow(WIN_YOLO, g[0], g[1]) + cv2.createTrackbar("Progress", WIN_YOLO, + frame_count, max(total_frames-1,1), on_progress) + print(f"[2] YOLO: {'ON' if win_visible[WIN_YOLO] else 'OFF'}") + elif key_char == ord('3'): + win_visible[WIN_CMD] = not win_visible[WIN_CMD] + if not win_visible[WIN_CMD]: + win_geom[WIN_CMD] = get_window_rect(WIN_CMD) + cv2.destroyWindow(WIN_CMD) + else: + g = win_geom.get(WIN_CMD, INIT_GEOM[WIN_CMD]) + cv2.namedWindow(WIN_CMD, cv2.WINDOW_NORMAL) + cv2.resizeWindow(WIN_CMD, g[2], g[3]) + cv2.moveWindow(WIN_CMD, g[0], g[1]) + print(f"[3] Command: {'ON' if win_visible[WIN_CMD] else 'OFF'}") + + # Arrow key seek + elif key in (2424832, 63234, 65361): # ← + seek_delta(-5) + elif key in (2555904, 63235, 65363): # → + seek_delta(5) + elif key in (2162688, 63232, 65360): # Shift+← + seek_delta(-30) + elif key in (2293760, 63233, 65367): # Shift+→ + seek_delta(30) + + # Cleanup + stop_audio() + cap.release() + cv2.destroyAllWindows() + print("Done!") + + +def main(): + if len(sys.argv) < 3: + print(f"Usage: python {sys.argv[0]} ") + print(f"Example: python {sys.argv[0]} video.mp4 yolov8n.pt") + sys.exit(1) + + video_path = sys.argv[1] + model_path = sys.argv[2] + + print("\n" + "=" * 60) + print("Video YOLO Player v" + BUILD_VERSION) + print("=" * 60) + + # Load probe data + probe_data = load_probe_data(video_path) + if probe_data: + print(f"✓ Found .probe.json") + else: + print(f"⚠ No .probe.json found (run video_probe.py first)") + + # Load YOLO pre-scan data + yolo_data = load_yolo_data(video_path) + if yolo_data: + print(f"✓ Found .yolo.json") + else: + print(f"⚠ No .yolo.json found (run video_yolo_object_prescan.py first)") + + print("=" * 60) + + # Play video + play_video(video_path, model_path, probe_data, yolo_data) + + +if __name__ == "__main__": + main() diff --git a/yolo11n.pt b/yolo11n.pt new file mode 100644 index 0000000..45b273b Binary files /dev/null and b/yolo11n.pt differ diff --git a/yolov8n.pt b/yolov8n.pt new file mode 100644 index 0000000..0db4ca4 Binary files /dev/null and b/yolov8n.pt differ